Automatic inspection of cracks on pavement surfaces based on improved segmentation model

Periodic crack inspections are greatly required for the operation and maintenance of the pavement. With the rapid development of the deep learning (DL) method and the computer hardware, manual visual inspections have been gradually replaced by DL-based inspections due to their high efficiency and low cost. In this study, a novel segmentation model was proposed to conduct precise identification of cracks. Firstly, the YOLOv5 was adopted as basic detector for the coarse prediction of the crack type and crack location. Secondly, a mask branch was customized and incorporated into the aforementioned detector for parallel mask production. In addition, the neck, a feature fusion component, was upgraded to produce higher-quality masks, and a sampling-free approach was investigated to reduce class imbalance's influence. Compared to a normal segmentation model, the proposed method demonstrated a better balance between accuracy and speed, with a speed of 33.2 frames per second and an accuracy of 87.13 mAP.