RMH-YOLO:在无人机空中图像中用于小目标检测的精致的多尺度架构
Fan Yang1, Min He2,3, Jiuxian Liu1
1Highway School, Chang'an University, Xi'an 710064, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究介绍了RMH-YOLO,这是一种改进的无人飞行器 (UAV) 视觉系统,用于检测小目标. 它在复杂的空中场景中显著提高了准确性,同时保持了计算效率.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器人技术 机器人技术 机器人技术
背景情况:
- 无人驾驶飞行器 (UAV) 视觉系统在小目标检测方面面临挑战,原因是像素数量低,尺度变化和复杂的背景.
- 现有的物体检测算法在无人机图像中对小于32x32像素的目标缺乏准确性,特别是在混乱的环境中.
研究的目的:
- 开发一个先进的多尺度架构,RMH-YOLO,用于在无人机图像中改进小目标检测.
- 增强特征表示和保存空间细节,以更好地识别小目标.
主要方法:
- 拟议的RMH-YOLO架构与精细功能模块 (RFM) 进行基于注意力的功能增强.
- 实施了多尺度聚焦和扩散 (MFFD) 网络,以保持细致的细节.
- 使用了高效的CS-Head检测架构和优化的损失函数 (NWD + InnerCIoU).
主要成果:
- 在VisDrone2019数据集上,RMH-YOLO实现了53.0%的精度和40.4%的回忆,表现优于YOLOv8n.
- 实现了42.4%的mAP50和25.7%的mAP50:95,显示出显著的改善.
- 保持了1.3M参数和16.7G FLOP的计算效率.
结论:
- 在无人机图像中,RMH-YOLO有效地提高了小目标检测精度.
- 该模型表现出强大的性能和计算效率,适合于现实世界的空中监控应用.
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