Research on road crack detection algorithm based on YOLO-SW

Efficient and highly accurate road crack detection algorithms are particularly important in road inspection systems. However, some of their limitations have gradually come to the fore as the target detection aspect has become more in-depth. Existing road target detection algorithms face the difficulty of capturing long-range dependencies, resulting in limited feature expressiveness and high leakage rates in small target detection scenarios ( e.g ., road fine crack identification). Therefore, in this article, we propose an improved model, You Only Look Once (YOLO)-SW, based on YOLO11n. Firstly, based on the structure of the C2f module, we form a deep feature processing chain by stacking n sp modules through nn.ModuleList (the module is responsible for multiscale feature aggregation and attention mechanism), and propose the CSP_PMSFA module, which enhances the ability of small-target detection. The method replaces C3k2 of YOLO11n with the Cross Stage Partial Path Multiscale Spatial Feature Aggregation (CSP_PMSFA) module that enhances the feature expression ability by combining multi-scale feature aggregation and attention mechanism, which effectively improves the feature expression accuracy of small targets. At the same time, the CGAFusion module is added to enhance the feature expression ability by combining spatial attention, channel attention, and pixel attention. Experiments on the pavement crack detection Computer Vision Project dataset show that the mean average accuracy of the improved YOLO-SW model (mean average precision at Intersection of Union 0.5 (mAP@ 0.5)) reaches 58.2%, which is an improvement of 8.7 percentage points over the baseline model YOLO11n. The experimental results validate the significant advantages of YOLO-SW for crack detection in complex road scenarios.