Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,maki...Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.展开更多
Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for deton...Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for detonation. For this, the study uses a set of 22,000 X-ray scanned images. After preprocessing with filtering techniques to improve image quality, deep learning methods, such as Convolutional Neural Networks (CNNs), are applied for classification. The results are also compared with Autoencoder and Random Forest algorithms. The results are validated on a second dataset, highlighting the advantages of the adopted approach. Baggage screening is a very important part of the risk assessment and security screening process at airports. Automating the detection of dangerous objects from passenger baggage X-ray scanners can speed up and increase the efficiency of the entire security procedure.展开更多
With the growth of the online market,demand for logistics and courier cargo is increasing rapidly.Accordingly,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly occu...With the growth of the online market,demand for logistics and courier cargo is increasing rapidly.Accordingly,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly occurring.The joint courier logistics system,a plan to solve this problem,aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies.However,several courier companies use different types of courier invoices.Such a system has a problem of information data transmission interruption.Therefore,the data processing process was systematically analyzed,a practically feasible methodology was devised,and delivery invoice information processing standards were established for this.In addition,the importance of this paper can be emphasized in terms of data processing in the logistics sector,which is expected to grow rapidly in the future.The results of this study can be used as basic data for the implementation of the logistics joint delivery terminal system in the future.And it can be used as a basis for securing the operational reliability of the joint courier logistics system.展开更多
With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure.In order to achieve faster and m...With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure.In order to achieve faster and more accurate identification of traffic vehicles,computer vision and deep learning technology play a vital role and have made significant advancements.This study summarizes the current research status,latest findings,and future development trends of traditional detection algorithms and deep learning-based detection algorithms.Among the detection algorithms based on deep learning,this study focuses on the representative convolutional neural network models.Specifically,it examines the two-stage and one-stage detection algorithms,which have been extensively utilized in the field of intelligent transportation systems.Compared to traditional detection algorithms,deep learning-based detection algorithms can achieve higher accuracy and efficiency.The single-stage detection algorithm is more efficient for real-time detection,while the two-stage detection algorithm is more accurate than the single-stage detection algorithm.In the follow-up research,it is important to consider the balance between detection efficiency and detection accuracy.Additionally,vehicle missed detection and false detection in complex scenes,such as bad weather and vehicle overlap,should be taken into account.This will ensure better application of the research findings in engineering practice.展开更多
Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is propose...Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.展开更多
To accurately and efficiently detect dead caged laying ducks,thereby reducing reliance on manual inspection,this study proposes a method that integrates infrared thermography with deep learning technology.A lightweigh...To accurately and efficiently detect dead caged laying ducks,thereby reducing reliance on manual inspection,this study proposes a method that integrates infrared thermography with deep learning technology.A lightweight object detection algorithm is developed,utilizing YOLO v8n as the baseline model.The backbone network is replaced with StarNet,which is based on“Star Operate”.Additionally,the C2f-Star structure is designed by combining the Star Block from StarNet with the C2f module,and it is inserted into the Neck structure of the baseline model.Lightweight module L-SPPF replaces the SPPF module in the baseline model to enhance feature augmentation.Furthermore,a lightweight shared convolutional detection head,termed SCSB-Head,is introduced to reduce computational complexity.These improvements collectively form a