An efficient method of pavement distress detection based on improved YOLOv7

Abstract

Abstract Pavements play a pivotal role in infrastructure construction, so pavement distress detection (PDD) will greatly affect pavement service life and vehicle operation safety. Traditional manual detection and computer detection methods have such disadvantages as low efficiency, high cost and error-proneness. Thus, they are not suitable for high-speed detection tasks due to a large number of defects. Defect detection methods based on deep learning can achieve end-to-end target detection, generalize and monitor targets in real time. On such a basis, this paper has proposed an efficient method of PDD based on improved YOLOv7. YOLOv7, which is the best-performing object detection model in the YOLO series, is known for its high efficiency, strong scalability, and support for panoramic detection. It lays a solid foundation for enhancing PDD models. In this paper, this model will be improved based on model speed and accuracy. Firstly, SimAM attention module is employed to weight feature images, which has greatly improved model accuracy. Secondly, Ghost module in place of a partial deep convolution module is used to improve model running speed. Then, SIoU, instead of the original localization loss function, is performed to optimize the model training process. Finally, the proposed improved YOLOv7 model is applied to different road defect datasets and compared with other methods, such as Faster R-CNN, CenterNet, DETR, YOLOv6 and the original YOLOv7 model. The results show that the proposed method has ubiquitous advantages over the above-mentioned methods, with the average mAP, F1 value and FPS value of 85.8%, 0.697 and 62.13 fps respectively. Furthermore, the values of the parameters Params and FLOPs also decrease to some degree.