在桥上的多个摄像头之间识别和跟踪车辆,使用基于YOLOv4和OSNet的方法
Tao Jin1, Xiaowei Ye1, Zhexun Li1
1Department of Civil Engineering, Zhejiang University, Hangzhou 310058, China.
Sensors (Basel, Switzerland)
|July 8, 2023
概括
这项研究引入了一种计算机视觉方法,用于通过多个桥梁摄像头跟踪车辆,增强结构健康监测. 该方法在车辆检测和多摄像头跟踪中实现了高精度,用于负载估计.
科学领域:
- 工程 工程师 工程师 工程师
- 计算机科学 计算机科学
- 交通运输 运输 运输 运输
背景情况:
- 车辆负载估计对于桥梁结构健康监测 (SHM) 至关重要.
- 现有的方法,如桥梁重量移动 (BWIM) 缺乏车辆位置数据.
- 多摄像头在桥上追踪车辆是很有挑战性的,因为视野不重叠.
研究的目的:
- 开发一套强大的计算机视觉系统,用于在多个桥上的多个,不重叠的摄像头视图中检测和跟踪车辆.
- 为了提高车辆位置和移动数据的准确性,用于全面的桥梁SHM.
- 为了能够在整个桥上获得车辆的时间空间负载分布.
主要方法:
- 基于You Only Look Once v4 (YOLOv4) 和全尺度网络 (OSNet) 的模型用于车辆检测和跟踪.
- 一个经过修改的交叉与欧盟 (IoU) 基于的追踪器集成了车辆外观和边界框重叠,用于内摄像头追踪.
- 匈牙利的算法促进了车辆在不同的摄像头视频源中进行匹配.
主要成果:
- 拟议的方法在单摄像头车辆跟踪方面实现了97.7%的准确性.
- 多摄像头车辆跟踪精度超过92.5%.
- 为模型培训和评估,创建了一个包含1727辆汽车的25080张图像的专用数据集.
结论:
- 开发的系统有效地通过多个摄像头追踪车辆,克服视野限制.
- 这项技术显著提高了监测车辆负载及其在桥上时空分布的能力.
- 这些发现有助于更准确,更全面地监测桥梁结构健康状况.
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