LAF-YOLOv10专为无人机小目标检测优化，四技合一提升精度与效率。
LAF-YOLOv10 with Partial Convolution Backbone, Attention-Guided Feature Pyramid, Auxiliary P2 Head, and Wise-IoU Loss for Small Object Detection in Drone Aerial Imagery
- 用部分卷积压缩骨干计算，保留关键特征
- 加辅助头+注意力金字塔，小目标定位更准
- 适合嵌入式无人机部署，推理快且抗标注噪声
无人飞行器作为监控、交通管理及灾害响应的主要感知平台，使航拍目标检测成为应用计算机视觉的核心问题。现有检测器难以应对无人机特有挑战：目标仅占几像素、背景杂乱、严重遮挡以及严格的机载计算预算。本文提出LAF-YOLOv10，基于YOLOv10n，集成四项互补技术以提升航拍图像中小目标检测性能。部分卷积C2f（PC-C2f）模块将空间卷积限制在骨干通道的四分之一，减少冗余计算同时保持判别能力。注意力引导特征金字塔网络（AG-FPN）在多尺度融合前引入Squeeze-and-Excitation通道门控，并用DySample替代最近邻上采样实现内容感知插值。在160×160分辨率增设辅助P2检测头，扩展对8×8像素以下目标的定位能力，同时移除原P5头以重新分配参数。Wise-IoU v3取代CIoU进行边界框回归，在密集航拍场景中抑制噪声标注带来的梯度干扰。四个模块分别解决非重叠瓶颈：PC-C2f压缩骨干计算，AG-FPN优化跨尺度融合，P2头恢复空间分辨率，Wise-IoU稳定回归过程。各组件单独均非首创，贡献在于在单一YOLOv10框架内协同集成。三次训练（种子42, 123, 256）显示，LAF-YOLOv10在VisDrone-DET2019上达35.1±0.3% [email protected]，参数量仅2.3M，较YOLOv10n提升3.3点；跨数据集测试于UAVDT得35.8±0.4% [email protected]。NVIDIA Jetson Orin Nano基准测试表明，FP16下可达24.3 FPS，证实其适用于嵌入式无人机部署。
原文摘要 · Abstract (English)
Unmanned aerial vehicles serve as primary sensing platforms for surveillance, traffic monitoring, and disaster response, making aerial object detection a central problem in applied computer vision. Current detectors struggle with UAV-specific challenges: targets spanning only a few pixels, cluttered backgrounds, heavy occlusion, and strict onboard computational budgets. This study introduces LAF-YOLOv10, built on YOLOv10n, integrating four complementary techniques to improve small-object detection in drone imagery. A Partial Convolution C2f (PC-C2f) module restricts spatial convolution to one quarter of backbone channels, reducing redundant computation while preserving discriminative capacity. An Attention-Guided Feature Pyramid Network (AG-FPN) inserts Squeeze-and-Excitation channel gates before multi-scale fusion and replaces nearest-neighbor upsampling with DySample for content-aware interpolation. An auxiliary P2 detection head at 160$\times$160 resolution extends localization to objects below 8$\times$8 pixels, while the P5 head is removed to redistribute parameters. Wise-IoU v3 replaces CIoU for bounding box regression, attenuating gradients from noisy annotations in crowded aerial scenes. The four modules address non-overlapping bottlenecks: PC-C2f compresses backbone computation, AG-FPN refines cross-scale fusion, the P2 head recovers spatial resolution, and Wise-IoU stabilizes regression under label noise. No individual component is novel; the contribution is the joint integration within a single YOLOv10 framework. Across three training runs (seeds 42, 123, 256), LAF-YOLOv10 achieves 35.1$\pm$0.3\% [email protected] on VisDrone-DET2019 with 2.3\,M parameters, exceeding YOLOv10n by 3.3 points. Cross-dataset evaluation on UAVDT yields 35.8$\pm$0.4\% [email protected]. Benchmarks on NVIDIA Jetson Orin Nano confirm 24.3 FPS at FP16, demonstrating viability for embedded UAV deployment.
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