A Two-stage Industrial Anomaly Detection Method Based on Diffusion Generative Model

Zeng Weifeng; Cheng Lianglun; Huang Guoheng

doi:10.12052/gdutxb.230204

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A Two-stage Industrial Anomaly Detection Method Based on Diffusion Generative Model

更新时间：2026-05-27

- A Two-stage Industrial Anomaly Detection Method Based on Diffusion Generative Model
- Vol. 42, Issue 2, Pages: 11-19(2025)
- 作者机构：
  
  广东工业大学计算机学院,广东,广州,510006
- 作者简介：
- 基金信息：
- DOI：10.12052/gdutxb.230204
  CLC： TP391.4
- Published：2025
- 稿件说明：
移动端阅览
曾炜峰, 程良伦, 黄国恒. 基于扩散生成的两阶段工业异常检测方法[J]. 广东工业大学学报, 2025, 42(2): 11–19.

Zeng Weifeng, Cheng Lianglun, Huang Guoheng. A Two-stage Industrial Anomaly Detection Method Based on Diffusion Generative Model[J]. 2025, 42(2): 11-19.
曾炜峰, 程良伦, 黄国恒. 基于扩散生成的两阶段工业异常检测方法[J]. 广东工业大学学报, 2025, 42(2): 11–19. DOI： 10.12052/gdutxb.230204

Zeng Weifeng, Cheng Lianglun, Huang Guoheng. A Two-stage Industrial Anomaly Detection Method Based on Diffusion Generative Model[J]. 2025, 42(2): 11-19. DOI： 10.12052/gdutxb.230204.

摘要

现有的工业异常检测方法大多是采用图像修复的思路，在训练阶段利用人工合成伪异常样本的方式，将人工缺陷图像和模型修复后的重建图像进行判别，并计算判别的偏差，得到异常区域。然而，人工合成的缺陷图像与实际缺陷语义关联度不高，不能准确覆盖实际缺陷类型，导致模型鲁棒性不足。为了解决这个问题，本文提出了基于扩散生成的两阶段工业异常检测模型——DADNet(Diffusion Anomaly Detection Network)。首先，利用语义引导的异常生成模块合成已知缺陷，并以此作为异常检测的先验信息。第一阶段采用图像重建模型对工件异常区域进行修复。第二阶段则训练基于修复图像与初始图像的判别模型，用来检测异常区域。此外，本文通过联合关注机制聚合特征，进一步强化重建模型的性能。DADNet在公开的不同材质工件数据集上的性能表现都优于现有模型，并在工业异常缺陷检测中更有应用前景。

Abstract

Most existing industrial anomaly detection methods adopt the idea of image restoration

which utilizes artificial synthesis of pseudo anomaly samples in the training stage to discriminate the artificial defect image from the reconstructed images after model restoration

and calculates the deviation of the discrimination to obtain the anomalous region. However

the artificial defect images do not correlate well with the actual defect semantics and cannot accurately cover the actual defect types

resulting in less robustness of the model. In order to solve this problem

this paper proposes a two-stage industrial Diffusion Anomaly Detection network (DADNet). Firstly

a semantically-guided anomaly generation module is utilized to synthesize the known defects

and this is used as the a priori information for anomaly detection. In the first stage

an image reconstruction model is used to repair the abnormal region of the workpiece. In the second stage

a discriminative model based on the repaired image and the initial image is trained and used to detect the anomalous regions. In addition

this paper further enhances the performance of the reconstruction model by aggregating features through the joint attention mechanism. DADNet outperforms the existing models on the publicly available datasets of workpieces with different materials

promising prospect for industrial anomaly defect detection.

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Keywords

references

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