列车距离估计在投票区基于单眼视觉的列车距离估计
Yang Hao1,2, Tao Tang1, Chunhai Gao2
1School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China.
Sensors (Basel, Switzerland)
|November 14, 2023
概括
本研究引入了一种单眼视觉方法,用于在铁路转处准确估计列车距离. 该方法通过精确定位列车并防止碰撞,确保了更安全的自治城市交通.
科学领域:
- 计算机视觉 计算机视觉
- 机器人技术 机器人技术 机器人技术
- 运输工程 运输工程
背景情况:
- 在城市铁路运输中自动驾驶需要精确的列车距离估计,特别是在人流地区.
- 传统的方法,如激光雷达和立体视觉,由于巨大的入射角度和转的距离,难以准确.
研究的目的:
- 提出一种基于单眼视觉的强有力的方法,用于估计在投票区的火车距离.
- 提高自治城市铁路运输系统的安全性和效率.
主要方法:
- 使用YOLOv8检测火车侧窗的实例细分.
- 功能提取以识别窗户的垂直方向,上边和下边.
- 针孔摄像机模型应用程序用于精确的距离计算.
主要成果:
- 创建了2477张图像的数据集,实现了92.43%的MIoU和97.47%的MPA,例如细分.
- 该方法表明,在四个典型的投票场景中,列车距离估计的平均RMSE为0.9523m.
- 取得的准确性足以确定参与率地区的交叉占用率.
结论:
- 拟议的单眼视觉方法为铁路转中列车距离估计提供了可行且准确的解决方案.
- 这项技术对自主城市铁路运营的安全性做出了重大贡献.
- 该方法的性能验证了其在现实世界地铁环境中的有效性.
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