Road crack recognition based on object detection

Abstract

At present, road crack has become the main threatening factor affecting highway quality. The traditional manual detection method has low efficiency and produces larger errors. Aiming at this problem, this paper presents an improved object detection algorithm based on YOLOv5s network, fused with SE attention mechanism, which strengthens the important characteristic of the fractures of the target and suppresses general characteristics. Finally, we use the accuracy and recall rate as the evaluated parameters. Compared with the original network, the result has improved significantly, which greatly reduce the probability of crack leak fault detection. The location and type of cracks are marked out in the test results of this model, which effectively replaces the traditional manual detection method and optimizes the efficiency of road crack identification. After optimization, the lightweight network can be deployed on various mobile terminal platforms, making full use of the platform computing power, which owns high speed of identification and high precision, and has broad application prospects.