lightweight object detection algorithm named SLSS-YOLO.Experimental results show that SLSS-YOLO achieves mAP@50%-95%,precision,and recall scores of 80.50%,99.44%,and 98.46%,respectively.Compared to the baseline model,these metrics improve by 1%,1.98%,and 0.26%,respectively.In terms of model size and detection speed,SLSS-YOLO has 1.44 M parameters and 4.6 G FLOPs,achieving an FPS rate of 134.9 f/s.This represents a reduction of 52.16%and 43.90%in parameters and FLOPs,respectively,while increasing FPS by 5.4 f/s compared to the baseline model.Moreover,an object tracking model is constructed using SLSS-YOLO and Hybrid-SORT.Tracking tests demonstrate that Hybrid-SORT achieves zero ID-Switches,with a detection speed of 10.9 ms/f.It outperforms Bot-SORT,ByteTrack,Deep OC-SORT,and OC-SORT in terms of tracking performance.Therefore,the proposed thermal infrared detection method can effectively identify and track dead ducks in complex cage environments,providing a reference for automated inspection in caged duck farms.展开更多
A climatology of extratropical cyclones （ECs） over East Asia （20~ 75~N, 60^-160~E） is analyzed by applying an improved objective detection and tracking algorithm to the 4-time daily sea level pressure fields from ...A climatology of extratropical cyclones （ECs） over East Asia （20~ 75~N, 60^-160~E） is analyzed by applying an improved objective detection and tracking algorithm to the 4-time daily sea level pressure fields from the European Centre for Medium-range Weather Forecasts （ECMWF） reanalysis data. A total of 12914 EC processes for the period of 1958-2001 are identified, with an EC database integrated and EC activities reanalyzed using the objective algorithm. The results reveal that there are three major cyclogenesis regions： West Siberian Plain, Mongolia （to the south of Lake Baikal）, and the coastal region of East China; whereas significant cyclolysis regions are observed in Siberia north of 60~N, Northeast China, and Okhotsk Se^Northwest Pacific. It is found that the EC lifetime is largely 1 7 days while winter ECs have the shortest lifespan. The ECs are the weakest in summer among the four seasons. Strong ECs often appear in West Siberia, Northeast China, and Okhotsk Sea-Northwest Pacific. Statistical analysis based on k-means clustering has identified 6 dominating trajectories in the area south of 55~N and east of 80~E, among which 4 tracks have important impacts on weather/climate in China. ECs occurring in spring （summer） tend to travel the longest （shortest）. They move the fastest in winter, and the slowest in summer. In winter, cyclones move fast in Northeast China, some areas of the Yangtze-Huaihe River region, and the south of Japan, with speed greater than 15 m s-1. Explosively-deepening cyclones are found to occur frequently along the east coast of China, Japan, and Northwest Pacific, but very few storms occur over the inland area. Bombs prefer to occur in winter, spring, and autumn. Their annual number and intensity in 1990 and 1992 in East Asia （EA） are smaller and weaker than their counterparts in North America.展开更多
基金National Key Research and Development Program of China(Nos.2022YFB4700600 and 2022YFB4700605)National Natural Science Foundation of China(Nos.61771123 and 62171116)+1 种基金Fundamental Research Funds for the Central UniversitiesGraduate Student Innovation Fund of Donghua University,China(No.CUSF-DH-D-2022044)。
文摘Defect detection is vital in the nonwoven material industry,ensuring surface quality before producing finished products.Recently,deep learning and computer vision advancements have revolutionized defect detection,making it a widely adopted approach in various industrial fields.This paper mainly studied the defect detection method for nonwoven materials based on the improved Nano Det-Plus model.Using the constructed samples of defects in nonwoven materials as the research objects,transfer learning experiments were conducted based on the Nano DetPlus object detection framework.Within this framework,the Backbone,path aggregation feature pyramid network(PAFPN)and Head network models were compared and trained through a process of freezing,with the ultimate aim of bolstering the model's feature extraction abilities and elevating detection accuracy.The half-precision quantization method was used to optimize the model after transfer learning experiments,reducing model weights and computational complexity to improve the detection speed.Performance comparisons were conducted between the improved model and the original Nano Det-Plus model,YOLO,SSD and other common industrial defect detection algorithms,validating that the improved methods based on transfer learning and semi-precision quantization enabled the model to meet the practical requirements of industrial production.
文摘Baggage screening is crucial for airport security. This paper examines various algorithms for firearm detection in X-ray images of baggage. The focus is on identifying steel barrel bores, which are essential for detonation. For this, the study uses a set of 22,000 X-ray scanned images. After preprocessing with filtering techniques to improve image quality, deep learning methods, such as Convolutional Neural Networks (CNNs), are applied for classification. The results are also compared with Autoencoder and Random Forest algorithms. The results are validated on a second dataset, highlighting the advantages of the adopted approach. Baggage screening is a very important part of the risk assessment and security screening process at airports. Automating the detection of dangerous objects from passenger baggage X-ray scanners can speed up and increase the efficiency of the entire security procedure.
基金supported by a grant from R&D program of the Korea Evaluation Institute of Industrial Technology(20015047).
文摘With the growth of the online market,demand for logistics and courier cargo is increasing rapidly.Accordingly,in the case of urban areas,road congestion and environmental problems due to cargo vehicles are mainly occurring.The joint courier logistics system,a plan to solve this problem,aims to establish an efficient logistics transportation system by utilizing one joint logistics delivery terminal by several logistics and delivery companies.However,several courier companies use different types of courier invoices.Such a system has a problem of information data transmission interruption.Therefore,the data processing process was systematically analyzed,a practically feasible methodology was devised,and delivery invoice information processing standards were established for this.In addition,the importance of this paper can be emphasized in terms of data processing in the logistics sector,which is expected to grow rapidly in the future.The results of this study can be used as basic data for the implementation of the logistics joint delivery terminal system in the future.And it can be used as a basis for securing the operational reliability of the joint courier logistics system.
基金supported by the National Natural Science Foundation of China(No.52062027)the Key Research and Development Project of Gansu Province(No.22YF7GA142)+2 种基金the Soft Science Special Project of Gansu Basic Research Plan(No.22JR4ZA035)the Gansu Provincial Science and Technology Major Special Project-Enterprise Innovation Consortium Project(Nos.22ZD6GA010 and 21ZD3GA002)the Natural Science Foundation of Gansu Province(No.22JR5RA343).
文摘With the increasing number of vehicles,there has been an unprecedented pressure on the operation and maintenance of intelligent transportation systems and transportation infrastructure.In order to achieve faster and more accurate identification of traffic vehicles,computer vision and deep learning technology play a vital role and have made significant advancements.This study summarizes the current research status,latest findings,and future development trends of traditional detection algorithms and deep learning-based detection algorithms.Among the detection algorithms based on deep learning,this study focuses on the representative convolutional neural network models.Specifically,it examines the two-stage and one-stage detection algorithms,which have been extensively utilized in the field of intelligent transportation systems.Compared to traditional detection algorithms,deep learning-based detection algorithms can achieve higher accuracy and efficiency.The single-stage detection algorithm is more efficient for real-time detection,while the two-stage detection algorithm is more accurate than the single-stage detection algorithm.In the follow-up research,it is important to consider the balance between detection efficiency and detection accuracy.Additionally,vehicle missed detection and false detection in complex scenes,such as bad weather and vehicle overlap,should be taken into account.This will ensure better application of the research findings in engineering practice.
文摘Aimed at the long and narrow geometric features and poor generalization ability of the damage detection in conveyor belts with steel rope cores using the X-ray image,a detection method of damage X-ray image is proposed based on the improved fully convolutional one-stage object detection(FCOS)algorithm.The regression performance of bounding boxes was optimized by introducing the complete intersection over union loss function into the improved algorithm.The feature fusion network structure is modified by adding adaptive fusion paths to the feature fusion network structure,which makes full use of the features of accurate localization and semantics of multi-scale feature fusion networks.Finally,the network structure was trained and validated by using the X-ray image dataset of damages in conveyor belts with steel rope cores provided by a flaw detection equipment manufacturer.In addition,the data enhancement methods such as rotating,mirroring,and scaling,were employed to enrich the image dataset so that the model is adequately trained.Experimental results showed that the improved FCOS algorithm promoted the precision rate and the recall rate by 20.9%and 14.8%respectively,compared with the original algorithm.Meanwhile,compared with Fast R-CNN,Faster R-CNN,SSD,and YOLOv3,the improved FCOS algorithm has obvious advantages;detection precision rate and recall rate of the modified network reached 95.8%and 97.0%respectively.Furthermore,it demonstrated a higher detection accuracy without affecting the speed.The results of this work have some reference significance for the automatic identification and detection of steel core conveyor belt damage.
