CRACK DETECTION AND DIMENSIONAL ASSESSMENT USING SMARTPHONE SENSORS AND DEEP LEARNING

Abstract

This paper addresses the crucial need for effective crack detection and dimensional assessment in civil infrastructure materials to ensure safety and functionality. It proposes a cost-effective solution for crack detection and dimensional assessment by applying state-of-the-art deep learning on smartphone sensor imagery and positioning data. The proposed methodology integrates 3D data from LiDAR sensors with Mask R-CNN and YOLOv8 object detection networks, for automated crack detection in concrete structures, allowing for accurate measurement of crack dimensions, including length, width, and area. The calculated crack-straight-length closely aligns with the ground-truth straight-length, with an average error of 1.5%. This research has the potential to advance concrete infrastructure inspection, bridge knowledge gaps, and contribute to innovative solutions for precise structural integrity assessment and maintenance.