Integrated utilization of images and point clouds for crack length estimation and component detection

Abstract

In this study, we propose a method for detecting cracks and extracting damage information by integrating point cloud data with corresponding images, aiming to enhance the efficiency of inspection and maintenance of aging bridge structures. To improve detection accuracy, the Segment Anything Model (SAM) was employed as a supplemental segmentation tool. Image-based crack detection was performed using deep learning, and the results were projected onto the point cloud data through geometric calibration． Utilizing the spatial resolution of the point cloud，the length of each detected crack was quantitatively estimated. The proposed framework enables accurate damage assessment and offers practical potential for automating bridge inspection tasks.