Intelligent Identification of Tunnel Lining Cracks Based on Improved YOLOv7
In order to overcome the inconvenience of the traditional manual visual inspection method,this paper proposes Tunnel Lining Crack-YOLO(TLC-YOLO),an improved YOLOv7 algorithm model for tunnel lining crack characteristics.This paper compares the detection effect of four types of backbone networks on tunnel lining cracks,and concludes that crack detection in complex environments has problems such as strong background interference and imbalance in the quality of training samples.By using lightweight convolutional GSConv and Slim-neck architectures in the TLC-YOLO model,and by embedding a dynamic sparse attention module,BiFormer,and by enhancing the transmission of channel information,we can improve the real-time response speed and detection accuracy of the model,enabling more flexible computation allocation and content awareness.Wise-IoU v3 is used as a coordinate regression loss function to improve the generalization ability of the model by assigning gradients to better training samples and suppressing poorer training instances.Results show that after training through the tunnel crack dataset,the TLC-YOLO model simultaneously improves the accuracy,recall,and F1 values,and mAP@0.5 values of the detection results for tunnel crack lesions in multiple sets of experiments compared to YOLOv7,proving that TLC-YOLO has a better ability to detect and classify tunnel lining cracks.
tunnel lining cracksdeep learningtarget detectionGSConvBiFormerWise-IoU v3 loss
NETL
NSTL
万方数据
