Purpose–The purpose of this study is to study the quantitative evaluation method of contact wire cracks by analyzing the changing law of eddy current signal characteristics under different cracks of contact wire of h...Purpose–The purpose of this study is to study the quantitative evaluation method of contact wire cracks by analyzing the changing law of eddy current signal characteristics under different cracks of contact wire of high-speed railway so as to provide a new way of thinking and method for the detection of contact wire injuries of high-speed railway.Design/methodology/approach–Based on the principle of eddy current detection and the specification parameters of high-speed railway contact wires in China,a finite element model for eddy current testing of contact wires was established to explore the variation patterns of crack signal characteristics in numerical simulation.A crack detection system based on eddy current detection was built,and eddy current detection voltage data was obtained for cracks of different depths and widths.By analyzing the variation law of eddy current signals,characteristic parameters were obtained and a quantitative evaluation model for crack width and depth was established based on the back propagation(BP)neural network.Findings–Numerical simulation and experimental detection of eddy current signal change rule is basically consistent,based on the law of the selected characteristics of the parameters in the BP neural network crack quantitative evaluation model also has a certain degree of effectiveness and reliability.BP neural network training results show that the classification accuracy for different widths and depths of the classification is 100 and 85.71%,respectively,and can be effectively realized on the high-speed railway contact line cracks of the quantitative evaluation classification.Originality/value–This study establishes a new type of high-speed railway contact wire crack detection and identification method,which provides a new technical means for high-speed railway contact wire injury detection.The study of eddy current characteristic law and quantitative evaluation model for different cracks in contact line has important academic value and practical significance,and it has certain guiding significance for the detection technology of contact line in high-speed railway.展开更多
Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,whi...Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.展开更多
The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality perc...The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.展开更多
Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of cr...Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.展开更多
Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects.In practice,however,this task faces the challenge of finding a balance between detection ac...Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects.In practice,however,this task faces the challenge of finding a balance between detection accuracy and efficiency.To alleviate this problem,a lightweight and efficient real-time crack segmentation framework was developed.Specifically,in the network model system based on an encoding-decoding structure,the encoding network is equipped with packet convolution and attention mechanisms to capture features of different visual scales in layers,and in the decoding process,we also introduce a fusion module based on spatial attention to effectively aggregate these hierarchical features.Codecs are connected by pyramid pooling model(PPM)filtering.The results show that the crack segmentation accuracy and real-time operation capability larger than 76%and 15 fps,respectively,are validated by three publicly available datasets.These wide-ranging results highlight the potential of the model for the intelligent O&M for cross-sea bridge.展开更多
Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine ...Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.展开更多
This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network(1D-CNN)and long short-term memory(LSTM)method in the image frequen...This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network(1D-CNN)and long short-term memory(LSTM)method in the image frequency domain.The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks.In order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing phase.The algorithm is then calibrated using the flattened frequency data.LSTM is used to improve the performance of the developed network for long sequence data.The accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing data.An implementation framework is further developed for future application of the trained model for large-scale images.The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time.The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.展开更多
Highway tunnels play a very important role in people's daily life.Among them,lining is an essential part of tunnel engineering,and the quality of lining greatly affects the overall quality of the tunnel.On this ba...Highway tunnels play a very important role in people's daily life.Among them,lining is an essential part of tunnel engineering,and the quality of lining greatly affects the overall quality of the tunnel.On this basis,the causes of lining cracks and the detection methods of existing highway tunnel lining cracks are analyzed,and the treatment countermeasures for highway tunnel lining cracks are proposed.展开更多
