BT-Crack2000 pavement crack semantic segmentation dataset

This study constructs a crack image dataset based on urban roads in Baotou City, China. The data collection was conducted using an onboard camera for field shooting, acquiring a total of 2102 high-definition road images. To enhance data diversity, the original dataset was expanded to 6500 multi-resolution images through data augmentation techniques such as image translation, selective cropping, and scaling. This dataset exhibits the following characteristics: (1) It includes a variety of background interference factors, such as lane markings, lighting variations (dim and glare conditions), and shadows in complex scenes; (2) These interference factors significantly increase the difficulty of image segmentation tasks, providing a challenging test benchmark for evaluating algorithm robustness. The construction of the dataset fully considers various interference factors in real road environments, offering reliable experimental data support for road crack detection research.