针对无人机小目标检测，提出跨尺度协同框架提升精度与效率。
CollabOD: Collaborative Multi-Backbone with Cross-scale Vision for UAV Small Object Detection
- 多骨干网络协作，融合前对齐特征，保留细节
- 在VisDrone上达29.9 AP50:95，UAVDT上17.4 AP50:95
- 适合计算资源受限的实时无人机视觉系统
无人机影像中的小目标检测面临高空视角导致的尺度剧烈变化、结构线索弱和计算预算紧等挑战。现有轻量级检测器通常在下采样后融合多尺度特征，此时边界与纹理细节已衰减，异构特征流可能空间错位。为此，我们提出CollabOD：一种协作式检测框架，通过保留结构细节、在特征融合前对齐跨路径信息，并在推理时保持检测头轻量化。该框架结合双路径融合主干、密集聚合模块、双边重加权模块与统一细节感知头，强化定位导向表征同时控制额外计算量。在VisDrone上取得52.4 AP50、30.8 AP75、29.9 AP50:95，计算量为65.5 GFLOPs；在UAVDT上达31.2 AP50、17.4 AP50:95；在AI-TOD上达45.4 AP50、20.0 AP50:95，推理速度达137 FPS。代码已开源。
原文摘要 · Abstract (English)
Small object detection in unmanned aerial vehicle (UAV) imagery is challenging because high-altitude viewpoints produce severe scale variation, weak structural cues, and tight computational budgets. Existing lightweight detectors usually fuse multi-scale features after downsampling, where boundary and texture details have already been attenuated and heterogeneous feature streams may be spatially misaligned. To address these issues, we propose CollabOD, a collaborative detection framework that preserves structural details, aligns cross-path features before fusion, and keeps the detection head lightweight at inference time. CollabOD combines a Dual-Path Fusion Stem, a Dense Aggregation Block, a Bilateral Reweighting Module, and a Unified Detail-Aware Head to strengthen localization-oriented representation while limiting extra computation. On VisDrone, CollabOD obtains 52.4 AP50, 30.8 AP75, and 29.9 AP50:95 with 65.5 GFLOPs; on UAVDT it reaches 31.2 AP50 and 17.4 AP50:95; and on AI-TOD it reaches 45.4 AP50 and 20.0 AP50:95 at 137 FPS. The code is available at: https://github.com/Bai-Xuecheng/CollabOD.
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