As a current popular method,intelligent detection of cracks is of great significance to road safety,so deep learning has gradually attracted attention in the field of crack image detection.The nonlinear structure,low ...As a current popular method,intelligent detection of cracks is of great significance to road safety,so deep learning has gradually attracted attention in the field of crack image detection.The nonlinear structure,low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep learning.Therefore,an end-to-end deep convolutional neural network(AttentionCrack)is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack pixels.The AttentionCrack network is built on U-Net based encoder-decoder architecture,and an attention mechanism is incorporated into the multi-scale convolutional feature to enhance the recognition of crack region.Additionally,a dilated convolution module is introduced in the encoder-decoder architecture to reduce the loss of crack detail due to the pooling operation in the encoder network.Furthermore,since up-sampling will lead to the loss of crack boundary information in the decoder network,a depthwise separable residual module is proposed to capture the boundary information of pavement crack.The AttentionCrack net on public pavement crack image datasets named CrackSegNet and Crack500 is trained and tested,the results demonstrate that the AttentionCrack achieves F1 score over 0.70 on the CrackSegNet and 0.71 on the Crack500 in average and outperforms the current state-of-the-art methods.展开更多
Regular inspection of bridge cracks is crucial to bridge maintenance and repair.The traditional manual crack detection methods are timeconsuming,dangerous and subjective.At the same time,for the existing mainstream vi...Regular inspection of bridge cracks is crucial to bridge maintenance and repair.The traditional manual crack detection methods are timeconsuming,dangerous and subjective.At the same time,for the existing mainstream vision-based automatic crack detection algorithms,it is challenging to detect fine cracks and balance the detection accuracy and speed.Therefore,this paper proposes a new bridge crack segmentationmethod based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+network framework.First,the improved lightweight MobileNetv2 network and dilated separable convolution are integrated into the original DeeplabV3+network to improve the original backbone network Xception and atrous spatial pyramid pooling(ASPP)module,respectively,dramatically reducing the number of parameters in the network and accelerates the training and prediction speed of the model.Moreover,we introduce the parallel attention mechanism into the encoding and decoding stages.The attention to the crack regions can be enhanced from the aspects of both channel and spatial parts and significantly suppress the interference of various noises.Finally,we further improve the detection performance of the model for fine cracks by introducing a multi-scale features fusion module.Our research results are validated on the self-made dataset.The experiments show that our method is more accurate than other methods.Its intersection of union(IoU)and F1-score(F1)are increased to 77.96%and 87.57%,respectively.In addition,the number of parameters is only 4.10M,which is much smaller than the original network;also,the frames per second(FPS)is increased to 15 frames/s.The results prove that the proposed method fits well the requirements of rapid and accurate detection of bridge cracks and is superior to other methods.展开更多
Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural integrity.Despite being the most common practice for fatigue crack detection,human vi...Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural integrity.Despite being the most common practice for fatigue crack detection,human visual inspection is known to be labor intensive,time-consuming,and prone to error.In this study,a computer vision-based fatigue crack detection approach using a short video recorded under live loads by a moving consumer-grade camera is presented.The method detects fatigue crack by tracking surface motion and identifies the differential motion pattern caused by opening and closing of the fatigue crack.However,the global motion introduced by a moving camera in the recorded video is typically far greater than the actual motion associated with fatigue crack opening/closing,leading to false detection results.To overcome the challenge,global motion compensation(GMC)techniques are introduced to compensate for camera-induced movement.In particular,hierarchical model-based motion estimation is adopted for 2D videos with simple geometry and a new method is developed by extending the bundled camera paths approach for 3D videos with complex geometry.The proposed methodology is validated using two laboratory test setups for both in-plane and out-of-plane fatigue cracks.The results confirm the importance of motion compensation for both 2D and 3D videos and demonstrate the effectiveness of the proposed GMC methods as well as the subsequent crack detection algorithm.展开更多
Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;howe...Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains challenging.In this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for classification.The performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation metrics.Nevertheless,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two models.These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications.We applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of 0.984.Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement cracks.By leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance measures.Lastly,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.展开更多
Detection of cracks at the early stage is considered as very constructive since precautionary steps need to be taken to avoid the damage to the civil structures.Moreover,identifying and classifying the severity level ...Detection of cracks at the early stage is considered as very constructive since precautionary steps need to be taken to avoid the damage to the civil structures.Moreover,identifying and classifying the severity level of cracks is inevitable in order to find the stability of buildings.Hence,this paper proposes an efficient strategy to classify the cracks into fine,medium,and thick using a novel bilayer crack detection algorithm.The bilayer crack detection algorithm helps in extracting the requisite features from the crack for efficient classification.The proposed algorithm works well in the dark background and connects the discontinued cracks too.The first layer is used to detect cracks under texture variations and manufacturing defects,through segmented adaptive thresholding and morphological operations.The residual noise present in the output of the first layer is removed in the second layer of crack detection.The second layer includes the double scan and the noise reduction algorithms and is used to join the missed crack parts.As a result,a segmented crack is formed.Further classification is done using an ensemble classifier with bagging,and decision tree techniques by extracting the geometrical features and the weaker crack criterion from the segmented part.The results of the proposed technique are compared with the existing techniques for different datasets and have obtained a rise in True Positive Rate(TPR),accuracy and precision value.The proposed technique is also implemented in Raspberry Pi for further real-time evaluation.展开更多
Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades.The cracks on the blades can endanger the shafting of the generator set,the towe...Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades.The cracks on the blades can endanger the shafting of the generator set,the tower and other components,and even cause the tower to collapse.To achieve high-precision wind blade crack detection,this paper proposes a crack fault-detection strategy that integratesGated ResidualNetwork(GRN),a fusionmodule and Transformer.Firstly,GRNcan reduce unnecessary noisy inputs that could negatively impact performancewhile preserving the integrity of feature information.In addition,to gain in-depth information about the characteristics of wind turbine blades,a fusionmodule is suggested to implement the information fusion of wind turbine features.Specifically,each fan feature ismapped to a one-dimensional vector with the same length,and all one-dimensional vectors are concatenated to obtain a two-dimensional vector.And then,in the fusion module,the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP,and the information fusion of different characteristic variables in the same channel is realized through the ColumnmixingMLP.Finally,the fused feature vector is input into the Transformer for feature learning,which enhances the influence of important feature information and improves the model’s anti-noise ability and classification accuracy.Extensive experimentswere conducted on the wind turbine supervisory control and data acquisition(SCADA)data froma domesticwind field.The results show that compared with other state-of-the-artmodels,including XGBoost,LightGBM,TabNet,etc.,the F1-score of proposed gated fusion based Transformer model can reach 0.9907,which is 0.4%-2.09% higher than the comparedmodels.Thismethod provides amore reliable approach for the condition detection and maintenance of fan blades in wind farms.展开更多
Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and in...Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.展开更多
This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to pr...This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.展开更多
This paper introduces a crack detection example of the prestressed box girder in a certain project. The morphology of the box girder cracks was surveyed and mapped. The length, width, and depth of the cracks were insp...This paper introduces a crack detection example of the prestressed box girder in a certain project. The morphology of the box girder cracks was surveyed and mapped. The length, width, and depth of the cracks were inspected, and the strength and reinforcement configuration of the components were tested. The test results indicate that the strength and reinforcement configuration of the inspected components meet the design requirements. The crack at the end of the top plate of the box girder is a local compressive crack at the anchorage end. The width and length of the crack on the bottom surface of the top plate are not significant, and the depth is relatively shallow. Judging from the crack morphology, this crack is identified as a temperature crack. Additionally, based on the treatment measures for cracks of different widths, the treatment measures for the cracks of the components in this project are derived, providing a reference basis for similar projects in the future.展开更多
With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection...With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection were analyzed considering cracks qualities.A computer program was developed by visual C++6.0 programming language to detect the cracks,which was tested by 15cases of bridge video images.The results indicate that the relative error is within 6%for cracks larger than 0.3 mm cracks and it is less than 10%for crack width between 0.2 mm and 0.3 mm.In addition,for the crack below 0.1 mm,the relative error is more than30%because the bridge is in safe stage and it is very difficult to detect the actual width of crack.展开更多
Vibration-based pavement condition(roughness and obvious anomalies)monitoring has been expanding in road engineering.However,the indistinctive transverse cracking has hardly been considered.Therefore,a vehicle-based n...Vibration-based pavement condition(roughness and obvious anomalies)monitoring has been expanding in road engineering.However,the indistinctive transverse cracking has hardly been considered.Therefore,a vehicle-based novel method is proposed for detecting the transverse cracking through signal processing techniques and support vector machine(SVM).The vibration signals of the car traveling on the transverse-cracked and the crack-free sections were subjected to signal processing in time domain,frequency domain and wavelet domain,aiming to find indices that can discriminate vibration signal between the cracked and uncracked section.These indices were used to form 8 SVM models.The model with the highest accuracy and F1-measure was preferred,consisting of features including vehicle speed,range,relative standard deviation,maximum Fourier coefficient,and wavelet coefficient.Therefore,a crack and crack-free classifier was developed.Then its feasibility was investigated by 2292 pavement sections.The detection accuracy and F1-measure are 97.25%and 85.25%,respectively.The cracking detection approach proposed in this paper and the smartphone-based detection method for IRI and other distress may form a comprehensive pavement condition survey system.展开更多
Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points alo...Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points along the length of a single fiber.Multiplexing provides for single channel detection of cracks and their locations in large structural systems. An algorithm was developed for signal recognition and tagging of the AE waveforms for detection of' crack locations,Labora- tory experiments on plain concrete beams and post-tensioned FRP tendons were pcrlormed to evaluate the crack detection capability of the sensor system.The acoustic emission sensor was able to detect initiation,growth and location of the cracks in concrete as well as in the FRP tendons.The AE system is potentially suitable lot applications involving health monitoring of structures following an earthquake.展开更多
文摘Purpose–The purpose of this study is to study the quantitative evaluation method of contact wire cracks by analyzing the changing law of eddy current signal characteristics under different cracks of contact wire of high-speed railway so as to provide a new way of thinking and method for the detection of contact wire injuries of high-speed railway.Design/methodology/approach–Based on the principle of eddy current detection and the specification parameters of high-speed railway contact wires in China,a finite element model for eddy current testing of contact wires was established to explore the variation patterns of crack signal characteristics in numerical simulation.A crack detection system based on eddy current detection was built,and eddy current detection voltage data was obtained for cracks of different depths and widths.By analyzing the variation law of eddy current signals,characteristic parameters were obtained and a quantitative evaluation model for crack width and depth was established based on the back propagation(BP)neural network.Findings–Numerical simulation and experimental detection of eddy current signal change rule is basically consistent,based on the law of the selected characteristics of the parameters in the BP neural network crack quantitative evaluation model also has a certain degree of effectiveness and reliability.BP neural network training results show that the classification accuracy for different widths and depths of the classification is 100 and 85.71%,respectively,and can be effectively realized on the high-speed railway contact line cracks of the quantitative evaluation classification.Originality/value–This study establishes a new type of high-speed railway contact wire crack detection and identification method,which provides a new technical means for high-speed railway contact wire injury detection.The study of eddy current characteristic law and quantitative evaluation model for different cracks in contact line has important academic value and practical significance,and it has certain guiding significance for the detection technology of contact line in high-speed railway.
基金supported in part by the 14th Five-Year Project of Ministry of Science and Technology of China(2021YFD2000304)Fundamental Research Funds for the Central Universities(531118010509)Natural Science Foundation of Hunan Province,China(2021JJ40114)。
文摘Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving safety.The task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection performance.Till to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the shadows.To fill in the gap,we made several contributions as follows.First,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size combinations.It also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection methods.Second,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting it.In addition to shadows,the method can cope with other noise disturbances.Third,we explored the mechanism of how shadows affect crack detection.Based on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather changes.Finally,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model overall.We compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.
基金Hunan Provincial Science and Technology Innovation Leader Project,Grant/Award Number:2021RC4025National Natural ScienceFoundation of China,Grant/Award Number:51808209Hunan Provincial Innovation Foundation for Postgraduate,Grant/Award Number:QL20210106.
文摘The increasing global population at a rapid pace makes road trafficdense;managing such massive traffic is challenging. In developing countrieslike Pakistan, road traffic accidents (RTA) have the highest mortality percentageamong other Asian countries. The main reasons for RTAs are roadcracks and potholes. Understanding the need for an automated system forthe detection of cracks and potholes, this study proposes a decision supportsystem (DSS) for an autonomous road information system for smart citydevelopment with the use of deep learning. The proposed DSS works in layerswhere initially the image of roads is captured and coordinates attached to theimage with the help of global positioning system (GPS), communicated tothe decision layer to find about the cracks and potholes in the roads, andeventually, that information is passed to the road management informationsystem, which gives information to drivers and the maintenance department.For the decision layer, we projected a CNN-based model for pothole crackdetection (PCD). Aimed at training, a K-fold cross-validation strategy wasused where the value of K was set to 10. The training of PCD was completedwith a self-collected dataset consisting of 6000 images from Pakistani roads.The proposed PCD achieved 98% of precision, 97% recall, and accuracy whiletesting on unseen images. The results produced by our model are higher thanthe existing model in terms of performance and computational cost, whichproves its significance.
基金supported by the National Natural Science Foundation of China(No.62176034)the Science and Technology Research Program of Chongqing Municipal Education Commission(No.KJZD-M202300604)the Natural Science Foundation of Chongqing(Nos.cstc2021jcyj-msxmX0518,2023NSCQ-MSX1781).
