Defect Quantification Using Novel Civil RGB-D Dataset

Routine visual structural inspection is a tedious but vital part of structural health monitoring. Many researchers have proposed novel methodologies to automatically classify, detect, and segment structural defects (i.e., crack, spall, etc.) from images. Yet, the scale from single images is ambiguous, which is an important prior for severity classification. While monocular depth estimation is an ill-posed problem, deep learning methods have made great progress field, greatly spurred by a plethora of open RGB-D datasets. However, to the best knowledge of the authors, there is no RGB-D dataset in the civil engineering domain. In this work, the authors seek to develop an efficient method to build an RGB-D dataset for the civil research community. The authors review popular RGB-D data collection paradigms and propose a LiDAR-based data collection method. Finally, the authors use the collected data to create a deep convolutional monocular depth estimation model for defect quantification. The authors hope this work can help other researchers incorporate depth information in their projects and to create a community to share civil RGB-D data to help advance the state-of-the-art in the automated visual structural inspection.