Pavement distress detection based on improved feature fusion network

Efficiently detecting pavement distress in complex environments is crucial for the intelligent operation of transportation infrastructure. This study proposed a novel pavement distress detection model based on You Only Look Once version 5 (YOLOv5) incorporating a novel lightweight feature fusion network named crossed feature pyramid network (CFPN) and an improved loss function to enhance pavement distress detection efficiency in complex environments. The proposed model was evaluated by a dataset comprising 7076 images representing four common pavement distress classes. The experimental results indicate the proposed model outperforms in challenging working conditions such as shadows and overlapped multi-object bounding boxes. The proposed model achieves mean average precision (mAP), recall, precision, and frames per second (FPS) values of 69.3 %, 65.7 %, 73.3 %, and 118, respectively. These values are 4.0 %, 0.7 %, 4.2 %, and 9.3 % higher than those of YOLOv5s, but the parameters are squeezed by 27.1 %, expanding its application in non-destructive automatic pavement distress detection.