基于深度学习的方法,用于在降雪期间检测流量参数
Cheng Jian1,2, Tiancheng Xie2,3,4, Xiaojian Hu2,3,4
1Nanjing LES Information Technology Co., Ltd., Nanjing 211189, China.
Journal of imaging
|December 27, 2024
概括
这项研究引入了一个新的框架,用于分析雪地条件下的交通流量. 该方法提高了车辆检测和交通参数的准确性,改进了智能交通系统.
科学领域:
- 计算机视觉 计算机视觉
- 智能运输系统 智能运输系统
背景情况:
- 降雪显著降低视频质量,挑战自动化交通数据收集.
- 精确的车辆检测和交通参数提取对于智能运输至关重要,特别是在恶劣的天气中.
研究的目的:
- 开发一个强大的分析框架,从降雪期间拍摄的视频中提取交通流量参数.
- 为了应对雪造成的图像退化和识别精度降低的挑战.
主要方法:
- 一个四个阶段的框架,利用深度学习网络去除雪 (颗粒和条纹).
- 实施YOLOv5用于车辆识别和虚拟线圈方法用于流量参数估计.
主要成果:
- 在除雪后,在中度雪条件下,车辆识别准确度提高了8.6%.
- 显著提高了运营速度.
- 在温和的雪地条件下,在交通流量参数估计中获得了97.2%的准确性.
结论:
- 拟议的框架有效地克服了雪引起的图像退化,以进行准确的交通分析.
- 这一进步对于在积雪环境中运行的可靠的智能运输系统至关重要.
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