计算机视觉和动态应变融合方法用于城市桥梁权衡运动
Yanjie Zhu1, Yuchen Wang1, Chenglin Gao1
1Department of Bridge Engineering, School of Transportation, Southeast University, Nanjing, China.
Communications engineering
|December 13, 2025
概括
一个新的视频压力系统使用计算机视觉和桥梁传感器在密集的交通中准确地称重车辆. 这种低成本技术提高了桥梁的安全性,并支持智能城市基础设施.
科学领域:
- 结构工程 结构工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 目前的动态称重系统对于密集的城市交通来说是昂贵的或不可靠的.
- 准确的车载负载监控对于桥梁安全和维护至关重要.
研究的目的:
- 开发一种低成本,可靠的系统,用于测量桥上的动态车载负荷.
- 集成计算机视觉和结构应变传感器,以准确估计重量.
主要方法:
- 利用轻量级的深度学习模型进行车辆特征识别.
- 采用多对象追踪来确定车辆轨迹和车道位置.
- 开发了一个分析算法,通过桥梁应变数据来估计车辆的重量.
主要成果:
- 实现了接近基线的车辆识别精度,模型参数显著减少,速度增加.
- 识别了近乎完美的车道准确性 (0.56%的错过检测率).
- 车辆总重量的估计,准确度高 (误差在2%以内).
结论:
- 视频应变逆估计系统为智能桥梁监控提供了具有成本效益和可靠的解决方案.
- 这项技术促进了对关键城市基础设施的数字双胞胎的创建.
- 该系统在交通繁忙的城市环境中证明了其实际适用性.
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