Unsupervised PatchCore-based anomaly detection method for detecting diverse types of damage in riverbank revetments

Abstract

In recent years, the aging of infrastructure has made establishment of efficient maintenance and management methods an urgent issue. This study aims to reduce human labor by automating inspection tasks through the use of digital data, with a particular focus on the automatic detection of damages in riverbank revetments. In image recognition tasks where collecting damage data is challenging, unsupervised anomaly detection methods that build models using only normal data have proven effective. However, existing methods often struggle to accommodate the diverse types of damages found in riverbank revetments. To address this issue, we propose a novel anomaly detection method based on PatchCore, a representative unsupervised anomaly detection technique. The proposed method introduces two key improvements: the construction of the normal dataset and the selection of intermediate layer depth in the feature extractor. Experimental results demonstrate that the proposed method achieves higher accuracy in detecting diverse damages in riverbank revetments compared to existing methods.