Application of deep learning-based anomaly detection using UAV images for specific road earthwork structures

Abstract

In the Guidelines for Inspection of Road Earthwork Structures published by Ministry of Land, Infrastructure, Transport and Tourism, the basic method for inspecting deformations is close visual inspection. However, it is also permissible to adopt new inspection technologies if they are deemed reasonable from the perspective of the inspection guidelines, based on collected information on the development trends of new inspection technologies. In this study, we quantitatively analyzed the relationship between various conditions given to PaDiM (Patch Distribution Modeling Framework for Anomaly Detection), a deep learning-based anomaly detection method, for detecting cracks on road reinforced slopes using UAV images as training data and the detection results. The results showed that cracks could be detected by providing UAV images to PaDiM, and we were able to find recommendations regarding the observation methods of road slopes using UAVs.