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.

翻译：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。提高效率并节省人力成本在实践中日益受到关注，近年来基于深度学习的算法表现优于传统视觉检测方法。然而现有基于深度学习的算法偏向于监督学习，这不仅需要大量标注数据和人力投入，还带来了效率低下和局限性。相比之下，近期研究表明无监督学习在解决上述视觉工业异常检测的不足方面具有巨大潜力。本综述总结了当前面临的挑战，并全面概述了近期提出的五类无监督视觉工业异常检测算法，详细描述了其创新点与框架。同时介绍了公开可用的工业异常检测数据集。通过对比不同类别方法，归纳了异常检测算法的优势与不足。基于现有研究框架，我们指出了尚待解决的核心问题并提供了改进方向。此外，根据最新技术趋势，我们展望了未来研究方向，旨在帮助学术界和工业界建立更广阔的跨域视角。