• 主题
高级搜索  >
历史搜索 清空历史
首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data
【2h】

An Automated Machine-Learning Approach for Road Pothole Detection Using Smartphone Sensor Data

机译：使用智能手机传感器数据的道路坑道检测自动化机器学习方法

代理获取
本网站仅为用户提供外文OA文献查询和代理获取服务，本网站没有原文。下单后我们将采用程序或人工为您竭诚获取高质量的原文，但由于OA文献来源多样且变更频繁，仍可能出现获取不到、文献不完整或与标题不符等情况，如果获取不到我们将提供退款服务。请知悉。
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Road surface monitoring and maintenance are essential for driving comfort, transport safety and preserving infrastructure integrity. Traditional road condition monitoring is regularly conducted by specially designed instrumented vehicles, which requires time and money and is only able to cover a limited proportion of the road network. In light of the ubiquitous use of smartphones, this paper proposes an automatic pothole detection system utilizing the built-in vibration sensors and global positioning system receivers in smartphones. We collected road condition data in a city using dedicated vehicles and smartphones with a purpose-built mobile application designed for this study. A series of processing methods were applied to the collected data, and features from different frequency domains were extracted, along with various machine-learning classifiers. The results indicated that features from the time and frequency domains outperformed other features for identifying potholes. Among the classifiers tested, the Random Forest method exhibited the best classification performance for potholes, with a precision of 88.5% and recall of 75%. Finally, we validated the proposed method using datasets generated from different road types and examined its universality and robustness.
机译：道路表面监测和维护对于驾驶舒适，运输安全和维护基础设施完整性至关重要。经常通过专门设计的仪器车辆定期进行传统的道路状况监测，这需要时间和金钱，只能涵盖道路网络的有限比例。鉴于智能手机的无处不在的使用，本文提出了一种自动坑洞检测系统，利用智能手机内置振动传感器和全球定位系统接收器。我们使用专用的车辆和智能手机收集了一个城市的道路状况数据，专为这项研究设计了专门的移动应用程序。将一系列处理方法应用于收集的数据，提取来自不同频域的特征，以及各种机器学习分类器。结果表明，来自时间和频率域的功能优于识别坑坑的其他功能。在测试的分类器中，随机森林方法表现出坑洼最佳分类性能，精度为88.5％，召回为75％。最后，我们使用不同道路类型产生的数据集进行了验证了该方法，并检查了其普遍性和鲁棒性。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
代理获取

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号