Crack detection and dimensional assessment using smartphone sensors and deep learning

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University of New Brunswick


This thesis addresses the critical challenge of deteriorating civil infrastructure due to natural processes and aging, emphasizing the importance of early detection for public safety. Surface cracks in concrete structures serve as vital indicators of deterioration, prompting the development of automatic defect detection using deep learning. Manual inspections, the basis of structural health monitoring, struggle with the complexities of crack patterns. The first part of this thesis focuses on training a Mask R-CNN network for crack detection, using augmented real-world data to enhance accuracy. The second part introduces a cost-effective methodology utilizing smartphone sensors' imagery and 3D data for automated crack detection and precise dimension assessment with YOLOv8 and Mask R-CNN. This research aims to advance a multi-modal approach combining LiDAR observations with image masks for accurate 3D crack measurements, establishing a pipeline for dimensional assessment, and evaluating state-of-the-art CNN-based networks for crack detection in real-life images.