EDNet提升无人机小目标检测速度与精度，适配手机端实时推理。
EDNet: Edge-Optimized Small Target Detection in UAV Imagery -- Faster Context Attention, Better Feature Fusion, and Hardware Acceleration
- 基于YOLOv10改进，用跨连接融合与快速上下文注意力增强特征提取。
- 在iPhone 12上达16-55帧/秒，小目标mAP@50最高提升5.6%。
- 支持从Tiny到XL七种尺寸，适合边缘设备部署，保护数据隐私。
无人机影像中小目标检测面临分辨率低、背景复杂和动态场景的挑战。我们提出EDNet，一种基于增强YOLOv10架构的边缘优化检测框架，无需后处理即可实现实时应用。EDNet采用XSmall检测头和跨连接融合策略，提升多尺度上下文感知与特征融合能力。其独特的C2f-FCA模块引入快速上下文注意力机制，在降低计算量的同时增强特征提取。采用WIoU损失函数优化边界框回归。模型提供从Tiny到XL共七种尺寸，适用于不同部署环境，支持本地实时推理并保障数据隐私。在iPhone 12上，各版本运行速度达16至55 FPS，小目标检测mAP@50最高提升5.6%。代码与预训练模型已开源：https://github.com/zsniko/EDNet。
原文摘要 · Abstract (English)
Detecting small targets in drone imagery is challenging due to low resolution, complex backgrounds, and dynamic scenes. We propose EDNet, a novel edge-target detection framework built on an enhanced YOLOv10 architecture, optimized for real-time applications without post-processing. EDNet incorporates an XSmall detection head and a Cross Concat strategy to improve feature fusion and multi-scale context awareness for detecting tiny targets in diverse environments. Our unique C2f-FCA block employs Faster Context Attention to enhance feature extraction while reducing computational complexity. The WIoU loss function is employed for improved bounding box regression. With seven model sizes ranging from Tiny to XL, EDNet accommodates various deployment environments, enabling local real-time inference and ensuring data privacy. Notably, EDNet achieves up to a 5.6% gain in mAP@50 with significantly fewer parameters. On an iPhone 12, EDNet variants operate at speeds ranging from 16 to 55 FPS, providing a scalable and efficient solution for edge-based object detection in challenging drone imagery. The source code and pre-trained models are available at: https://github.com/zsniko/EDNet.
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