DOMino-YOLO: A Deformable Occlusion-Aware Framework for Vehicle Detection in Aerial Imagery

Occlusion-aware vehicle detection in UAV imagery is challenging due to partial visibility from varied viewpoints, dense scenes, and limited features. To address this, we introduce two contributions. First, VOD-UAV, the first UAV-based vehicle detection dataset focused on occlusion, containing 712 synthetic and 1219 real-world images, each annotated with five discrete occlusion levels. These fine-grained labels enable structured supervision and detailed analysis under varying visibility conditions. Second, DOMino-YOLO, a YOLOv11-based detection framework, enhances occlusion robustness via three components: the Deformable Convolution Enhanced Module (DCEM) for spatial alignment, the Visibility-Aware Structural Aggregation (VASA) module for multi-scale feature extraction from partially visible regions, and the Context-Suppressed Implicit Modulation Head (CSIM-Head) for reducing false activations by adaptive channel reweighting. An Occlusion-Aware Repulsion Loss (OAR-Loss) combines Repulsion Loss and Visibility-Weighted Classification Loss to suppress redundant predictions and emphasize heavily occluded objects. Extensive experiments on VOD-UAV demonstrate that DOMino-YOLO significantly improves detection accuracy and robustness under occlusion. The dataset and code will publicly available to support future research.