Vehicle Detection in Drone Aerial Views Based on Lightweight YOLOv10-IAD

UAV-based vehicle detection faces challenges of small targets, dense distribution, and occlusions. Built upon YOLOv10n, this paper proposes YOLOv10-IAD by integrating three modules: (1) Involution convolution in the backbone to enlarge the receptive field and enhance spatial perception for small targets; (2) ACmix (Attention and Convolution Mixed) in the neck to fuse local details with global context; (3) DyHead (Dynamic Head) that recalibrates features via scale-, space-, and task-aware attention, improving localization for occluded objects. On VisDrone2019 and UAVDT datasets, YOLOv10-IAD improves mAP50 by 3.7% (to 47.2%) and 3.5% (to 52.0%), and recall by 3.1% and 2.0%, respectively, with only a modest increase in parameters (2.9 M) and computational cost. Compared to other YOLO series, it achieves a favorable trade-off between detection accuracy and computational efficiency. These advancements make it suitable for deployment on hardware onboard UAVs for real-time road vehicle detection.