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The study introduces an advanced approach to pothole detection and classification implemented by YOLOv12 and shows greater performance improvements than YOLOv8. A new data set was created containing 1,000 pothole images (water and no water). The images, along with staggered ground truths, were recorded manually. We ran five different models on this data merged from YOLOv8 and YOLOv12. While, YOLOv12 surpasses YOLOv8 in detection and generalization, YOLOv8 outperformed YOLOv12 in classifying ponds. These results establish a foundation for integrating YOLO-based pothole detection into intelligent transportation systems, enhancing road safety and infrastructure maintenance. Future research may focus on further architectural optimizations and real-world deployment strategies to enhance system efficiency.

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Pothole Detection and Classification Using YOLO Models

  • 会议论文

  • 发表日期：

    2025年

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  • Thien Thanh Tran Ngoc,
  • Tien Thanh Do,
  • Ngoc Anh Le,
  • Thi Nhung Vuong,
  • Ngoc Thanh Pham,
  • Doan Dong Nguyen,
  • Phuong Anh Nguyen

摘要

The study introduces an advanced approach to pothole detection and classification implemented by YOLOv12 and shows greater performance improvements than YOLOv8. A new data set was created containing 1,000 pothole images (water and no water). The images, along with staggered ground truths, were recorded manually. We ran five different models on this data merged from YOLOv8 and YOLOv12. While, YOLOv12 surpasses YOLOv8 in detection and generalization, YOLOv8 outperformed YOLOv12 in classifying ponds. These results establish a foundation for integrating YOLO-based pothole detection into intelligent transportation systems, enhancing road safety and infrastructure maintenance. Future research may focus on further architectural optimizations and real-world deployment strategies to enhance system efficiency.