An improved Yolov5-based algorithm for pavement disease detection in airport flight areas

An improved YOLOv5 network with SPP and CBAM added to the YOLOv5 network is proposed to identify airport service lane damages accurately, quickly, and completely on the basis of ground-penetrating radar technology. First, the structure and performance of YOLOv5 are described in detail, while the common hidden defects are marked in combination with the actual GPR image data acquired at the airport. The improved network achieved a 6.2% improvement in network detection accuracy, a 5.7% improvement in recall rate, and a 5.1% improvement in mAP compared to the original one. These findings demonstrated the high accuracy and feasibility of the proposed optimized network for the detection of hidden defects in airport service lanes.