A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images

TL;DR

This survey reviews recent unsupervised anomaly detection algorithms for industrial images, highlighting challenges, datasets, and future directions to improve efficiency and reduce reliance on labeled data in Industry 4.0 applications.

Contribution

It provides a comprehensive overview of unsupervised algorithms, compares their advantages and disadvantages, and discusses future research directions in industrial anomaly detection.

Findings

Unsupervised methods reduce need for labeled data.

Different algorithms have varied strengths and limitations.

Future research should focus on core unresolved issues.

Abstract

In line with the development of Industry 4.0, surface defect detection/anomaly detection becomes a topical subject in the industry field. Improving efficiency as well as saving labor costs has steadily become a matter of great concern in practice, where deep learning-based algorithms perform better than traditional vision inspection methods in recent years. While existing deep learning-based algorithms are biased towards supervised learning, which not only necessitates a huge amount of labeled data and human labor, but also brings about inefficiency and limitations. In contrast, recent research shows that unsupervised learning has great potential in tackling the above disadvantages for visual industrial anomaly detection. In this survey, we summarize current challenges and provide a thorough overview of recently proposed unsupervised algorithms for visual industrial anomaly detection covering five categories, whose innovation points and frameworks are described in detail. Meanwhile, publicly available datasets for industrial anomaly detection are introduced. By comparing different classes of methods, the advantages and disadvantages of anomaly detection algorithms are summarized. Based on the current research framework, we point out the core issue that remains to be resolved and provide further improvement directions. Meanwhile, based on the latest technological trends, we offer insights into future research directions. It is expected to assist both the research community and industry in developing a broader and cross-domain perspective.