基金supported by the Fundamental Research Funds for the Central Universities(Grant No.2662022GXYJ004).
文摘To accurately and efficiently detect dead caged laying ducks,thereby reducing reliance on manual inspection,this study proposes a method that integrates infrared thermography with deep learning technology.A lightweight object detection algorithm is developed,utilizing YOLO v8n as the baseline model.The backbone network is replaced with StarNet,which is based on“Star Operate”.Additionally,the C2f-Star structure is designed by combining the Star Block from StarNet with the C2f module,and it is inserted into the Neck structure of the baseline model.Lightweight module L-SPPF replaces the SPPF module in the baseline model to enhance feature augmentation.Furthermore,a lightweight shared convolutional detection head,termed SCSB-Head,is introduced to reduce computational complexity.These improvements collectively form a lightweight object detection algorithm named SLSS-YOLO.Experimental results show that SLSS-YOLO achieves mAP@50%-95%,precision,and recall scores of 80.50%,99.44%,and 98.46%,respectively.Compared to the baseline model,these metrics improve by 1%,1.98%,and 0.26%,respectively.In terms of model size and detection speed,SLSS-YOLO has 1.44 M parameters and 4.6 G FLOPs,achieving an FPS rate of 134.9 f/s.This represents a reduction of 52.16%and 43.90%in parameters and FLOPs,respectively,while increasing FPS by 5.4 f/s compared to the baseline model.Moreover,an object tracking model is constructed using SLSS-YOLO and Hybrid-SORT.Tracking tests demonstrate that Hybrid-SORT achieves zero ID-Switches,with a detection speed of 10.9 ms/f.It outperforms Bot-SORT,ByteTrack,Deep OC-SORT,and OC-SORT in terms of tracking performance.Therefore,the proposed thermal infrared detection method can effectively identify and track dead ducks in complex cage environments,providing a reference for automated inspection in caged duck farms.
基金Supported by the National Science and Technology Support Program of China (2007BAC03A01 and 2009BAC51B01)
文摘A climatology of extratropical cyclones （ECs） over East Asia （20~ 75~N, 60^-160~E） is analyzed by applying an improved objective detection and tracking algorithm to the 4-time daily sea level pressure fields from the European Centre for Medium-range Weather Forecasts （ECMWF） reanalysis data. A total of 12914 EC processes for the period of 1958-2001 are identified, with an EC database integrated and EC activities reanalyzed using the objective algorithm. The results reveal that there are three major cyclogenesis regions： West Siberian Plain, Mongolia （to the south of Lake Baikal）, and the coastal region of East China; whereas significant cyclolysis regions are observed in Siberia north of 60~N, Northeast China, and Okhotsk Se^Northwest Pacific. It is found that the EC lifetime is largely 1 7 days while winter ECs have the shortest lifespan. The ECs are the weakest in summer among the four seasons. Strong ECs often appear in West Siberia, Northeast China, and Okhotsk Sea-Northwest Pacific. Statistical analysis based on k-means clustering has identified 6 dominating trajectories in the area south of 55~N and east of 80~E, among which 4 tracks have important impacts on weather/climate in China. ECs occurring in spring （summer） tend to travel the longest （shortest）. They move the fastest in winter, and the slowest in summer. In winter, cyclones move fast in Northeast China, some areas of the Yangtze-Huaihe River region, and the south of Japan, with speed greater than 15 m s-1. Explosively-deepening cyclones are found to occur frequently along the east coast of China, Japan, and Northwest Pacific, but very few storms occur over the inland area. Bombs prefer to occur in winter, spring, and autumn. Their annual number and intensity in 1990 and 1992 in East Asia （EA） are smaller and weaker than their counterparts in North America.
