Application of YOLOv5 algorithm in road crack detection

Abstract

Pavement crack detection is a key technology used to judge whether the road is safe or not. Due to the complex and diverse background of cracks, the traditional crack detection algorithm is difficult to accurately detect cracks. In this paper, YOLOv5 algorithm with strong portability is used to detect road cracks. Mosaic data enhancement method is used to enrich the background of detection targets at the input end and improve the detection effect of small targets. The Backbone CSP structure divides the input into two branches, which greatly reduces the computational load while enhancing the learning performance of the entire convolutional neural network. The experimental results show that the model trained on the public dataset achieves 92% detection accuracy on the test set, and the detection time is 0.04s. The model is directly applied to the vehicle and pedestrian detection datasets, and the detection accuracy is improved by 2%, and the detection time is shortened to 0.028s, indicating that the model has good generalization performance. It can be used for crack detection and quality assessment in complex road scenarios.