A dual-path lightweight detector with hybrid attention for real-time object detection

초록

Unmanned aerial vehicle (UAV)-based object detection presents significant challenges, including pronounced variations in object scale and limited computational resources. To address these issues, this paper proposes the Dual-path and Bimodal-attention-enhanced Network (DBYNet), a real-time detection framework optimized for UAV applications. DBYNet adopts a deployment-oriented design that integrates a dual-path backbone for spatial–semantic feature decoupling, a hybrid attention mechanism for enhanced contextual modeling, and lightweight optimization strategies to improve inference efficiency. Specifically, a shallow lightweight branch preserves fine-grained spatial details, while a deep branch with deformable convolutions captures high-level semantic features, and the proposed hybrid attention combines Overlapping Cross Attention (OCA) and Channel-spatial Bimodal Attention (CAB) to strengthen feature interaction. In addition, Quantization-Aware Training and temperature-aware distillation are employed to reduce model complexity without compromising accuracy. Extensive experiments on the VisDrone2019 dataset demonstrate that DBYNet achieves a favorable accuracy–efficiency trade-off, particularly improving robustness for small, densely distributed, and low-visibility targets in challenging UAV scenarios.

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