Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offis...Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offish to be supplied, most seafood processing companies have yet to installautomation equipment. Such absence of automation equipment for seafoodprocessing incurs a considerable cost regarding labor force, economy, andtime. Moreover, workers responsible for fish processing are exposed to risksbecause fish processing tasks require the use of dangerous tools, such aspower saws or knives. To solve these problems observed in the fish processingfield, this study proposed a fish cutting point prediction method based onAI machine vision and target weight. The proposed method performs threedimensional(3D) modeling of a fish’s form based on image processing techniquesand partitioned random sample consensus (RANSAC) and extracts 3Dfeature information. Then, it generates a neural network model for predictingfish cutting points according to the target weight by performing machinelearning of the extracted 3D feature information and measured weight information.This study allows for the direct cutting of fish based on cutting pointspredicted by the proposed method. Subsequently, we compared the measuredweight of the cut pieces with the target weight. The comparison result verifiedthat the proposed method showed a mean error rate of approximately 3%.展开更多
The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographica...The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.展开更多
Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still inv...Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.展开更多
The objective of this study was to develop an online tool-wear-measurement scheme for small diameter end-mills based on machine vision to increase tool life and the production efficiency. The geometrical features of w...The objective of this study was to develop an online tool-wear-measurement scheme for small diameter end-mills based on machine vision to increase tool life and the production efficiency. The geometrical features of wear zone of each end mill were analyzed, and three tool wear criterions of small-diameter end mills were defined. With the uEye camera, macro lens and 3-axis micro milling machine, it was proved the feasibility of measuring flank wear with the milling tests on a 45# steel workpiece. The design of experiment (DOE) showed that Vc was the most remarkable effect factor for the flank wear of small-diameter end mill. The wear curve of the experiments of milling was very similar to the Taylor curve.展开更多
This study assessed the feasibility of developing a machine vision system equipped with ultraviolet (UV) light, using changes in fish-surface color to predict aerobic plate count (APC, a standard freshness indicator) ...This study assessed the feasibility of developing a machine vision system equipped with ultraviolet (UV) light, using changes in fish-surface color to predict aerobic plate count (APC, a standard freshness indicator) during storage. The APC values were tested and images of the fish surface were taken when fish were stored at room temperature. Then, images</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span><span><span><span> color-space conversion among RGB, HSV, and L*a*b* color spaces was carried out and analyzed. The results revealed that a* and b* values from the UV-light image decreased linearly during storage. A further regression analysis of these two parameters with APC value demonstrated a good exponential relationship between the a* value and the APC value (R</span><sup><span>2</span></sup><span> = 0.97), followed by the b* (R</span><sup><span>2</span></sup><span> = 0.85). Therefore, our results suggest that the change in color of the fish surface under UV light can be used to assess fish freshness during storage.展开更多
Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener...Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.展开更多
Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizati...Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.展开更多
Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to ...Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field.Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric imaging.This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping.This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.In this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods.This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.展开更多
Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield.Therefore,the study explores corn identification and positioning methods based on machine vision.The ultra-green fea...Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield.Therefore,the study explores corn identification and positioning methods based on machine vision.The ultra-green feature algorithm and maximum betweenclass variance method(OTSU)were used to segment maize corn,weeds,and land;the segmentation effect was significant and can meet the following shape feature extraction requirements.Finally,the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method.The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h,the recognition accuracy can reach 94.1%.The technique used in this study is accessible for normal cases and can make a good recognition effect;the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.展开更多
The uniformity of appearance attributes of bell peppers is significant for consumers and food industries.To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and ...The uniformity of appearance attributes of bell peppers is significant for consumers and food industries.To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers,developing an appropriate sorting system would be of high importance and influence.According to standards and export needs,the bell pepper should be graded based on maturity levels and size to five classes.This research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples,with the ability to predict the maturity level and the size of the desirable bell peppers.Multilayer perceptron(MLP)artificial neural networks(ANNs)as the nonlinear modelswere designed for that purpose.TheMLP modelswere trained and evaluated through five-fold cross-validation method.The optimum MLP classifier was compared with a linear discriminant analysis(LDA)model.The results showed that the MLP outperforms the LDA model.The processing time to classify each captured image was estimated as 0.2 s/sample,which is fast enough for in-line application.Accordingly,the optimum MLP model was integrated with a machine vision-based sorting machine,and the developed system was evaluated in the in-line phase.The performance parameters,including accuracy,precision,sensitivity,and specificity,were 93.2%,86.4%,84%,and 95.7%,respectively.The total sorting rate of the bell pepper was also measured as approximately 3000 samples/h.展开更多
