基于无监督学习的工业图像异常检测研究综述

许建; 冯健聪; 侯富豪; 杨庚

doi:10.11959/j.issn.2096-109x.AQ25287

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基于无监督学习的工业图像异常检测研究综述

综述 | 更新时间：2026-07-15

- 基于无监督学习的工业图像异常检测研究综述
- Survey on industrial image anomaly detection based on unsupervised learning
- 网络与信息安全学报 2026年12卷第3期页码：1-22
- 作者机构：
  
  1.南京邮电大学计算机学院，江苏南京 210023
  2.南京邮电大学大数据安全与智能处理江苏省高校重点实验室，江苏南京 210023
- 作者简介：
  
  许建（1980- ），男，博士，南京邮电大学副教授，主要研究方向为深度学习目标检测、网络空间安全等。
  冯健聪（2002- ），男，南京邮电大学硕士生，主要研究方向为工业图像异常检测。
  侯富豪（2002- ），男，南京邮电大学硕士生，主要研究方向为深度学习目标检测。
  杨庚（1961- ），男，博士，南京邮电大学教授，主要研究方向为计算机通信与网络、并行与分布式计算、信息安全等。
- 基金信息：
  
  国家自然科学基金资助项目(62372244)
- DOI：10.11959/j.issn.2096-109x.AQ25287
  中图分类号： TP393
- 收稿：2025-12-28，
  
  修回：2026-04-15，
  
  录用：2026-04-16，
  
  纸质出版：2026-06-25
- 稿件说明：
移动端阅览
许建, 冯健聪, 侯富豪, 等. 基于无监督学习的工业图像异常检测研究综述[J]. 网络与信息安全学报, 2026, 12(3): 1-22.

Xu Jian, Feng Jiancong, Hou Fuhao, et al. Survey on industrial image anomaly detection based on unsupervised learning[J]. Chinese Journal of Network and Information Security, 2026, 12(3): 1-22.
许建, 冯健聪, 侯富豪, 等. 基于无监督学习的工业图像异常检测研究综述[J]. 网络与信息安全学报, 2026, 12(3): 1-22. DOI： 10.11959/j.issn.2096-109x.AQ25287.

Xu Jian, Feng Jiancong, Hou Fuhao, et al. Survey on industrial image anomaly detection based on unsupervised learning[J]. Chinese Journal of Network and Information Security, 2026, 12(3): 1-22. DOI： 10.11959/j.issn.2096-109x.AQ25287.

摘要

工业4.0推动了智能制造的快速发展，传统人工质检存在效率低、成本高和稳定性差等问题，而基于监督学习的工业图像异常检测方法高度依赖精确标注的异常样本，难以适应真实工业场景中缺陷稀少、多变的特点。相比之下，基于无监督学习的工业图像异常检测方法仅需利用正常样本建模正常产品的分布特征与内在模式，因而成为近年来学术界与工业界的研究热点。首先，围绕单类学习设定对基于无监督学习的工业图像异常检测方法进行系统综述，并依据异常分数来源将现有方法归纳为特征嵌入和图像重建两大范式，对相关研究的发展脉络与核心思想进行梳理；其次，在上述分类框架下，对两类方法的技术演进与研究趋势进行归纳，总结不同方法在特征表示、异常评分机制和定位策略等方面的共性与差异，并对现有研究成果的主要优势与局限进行综合分析，从整体上梳理当前基于无监督学习的工业图像异常检测方法的发展特点与研究重点；再次，归纳了该领域常用的基准数据集与评价指标；最后，从实际工业应用需求出发，对当前研究面临的主要问题进行概括，并对未来的发展方向进行展望，以期为该领域的后续研究与应用提供参考。

Abstract

With the rapid advancement of Industry 4.0

Intelligent manufacturing has become increasingly important. Traditional manual inspection still suffers from low efficiency

high cost

and limited stability

while supervised learning methods rely heavily on accurately labeled anomalous samples

which are scarce and diverse in real industrial scenarios. In contrast

unsupervised industrial image anomaly detection learns the distribution and intrinsic patterns of normal samples only

and has therefore attracted growing attention from both academia and industry.First

a systematic review of unsupervised industrial image anomaly detection under the one-class learning setting was provided. Existing methods were categorized into two main paradigms according to the source of anomaly scores: feature embedding and image reconstruction

and the development of representative approaches was summarized.Second

based on this taxonomy

the evolution and research trends of these two paradigms were analyzed. The common characteristics and differences among existing methods in terms of feature representation

anomaly scoring mechanisms

and localization strategies were summarized to provide a structured understanding of current research.Then

commonly used benchmark datasets and evaluation metrics in this field were reviewed.Finally

the main challenges in current research were discussed from the perspective of practical industrial applications

and potential future research directions were outlined to facilitate further development and deployment of unsupervised industrial image anomaly detection methods.

关键词

Keywords

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