Anomaly Detection for Automated Visual Inspection: A Review

Abstract

AbstractAnomaly detection (AD) methods that are based on deep learning (DL) have considerably improved the state of the art in AD performance on natural images recently. Combined with the public release of large-scale datasets that target AD for automated visual inspection (AVI), this has triggered the development of numerous, novel AD methods specific to AVI. However, with the rapid emergence of novel methods, the need to systematically categorize them arises. In this review, we perform such a categorization, and identify the underlying assumptions as well as working principles of DL-based AD methods that are geared towards AVI. We perform this for 2D AVI setups, and find that the majority of successful AD methods currently combines features generated by pre-training DL models on large-scale, natural image datasets with classical AD methods in hybrid AD schemes. Moreover, we give the main advantages and drawbacks of the two identified model categories in the context of AVI’s inherent requirements. Last, we outline open research questions, such as the need for an improved detection performance of semantic anomalies, and propose potential ways to address them.