In order to enhance intelligent analysis of bridge long-term service performance and solve insufficient accuracy of existing defect detection algorithms in complex scenarios，an improved YOLOv8s-based algorithm（YOLOv8s-RC） was proposed for bridge surface disease detection. The large kernel separable convolutional attention mechanism（LSKA） was introduced into the spatial pyramid pooling-fast module （SPPF） of the backbone network to enhance defect feature extraction capability. The weighted feature fusion principle of the bidirectional feature pyramid network（BiFPN） was applied to optimize feature fusion in the neck network，thereby enhancing fusion effectiveness. The original CIoU loss function was replaced by the SIoU loss function to improve bounding box localization accuracy. Ablation experiments on the CODEBRIM dataset demonstrate that the YOLOv8s-RC model achieves 2.3% higher precision，1.7% higher recall，2.0% higher F1-score，and 1.6% higher mAP@0.5 compared with the baseline model. The results show that this algorithm exhibits stronger detection capabilities for small target defects and weak feature defects. With 12.2×10⁶ parameters and 107.5 FPS inference speed，the model is verified to meet real-time monitoring requirements on lightweight devices. Generalization tests on the DACL10K dataset indicate superior performance over Faster-RCNN，SSD，YOLOv5s and YOLOv8s models in various bridge defect detection scenarios. It is concluded that the YOLOv8s-RC algorithm provides an effective technical solution for identifying bridge surface defects in complex environments.