Detecting small-scale objects remains a critical challenge with limited pixel information, complex backgrounds, and varying imaging conditions. To tackle these challenges, we propose an innovative high-precision detection framework (DRF-YOLO) that integrates a dilated-wise residual (DWR) module and an asymptotic feature pyramid network (AFPN). The DWR module enhances contextual representation and enriches spatial detail, while AFPN optimizes multi-scale feature fusion and semantic alignment. Extensive evaluations were carried out on the DUT-Anti-UAV and Det-Fly datasets, which contain images taken in complex aerial environments. The DRF-YOLO model achieved an mAP@50 of 86.9 and 91.1% on the two respective datasets, showing performance gains of 1.5% and 3.3% compared to the YOLOv8 reference model, and yielded mAP@50:95 gains of 1.1 and 2.3%, respectively. The synergistic effect of the DWR module and AFPN architecture enables significant enhancement in mAP@50, mAP@50:95, precision, and recall, demonstrating an optimal balance between accuracy and object coverage. The model also demonstrates improved robustness under complex backgrounds and occlusion, underscoring its potential for accurate UAV detection.
Wang et al. (2025) studied this question.