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基于深度学习的方法用于自由流通通道系统中的轴计数器
Bruno José Souza1,2, Guinther Kovalski da Costa3, Anderson Luis Szejka3
1Industrial and Systems Engineering Graduate Program, Pontifical Catholic University of Parana-PUCPR, Curitiba, Paraná, 80215-901, Brazil. bruno.jose@pucpr.edu.br.
Scientific reports
|February 9, 2024
概括
本研究引入了用于收费系统的自动轴计数器,使用计算机视觉从图像中识别车辆轴. 该系统通过精确计数轴和产生收费,实现了自由流通的收费,改善了交通流动性.
科学领域:
- 运输工程 运输工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 收费站会造成交通瓶,阻碍有效的流动.
- 收费自动化对于改善交通流量和运营效率至关重要.
研究的目的:
- 为自由流通的收费系统开发自动轴计数器.
- 加强车辆识别和收费过程在收费站.
主要方法:
- 利用计算机视觉算法和你只看一次 (YOLO) 模型来检测车辆的轮子和轴.
- 与YOLOv5,YOLOv6,YOLOv7和YOLOv8进行比较,其中YOLOv5m表现最好.
- 开发了一个通道管理器和图像重建模块,用于完整的车辆成像和轴数计.
主要成果:
- 在识别车辆轮子时,YOLOv5m模型实现了高精度 (99.40%) 和回忆 (98.20%).
- 该系统准确地计数车辆轴,并识别车辆通行.
- 根据轴数量成功生成了适当的负载.
结论:
- 开发的自动轴计数器有效地促进了自由流动的收费系统.
- 这项技术通过减少收费站拥堵来提高交通流动性.
- 计算机视觉,特别是YOLO模型,为收费应用中的自动化车辆评估提供了可行的解决方案.
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