YOLOv8和点云融合用于增强道路坑洞检测和量化
Junkui Zhong1,2, Deyi Kong3, Yuliang Wei4
1Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei, 230031, China.
Scientific reports
|April 2, 2025
概括
这项研究引入了一种使用深度摄像头和点云检测道路坑洞的新方法,显著提高了智能交通系统的准确性并减少了误报.
科学领域:
- 计算机视觉 计算机视觉
- 道路基础设施监测 道路基础设施监测
- 智能运输系统 智能运输系统
背景情况:
- 自动探测坑洞对于道路维护和增强智能交通系统中的环境感知至关重要.
- 减少虚假阳性是优化道路表面异常检测精度的关键.
- 现有的方法可能会与不规则的坑作斗争,并将它们与其他表面特征区分开来.
研究的目的:
- 通过集成的深度摄像头图像和点云数据,引入一种用于检测不规则坑洞的新方法.
- 通过有效过出错误阳性来提高坑洞检测的准确性.
- 为了提供精确的测量坑洞尺寸,如周长,表面积和深度.
主要方法:
- 使用YOLOv8进行初始的二维物体检测,以确定候选坑区和相关的三维点云.
- 应用表面光滑度分析来确定坑道边界轮并提取相关的点云数据.
- 实施高度值来评估坑洞深度,并过出像污点和补丁这样的非坑洞特征.
主要成果:
- 拟议的方法证明了与独立的YOLOv8.8相比,在维护良好的道路上,[公式:参见文本]的检测准确度得到了提高.
- 实现了高性能指标:[公式:见文本]的精度,[公式:见文本]的回忆,以及[公式:见文本]的F1得分.
- 该模型在0.23秒内处理单个图像,对周边 ([公式:见文本]),表面积 ([公式:见文本]) 和深度 ([公式:见文本]) 的错误率较低.
结论:
- 深度摄像头图像和点云数据的综合方法为准确的坑洞检测提供了强大的解决方案.
- 该方法有效地减少了假阳性,提高了自动化道路评估的可靠性.
- 这项技术有助于更高效的道路维护和改进的智能交通系统.
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