Detection and Statistics System of Pavement Distresses Based on Street View Videos

Timely detection and statistical analysis of pavement distresses are essential for improving road maintenance efficiency. However, traditional methods for pavement defect detection face challenges such as inefficiency and high equipment costs. In response to these challenges, this paper proposes a pavement defect detection and statistical system based on street view videos. Initially, we introduce an enhanced algorithm named SN-YOLO (Slim-neck YOLO) designed to address the issue of low model detection accuracy in complex background environments meanwhile achieve model lightweighting. Specifically, the GSConv lightweight convolution module is employed to minimize the model size, while the VoVGSCSP and VoVGSCSP-cheap modules are incorporated to augment the original C2f module, thereby refining the model's recognition capabilities in intricate backgrounds. Moreover, by incorporating Soft-NMS for post-processing optimization, the model's robustness in detecting multi-scale defects is enhanced. Experimental results on the open-source dataset RDD2022 and a proprietary dataset demonstrate that the improved SN-YOLO algorithm surpasses current state-of-the-art methods. Furthermore, by leveraging the SN-YOLO algorithm and the Deep oc-sort tracking algorithm, we develop a deployable pavement distress detection and statistic system. In the application to real-world road street view video analysis, the system exhibits unparalleled accuracy and efficiency in defect detection and data compilation, presenting a robust solution for expedited, large-scale assessment of pavement conditions.