It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not.A convenient and fast method based on line segment detector(LSD)and the least square...It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not.A convenient and fast method based on line segment detector(LSD)and the least square curve fitting to identify the rail in the image is proposed in this paper.The image in front of the train can be obtained through the camera on-board.After preprocessing,it will be divided equally along the longitudinal axis.Utilizing the characteristics of the LSD algorithm,the edges are approximated into multiple line segments.After screening the terminals of the line segments,it can generate the mathematical model of the rail in the image based on the least square.Experiments show that the algorithm in this paper can fit the rail curve accurately and has good applicability and robustness.展开更多
Physical contamination of food occurs when it comes into contact with foreign objects.Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as bro...Physical contamination of food occurs when it comes into contact with foreign objects.Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking.Therefore,a preventive method that can detect and remove foreign objects in advance is required.Several studies have attempted to detect defective products using deep learning networks.Because it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning methods.This paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning network.In this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste manner.Based on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test data.We evaluated this method on images of pasta,snacks,pistachios,and red beans under the same conditions.The results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged foods.For detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly detection.In addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.展开更多
The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conven...The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conventional comprehensive video monitoring systems for railways,a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies.In a bid to raise detection accuracy,the convolutional block attention module(CBAM),including spatial and channel attention modules,is seamlessly integrated into the YOLOv5 model,giving rise to the CBAM-YOLOv5 model.Furthermore,the distance intersection-over-union_non-maximum suppression(DIo U_NMS)algorithm is employed in lieu of the weighted nonmaximum suppression algorithm,resulting in improved detection performance for intrusive targets.To accelerate detection speed,the model undergoes pruning based on the batch normalization(BN)layer,and Tensor RT inference acceleration techniques are employed,culminating in the successful deployment of the algorithm on edge devices.The CBAM-YOLOv5 model exhibits a notable 2.1%enhancement in detection accuracy when evaluated on a selfconstructed railway dataset,achieving 95.0%for mean average precision(m AP).Furthermore,the inference speed on edge devices attains a commendable 15 frame/s.展开更多
With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WP...With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WPT)because of its high reliability,safety,and convenience,especially high suitability for the future self-driving scenario.Foreign object detection(FOD),mainly including metal object detection and living object detection,is required urgently and timely for the practical application of EV-WPT technology to ensure electromagnetic safety.In the last decade,especially in the past three years,many pieces of research on FOD have been reported.This article reviews FOD state-of-the-art technology for EV-WPT and compares the pros and cons of different approaches in terms of sensitivity,reliability,adaptability,complexity,and cost.Future challenges for research and development are also discussed to encourage commercialisation of EV-WPT technique.展开更多
基金National Natural Science Foundation of China(No.61763023).
文摘It is necessary to rely on the rail gauge to determine whether the object beside the track will affect train operation safety or not.A convenient and fast method based on line segment detector(LSD)and the least square curve fitting to identify the rail in the image is proposed in this paper.The image in front of the train can be obtained through the camera on-board.After preprocessing,it will be divided equally along the longitudinal axis.Utilizing the characteristics of the LSD algorithm,the edges are approximated into multiple line segments.After screening the terminals of the line segments,it can generate the mathematical model of the rail in the image based on the least square.Experiments show that the algorithm in this paper can fit the rail curve accurately and has good applicability and robustness.
基金supported by Basic Science Research Program through the National Research Foundation(NRF)of Korea funded by the Ministry of Education(grant number 2020R1A6A1A03040583,Kangjik Kim,www.nrf.re.kr)this research was also supported by the Soonchunhyang University Research Fund.
文摘Physical contamination of food occurs when it comes into contact with foreign objects.Foreign objects can be introduced to food at any time during food delivery and packaging and can cause serious concerns such as broken teeth or choking.Therefore,a preventive method that can detect and remove foreign objects in advance is required.Several studies have attempted to detect defective products using deep learning networks.Because it is difficult to obtain foreign object-containing food data from industry,most studies on industrial anomaly detection have used unsupervised learning methods.This paper proposes a new method for real-time anomaly detection in packaged food products using a supervised learning network.In this study,a realistic X-ray image training dataset was constructed by augmenting foreign objects with normal product images in a cut-paste manner.Based on the augmented training dataset,we trained YOLOv4,a real-time object detection network,and detected foreign objects in the test data.We evaluated this method on images of pasta,snacks,pistachios,and red beans under the same conditions.The results show that the normal and defective products were classified with an accuracy of at least 94%for all packaged foods.For detecting foreign objects that are typically difficult to detect using the unsupervised learning and traditional methods,the proposed method achieved high-performance realtime anomaly detection.In addition,to eliminate the loss in high-resolution X-ray images,the false positive rate and accuracy could be lowered to 5%with patch-based training and a new post-processing algorithm.
基金supported in part by the Science and Technology Innovation Project of CHN Energy Shuo Huang Railway Development Company Ltd(No.SHTL-22-28)the Beijing Natural Science Foundation Fengtai Urban Rail Transit Frontier Research Joint Fund(No.L231002)the Major Project of China State Railway Group Co.,Ltd.(No.K2023T003)。
文摘The detection of foreign object intrusion is crucial for ensuring the safety of railway operations.To address challenges such as low efficiency,suboptimal detection accuracy,and slow detection speed inherent in conventional comprehensive video monitoring systems for railways,a railway foreign object intrusion recognition and detection system is conceived and implemented using edge computing and deep learning technologies.In a bid to raise detection accuracy,the convolutional block attention module(CBAM),including spatial and channel attention modules,is seamlessly integrated into the YOLOv5 model,giving rise to the CBAM-YOLOv5 model.Furthermore,the distance intersection-over-union_non-maximum suppression(DIo U_NMS)algorithm is employed in lieu of the weighted nonmaximum suppression algorithm,resulting in improved detection performance for intrusive targets.To accelerate detection speed,the model undergoes pruning based on the batch normalization(BN)layer,and Tensor RT inference acceleration techniques are employed,culminating in the successful deployment of the algorithm on edge devices.The CBAM-YOLOv5 model exhibits a notable 2.1%enhancement in detection accuracy when evaluated on a selfconstructed railway dataset,achieving 95.0%for mean average precision(m AP).Furthermore,the inference speed on edge devices attains a commendable 15 frame/s.
基金Key R&D Program of Guangdong Province,China(No.2020B0404030004)partly by the open research fund from Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ)(No.GML-KF-22-19)partly by the National Natural Science Foundation of China(No.62001301).
文摘With the rapid development and widespread application of electric vehicles(EVs)around the world,the wireless power transfer(WPT)technology is also accelerating for commercial applications in EV wireless charging(EV-WPT)because of its high reliability,safety,and convenience,especially high suitability for the future self-driving scenario.Foreign object detection(FOD),mainly including metal object detection and living object detection,is required urgently and timely for the practical application of EV-WPT technology to ensure electromagnetic safety.In the last decade,especially in the past three years,many pieces of research on FOD have been reported.This article reviews FOD state-of-the-art technology for EV-WPT and compares the pros and cons of different approaches in terms of sensitivity,reliability,adaptability,complexity,and cost.Future challenges for research and development are also discussed to encourage commercialisation of EV-WPT technique.