Owing to the requirements of a high yield and high-quality tomatoes, tomato grading is important-particularly for fruit morphology, and accuracy has become the focus of attention. Machine vision provides a fast and no...Owing to the requirements of a high yield and high-quality tomatoes, tomato grading is important-particularly for fruit morphology, and accuracy has become the focus of attention. Machine vision provides a fast and nondestructive manner to address this demand. In this study, the gamma correction method was used for preprocessing to enhance the edge information of tomatoes, and Otsu’s method was used to eliminate the tomato-image background in the A-component image under the LAB color model. On this basis, two levels of exploration were conducted. First, new evaluation indices were proposed for tomato shapes from different views. For the top view, two shape-evaluation indices were established: the area ratio of the maximum inscribed circle to the maximum circumscribed circle and the dispersion of the contour centroid distance (range and coefficient of variation), the highest accuracy was 94%. For the side view, the difference between the maximum and minimum centroid distances in the contour was established as a shape index, the highest accuracy was 91.91%. Second, an evaluation method based on multi-view fusion was developed by combining the advantage indices for different views. The classification accuracy reached 96%, with the highest identification accuracy of unqualified tomatoes. The results show that the proposed evaluation method combining top views (dispersion of centroid distance) with side views (difference between maximum and minimum centroid distances) is effective for classifying tomatoes.展开更多
This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divi...This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divided into two parts.The first part was utilized to precisely position the cow’s back by employing a dedicated deep learning model named GhostNet_YOLOv4,which can be implemented on mobile or embedded devices.The second part was used with the Visual Background Extractor(Vibe)algorithm,incorporating additional morphological processing techniques.Enhancing the Vibe algorithm,a widely used background subtraction algorithm for image sequences,achieved more accurate recognition of the specific pixel areas of cows.Subsequently,cow shape-related feature parameters were extracted from the back area using the combined approach.These parameters were used to calculate the average curvature,which describes the degree of curvature of the cow’s back contour during walking.The differences in curvature values were employed for classification to detect lameness.Through extensive experimentation,distinct average curvature ranges of[−0.025,−0.125],[−0.025,+∞],and[−∞,−0.125]were established for normal cows,early lameness,and moderate-severe lameness,respectively.The algorithm’s effectiveness was validated by processing 600 image sequences of dairy cows,resulting in a lameness detection accuracy of 91.67%.These findings can serve as a reference for the timely and accurate recognition of lameness in dairy cows.展开更多
Machine vision measurement(MVM)is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control.The result of MVM is determined by its configuration,e...Machine vision measurement(MVM)is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control.The result of MVM is determined by its configuration,especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing.In a traditional workflow,engineers constantly adjust and verify the configuration for an acceptable result,which is time-consuming and significantly depends on expertise.To address these challenges,we propose a target-independent approach,visual interactive image clustering,which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters.Our approach has four steps:data preparation,data sampling,data processing,and visual analysis with our visualization system.During preparation,engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images.Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm.The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters.Based on the image relationships,we develop VMExplorer,a visual analytics system that assists engineers in grouping images into clusters and exploring parameters.Finally,engineers can determine an appropriate lighting scheme with robust parameter combinations.To demonstrate the effiectiveness and usability of our approach,we conduct a case study with engineers and obtain feedback from expert interviews.展开更多
Manufacturing and agricultural industries use manual methods to count materials. This leads to low accuracy and inefficiency. This paper proposes a secondary counting method that combines main and differential countin...Manufacturing and agricultural industries use manual methods to count materials. This leads to low accuracy and inefficiency. This paper proposes a secondary counting method that combines main and differential counting. The area-fill identification algorithm is applied to mark the counted materials. To verify the effectiveness of the proposed counting algorithm, numbers of countings are conducted for different materials, such as the screws, hole gaskets, beans, jujube, etc. The results show that the counting accuracy reaches 98% for materials with size of 2—20 mm. The method has delivered a high-efficiency and high-accuracy automatic intelligent counting, with a wide range of application prospects and reference value.展开更多
Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than ot...Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than other traditional machine learning(ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.展开更多
