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A high-resolution perspective-view road image dataset for pothole detection.

Hanshen Chen1, Zhoulin Tu2, Yu Zhao3

  • 1College of Intelligent Transportation, Zhejiang Institute of Communications, Hangzhou, 311112, China. chenhs@zjvtit.edu.cn.

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|April 28, 2026
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Summary

This study introduces HRP4K, a large road image dataset for training AI pothole detectors. The dataset aids in developing automated systems for critical infrastructure monitoring and road safety.

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Area of Science:

  • Computer Vision
  • Infrastructure Monitoring
  • Machine Learning

Background:

  • Potholes pose significant risks to vehicle safety and infrastructure integrity.
  • Automated pothole detection is crucial for efficient road maintenance.
  • Lack of large-scale, high-quality datasets hinders computer vision progress in this area.

Purpose of the Study:

  • Introduce HRP4K, a novel high-resolution road image dataset.
  • Provide a benchmark for developing and evaluating pothole detection algorithms.
  • Facilitate advancements in automated infrastructure monitoring.

Main Methods:

  • Collected 6,003 images across 1,100 km of diverse roads in China.
  • Utilized vehicle-mounted cameras for perspective-view data capture.
  • Employed a human-in-the-loop pipeline for high-fidelity annotation of 7,217 pothole instances.

Main Results:

  • HRP4K dataset features realistic distributions of small and ultra-small potholes.
  • Dataset includes 4,003 positive and 2,000 negative images, annotated in YOLO and COCO formats.
  • Baseline performance of six modern object detectors reported for benchmarking.

Conclusions:

  • HRP4K offers a challenging benchmark for robust pothole detection.
  • The dataset supports the development of scalable, automated road maintenance solutions.
  • Enables standardized comparison and evaluation of computer vision models for infrastructure monitoring.