基于双向约束蒸馏的无监督图像异常检测

李波; 李泽超; 邢鹏; 唐金辉

doi:10.12263/DZXB.20240733

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基于双向约束蒸馏的无监督图像异常检测

学术论文 | 更新时间：2025-12-08

- 基于双向约束蒸馏的无监督图像异常检测
- Unsupervised Image Anomaly Detection Based on Constrained BidiRectional Distillation
- 电子学报 2025年53卷第3期页码：895-909
- 作者机构：
  
  南京理工大学计算机科学与工程学院，江苏南京 210094
- 作者简介：
  
  [ "李波男，2000年4月出生.现为南京理工大学计算机科学与工程学院硕士研究生.主要研究方向为异常检测、计算机视觉. E-mail: 122106010714@njust.edu.cn" ]
  [ "李泽超男，1985年5月出生.现为南京理工大学计算机科学与工程学院教授，副院长，博士生导师.主要研究方向为图像视频分析、目标检测、模式识别.中国电子学会会员编号：E190031283S.E-mail: zechao.li@njust.edu.cn" ]
  [ "邢鹏男，1998年9月出生.现为南京理工大学计算机科学与工程学院博士研究生.主要研究方向为异常检测、计算机视觉、AIGC. E-mail: xingp_ng@njust.edu.cn" ]
  [ "唐金辉男，1981年2月出生.现为南京理工大学计算机科学与工程学院教授，院长.主要研究方向为多媒体分析与计算机视觉相关工作.中国电子学会会员编号：E190031289M.E-mail: jinhuitang@njust.edu.cn" ]
- 基金信息：
  
  国家自然科学基金(U20B2064;U21B2043)
- DOI：10.12263/DZXB.20240733
  中图分类号： TP37;TP391.4
- 收稿：2024-08-07，
  
  修回：2024-10-22，
  
  纸质出版：2025-03-25
- 稿件说明：
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李波, 李泽超, 邢鹏, 等. 基于双向约束蒸馏的无监督图像异常检测[J]. 电子学报, 2025, 53(03): 895-909.

LI Bo, LI Ze-chao, XING Peng, et al. Unsupervised Image Anomaly Detection Based on Constrained BidiRectional Distillation[J]. Acta Electronica Sinica, 2025, 53(03): 895-909.
李波, 李泽超, 邢鹏, 等. 基于双向约束蒸馏的无监督图像异常检测[J]. 电子学报, 2025, 53(03): 895-909. DOI：10.12263/DZXB.20240733

LI Bo, LI Ze-chao, XING Peng, et al. Unsupervised Image Anomaly Detection Based on Constrained BidiRectional Distillation[J]. Acta Electronica Sinica, 2025, 53(03): 895-909. DOI：10.12263/DZXB.20240733

摘要

异常检测是一项重要的计算机视觉任务，它的目标是检测异常样本同时定位异常区域.近期，主流的无监督异常检测方案通常基于蒸馏方法和重构方法.然而，它们仍存在相似的局限.在基于蒸馏方法的异常检测中，学生网络通常能学习到教师网络相似的表征能力，无法针对某些异常区域产生与教师网络有明显差异的表征.在重构模型中，编码-解码结构容易学习到简单的复原捷径，导致复原图像与输入相似，无法有效地检

测异常.为了解决上述挑战，本文提出基于双向约束蒸馏的无监督图像异常检测方法

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=83539255&type=

2.20133328

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=83539256&type=

2.70933342

-Net，它通过双向蒸馏模块和多级过滤模块缓解了上述局限.具体地，在教师学生网络中，本文首先提出蒸馏适应域特征而非原始域特征，它通过双向蒸馏分支保证了正常适应域特征的高效对齐.然后，本文提出多级过滤模块，通过查询和压缩的方式过滤异常特征，进一步增强学习正常语义特征分布的能力，提升异常检测性能.最后，本文在两个基准异常检测数据集MVTec和VisA上进行了大量实验，结果表明所提方法在异常检测和定位任务上取得了先进的性能.

Abstract

Anomaly detection has been widely studied and applied to various visual scenes. Recently

the mainstream unsupervised anomaly detection schemes are usually based on distillation methods and reconstruction methods. However

they still have some limitations. In distillation model

the student network can usually learn the strong representation ability of the teacher network

thus can not represent differently for the abnormal regions. In reconstruction model

the encoder-decoder model can easily learn a restoration shortcut and recover features indiscriminately. To address the above challenges

we propose

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=83539255&type=

2.20133328

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=83539256&type=

2.70933342

-Net

which integrates the advantages of above two methods and

alleviates limitations through the bidirectional distillation module and the multistage filtration mechanism. Specifically

in the teacher-student network

this paper first proposes distilling adaptive domain features instead of original domain features

which ensures efficient alignment of normal adaptive domain features through bidirectional distillation branches. Then

we propose a multilevel filtering module to filter abnormal features through query and compression to further enhance the ability to learn normal semantic feature distribution and improve the anomaly detection performance. Finally

a large number of experiments are carried out on two benchmark anomaly detection datasets

MVTec and VisA. The results show that the proposed method achieves advanced performance in anomaly detection and location tasks.

关键词

Keywords

references

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