文摘Automatic crack detection of cement pavement chiefly benefits from the rapid development of deep learning,with convolutional neural networks(CNN)playing an important role in this field.However,as the performance of crack detection in cement pavement improves,the depth and width of the network structure are significantly increased,which necessitates more computing power and storage space.This limitation hampers the practical implementation of crack detection models on various platforms,particularly portable devices like small mobile devices.To solve these problems,we propose a dual-encoder-based network architecture that focuses on extracting more comprehensive fracture feature information and combines cross-fusion modules and coordinated attention mechanisms formore efficient feature fusion.Firstly,we use small channel convolution to construct shallow feature extractionmodule(SFEM)to extract low-level feature information of cracks in cement pavement images,in order to obtainmore information about cracks in the shallowfeatures of images.In addition,we construct large kernel atrous convolution(LKAC)to enhance crack information,which incorporates coordination attention mechanism for non-crack information filtering,and large kernel atrous convolution with different cores,using different receptive fields to extract more detailed edge and context information.Finally,the three-stage feature map outputs from the shallow feature extraction module is cross-fused with the two-stage feature map outputs from the large kernel atrous convolution module,and the shallow feature and detailed edge feature are fully fused to obtain the final crack prediction map.We evaluate our method on three public crack datasets:DeepCrack,CFD,and Crack500.Experimental results on theDeepCrack dataset demonstrate the effectiveness of our proposed method compared to state-of-the-art crack detection methods,which achieves Precision(P)87.2%,Recall(R)87.7%,and F-score(F1)87.4%.Thanks to our lightweight crack detectionmodel,the parameter count of the model in real-world detection scenarios has been significantly reduced to less than 2M.This advancement also facilitates technical support for portable scene detection.
基金supported by the National Key Research and Development Program of China(Grant Nos.2019YFB1600700 and 2019YFB1600701)the Wuhan Maritime Communication Research Institute(Grant No.2020MG001/050-22-CF).
文摘Rapid and accurate segmentation of structural cracks is essential for ensuring the quality and safety of engineering projects.In practice,however,this task faces the challenge of finding a balance between detection accuracy and efficiency.To alleviate this problem,a lightweight and efficient real-time crack segmentation framework was developed.Specifically,in the network model system based on an encoding-decoding structure,the encoding network is equipped with packet convolution and attention mechanisms to capture features of different visual scales in layers,and in the decoding process,we also introduce a fusion module based on spatial attention to effectively aggregate these hierarchical features.Codecs are connected by pyramid pooling model(PPM)filtering.The results show that the crack segmentation accuracy and real-time operation capability larger than 76%and 15 fps,respectively,are validated by three publicly available datasets.These wide-ranging results highlight the potential of the model for the intelligent O&M for cross-sea bridge.
基金supported in part by the National Natural Foundation of China(No.62176147)。
文摘Automatic pavement crack detection plays an important role in ensuring road safety.In images of cracks,information about the cracks can be conveyed through high-frequency and low-fre-quency signals that focus on fine details and global structures,respectively.The output features obtained from different convolutional layers can be combined to represent information about both high-frequency and low-frequency signals.In this paper,we propose an encoder-decoder framework called octave hierarchical network(Octave-H),which is based on the U-Network(U-Net)architec-ture and utilizes an octave convolutional neural network and a hierarchical feature learning module for performing crack detection.The proposed octave convolution is capable of extracting multi-fre-quency feature maps,capturing both fine details and global cracks.We propose a hierarchical feature learning module that merges multi-frequency-scale feature maps with different levels(high and low)of octave convolutional layers.To verify the superiority of the proposed Octave-H,we employed the CrackForest dataset(CFD)and AigleRN databases to evaluate this method.The experimental results demonstrate that Octave-H outperforms other algorithms with satisfactory performance.
文摘This paper presents a vision-based crack detection approach for concrete bridge decks using an integrated one-dimensional convolutional neural network(1D-CNN)and long short-term memory(LSTM)method in the image frequency domain.The so-called 1D-CNN-LSTM algorithm is trained using thousands of images of cracked and non-cracked concrete bridge decks.In order to improve the training efficiency,images are first transformed into the frequency domain during a preprocessing phase.The algorithm is then calibrated using the flattened frequency data.LSTM is used to improve the performance of the developed network for long sequence data.The accuracy of the developed model is 99.05%,98.9%,and 99.25%,respectively,for training,validation,and testing data.An implementation framework is further developed for future application of the trained model for large-scale images.The proposed 1D-CNN-LSTM method exhibits superior performance in comparison with existing deep learning methods in terms of accuracy and computation time.The fast implementation of the 1D-CNN-LSTM algorithm makes it a promising tool for real-time crack detection.
文摘Highway tunnels play a very important role in people's daily life.Among them,lining is an essential part of tunnel engineering,and the quality of lining greatly affects the overall quality of the tunnel.On this basis,the causes of lining cracks and the detection methods of existing highway tunnel lining cracks are analyzed,and the treatment countermeasures for highway tunnel lining cracks are proposed.
