ODE-Net: Occluded traffic sign detection and occlusion percentage estimation via frequency-aware feature fusion

Traffic sign detection is a critical perception component in autonomous driving, yet it remains highly challenging due to occlusions caused by leaves, vehicles, and buildings. These visual obstructions can lead to catastrophic decision-making errors in autonomous vehicles, directly threatening passenger safety. To support reliable autonomous navigation under occlusion, this paper proposes ODE-Net. It is an advanced YOLOv11-based detection framework with three key contributions. First, the hierarchical multi-scale feature extraction module designed within the backbone network enhances the representation of partially visible features under various obstructions. Second, the high-low frequency fusion neck module introduced at the neck is incorporated to strengthen semantic information, enabling the capture of edge details and overall contours at occlusion boundaries. Finally, an occlusion ratio prediction branch integrated into the detection head, along with the improved area-aware regression loss, is proposed to optimize bounding box regression. We also construct the Occluded Traffic Sign Dataset, which contains 8699 images with precise occlusion-percentage annotations. Compared with recent detectors including YOLOv11, YOLOv8, HYPER-YOLO, and RT-DETR-l, experiments on CCTSDB and OTSD show that ODE-Net achieves an mAP50 of 80.6% on CCTSDB and an mAP50-95 of 53.5%, improving over YOLOv11 by 3.3 and 3.4 percentage points, respectively. On OTSD, ODE-Net reaches the best mAP50-95 of 64.5%, surpassing YOLOv11, RT-DETR-l, and HYPER-YOLO by 2.2, 2.5, and 1.6 percentage points, respectively. The model achieves 94.4% occlusion-prediction accuracy while maintaining a compact size of 2.58M parameters and a low computational cost of 6.7 GFLOPs, thereby achieving a superior accuracy-efficiency balance.