Pothole image detection based on YOLOv7 algorithm

Abstract

As one of the common road diseases, the accurate detection of potholes during inspections can help to make timely maintenance measures, which will greatly save road maintenance costs and reduce the incidence of traffic accidents. In order to improve the accuracy and timeliness of pothole detection and to facilitate the development of disease maintenance, the YOLOv7 model is improved. Firstly, the Efficient convolution operator (DSConv) is introduced to reconstruct the Backbone and Head parts of YOLOv7 to reduce the computational effort of the original YOLOv7 model and improve the detection speed of the model. Secondly, the SE attention mechanism is incorporated into the model to improve the model's ability to extract features from potholes. Finally, the latest v3 version of Wise-IoU is introduced as the loss function of the improved model to reduce the impact caused by sample annotation. The accuracy and mAP of the improved model improved by 3.04% and 1.34% respectively compared to the original YOLOv7 model, and the number of FLOPS of the model decreased from 105.1 to 48.8. The results show that the proposed improved method can effectively improve the speed and accuracy of the YOLOv7 model in detecting potholes, and is advanced compared to the current mainstream target detection algorithms.