Real-time pavement distress detection based on deep learning and visual sensors

Pavement ageing significantly affects traffic safety and requires timely maintenance. However, accurately identifying pavement conditions from images captured by in-vehicle cameras remains challenging. This paper proposes a lightweight pavement distress detection method, MASL-YOLO. To address multi-scale defects, the method employs multi-scale channel mix convolution to enhance feature fusion, thereby improving the sensitivity to pavement damage. A multi-path aggregation attention mechanism and deformable convolutions are used to enhance adaptive sampling, strengthening feature extraction in the backbone. Integrating a large-kernel separable structure into the spatial pyramid pooling fusion further enhances the network's generalisation capability in complex environments. Experiments show that MASL-YOLO achieves an mAP of 85.5%, a 5.1% improvement over YOLOv8n and operates at 73.7 frames per second, meeting real-time requirements. The model, integrated with the RealSense D455 visual sensor and the GNSS module on the detection vehicle, achieved fast and effective pavement distress detection in field tests.