Automated surface crack detection in historical constructions with various materials using deep learning-based YOLO network

Abstract

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 capable of detecting cracks in various CH materials is crucial for applying deep learning (DL) methods. 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 brick, 1116 concrete, 955 cob, 882 stone, and 208 tile images. YOLOv5 architecture, transfer learning, and object detection models were utilized for detecting cracks to observe and compare their performance in different materials. This study represents the first comparison of this kind using an original dataset. The 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 and detecting more damages across various materials.