Scholarlink

For transportation infrastructure to be safe, effective, and long-lasting, road condition monitoring is essential. Conventional techniques, which depend on human inspections, are frequently ineffective and prone to mistakes. To overcome these constraints, this study suggests a machine learning-based smart road condition monitoring system. Utilizing cameras installed on vehicles, the system gathers pictures and videos of the state of the roads, which are subsequently processed by sophisticated machine learning algorithms. These algorithms categorize surface conditions, identify irregularities in the road, and offer information on repair requirements. Through comprehensive field testing and data analysis, the study shows how effective the system is, showing notable gains in both the efficiency of maintenance procedures and the accuracy of identifying road issues. By concentrating on image and video analysis, this smart monitoring system offers a revolutionary solution for urban infrastructure management, opening the door for safer, more intelligent, and sustainable road maintenance procedures. This strategy not only lowers operating costs but also improves road safety and infrastructure sustainability.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Advanced Road Condition Monitoring Using Machine Learning and Computer Vision

  • 会议论文

  • 发表日期：

    2026年

查看全文
  • Vedant Chandore,
  • Niranjan Pardeshi,
  • Sai Sinare,
  • Samruddhi Akude,
  • Sahil Dhawane,
  • Kartik Gawande,
  • Rahul Sadgir,
  • Shravani Nigade,
  • Ajay Talele

摘要

For transportation infrastructure to be safe, effective, and long-lasting, road condition monitoring is essential. Conventional techniques, which depend on human inspections, are frequently ineffective and prone to mistakes. To overcome these constraints, this study suggests a machine learning-based smart road condition monitoring system. Utilizing cameras installed on vehicles, the system gathers pictures and videos of the state of the roads, which are subsequently processed by sophisticated machine learning algorithms. These algorithms categorize surface conditions, identify irregularities in the road, and offer information on repair requirements. Through comprehensive field testing and data analysis, the study shows how effective the system is, showing notable gains in both the efficiency of maintenance procedures and the accuracy of identifying road issues. By concentrating on image and video analysis, this smart monitoring system offers a revolutionary solution for urban infrastructure management, opening the door for safer, more intelligent, and sustainable road maintenance procedures. This strategy not only lowers operating costs but also improves road safety and infrastructure sustainability.