通过应用基于道路振动数据的卷积神经网络方法来检测道路坑洞
Furkan Ozoglu1, Türkay Gökgöz1
1Department of Geomatic Engineering, Yildiz Technical University, 34349 Istanbul, Turkey.
Sensors (Basel, Switzerland)
|November 25, 2023
概括
这项研究介绍了一种基于智能手机的系统,用于使用振动传感器和GPS检测道路坑洞. 卷积神经网络实现了高精度,使得有效的道路表面异常检测.
科学领域:
- 道路运输安全道路运输安全
- 对振动进行分析.
- 机器学习用于基础设施监控.
背景情况:
- 坑洞对驾驶舒适性,车辆维护成本和道路安全构成重大风险.
- 现有的检测方法通常需要专门的设备,增加车主成本.
研究的目的:
- 利用现有智能手机技术开发一个具有成本效益的坑洞检测系统.
- 引入卷积神经网络 (CNN) 的新型应用,用于分析路面振动.
主要方法:
- 开发了一个iOS应用程序来收集振动数据 (加速计,陀螺仪) 和GPS坐标.
- 道路振动数据被转换成视觉表示 (基于像素的图像).
- 训练并验证了具有不同架构的多个CNN模型,以识别坑洞.
主要成果:
- 在初始验证过程中,CNN模型表现出高精度 (93.24%) 和低损失 (0.2948).
- 一个两阶段的验证过程证实了系统在现实环境中的有效性.
- 现场研究显示,洞检测成功率在80%至87%之间.
结论:
- 智能手机集成的振动传感与CNN相结合,为洞穴检测提供了可行的和准确的解决方案.
- 这种方法减少了对昂贵的专用硬件的需求,使道路监控更容易获得.
- 开发的系统有助于提高道路安全和降低运输成本.
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