Automated Surface Crack Detection in Historical Constructions with Various Materials Using Deep Learning-Based YOLO Network

Cultural heritage (CH) constructions involve the use of diverse masonry materials. Under natural and human influences, masonry materials can undergo various types of damages, with crack damages being most prevalent. Developing a robust model using deep learning (DL) capable of detecting cracks in various CH materials is crucial. In this study, we compared the performance of the DL method You Only Look Once (YOLO) object detection network based on images in different masonry materials (stone, brick, cob, and tile) with that in a modern material (concrete). The dataset used in the study comprised 1213 bricks, 1116 concretes, 955 cobs, 882 stones, and 208 tile images. YOLOv5 architecture, transfer learning, and object detection models were utilized for detecting cracks and compare their performance in different materials. This study represents the first comparison of this kind using an original dataset. The DL model achieved mean average precision values of 94.4%, 93.9%, 92.7%, 87.2%, 83.4%, 81.6%, and 70.3% for concrete; concrete and cob, cob; stone; stone and brick; brick; and tile, respectively. The findings of this study indicate considerable potential for the widespread use of DL techniques in identifying cracks from images across various CH materials and assisting inspection professionals in damage surveys.