With the decrease of agricultural labor and the increase of production cost,the researches on citrus harvesting robot(CHR)have received more and more attention in recent years.For the success of robotic harvesting and...With the decrease of agricultural labor and the increase of production cost,the researches on citrus harvesting robot(CHR)have received more and more attention in recent years.For the success of robotic harvesting and the safety of robot,the identification of mature citrus fruit and obstacle is the priority of robotic harvesting.In this work,a machine vision system,which consisted of a color CCD camera and a computer,was developed to achieve these tasks.Images of citrus trees were captured under sunny and cloudy conditions.Due to varying degrees of lightness and position randomness of fruits and branches,red,green,and blue values of objects in these images are changed dramatically.The traditional threshold segmentation is not efficient to solve these problems.Multi-class support vector machine(SVM),which succeeds by morphological operation,was used to simultaneously segment the fruits and branches in this study.The recognition rate of citrus fruit was 92.4%,and the branch of which diameter was more than 5 pixels,could be recognized.The results showed that the algorithm could be used to detect the fruits and branches for CHR.展开更多
The accuracy of extracting projected pig area is critical to the accuracy of the weight measurement of pigs by machine vision.The capability of both the conventional and the edge detection methods for extracting pig a...The accuracy of extracting projected pig area is critical to the accuracy of the weight measurement of pigs by machine vision.The capability of both the conventional and the edge detection methods for extracting pig area was examined using the images of 47 pigs of different weights.Relationship between the threshold value and the extracted area was numerically analyzed for both methods.It was found that the accuracy of the conventional method depended heavily on the threshold value,while choice of threshold value in the edge detection approach had no influence on the extracted area over a wide range.In normal lighting conditions,both methods yielded comparable values of predicted weight;however,under variable light intensities,the edge detection method was superior to the conventional method,because the former was proven to be independent of light intensities.This makes edge detection an ideal method for area extraction during the walk-through weighing process where pigs are allowed to move around.展开更多
Chemical sucker control has been proven to be a more efficient method than manual and mechanical removals.The quick and effective identification and location of suckers are key technologies for targeted spray that can...Chemical sucker control has been proven to be a more efficient method than manual and mechanical removals.The quick and effective identification and location of suckers are key technologies for targeted spray that can reduce chemical applications and alleviate potential problems.The goal of this research was to improve the accuracy of identification and location algorithm of grapevine suckers for real-time mobile targeted spray based on information fusion of two dimensional(2D)laser scanner and camera machine vision.A triangle white calibration board was used to determine the invisible laser scanning line.The positions of the terminated points of the scanning line on the calibration board in the laser scanner’s coordinates were calculated.Suckers size and center location were obtained by ExGExR segmentation,then the relative position between the suckers and triangle calibration board was determined in the image coordinates.Eventually,the actual size and relative position between the identified suckers and the platform were calculated by integrating the laser line and image information.The results of the field trials showed that the consumed time of the developed algorithm was 0.787 s,the width recognition rate 91.8%,height recognition rate 88.2%,and the relative position accuracies 92.0%,87.3%,which could meet the requirement of grapevine sucker precision targeted spray.展开更多
In order to realize the automatic monitoring of ruminant activities of cows,an automatic detection method for the mouth area of ruminant cows based on machine vision technology was studied.Optical flow was used to cal...In order to realize the automatic monitoring of ruminant activities of cows,an automatic detection method for the mouth area of ruminant cows based on machine vision technology was studied.Optical flow was used to calculate the relative motion speed of each pixel in the video frame images.The candidate mouth region with large motion ranges was extracted,and a series of processing methods,such as grayscale processing,threshold segmentation,pixel point expansion and adjacent region merging,were carried out to extract the real area of cows’mouth.To verify the accuracy of the proposed method,six videos with a total length of 96 min were selected for this research.The results showed that the highest accuracy was 87.80%,the average accuracy was 76.46%and the average running time of the algorithm was 6.39 s.All the results showed that this method can be used to detect the mouth area automatically,which lays the foundation for automatic monitoring of cows’ruminant behavior.展开更多
This research investigated the size detecting and online grading of Red Globe grapes using images of entire cases,rather than individual grapes.Method of ellipse fitting based on iterative least median squares was pro...This research investigated the size detecting and online grading of Red Globe grapes using images of entire cases,rather than individual grapes.Method of ellipse fitting based on iterative least median squares was proposed and the process of grape grading includes the following four steps:stem removal from the RGB and NIR images collected by the 2-CCD camera;edge extraction by multiple methods of edge detection,image binarization,morphological processing,et al.;size determination of individual grapes by using image segmentation and ellipse fitting to calculate short axis length;Finally,grading based on the 15%downgrade principle,this means that if the case contains more than 15%of multiple grades,then the case is re-evaluated.Thirty-eight cases of Red Globe grapes were graded using these methods and 35 cases were correctly graded with an accuracy rate reaching 92.1%.The results showed that the accuracy and speed meet the requirements of grape automatic online detection.展开更多
基金supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF)funded by the Ministry of Education (NRF-2020R1I1A3073313).
