Recent YOLO-based object detection methods have demonstrated strong performance in real-time applications due to their efficient end-to-end architecture. However, in complex scenarios such as VisDrone2019, existing methods still face limitations in small object detection and multi-scale feature modeling capability. These performance bottlenecks are not only attributed to model-level constraints, such as the loss of low-level spatial details during progressive downsampling and the insufficient preservation of fine-grained structural information in high-level semantic representations during feature propagation, which consequently limits multi-scale feature representation and fusion, but are also influenced by data-level factors, including long-tailed distributions and spatial distribution bias. To address these limitations, this paper proposes an improved model named MD-YOLO. First, a Multi-scale Adaptive Channel (MAC) module is introduced into the backbone to replace conventional stride-based downsampling, enhancing multi-scale feature representation while preserving fine-grained information. Second, a Dual Attention Feature Fusion (DAFA) module is designed to align features across different resolutions and further enhance fused representations using both channel and spatial attention mechanisms. Furthermore, a high-resolution P2 detection head is incorporated to enhance the detection capability for dense small objects. Experimental results on the VisDrone2019 dataset demonstrate that the proposed method substantially outperforms the YOLOv11s baseline, improving mAP@0.5 from 38.5% to 45.6% and mAP@0.5:0.95 from 22.8% to 27.1%, while maintaining a reasonable computational cost.
Zhou et al. (Thu,) studied this question.
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