<p>Potholes are a critical infrastructure distress that damages vehicles and creates safety risks, motivating timely detection for effective maintenance. Automated detection via computer vision is a scalable solution, but its progress is hindered by the scarcity of high-quality, large-scale public datasets. Here we present HRP4K, a high-resolution, perspective-view road image dataset for developing and evaluating pothole detectors. Images are captured by vehicle-mounted full-frame mirrorless cameras across 1,100 km of urban and rural roads in China. The dataset comprises 6,003 images, including 4,003 positive images containing at least one pothole and 2,000 negative images of pothole-absent road surfaces. In total, HRP4K provides 7,217 pothole instances annotated with bounding boxes and is released in both YOLO and COCO formats. A human-in-the-loop pipeline involving algorithmic preprocessing, privacy anonymization, and iterative model-assisted annotation ensured consistent, high-fidelity labels. The data exhibit realistic long-tailed distributions of small and ultra-small potholes in visually complex scenes, providing a challenging benchmark for object detection. We report baseline results using six modern detectors to facilitate standardized comparison and benchmarking in automated infrastructure monitoring.</p>