文摘Food processing companies pursue the distribution of ingredientsthat were packaged according to a certain weight. Particularly, foods like fishare highly demanded and supplied. However, despite the high quantity offish to be supplied, most seafood processing companies have yet to installautomation equipment. Such absence of automation equipment for seafoodprocessing incurs a considerable cost regarding labor force, economy, andtime. Moreover, workers responsible for fish processing are exposed to risksbecause fish processing tasks require the use of dangerous tools, such aspower saws or knives. To solve these problems observed in the fish processingfield, this study proposed a fish cutting point prediction method based onAI machine vision and target weight. The proposed method performs threedimensional(3D) modeling of a fish’s form based on image processing techniquesand partitioned random sample consensus (RANSAC) and extracts 3Dfeature information. Then, it generates a neural network model for predictingfish cutting points according to the target weight by performing machinelearning of the extracted 3D feature information and measured weight information.This study allows for the direct cutting of fish based on cutting pointspredicted by the proposed method. Subsequently, we compared the measuredweight of the cut pieces with the target weight. The comparison result verifiedthat the proposed method showed a mean error rate of approximately 3%.
文摘The inter-class face classification problem is more reasonable than the intra-class classification problem.To address this issue,we have carried out empirical research on classifying Indian people to their geographical regions.This work aimed to construct a computational classification model for classifying Indian regional face images acquired from south and east regions of India,referring to human vision.We have created an Automated Human Intelligence System(AHIS)to evaluate human visual capabilities.Analysis of AHIS response showed that face shape is a discriminative feature among the other facial features.We have developed a modified convolutional neural network to characterize the human vision response to improve face classification accuracy.The proposed model achieved mean F1 and Matthew Correlation Coefficient(MCC)of 0.92 and 0.84,respectively,on the validation set,outperforming the traditional Convolutional Neural Network(CNN).The CNN-Contoured Face(CNN-FC)model is developed to train contoured face images to investigate the influence of face shape.Finally,to cross-validate the accuracy of these models,the traditional CNN model is trained on the same dataset.With an accuracy of 92.98%,the Modified-CNN(M-CNN)model has demonstrated that the proposed method could facilitate the tangible impact in intra-classification problems.A novel Indian regional face dataset is created for supporting this supervised classification work,and it will be available to the research community.
基金Supported by the Fundamental Public Welfare Research Program of Zhejiang Provincial Natural Science Foundation,China(LGN18C140007 and Y20C140024)the National High Technology Research and Development Program of China(863 Program,2013AA102402)the Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural Sciences.
文摘Monitring pest populations in paddy fields is important to effectively implement integrated pest management.Light traps are widely used to monitor field pests all over the world.Most conventional light traps still involve manual identification of target pests from lots of trapped insects,which is time-consuming,labor-intensive and error-prone,especially in pest peak periods.In this paper,we developed an automatic monitoring system for rice light-trap pests based on machine vision.This system is composed of an itelligent light trap,a computer or mobile phone client platform and a cloud server.The light trap firstly traps,kills and disperses insects,then collects images of trapped insects and sends each image to the cloud server.Five target pests in images are automatically identifed and counted by pest identification models loaded in the server.To avoid light-trap insects piling up,a vibration plate and a moving rotation conveyor belt are adopted to disperse these trapped insects.There was a close correlation(r=0.92)between our automatic and manual identification methods based on the daily pest number of one-year images from one light trap.Field experiments demonstrated the effectiveness and accuracy of our automatic light trap monitoring system.
基金Supported by the Ministerial Level Advanced Research Foundation(51318020309)
文摘The objective of this study was to develop an online tool-wear-measurement scheme for small diameter end-mills based on machine vision to increase tool life and the production efficiency. The geometrical features of wear zone of each end mill were analyzed, and three tool wear criterions of small-diameter end mills were defined. With the uEye camera, macro lens and 3-axis micro milling machine, it was proved the feasibility of measuring flank wear with the milling tests on a 45# steel workpiece. The design of experiment (DOE) showed that Vc was the most remarkable effect factor for the flank wear of small-diameter end mill. The wear curve of the experiments of milling was very similar to the Taylor curve.
