基于偏差扩散与双流对比学习的工业异常检测方法

doi:10.16451/j.cnki.issn1003-6059.202511003

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Abstract

Industrial anomaly detection is crucial to intelligent manufacturing and quality control. However, due to the scarcity and diverse distribution of anomaly samples, existing methods are prone to reconstruction distortion or feature confusion in multi-class scenarios, leading to inaccurate anomaly localization. To address these issues, an industrial anomaly detection method based on deviation diffusion and dual-stream contrastive learning is proposed. The deviation direction from noisy features to normal features in the latent space is directly learned through a deviation diffusion model. The normal structure of specific classes is captured by combining class embedding. A robust anomaly heatmap is generated through fusing the differences between the latent space domain and the image domain during inference. Simultaneously, dual-stream contrastive learning is introduced to widen the representation gap between normal samples and anomalies. Furthermore, a foreground-aware anomaly synthesis strategy is designed to construct complementary anomalies with structural and textural characteristics to provide high-quality anomaly samples for the contrastive learning of the normal flow-synthesized anomaly flow. The experimental results on MVTec-AD and VisA datasets show that the proposed method achieves excellent performance on multiple evaluation metrics and exhibits strong generalization ability.

Key words： Anomaly Detection Industrial Vision Deviation Diffusion Model Dual-Stream Contrastive Learning

Received: 18 October 2025

ZTFLH:

TP311

Fund:

Supported by Yunnan Fundamental Research Project(No.202301AV070004)

Corresponding Authors: LI Huafeng, Ph.D., professor. His research interests include image processing and computer vision.

About author:: ZHU Sheng, Master student. His research interests include computer vision and image processing.
ZHANG Yafei, Ph.D., professor. Her research interests include image processing and pattern recognition.

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Cite this article:

ZHU Sheng,ZHANG Yafei,LI Huafeng. Industrial Anomaly Detection Method Based on Deviation Diffusion with Two-Stream Contrastive Learning[J]. Pattern Recognition and Artificial Intelligence, 2025, 38(11): 986-998.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202511003 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2025/V38/I11/986

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