Automated low-cost framework for crack measurements in RC structures using deep learning approach

This research presents a comprehensive and automated framework for detecting surface cracks and measuring their widths in reinforced concrete (RC) members using a modified YOLO-V11 deep learning (DL) architecture and presents a customized image calibration and scaling approach to transfer crack dimensions from pixel-size to real-size using low-cost imaging devices and tools.