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The study introduces a machine learning methodology that uses deep learning algorithms to identify and categorise potholes. The primary objective is to create an automated system that can precisely detect potholes in real-time or for data gathering without an internet connection, assisting in vehicle management and road upkeep. The suggested method utilises Convolutional Neural Networks (CNN) and image processing techniques using OpenCV to achieve a high level of reliability and accuracy in detecting potholes. This method tackles a notable problem in transportation networks by improving vehicle safety and decreasing repair expenses.

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Smart Road Sensing: A Machine Learning Approach for Pothole Detection and Classification

  • 会议论文

  • 发表日期：

    2025年

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  • Neel H. Dholakia,
  • Vipul Ladva,
  • Madhu Shukla,
  • Nishant Kothari,
  • Uvesh Sipai,
  • Akshay Ranpariya,
  • Simrin Syed

摘要

The study introduces a machine learning methodology that uses deep learning algorithms to identify and categorise potholes. The primary objective is to create an automated system that can precisely detect potholes in real-time or for data gathering without an internet connection, assisting in vehicle management and road upkeep. The suggested method utilises Convolutional Neural Networks (CNN) and image processing techniques using OpenCV to achieve a high level of reliability and accuracy in detecting potholes. This method tackles a notable problem in transportation networks by improving vehicle safety and decreasing repair expenses.