Road surface crack detection method based on improved YOLOv5 and vehicle-mounted images

Abstract

Road surface crack detection methods using vehicle-mounted images have gained substantial attention recently. Notably, YOLO-based techniques have exhibited effectiveness and real-time performance. However, current YOLO-based approaches encounter challenges like blurriness of small cracks and incomplete information extraction from vehicle-mounted images. Therefore, this paper proposes a novel detection method based on the improved YOLOv5 and vehicle-mounted images. In this method, the Slim-Neck structure enhances crack focus through a weighted attention mechanism while optimizing network efficiency. The integration of the C2f structure and Decoupled Head better harnesses upper layer output information. Moreover, the SPPCSPC structure is bifurcated to augment model efficiency and accuracy. The training process is optimized by using the Silu activation function and CIoU loss function. This approach is applied to vehicle-mounted images, with its efficacy and feasibility affirmed through extensive comparative and ablation experiments. Importantly, compared to five other advanced methods, notable enhancements are observed in various evaluation metrics.