Visual anomaly detection algorithms: Development and Frontier review

Abstract

Visual anomaly detection includes image anomaly detection and video anomaly detection, focusing on identifying and locating anomalous patterns or events in images or videos. This technology finds widespread applications across multiple domains, including industrial surface defect inspection, medical image lesion analysis, and security surveillance systems. By identifying patterns that do not conform to normal conditions, it helps to detect anomalies in a timely manner and reduce risks and losses. This paper provides a comprehensive review of existing visual anomaly detection algorithms. It introduces a taxonomy of algorithms from a new perspective: statistical-based algorithms, measurement-based algorithms, generative-based algorithms, and representation-based algorithms. Furthermore, this paper systematically introduces datasets for visual anomaly detection and compares the performance of various algorithms on different datasets under typical evaluation metrics. By analyzing existing algorithms, we identify current challenges and suggest promising future research directions.