提升无人机图像小目标检测精度，兼顾速度与细节保留
RT-DETR++ for UAV Object Detection
- 采用双路通道门控注意力上/下采样，减少特征传播误差
- 引入CSP-PAC并行空洞卷积，融合多尺度局部与上下文信息
- 在不增加计算量前提下实现实时高精度检测，适合无人机场景
无人机影像中的目标检测面临诸多挑战，如小目标密集、尺度变化大和遮挡问题。本文提出RT-DETR++，对RT-DETR模型的编码器进行改进。首先，设计基于通道门控注意力的上/下采样（AU/AD）机制，通过双路径结构降低特征层传播过程中的误差，更好保留细节。其次，在特征融合阶段引入CSP-PAC模块，利用并行空洞卷积在同一层内处理局部与上下文信息，实现多尺度特征的有效融合。实验表明，该新型颈部结构在检测小而密集目标方面表现优异，同时保持实时检测速度，未增加计算复杂度。本研究为实时检测系统中的特征编码设计提供了有效方案。
原文摘要 · Abstract (English)
Object detection in unmanned aerial vehicle (UAV) imagery presents significant challenges. Issues such as densely packed small objects, scale variations, and occlusion are commonplace. This paper introduces RT-DETR++, which enhances the encoder component of the RT-DETR model. Our improvements focus on two key aspects. First, we introduce a channel-gated attention-based upsampling/downsampling (AU/AD) mechanism. This dual-path system minimizes errors and preserves details during feature layer propagation. Second, we incorporate CSP-PAC during feature fusion. This technique employs parallel hollow convolutions to process local and contextual information within the same layer, facilitating the integration of multi-scale features. Evaluation demonstrates that our novel neck design achieves superior performance in detecting small and densely packed objects. The model maintains sufficient speed for real-time detection without increasing computational complexity. This study provides an effective approach for feature encoding design in real-time detection systems.
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