轻量级物体检测算法用于无人机空中图像
Jian Wang1,2, Fei Zhang1, Yuesong Zhang1
1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650504, China.
Sensors (Basel, Switzerland)
|July 14, 2023
概括
本研究介绍了MFP-YOLO,这是一种轻量级算法,用于无人机 (UAV) 探测空中图像. 它显著提高了检测准确度,并减少了模型大小,优于现有方法.
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:
- 无人机空中影像带来了诸如低检测精度和大参数体积等挑战,原因是高分辨率,尺度变化和复杂的背景.
- 现有的算法难以有效地处理这些复杂性,导致对象检测任务的性能不足.
研究的目的:
- 开发一个轻量级和精确的对象检测算法,用于无人机空中图像.
- 解决空中图像分析中尺度变化和复杂背景的挑战.
- 为了提高检测准确度,并减少实时应用的计算负载.
主要方法:
- 推出了基于YOLOv5s的轻量级算法MFP-YOLO.
- 设计了一种多路径反向残余模块,具有处理尺度变化和背景干扰的注意力机制.
- 采用并行解卷空间金字塔聚合用于多尺度目标检测.
- 利用焦点-EIoU损失函数来加强对高质量的样本的关注,并改善培训.
- 采用轻量级的脱头来加快收和提高精度.
主要成果:
- 与YOLOv5s相比,MFP-YOLO在VisDrone 2019验证套件上提高了mAP50,在测试套件上提高了12.9%,在测试套件上提高了8.0%.
- 参数体积减少了79.2%,重量大小减少了73.7%.
- 在无人机空中图像检测中,在主流算法上表现出优越的性能.
结论:
- MFP-YOLO有效地解决了无人机空中图像检测的挑战,在显著减少的模型尺寸下提供高精度.
- 拟议的算法显示了对现实世界应用的巨大潜力,这些应用需要从空中平台高效准确地检测物体.
- MFP-YOLO代表了用于空中图像分析的轻量级深度学习模型的重大进步.
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