Road Surface Damage Detection Based on Improved YOLOv5 Algorithm

In view of the shortcomings of YOLOv5 road surface damage detection algorithm in detection accuracy and practicability, and combined with the characteristics of road surface damage, YOLOv5 model is improved. Preprocess the collected road image to improve the image quality. Add a squeeze incentive attention mechanism module between the last C3 module and the SPPF module of the backbone network to build an improved YOLOv5 model. The improved model can focus on the channel features with the largest amount of information and suppress unnecessary channel features, thus greatly improving the accuracy of damaged target detection. The improved YOLOv5 model is used for detection experiments on open source datasets and actual scene damaged images. The experimental results show that the improved model shows good adaptability and robustness, and the detection accuracy is also higher, which can more effectively detect damaged targets on the road surface.