CrackBD: A Dataset for Crack Detection in Historical Buildings of Bangladesh

Experts involved in monitoring the condition of historical structures periodically look for automated techniques to speed up the process of detecting and analyzing cracks. These individuals are currently required to physically examine significant paperwork and do visual inspections, investing a substantial amount of effort to identify both small and severe cracking issues. The laborious method has become a growing concern, prompting experts to increasingly demand automated crack detection technology for precise analysis of the structural integrity of old structures. The Current datasets have been essential for researching automated crack identification in historical structures. However, they have some limitations, including a lack of variety in the structures, materials, and architectural styles they cover. Consequently, the models trained on these datasets struggle to apply their insights to new data beyond their training set. In order to eliminate these limitations, we propose a comprehensive and diverse dataset. The collection consists of 71,400 photos of cracks in different architectural styles and construction materials. It consists of 5,100 high-quality images that capture various types and locations of cracks in historic buildings. Additionally, we have generated 66,300 crack images using multiple proficient data augmentation approaches to enhance the robustness of the dataset.