广东工业大学学报2025，Vol.42Issue(2)：11-19,9.
基于扩散生成的两阶段工业异常检测方法
A Two-stage Industrial Anomaly Detection Method Based on Diffusion Generative Model
摘要
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.关键词
异常检测/工业检测/图像生成/特征聚合Key words
anomaly detection/industrial detection/image generation/feature aggregation分类
计算机与自动化引用本文复制引用
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):11-19,9.基金项目
广东省重点领域研发计划项目(2018B010109007) （2018B010109007）
佛山市重点领域科技攻关项目(2020001006832) （2020001006832）
