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In a populous nation like India, one fundamental need is travel so travel encompasses road, rail and water. Road transport is most utilized and also the prime reason people get to lead simple lives. Even after studying the present situation, we found a major problem on the roads: potholes. These potholes have become a source of harm to the condition of the roads and an additional threat of accidents on the roads. The detection of potholes is vital for safety on roads. The best method for pothole detection is using the real-time accurate efficient YOLOv10 model. A Raspberry Pi Camera Module can record real-time video and images of the road. Further, the Raspberry Pi can be integrated with a GPS module to find the precise coordinates of any potholes. The data generated by the GPS module is helpful in making repairs and guiding drivers in choosing routes. The system relies on a Convolution Neural Network (CNN) model, which assists in pothole detection using YOLO models.

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Evaluating Pothole Detection Performance Across Different Yolo Models

  • 会议论文

  • 发表日期：

    2026年

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  • S. Sahana,
  • H. Umabharati,
  • G. Rakshita,
  • K. Vaishanvi,
  • Nikita Patil

摘要

In a populous nation like India, one fundamental need is travel so travel encompasses road, rail and water. Road transport is most utilized and also the prime reason people get to lead simple lives. Even after studying the present situation, we found a major problem on the roads: potholes. These potholes have become a source of harm to the condition of the roads and an additional threat of accidents on the roads. The detection of potholes is vital for safety on roads. The best method for pothole detection is using the real-time accurate efficient YOLOv10 model. A Raspberry Pi Camera Module can record real-time video and images of the road. Further, the Raspberry Pi can be integrated with a GPS module to find the precise coordinates of any potholes. The data generated by the GPS module is helpful in making repairs and guiding drivers in choosing routes. The system relies on a Convolution Neural Network (CNN) model, which assists in pothole detection using YOLO models.