DPCNet: A dual path cross perception network for small object detection in UAV imagery

Linfeng Jia, Yafeng Zhu and Bin Li

PLOS ONE, 2026, vol. 21, issue 3, 1-28

Abstract: Small object detection in unmanned aerial vehicle imagery is challenged by tiny target scales, dense layouts, and cluttered backgrounds that blur fine details and destabilize multiscale representations. We present DPCNet, a single-stage detector that combines dual-path cross perception with deep and shallow feature interaction and a decoupled detection head. The Dual-Path Cross Perception block separates a detail stream and a semantic stream and performs gated bidirectional fusion, preserving edges while enriching context. The Deep and Shallow Feature Interaction block aligns features across levels through dynamic up-sampling and down-sampling and similarity-guided masking, which strengthens cross-scale consistency. The Dual-Path Decoupled Detection Head keeps classification and regression separate yet enables lightweight cross-branch channel and spatial guidance, and bounding-box regression adopts a geometry-sensitive Shape-IoU loss. Experiments on VisDrone2019 and HIT-UAV show consistent gains over the YOLO11n baseline: DPCNet improves mAP@0.5 by 2.0% and 5.1%, respectively, with higher precision and recall, especially for small, dense, low-light, and occluded targets. Despite modest computational overhead from cross-path interactions, the parameter count is reduced by about 45%, indicating a compact and robust solution for small object detection in challenging UAV scenarios.

Date: 2026
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DOI: 10.1371/journal.pone.0344091

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