Geodesy

Benchmark (surveying)

Philosophy

Pure mathematics

Field (mathematics)

Anomaly detection

Computer science

A priori and a posteriori

Anomaly (physics)

Physics

Mathematics

Geography

Epistemology

Condensed matter physics

Data science

Artificial intelligence

Deep learning

Unsupervised learning

Machine learning

IEEE Transactions on Instrumentation and Measurement

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of a priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localization algorithms have become more widely used in industrial inspection tasks. This paper aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised anomaly localization in industrial images using deep learning. The survey reviews more than 120 significant publications covering different aspects of anomaly localization, mainly covering various concepts, challenges, taxonomies, benchmark datasets, and quantitative performance comparisons of the methods reviewed. In reviewing the achievements to date, this paper provides detailed predictions and analysis of several future research directions. This review provides detailed technical information for researchers interested in industrial anomaly localization and who wish to apply it to the localization of anomalies in other fields.

Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey