Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learni...Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.展开更多
This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,ena...This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,enabling a comprehensive safety assessment of gas storage caverns.Flexible concrete samples are prepared to simulate the crack characteristics of the sealing lining,providing data support for the recognition module.The Convolutional Block Attention Module is introduced into the ResNet-50 backbone to adaptively adjust feature map weights and enhance feature extraction.Additionally,the mask segmentation loss function is optimized by combining Binary Cross-Entropy loss and Dice loss to improve crack region recognition.Experimental results show that the improved Mask R-CNN model achieves a mean average precision of 89.3%,a 17.2%improvement over the original model,and an intersection over union of 88.41%.Compared to RCNN,Faster R-CNN,YOLOv5,and SSD,the improved model shows superior performance with higher average precision(AP)50:95,AP50,and AP75 values in crack recognition tasks.The proposed method effectively identifies cracks in the flexible concrete sealing lining of shallow-buried CAES caverns,contributing significantly to the prevention of gas storage leaks and providing a valuable approach for the comprehensive safety assessment of CAES gas storage caverns.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
文摘Detecting pavement cracks is critical for road safety and infrastructure management.Traditional methods,relying on manual inspection and basic image processing,are time-consuming and prone to errors.Recent deep-learning(DL)methods automate crack detection,but many still struggle with variable crack patterns and environmental conditions.This study aims to address these limitations by introducing the Masker Transformer,a novel hybrid deep learning model that integrates the precise localization capabilities of Mask Region-based Convolutional Neural Network(Mask R-CNN)with the global contextual awareness of Vision Transformer(ViT).The research focuses on leveraging the strengths of both architectures to enhance segmentation accuracy and adaptability across different pavement conditions.We evaluated the performance of theMaskerTransformer against other state-of-theartmodels such asU-Net,TransformerU-Net(TransUNet),U-NetTransformer(UNETr),SwinU-NetTransformer(Swin-UNETr),You Only Look Once version 8(YoloV8),and Mask R-CNN using two benchmark datasets:Crack500 and DeepCrack.The findings reveal that the MaskerTransformer significantly outperforms the existing models,achieving the highest Dice SimilarityCoefficient(DSC),precision,recall,and F1-Score across both datasets.Specifically,the model attained a DSC of 80.04%on Crack500 and 91.37%on DeepCrack,demonstrating superior segmentation accuracy and reliability.The high precision and recall rates further substantiate its effectiveness in real-world applications,suggesting that the Masker Transformer can serve as a robust tool for automated pavement crack detection,potentially replacing more traditional methods.
基金the financial supports of the National Natural Science Foundation of China Youth Science Foundation Project(52204152,52204111,52204153)the Postdoctoral Innovation Talent Support Program(BX2020275)the Postdoctoral Science Foundation(2020M683521).
文摘This paper proposes a detection method based on an improved Mask Region-based Convolutional Neural Network(Mask R-CNN)model for crack recognition in shallow-buried compressed air energy storage(CAES)cavern linings,enabling a comprehensive safety assessment of gas storage caverns.Flexible concrete samples are prepared to simulate the crack characteristics of the sealing lining,providing data support for the recognition module.The Convolutional Block Attention Module is introduced into the ResNet-50 backbone to adaptively adjust feature map weights and enhance feature extraction.Additionally,the mask segmentation loss function is optimized by combining Binary Cross-Entropy loss and Dice loss to improve crack region recognition.Experimental results show that the improved Mask R-CNN model achieves a mean average precision of 89.3%,a 17.2%improvement over the original model,and an intersection over union of 88.41%.Compared to RCNN,Faster R-CNN,YOLOv5,and SSD,the improved model shows superior performance with higher average precision(AP)50:95,AP50,and AP75 values in crack recognition tasks.The proposed method effectively identifies cracks in the flexible concrete sealing lining of shallow-buried CAES caverns,contributing significantly to the prevention of gas storage leaks and providing a valuable approach for the comprehensive safety assessment of CAES gas storage caverns.
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.
