YOLO-DMA: A Small-Object Detector Based on Multi-Scale Deformable Convolution and Linear Attention

Object detection in UAV aerial imagery presents significant challenges, including large-scale variations, complex background interference, object occlusion, and a high density of small targets. These factors restrict the generalization and localization capabilities of existing detectors. To address these issues, we propose YOLO-DMA, an efficient detection framework for aerial images. The framework incorporates three key improvements. First, we designed a Hierarchical Deformable Block (HDB), which uses adaptive sampling grids and a progressive multi-branch structure to capture features of irregular objects while preserving network depth, enabling richer hierarchical feature representation. Second, we proposed a Dual-Path Linear-complexity Perception (DPLP) module. One path employs a linear-complexity attention mechanism to model the global context efficiently, while the other utilizes lightweight convolutions to extract local details. This design effectively fuses shallow details with mid-level semantics, improving detection and localization accuracy. Third, we adopted the Wise-IoU v3 loss function, which dynamically adjusts optimization objectives, suppressing harmful gradients from low-quality samples and emphasizing small objects during training. Comprehensive experiments on the VisDrone dataset show that YOLO-DMA achieves 42.8% mAP50 and 25.7% mAP50:95. These correspond to improvements of 4.8% and 3.1% over YOLOv10. Experimental results demonstrate the effectiveness and practicality of the proposed framework.