Learning monocular depth estimation for defect measurement from civil RGB-D dataset

A large quantity of civil infrastructure in North America is near the end of their design life. Consequently, the routine visual structural inspection is increasingly necessary to ensure the safety and efficient management of the infrastructure stock. The increasing need for inspections and the laborious nature of the work has caused strain on the inspection industry. To improve inspection efficacy, various researchers have proposed novel deep learning methodologies to automatically classify, detect, and segment structural defects from images. After the defects are identified, it is often desirable to quantify the size of the defect, for severity classification and repair cost estimation. Yet, the measurement from a single image for quantification is not a trivial task, requiring supplementary data or sensor inputs, which may not be practical or economical in the current inspection process. In this study, we propose to recover the three-dimensional geometry of a scene from a single image, by using deep learning-based monocular depth estimation. The monocular depth estimation field has made great progress by leveraging deep learning and a plethora of open red, green, blue, and depth (RGB-D) datasets. However, there has not been a publicly available in situ Light Detection and Ranging (LiDAR) RGB-D dataset for the civil engineering domain, which is a barrier for researchers to develop and evaluate spatial computer vision methods in the civil engineering context. To bridge this gap, we build a LiDAR-based RGB-D dataset for training monocular depth estimators. Then using the civil RGB-D dataset, we test a solution for the real-world application of monocular depth estimation to quantify defects in civil infrastructure.