计算机工程与应用2025，Vol.61Issue(23)：38-58,21.DOI:10.3778/j.issn.1002-8331.2503-0133
工业图像表面异常定位的无监督学习方法综述
Review of Unsupervised Learning Methods for Surface Anomaly Localization in Industrial Images
摘要
Abstract
The rapid development of deep learning has marked a milestone for anomaly detection and localization in industrial images.The demand for comprehensive and in-depth exploration of specific methods and emerging trends in this field continues to grow in existing research,surpassing traditional supervised training paradigms.The background,current developments,and key challenges of anomaly localization methods based on self-supervised and unsupervised learning are discussed.The review offers a comprehensive analysis of existing outstanding research in the industrial domain,addressing aspects such as neural network architecture design,special application scenarios,loss function improvements,collection of public datasets,and the use of evaluation metrics.Furthermore,this paper focuses on the role of large visual-language models in few-shot learning for multi-class unified anomaly localization tasks,exploring their cognitive and reasoning capabilities.It summarizes current research findings and highlights future research directions,aiming to enhance the robustness of anomaly localization algorithms in complex real-world scenarios and improve the efficiency of system development.This comprehensive analysis seeks to bridge existing knowledge gaps,provide valuable insights for researchers,and contribute to shaping the future of industrial anomaly localization research.关键词
智能制造/工业图像处理/异常检测与定位/深度学习/无监督学习Key words
intelligent manufacturing/industrial image processing/anomaly detection and localization/deep learning/unsupervised learning分类
信息技术与安全科学引用本文复制引用
赵俊,赵涓涓..工业图像表面异常定位的无监督学习方法综述[J].计算机工程与应用,2025,61(23):38-58,21.基金项目
国家自然科学基金(U21A20469) （U21A20469）
山西省科技创新团队专项资金(202304051001009) （202304051001009）
中央指导地方科技发展资助项目(YDZJSX2022C004). （YDZJSX2022C004）
