工业图像无监督异常检测算法综述
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images
AI总结：
本综述针对工业4.0背景下工业图像异常检测需求，梳理五大类无监督异常检测算法的创新点与框架，介绍公开数据集，总结各类方法优劣，指出待解决问题与未来研究方向，为科研及工业领域提供跨领域参考。
摘要：
随着工业4.0的发展，表面缺陷检测/异常检测成为工业领域的热点课题。在实际应用中，提升效率同时节约人力成本一直是备受关注的问题，近年来基于深度学习的算法表现优于传统视觉检测方法。但现有基于深度学习的算法多偏向有监督学习，不仅需要大量标注数据和人力，还存在效率低下、局限性强的问题。与之相对，近期研究表明无监督学习在解决工业视觉异常检测的上述痛点方面具备巨大潜力。本综述梳理了当前面临的挑战，全面概述了近年提出的工业视觉异常检测无监督算法，覆盖五大类别，详细阐述了各类算法的创新点与框架。同时介绍了公开可用的工业异常检测数据集。通过对比不同类别的方法，总结了各类异常检测算法的优缺点。基于当前研究框架，指出了仍待解决的核心问题并给出进一步优化方向，同时结合最新技术趋势，对未来研究方向提出了见解。本综述有望帮助科研界与工业界建立更广阔的跨领域研究视角。
英文摘要：
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.
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