基金supported by the National Natural Science Foundation of China under Grant No.62001004the Key Provincial Natural Science Research Projects of Colleges and Universities in Anhui Province under Grant No.KJ2019A0768+2 种基金the Key Research and Development Program of Anhui Province under Grant No.202104A07020017the Research Project Reserve of Anhui Jianzhu University under Grant No.2020XMK04the Natural Science Foundation of the Anhui Higher Education Institutions of China,No.KJ2019A0789.
文摘As a current popular method,intelligent detection of cracks is of great significance to road safety,so deep learning has gradually attracted attention in the field of crack image detection.The nonlinear structure,low contrast and discontinuity of cracks bring great challenges to existing crack detection methods based on deep learning.Therefore,an end-to-end deep convolutional neural network(AttentionCrack)is proposed for automatic crack detection to overcome the inaccuracy of boundary location between crack and non-crack pixels.The AttentionCrack network is built on U-Net based encoder-decoder architecture,and an attention mechanism is incorporated into the multi-scale convolutional feature to enhance the recognition of crack region.Additionally,a dilated convolution module is introduced in the encoder-decoder architecture to reduce the loss of crack detail due to the pooling operation in the encoder network.Furthermore,since up-sampling will lead to the loss of crack boundary information in the decoder network,a depthwise separable residual module is proposed to capture the boundary information of pavement crack.The AttentionCrack net on public pavement crack image datasets named CrackSegNet and Crack500 is trained and tested,the results demonstrate that the AttentionCrack achieves F1 score over 0.70 on the CrackSegNet and 0.71 on the Crack500 in average and outperforms the current state-of-the-art methods.
基金This work was supported by the High-Tech Industry Science and Technology Innovation Leading Plan Project of Hunan Provincial under Grant 2020GK2026,author B.Y,http://kjt.hunan.gov.cn/.
文摘Regular inspection of bridge cracks is crucial to bridge maintenance and repair.The traditional manual crack detection methods are timeconsuming,dangerous and subjective.At the same time,for the existing mainstream vision-based automatic crack detection algorithms,it is challenging to detect fine cracks and balance the detection accuracy and speed.Therefore,this paper proposes a new bridge crack segmentationmethod based on parallel attention mechanism and multi-scale features fusion on top of the DeeplabV3+network framework.First,the improved lightweight MobileNetv2 network and dilated separable convolution are integrated into the original DeeplabV3+network to improve the original backbone network Xception and atrous spatial pyramid pooling(ASPP)module,respectively,dramatically reducing the number of parameters in the network and accelerates the training and prediction speed of the model.Moreover,we introduce the parallel attention mechanism into the encoding and decoding stages.The attention to the crack regions can be enhanced from the aspects of both channel and spatial parts and significantly suppress the interference of various noises.Finally,we further improve the detection performance of the model for fine cracks by introducing a multi-scale features fusion module.Our research results are validated on the self-made dataset.The experiments show that our method is more accurate than other methods.Its intersection of union(IoU)and F1-score(F1)are increased to 77.96%and 87.57%,respectively.In addition,the number of parameters is only 4.10M,which is much smaller than the original network;also,the frames per second(FPS)is increased to 15 frames/s.The results prove that the proposed method fits well the requirements of rapid and accurate detection of bridge cracks and is superior to other methods.
基金NCHRP Project,IDEA 223:Fatigue Crack Inspection using Computer Vision and Augmented Reality。
文摘Fatigue cracks that develop in civil infrastructure such as steel bridges due to repetitive loads pose a major threat to structural integrity.Despite being the most common practice for fatigue crack detection,human visual inspection is known to be labor intensive,time-consuming,and prone to error.In this study,a computer vision-based fatigue crack detection approach using a short video recorded under live loads by a moving consumer-grade camera is presented.The method detects fatigue crack by tracking surface motion and identifies the differential motion pattern caused by opening and closing of the fatigue crack.However,the global motion introduced by a moving camera in the recorded video is typically far greater than the actual motion associated with fatigue crack opening/closing,leading to false detection results.To overcome the challenge,global motion compensation(GMC)techniques are introduced to compensate for camera-induced movement.In particular,hierarchical model-based motion estimation is adopted for 2D videos with simple geometry and a new method is developed by extending the bundled camera paths approach for 3D videos with complex geometry.The proposed methodology is validated using two laboratory test setups for both in-plane and out-of-plane fatigue cracks.The results confirm the importance of motion compensation for both 2D and 3D videos and demonstrate the effectiveness of the proposed GMC methods as well as the subsequent crack detection algorithm.