文摘This study assessed the feasibility of developing a machine vision system equipped with ultraviolet (UV) light, using changes in fish-surface color to predict aerobic plate count (APC, a standard freshness indicator) during storage. The APC values were tested and images of the fish surface were taken when fish were stored at room temperature. Then, images</span></span><span style="font-family:Verdana;"><span style="font-family:Verdana;"><span style="font-family:Verdana;">’</span></span></span><span><span><span><span> color-space conversion among RGB, HSV, and L*a*b* color spaces was carried out and analyzed. The results revealed that a* and b* values from the UV-light image decreased linearly during storage. A further regression analysis of these two parameters with APC value demonstrated a good exponential relationship between the a* value and the APC value (R</span><sup><span>2</span></sup><span> = 0.97), followed by the b* (R</span><sup><span>2</span></sup><span> = 0.85). Therefore, our results suggest that the change in color of the fish surface under UV light can be used to assess fish freshness during storage.
基金funded by the Key Research and Development Project of China Academy of Railway Sciences Corporation Limited(2021YJ310).
文摘Purpose–This research aims to improve the performance of rail fastener defect inspection method for multi railways,to effectively ensure the safety of railway operation.Design/methodology/approach–Firstly,a fastener region location method based on online learning strategy was proposed,which can locate fastener regions according to the prior knowledge of track image and template matching method.Online learning strategy is used to update the template library dynamically,so that the method not only can locate fastener regions in the track images of multi railways,but also can automatically collect and annotate fastener samples.Secondly,a fastener defect recognition method based on deep convolutional neural network was proposed.The structure of recognition network was designed according to the smaller size and the relatively single content of the fastener region.The data augmentation method based on the sample random sorting strategy is adopted to reduce the impact of the imbalance of sample size on recognition performance.Findings–Test verification of the proposed method is conducted based on the rail fastener datasets of multi railways.Specifically,fastener location module has achieved an average detection rate of 99.36%,and fastener defect recognition module has achieved an average precision of 96.82%.Originality/value–The proposed method can accurately locate fastener regions and identify fastener defect in the track images of different railways,which has high reliability and strong adaptability to multi railways.
文摘Coal is heterogeneous in nature,and thus the characterization of coal is essential before its use for a specific purpose.Thus,the current study aims to develop a machine vision system for automated coal characterizations.The model was calibrated using 80 image samples that are captured for different coal samples in different angles.All the images were captured in RGB color space and converted into five other color spaces(HSI,CMYK,Lab,xyz,Gray)for feature extraction.The intensity component image of HSI color space was further transformed into four frequency components(discrete cosine transform,discrete wavelet transform,discrete Fourier transform,and Gabor filter)for the texture features extraction.A total of 280 image features was extracted and optimized using a step-wise linear regression-based algorithm for model development.The datasets of the optimized features were used as an input for the model,and their respective coal characteristics(analyzed in the laboratory)were used as outputs of the model.The R-squared values were found to be 0.89,0.92,0.92,and 0.84,respectively,for fixed carbon,ash content,volatile matter,and moisture content.The performance of the proposed artificial neural network model was also compared with the performances of performances of Gaussian process regression,support vector regression,and radial basis neural network models.The study demonstrates the potential of the machine vision system in automated coal characterization.
文摘Today there is a rapid development taking place in phenotyping of plants using non-destructive image based machine vision techniques.Machine vision based plant phenotyping ranges from single plant trait estimation to broad assessment of crop canopy for thousands of plants in the field.Plant phenotyping systems either use single imaging method or integrative approach signifying simultaneous use of some of the imaging techniques like visible red,green and blue(RGB)imaging,thermal imaging,chlorophyll fluorescence imaging(CFIM),hyperspectral imaging,3-dimensional(3-D)imaging or high resolution volumetric imaging.This paper provides an overview of imaging techniques and their applications in the field of plant phenotyping.This paper presents a comprehensive survey on recent machine vision methods for plant trait estimation and classification.In this paper,information about publicly available datasets is provided for uniform comparison among the state-of-the-art phenotyping methods.This paper also presents future research directions related to the use of deep learning based machine vision algorithms for structural(2-D and 3-D),physiological and temporal trait estimation,and classification studies in plants.
