YOLO11-Occ: An Improved Occlusion-Robust Object Detection Method Based on YOLO11

Object detection is widely applied in various fields, including intelligent surveillance, intelligent urban systems, and environmental perception for Advanced Driver Assistance Systems (ADAS). However, in road scenes, the presence of numerous objects and mutual occlusions leads to challenges in feature extraction and reduces detection accuracy for occluded objects. This paper proposes an occlusion-aware object detection algorithm based on YOLO11 to address these issues. Specifically, the input image is segmented into superpixels, and corresponding reactors are generated for each superpixel. A KNN-based adjacency graph is then constructed to extract graph features. Furthermore, we introduce an occlusion attention mechanism to guide the superpixel graph in focusing on occluded regions and computing occlusion loss. Our approach is evaluated on the KITTI dataset. Experimental results demonstrate that, compared to the state-of-the-art detection algorithm YOLO11, our method achieves a 10.28% improvement in mAP50, effectively enhancing detection performance under occlusion scenarios.