改进了基于YOLOv8的MASW YOLO模型,用于基于YOLOv8的无人机图像中检测小目标
Xianghe Meng1, Fei Yuan2, Dexiang Zhang3
1College of Electrical Engineering and Automation, Anhui University, Hefei, 230601, China.
Scientific reports
|July 11, 2025
概括
MASW-YOLO模型通过提高小目标准确性和减少错过的检测来增强无人机对象检测. 这种先进的算法提高了检测性能,同时降低了模型的复杂性.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 在无人机 (UAV) 图像中对象检测面临着小目标,错误检测和错误检测的挑战.
- 像YOLOv8n这样的现有模型需要改进,以便在复杂的空中场景中有效处理这些特定问题.
研究的目的:
- 提出和评估MASW-YOLO,这是一个基于YOLOv8n.n.的改进算法模型.
- 为了提高小型目标的检测准确度,并减少无人机视角特征检测中的错误阳性/负性.
主要方法:
- 在骨干中纳入了一个多尺度的卷积MSCA注意力机制,以改进小目标特征聚合.
- 使用AFPN渐进式金字塔网络重建了子网络,以解决多尺度特征融合的弱点.
- 用软NMS取代非最大抑制 (NMS),以更好地处理封闭和密集的目标.
- 优化了Wise-IoU的损失函数,以提高界限框回归精度,特别是对于具有尺度变化的目标.
主要成果:
- 在VisDrone2019数据集上,MASW-YOLO实现了38.3%的平均检测准确度,比基线YOLOv8n.改善了7.9%.
- 该模型显示,网络参数显著减少了19.6%,表明效率有所提高.
- 观察到小型,封闭和密集目标的增强检测能力.
结论:
- 拟议的MASW-YOLO模型有效地解决了无人机物体检测的关键局限性.
- 协同集成的MSCA注意力和AFPN,再加上软NMS和智能IoU,显著提高了检测性能和效率.
- MASW-YOLO为现实世界无人机监控和分析应用提供了一个有前途的解决方案.
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