A Lightweight YOLOv11n-Based Framework for Highway Pavement Distress Detection Under Occlusion Conditions

In response to the three main challenges in lightweight road pavement defect detection models—insufficient feature discriminability, weak environmental robustness, and low edge deployment efficiency—this paper proposes an innovative architecture, RS-YOLOv11n, based on YOLOv11n. Experimental results demonstrate significant improvements of RS-YOLOv11n over YOLOv11n on the RDD2022_Mix dataset: model parameters are reduced by 21.0%, computational complexity is decreased by 17.5%, mAP@0.5 is increased by 0.64%, and recall rate is improved by 1.03%. Firstly, a heterogeneous feature distillation backbone, RHGNetv2, is designed, incorporating RepConv reparameterized convolution to optimize computational efficiency. Secondly, a lightweight occlusion-aware module, SEAM, is introduced, significantly enhancing detection performance in occluded scenarios. RS-YOLOv11n provides a high-precision, low-resource, lightweight solution for intelligent road inspection.