基金the National Key Research and Development Program of China[Grant numbers:2019YFB1312303].
文摘Weeds that grow among crops are undesirable plants and have adversely affected crop growth and yield.Therefore,the study explores corn identification and positioning methods based on machine vision.The ultra-green feature algorithm and maximum betweenclass variance method(OTSU)were used to segment maize corn,weeds,and land;the segmentation effect was significant and can meet the following shape feature extraction requirements.Finally,the identification and positioning of corn were achieved by morphological reconstruction and pixel projection histogram method.The experiment reveals that when a weeding robot travels at a speed of 1.6 km/h,the recognition accuracy can reach 94.1%.The technique used in this study is accessible for normal cases and can make a good recognition effect;the accuracy and real-time requirements of robot recognition are improved and reduced the calculation time.
文摘The uniformity of appearance attributes of bell peppers is significant for consumers and food industries.To automate the sorting process of bell peppers and improve the packaging quality of this crop by detecting and separating the not likable low-color bell peppers,developing an appropriate sorting system would be of high importance and influence.According to standards and export needs,the bell pepper should be graded based on maturity levels and size to five classes.This research has been aimed to develop a machine vision-based system equipped with an intelligent modelling approach for in-line sorting bell peppers into desirable and undesirable samples,with the ability to predict the maturity level and the size of the desirable bell peppers.Multilayer perceptron(MLP)artificial neural networks(ANNs)as the nonlinear modelswere designed for that purpose.TheMLP modelswere trained and evaluated through five-fold cross-validation method.The optimum MLP classifier was compared with a linear discriminant analysis(LDA)model.The results showed that the MLP outperforms the LDA model.The processing time to classify each captured image was estimated as 0.2 s/sample,which is fast enough for in-line application.Accordingly,the optimum MLP model was integrated with a machine vision-based sorting machine,and the developed system was evaluated in the in-line phase.The performance parameters,including accuracy,precision,sensitivity,and specificity,were 93.2%,86.4%,84%,and 95.7%,respectively.The total sorting rate of the bell pepper was also measured as approximately 3000 samples/h.
基金supported by the Beijing Nova Program (Grant No.2023141)Yunnan Key Research and Development Program (Grant No.202202AE090066)the Project of Beijing Academy of Agricultural Sciences (Grant No.YXQN202304)。
文摘Owing to the requirements of a high yield and high-quality tomatoes, tomato grading is important-particularly for fruit morphology, and accuracy has become the focus of attention. Machine vision provides a fast and nondestructive manner to address this demand. In this study, the gamma correction method was used for preprocessing to enhance the edge information of tomatoes, and Otsu’s method was used to eliminate the tomato-image background in the A-component image under the LAB color model. On this basis, two levels of exploration were conducted. First, new evaluation indices were proposed for tomato shapes from different views. For the top view, two shape-evaluation indices were established: the area ratio of the maximum inscribed circle to the maximum circumscribed circle and the dispersion of the contour centroid distance (range and coefficient of variation), the highest accuracy was 94%. For the side view, the difference between the maximum and minimum centroid distances in the contour was established as a shape index, the highest accuracy was 91.91%. Second, an evaluation method based on multi-view fusion was developed by combining the advantage indices for different views. The classification accuracy reached 96%, with the highest identification accuracy of unqualified tomatoes. The results show that the proposed evaluation method combining top views (dispersion of centroid distance) with side views (difference between maximum and minimum centroid distances) is effective for classifying tomatoes.
基金This work was supported by Shanxi Province Basic Research Program(Free Exploration)Project(No:202103021224149)Shanxi Province Postgraduate Education Teaching Reform Project(2021YJJG087)Shanxi Province Educational Science“14th Five-Year Plan”Education Evaluation Special Project(PJ-21001)funded.
