Most image-based object detection methods employ horizontal bounding boxes(HBBs)to capture objects in tunnel images.However,these bounding boxes often fail to effectively enclose objects oriented in arbitrary directio...Most image-based object detection methods employ horizontal bounding boxes(HBBs)to capture objects in tunnel images.However,these bounding boxes often fail to effectively enclose objects oriented in arbitrary directions,resulting in reduced accuracy and suboptimal detection performance.Moreover,HBBs cannot provide directional information for rotated objects.This study proposes a rotated detection method for identifying apparent defects in shield tunnels.Specifically,the oriented region-convolutional neural network(oriented R-CNN)is utilized to detect rotated objects in tunnel images.To enhance feature extraction,a novel hybrid backbone combining CNN-based networks with Swin Transformers is proposed.A feature fusion strategy is employed to integrate features extracted from both networks.Additionally,a neck network based on the bidirectional-feature pyramid network(Bi-FPN)is designed to combine multi-scale object features.The bolt hole dataset is curated to evaluate the efficacyof the proposed method.In addition,a dedicated pre-processing approach is developed for large-sized images to accommodate the rotated,dense,and small-scale characteristics of objects in tunnel images.Experimental results demonstrate that the proposed method achieves a more than 4%improvement in mAP_(50-95)compared to other rotated detectors and a 6.6%-12.7%improvement over mainstream horizontal detectors.Furthermore,the proposed method outperforms mainstream methods by 6.5%-14.7%in detecting leakage bolt holes,underscoring its significant engineering applicability.展开更多
Rod insulators are vital parts of the catenary of high speed railways(HSRs).There are many different catenary insulators,and the background of the insulator image is complicated.It is difficult to recognise insulators...Rod insulators are vital parts of the catenary of high speed railways(HSRs).There are many different catenary insulators,and the background of the insulator image is complicated.It is difficult to recognise insulators and detect defects automatically.In this paper,we propose a catenary intelligent defect detection algorithm based on Mask region-convolutional neural network(R-CNN)and an image processing model.Vertical projection technology is used to achieve single shed positioning and precise cutting of the insulator.Gradient,texture,and gray feature fusion(GTGFF)and a K-means clustering analysis model(KCAM)are proposed to detect broken insulators,dirt,foreign bodies,and flashover.Using this model,insulator recognition and defect detection can achieve a high recall rate and accuracy,and generalized defect detection.The algorithm is tested and verified on a dataset of realistic insulator images,and the accuracy and reliability of the algorithm satisfy current requirements for HSR catenary automatic inspection and intelligent maintenance.展开更多
基金support from the National Natural Science Foundation of China(Grant Nos.52025084 and 52408420)the Beijing Natural Science Foundation(Grant No.8244058).
文摘Most image-based object detection methods employ horizontal bounding boxes(HBBs)to capture objects in tunnel images.However,these bounding boxes often fail to effectively enclose objects oriented in arbitrary directions,resulting in reduced accuracy and suboptimal detection performance.Moreover,HBBs cannot provide directional information for rotated objects.This study proposes a rotated detection method for identifying apparent defects in shield tunnels.Specifically,the oriented region-convolutional neural network(oriented R-CNN)is utilized to detect rotated objects in tunnel images.To enhance feature extraction,a novel hybrid backbone combining CNN-based networks with Swin Transformers is proposed.A feature fusion strategy is employed to integrate features extracted from both networks.Additionally,a neck network based on the bidirectional-feature pyramid network(Bi-FPN)is designed to combine multi-scale object features.The bolt hole dataset is curated to evaluate the efficacyof the proposed method.In addition,a dedicated pre-processing approach is developed for large-sized images to accommodate the rotated,dense,and small-scale characteristics of objects in tunnel images.Experimental results demonstrate that the proposed method achieves a more than 4%improvement in mAP_(50-95)compared to other rotated detectors and a 6.6%-12.7%improvement over mainstream horizontal detectors.Furthermore,the proposed method outperforms mainstream methods by 6.5%-14.7%in detecting leakage bolt holes,underscoring its significant engineering applicability.
基金supported by the National Natural Science Foundation of China(Nos.51677171,51637009,51577166 and 51827810)the National Key R&D Program of China(No.2018YFB0606000)+2 种基金the China Scholarship Council(No.201708330502)the Fund of Shuohuang Railway Development Limited Liability Company(No.SHTL-2020-13)the Fund of State Key Laboratory of Industrial Control Technology(No.ICT2022B29),China。
文摘Rod insulators are vital parts of the catenary of high speed railways(HSRs).There are many different catenary insulators,and the background of the insulator image is complicated.It is difficult to recognise insulators and detect defects automatically.In this paper,we propose a catenary intelligent defect detection algorithm based on Mask region-convolutional neural network(R-CNN)and an image processing model.Vertical projection technology is used to achieve single shed positioning and precise cutting of the insulator.Gradient,texture,and gray feature fusion(GTGFF)and a K-means clustering analysis model(KCAM)are proposed to detect broken insulators,dirt,foreign bodies,and flashover.Using this model,insulator recognition and defect detection can achieve a high recall rate and accuracy,and generalized defect detection.The algorithm is tested and verified on a dataset of realistic insulator images,and the accuracy and reliability of the algorithm satisfy current requirements for HSR catenary automatic inspection and intelligent maintenance.
