Improved YOLOV5-Based UAV Pavement Crack Detection

In terms of highway crack detection, the combination of UAV and deep learning network has become a powerful detection means. However, in the actual detection, in order to take into account the detection efficiency, it is necessary to ensure that the detection area is large enough, which makes the crack occupy few pixels in the image, and the image background is complex. Therefore, in this paper, DJI Mavic3 is used to establish the image data set of highway pavement cracks under complex background. And, the YOLOV5 deep learning model is improved by adding swin transformer structure and BIFPN feature pyramid. The improved YOLOV5 model achieved real-time pixel-level detection with detection accuracy of 90% and detection speed of 43.5 FPS. In terms of crack detection ability, the accuracy of the improved YOLOV5 reaches 4 pixels, and cracks of 1.2 mm can be detected in the experiment. Compared with the YOLOV7 model, the detection accuracy of the improved YOLOV5 model is increased by 15.4%. Compared with the YOLOV6 model, the calculated parameters of the improved YOLOV5 model are reduced by 59.25%. The proposed model is superior to other advanced models in crack detection.