文摘Pavement crack detection plays a crucial role in ensuring road safety and reducing maintenance expenses.Recent advancements in deep learning(DL)techniques have shown promising results in detecting pavement cracks;however,the selection of relevant features for classification remains challenging.In this study,we propose a new approach for pavement crack detection that integrates deep learning for feature extraction,the whale optimization algorithm(WOA)for feature selection,and random forest(RF)for classification.The performance of the models was evaluated using accuracy,recall,precision,F1 score,and area under the receiver operating characteristic curve(AUC).Our findings reveal that Model 2,which incorporates RF into the ResNet-18 architecture,outperforms baseline Model 1 across all evaluation metrics.Nevertheless,our proposed model,which combines ResNet-18 with both WOA and RF,achieves significantly higher accuracy,recall,precision,and F1 score compared to the other two models.These results underscore the effectiveness of integrating RF and WOA into ResNet-18 for pavement crack detection applications.We applied the proposed approach to a dataset of pavement images,achieving an accuracy of 97.16%and an AUC of 0.984.Our results demonstrate that the proposed approach surpasses existing methods for pavement crack detection,offering a promising solution for the automatic identification of pavement cracks.By leveraging this approach,potential safety hazards can be identified more effectively,enabling timely repairs and maintenance measures.Lastly,the findings of this study also emphasize the potential of integrating RF and WOA with deep learning for pavement crack detection,providing road authorities with the necessary tools to make informed decisions regarding road infrastructure maintenance.
文摘Detection of cracks at the early stage is considered as very constructive since precautionary steps need to be taken to avoid the damage to the civil structures.Moreover,identifying and classifying the severity level of cracks is inevitable in order to find the stability of buildings.Hence,this paper proposes an efficient strategy to classify the cracks into fine,medium,and thick using a novel bilayer crack detection algorithm.The bilayer crack detection algorithm helps in extracting the requisite features from the crack for efficient classification.The proposed algorithm works well in the dark background and connects the discontinued cracks too.The first layer is used to detect cracks under texture variations and manufacturing defects,through segmented adaptive thresholding and morphological operations.The residual noise present in the output of the first layer is removed in the second layer of crack detection.The second layer includes the double scan and the noise reduction algorithms and is used to join the missed crack parts.As a result,a segmented crack is formed.Further classification is done using an ensemble classifier with bagging,and decision tree techniques by extracting the geometrical features and the weaker crack criterion from the segmented part.The results of the proposed technique are compared with the existing techniques for different datasets and have obtained a rise in True Positive Rate(TPR),accuracy and precision value.The proposed technique is also implemented in Raspberry Pi for further real-time evaluation.
基金supported by the Jiangsu Provincial Key R&D Programme(BE2020034)China Huaneng Group Science and Technology Project(HNKJ20-H72).
文摘Harsh working environments and wear between blades and other unit components can easily lead to cracks and damage on wind turbine blades.The cracks on the blades can endanger the shafting of the generator set,the tower and other components,and even cause the tower to collapse.To achieve high-precision wind blade crack detection,this paper proposes a crack fault-detection strategy that integratesGated ResidualNetwork(GRN),a fusionmodule and Transformer.Firstly,GRNcan reduce unnecessary noisy inputs that could negatively impact performancewhile preserving the integrity of feature information.In addition,to gain in-depth information about the characteristics of wind turbine blades,a fusionmodule is suggested to implement the information fusion of wind turbine features.Specifically,each fan feature ismapped to a one-dimensional vector with the same length,and all one-dimensional vectors are concatenated to obtain a two-dimensional vector.And then,in the fusion module,the information fusion of the same characteristic variables in the different channels is realized through the Channel-mixing MLP,and the information fusion of different characteristic variables in the same channel is realized through the ColumnmixingMLP.Finally,the fused feature vector is input into the Transformer for feature learning,which enhances the influence of important feature information and improves the model’s anti-noise ability and classification accuracy.Extensive experimentswere conducted on the wind turbine supervisory control and data acquisition(SCADA)data froma domesticwind field.The results show that compared with other state-of-the-artmodels,including XGBoost,LightGBM,TabNet,etc.,the F1-score of proposed gated fusion based Transformer model can reach 0.9907,which is 0.4%-2.09% higher than the comparedmodels.Thismethod provides amore reliable approach for the condition detection and maintenance of fan blades in wind farms.