文摘This study proposed a method for detecting lameness in dairy cows based on machine vision,addressing the challenges associated with manual detection.Data from a dairy farm in Taigu,Shanxi,China were collected and divided into two parts.The first part was utilized to precisely position the cow’s back by employing a dedicated deep learning model named GhostNet_YOLOv4,which can be implemented on mobile or embedded devices.The second part was used with the Visual Background Extractor(Vibe)algorithm,incorporating additional morphological processing techniques.Enhancing the Vibe algorithm,a widely used background subtraction algorithm for image sequences,achieved more accurate recognition of the specific pixel areas of cows.Subsequently,cow shape-related feature parameters were extracted from the back area using the combined approach.These parameters were used to calculate the average curvature,which describes the degree of curvature of the cow’s back contour during walking.The differences in curvature values were employed for classification to detect lameness.Through extensive experimentation,distinct average curvature ranges of[−0.025,−0.125],[−0.025,+∞],and[−∞,−0.125]were established for normal cows,early lameness,and moderate-severe lameness,respectively.The algorithm’s effectiveness was validated by processing 600 image sequences of dairy cows,resulting in a lameness detection accuracy of 91.67%.These findings can serve as a reference for the timely and accurate recognition of lameness in dairy cows.
基金Project supported by the National Key R&D Program of China(No.2020YFB1707700)the Zhejiang Provincial Natural Science Foundation of China(No.LR23F020003)the National Nat-ural Science Foundation of China(Nos.61972356 and 62036009)。
文摘Machine vision measurement(MVM)is an essential approach that measures the area or length of a target efficiently and non-destructively for product quality control.The result of MVM is determined by its configuration,especially the lighting scheme design in image acquisition and the algorithmic parameter optimization in image processing.In a traditional workflow,engineers constantly adjust and verify the configuration for an acceptable result,which is time-consuming and significantly depends on expertise.To address these challenges,we propose a target-independent approach,visual interactive image clustering,which facilitates configuration optimization by grouping images into different clusters to suggest lighting schemes with common parameters.Our approach has four steps:data preparation,data sampling,data processing,and visual analysis with our visualization system.During preparation,engineers design several candidate lighting schemes to acquire images and develop an algorithm to process images.Our approach samples engineer-defined parameters for each image and obtains results by executing the algorithm.The core of data processing is the explainable measurement of the relationships among images using the algorithmic parameters.Based on the image relationships,we develop VMExplorer,a visual analytics system that assists engineers in grouping images into clusters and exploring parameters.Finally,engineers can determine an appropriate lighting scheme with robust parameter combinations.To demonstrate the effiectiveness and usability of our approach,we conduct a case study with engineers and obtain feedback from expert interviews.
基金supported by the Special Fund of Science and Technology Innovation Strategy of Guangdong Province in 2021 (No.pdjh2021a0284)the National Natural Science Foundation of China (No.52105436)+1 种基金the Guangzhou Science and Technology Plan (No.202102080184)the Guangdong Education Department Project (No.2019KTSCX086)。
文摘Manufacturing and agricultural industries use manual methods to count materials. This leads to low accuracy and inefficiency. This paper proposes a secondary counting method that combines main and differential counting. The area-fill identification algorithm is applied to mark the counted materials. To verify the effectiveness of the proposed counting algorithm, numbers of countings are conducted for different materials, such as the screws, hole gaskets, beans, jujube, etc. The results show that the counting accuracy reaches 98% for materials with size of 2—20 mm. The method has delivered a high-efficiency and high-accuracy automatic intelligent counting, with a wide range of application prospects and reference value.
基金supported by the Project SP2023/074 Application of Machine and Process Control Advanced Methods supported by the Ministry of Education,Youth and Sports,Czech Republic.
文摘Computer vision(CV)was developed for computers and other systems to act or make recommendations based on visual inputs,such as digital photos,movies,and other media.Deep learning(DL)methods are more successful than other traditional machine learning(ML)methods inCV.DL techniques can produce state-of-the-art results for difficult CV problems like picture categorization,object detection,and face recognition.In this review,a structured discussion on the history,methods,and applications of DL methods to CV problems is presented.The sector-wise presentation of applications in this papermay be particularly useful for researchers in niche fields who have limited or introductory knowledge of DL methods and CV.This review will provide readers with context and examples of how these techniques can be applied to specific areas.A curated list of popular datasets and a brief description of them are also included for the benefit of readers.
基金International Science&Technology Cooperation Program of China(2013DFA11470)the National Natural Science Foundation of China(30771243)+1 种基金International Science&Technology Cooperation Program of Chongqing(cstc2011gjhz80001)Fundamental Research Funds for the Central Universities(XDJK2013C102).
