Anomaly detection models for automatic crack detection and segmentation

Abstract

In periodic inspections of bridges, damage conditions are assessed through close visual inspection, followed by evaluations of damage extent and structural integrity. The use of deep learning models is expected to streamline the assessment of damage. Constructing high-performance deep learning models requires extensive data collection and annotation with class labels tailored to specific tasks. This article applies five representative anomaly detection models (Ganomaly, PaDiM, PatchCore, FastFlow, and EfficientAD) to the problems of crack detection and segmentation in concrete structures, proposing a method to build models using only normal data. Evaluation experiments assess the effectiveness of each model in crack detection and segmentation. Additionally, this article shows limitations of anomaly detection models and discusses challenges towards practical applications.