文摘Electromagnetic acoustic emission technology is one of nondestructive testing, which can be used for defect detection of metal specimens. In this study, round and cracked metal specimens, round metal specimens, and intact metal specimens were prepared. And the electromagnetic acoustic emission signals of the three specimens were collected. In addition, the local mean decomposition(LMD), Autoregressive model(AR model) and least squares support vector machine (LSSVM) algorithms were combined to identify the eletromagnetic acoustic emission signals of round and cracked, round, and intact specimens. According to the algorithm recognition results, the recognition accuracy of can reach above 97.5%, which has a higher recognition rate compared with SVM and BP neural network. The results of the study show that the algorithm is able to identify quickly and accurately crack defect in metal specimens.
文摘This study aimed to propose road crack detection method based on infrared image fusion technology.By analyzing the characteristics of road crack images,this method uses a variety of infrared image fusion methods to process different types of images.The use of this method allows the detection of road cracks,which not only reduces the professional requirements for inspectors,but also improves the accuracy of road crack detection.Based on infrared image processing technology,on the basis of in-depth analysis of infrared image features,a road crack detection method is proposed,which can accurately identify the road crack location,direction,length,and other characteristic information.Experiments showed that this method has a good effect,and can meet the requirement of road crack detection.
文摘This paper introduces a crack detection example of the prestressed box girder in a certain project. The morphology of the box girder cracks was surveyed and mapped. The length, width, and depth of the cracks were inspected, and the strength and reinforcement configuration of the components were tested. The test results indicate that the strength and reinforcement configuration of the inspected components meet the design requirements. The crack at the end of the top plate of the box girder is a local compressive crack at the anchorage end. The width and length of the crack on the bottom surface of the top plate are not significant, and the depth is relatively shallow. Judging from the crack morphology, this crack is identified as a temperature crack. Additionally, based on the treatment measures for cracks of different widths, the treatment measures for the cracks of the components in this project are derived, providing a reference basis for similar projects in the future.
基金Project(51178193)supported by the National Natural Science Foundation of ChinaProject(2009 353-344-570)supported by the Ministry of Transport of ChinaProject(2010-02-051)supported by the Transportation Department of Guangdong Province,China
文摘With the digital image technology,a crack detection method of reinforced concrete bridge was studied for the performance assessment.The effects including the image gray level,pixel rate,noise filter,and edge detection were analyzed considering cracks qualities.A computer program was developed by visual C++6.0 programming language to detect the cracks,which was tested by 15cases of bridge video images.The results indicate that the relative error is within 6%for cracks larger than 0.3 mm cracks and it is less than 10%for crack width between 0.2 mm and 0.3 mm.In addition,for the crack below 0.1 mm,the relative error is more than30%because the bridge is in safe stage and it is very difficult to detect the actual width of crack.
基金Project(51778482)supported by the National Natural Science Foundation of China。
文摘Vibration-based pavement condition(roughness and obvious anomalies)monitoring has been expanding in road engineering.However,the indistinctive transverse cracking has hardly been considered.Therefore,a vehicle-based novel method is proposed for detecting the transverse cracking through signal processing techniques and support vector machine(SVM).The vibration signals of the car traveling on the transverse-cracked and the crack-free sections were subjected to signal processing in time domain,frequency domain and wavelet domain,aiming to find indices that can discriminate vibration signal between the cracked and uncracked section.These indices were used to form 8 SVM models.The model with the highest accuracy and F1-measure was preferred,consisting of features including vehicle speed,range,relative standard deviation,maximum Fourier coefficient,and wavelet coefficient.Therefore,a crack and crack-free classifier was developed.Then its feasibility was investigated by 2292 pavement sections.The detection accuracy and F1-measure are 97.25%and 85.25%,respectively.The cracking detection approach proposed in this paper and the smartphone-based detection method for IRI and other distress may form a comprehensive pavement condition survey system.
基金National Science Foundation,Grant number CMS-9900338
文摘Development and testing of a serially multiplexed fiber optic sensor system is described.The sensor differs from conventional fiber optic acoustic systems,as it is capable of sensing AE emissions at several points along the length of a single fiber.Multiplexing provides for single channel detection of cracks and their locations in large structural systems. An algorithm was developed for signal recognition and tagging of the AE waveforms for detection of' crack locations,Labora- tory experiments on plain concrete beams and post-tensioned FRP tendons were pcrlormed to evaluate the crack detection capability of the sensor system.The acoustic emission sensor was able to detect initiation,growth and location of the cracks in concrete as well as in the FRP tendons.The AE system is potentially suitable lot applications involving health monitoring of structures following an earthquake.