文摘With the decrease of agricultural labor and the increase of production cost,the researches on citrus harvesting robot(CHR)have received more and more attention in recent years.For the success of robotic harvesting and the safety of robot,the identification of mature citrus fruit and obstacle is the priority of robotic harvesting.In this work,a machine vision system,which consisted of a color CCD camera and a computer,was developed to achieve these tasks.Images of citrus trees were captured under sunny and cloudy conditions.Due to varying degrees of lightness and position randomness of fruits and branches,red,green,and blue values of objects in these images are changed dramatically.The traditional threshold segmentation is not efficient to solve these problems.Multi-class support vector machine(SVM),which succeeds by morphological operation,was used to simultaneously segment the fruits and branches in this study.The recognition rate of citrus fruit was 92.4%,and the branch of which diameter was more than 5 pixels,could be recognized.The results showed that the algorithm could be used to detect the fruits and branches for CHR.
基金The project was supported in part by the National Research Initiative of the USDA Cooperative State Research,Education and Extension Service,grant number 2003-35503-13990.
文摘The accuracy of extracting projected pig area is critical to the accuracy of the weight measurement of pigs by machine vision.The capability of both the conventional and the edge detection methods for extracting pig area was examined using the images of 47 pigs of different weights.Relationship between the threshold value and the extracted area was numerically analyzed for both methods.It was found that the accuracy of the conventional method depended heavily on the threshold value,while choice of threshold value in the edge detection approach had no influence on the extracted area over a wide range.In normal lighting conditions,both methods yielded comparable values of predicted weight;however,under variable light intensities,the edge detection method was superior to the conventional method,because the former was proven to be independent of light intensities.This makes edge detection an ideal method for area extraction during the walk-through weighing process where pigs are allowed to move around.
基金National Natural Science Foundation of China(No.31600588)the Fundamental Research Funds for the Central Universities(No.2015ZCQ-GX-01).
文摘Chemical sucker control has been proven to be a more efficient method than manual and mechanical removals.The quick and effective identification and location of suckers are key technologies for targeted spray that can reduce chemical applications and alleviate potential problems.The goal of this research was to improve the accuracy of identification and location algorithm of grapevine suckers for real-time mobile targeted spray based on information fusion of two dimensional(2D)laser scanner and camera machine vision.A triangle white calibration board was used to determine the invisible laser scanning line.The positions of the terminated points of the scanning line on the calibration board in the laser scanner’s coordinates were calculated.Suckers size and center location were obtained by ExGExR segmentation,then the relative position between the suckers and triangle calibration board was determined in the image coordinates.Eventually,the actual size and relative position between the identified suckers and the platform were calculated by integrating the laser line and image information.The results of the field trials showed that the consumed time of the developed algorithm was 0.787 s,the width recognition rate 91.8%,height recognition rate 88.2%,and the relative position accuracies 92.0%,87.3%,which could meet the requirement of grapevine sucker precision targeted spray.
基金This work was supported by the National Key Research and Development Program of China(2017YFD0701603)Natural Science Foundation of China(61473235).
文摘In order to realize the automatic monitoring of ruminant activities of cows,an automatic detection method for the mouth area of ruminant cows based on machine vision technology was studied.Optical flow was used to calculate the relative motion speed of each pixel in the video frame images.The candidate mouth region with large motion ranges was extracted,and a series of processing methods,such as grayscale processing,threshold segmentation,pixel point expansion and adjacent region merging,were carried out to extract the real area of cows’mouth.To verify the accuracy of the proposed method,six videos with a total length of 96 min were selected for this research.The results showed that the highest accuracy was 87.80%,the average accuracy was 76.46%and the average running time of the algorithm was 6.39 s.All the results showed that this method can be used to detect the mouth area automatically,which lays the foundation for automatic monitoring of cows’ruminant behavior.
基金Natural Science Fund of Hubei Province(2012FKB02910)Research and Development Projects in Hubei Province(2011BHB01).
文摘This research investigated the size detecting and online grading of Red Globe grapes using images of entire cases,rather than individual grapes.Method of ellipse fitting based on iterative least median squares was proposed and the process of grape grading includes the following four steps:stem removal from the RGB and NIR images collected by the 2-CCD camera;edge extraction by multiple methods of edge detection,image binarization,morphological processing,et al.;size determination of individual grapes by using image segmentation and ellipse fitting to calculate short axis length;Finally,grading based on the 15%downgrade principle,this means that if the case contains more than 15%of multiple grades,then the case is re-evaluated.Thirty-eight cases of Red Globe grapes were graded using these methods and 35 cases were correctly graded with an accuracy rate reaching 92.1%.The results showed that the accuracy and speed meet the requirements of grape automatic online detection.
