Anomaly Detection Method for Visual Inspection using Self-Supervised Learning

Abstract

In industrial inspections such as visual and sensory inspections in the manufacturing industry, the automation of industrial inspection using computer vision technology is expected to reduce costs and minimize variations caused by human factors. For industrial products, where anomalies are rare and unknown anomalies may occur, unsupervised anomaly detection methods such as PaDiM and PatchCore have been proposed to detect anomalies based on normal products only. However, the models used in these methods are pre-trained on datasets like ImageNet and thus do not learn the features of the inspection target. This makes it difficult to adjust the models for performance improvement. Therefore, this paper proposes an anomaly detection method that uses self-supervised learning to capture the features of images targeted. Specifically, the model is fine-tuned with inspection images using self-supervised learning, and anomaly detection is performed using an unsupervised anomaly detection method with the fine-tuned model. In experiments, the proposed method achieved higher image-level AUC than existing methods for the visual inspection of breaker nameplates.