Research on detection model of lining crack based on deformable convolutional network and YOLOv8
In order to solve the problems of poor intelligent detection accuracy and efficiency caused by the factors such as high randomness of crack characteristic development,low resolution of annotation box,dense distribution and easy overlap,and relatively small target,the YOLOv8 backbone network was fused based on the improved deformable convolutional neural net-work,and a crack detection model D-YOLO that can adapt to complex tunnel scenes was proposed.The normalization step of spatial aggregation weight softmax in the deformable convolutional network v3(DCNv3)was removed to enhance the convolu-tional efficiency of network,and the new DCNv4 was used to fuse the C2f convolution module of backbone network to enhance the detail perception ability of different scale crack characteristics and spatial position change in the network images.The self-built crack dataset was used to compare and verify four detection models including SSD,Faster-RCNN,YOLOv5,and YOLOv8.The results show that the F1 score of D-YOLO is 80.82%,mAP@0.5 is 86.90%,and both of them are improved than those of SSD,Faster-RCNN,YOLOv5,and YOLOv8.The single image detection speed of D-YOLO is 20.36 ms,which is 37.06%,65.33%,45.22%,and 28.39%faster than those of various comparison models,respectively.Meanwhile,the attention range of image features of lining crack is increased through D-YOLO.The research results can provide new ide-as for the safety detection of lining during tunnel operation.
tunnel engineeringstructural safetydeformable convolutional networklining crackYou Only Look Once v8(YOLOv8)
NETL
NSTL
万方数据
