[1]JIANG Shijie,XIA Xiushan,ZHAI Wei,et al.ODE diffusion model for multiclass anomaly detection and localization[J].CAAI Transactions on Intelligent Systems,2025,20(2):376-388.[doi:10.11992/tis.202402022]
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ODE diffusion model for multiclass anomaly detection and localization

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 20 Number of periods: 2025 2 Page number: 376-388 Column: 学术论文—机器感知与模式识别 Public date: 2025-03-05
Title:
ODE diffusion model for multiclass anomaly detection and localization
Author(s):
JIANG Shijie1;  XIA Xiushan2;  ZHAI Wei1;  CAO Yang1
1. Department of Automation, University of Science & Technology of China, Hefei 230027, China;
2. Institute of Advanced Technology, University of Science & Technology of China, Hefei 230031, China
Keywords:
defect detection; anomaly detection; anomaly localization; diffusion model; denoising network; ordinary differential equations; unsupervised learning; timestep-perceptive
CLC:
TP391
DOI:
10.11992/tis.202402022
Abstract:
Multiclass anomaly detection and localization methods aim to train a single model capable of identifying anomalous regions that deviate from normal across multiple categories. Diffusion-based methods have recently attracted attention due to their excellent performance in this task. However, existing methods concentrate on enhancing the denoising network of the diffusion model by adding more constraints to ensure high consistency in multistep generation and achieve superior reconstruction performance. Additional sampling steps also lead to higher computational costs. We propose a novel multiclass anomaly detection and localization method called TimeNet to address these issues. It is based on the diffusion model of ordinary differential equations and achieves high-quality reconstruction with only one-step generation. We introduce a time-perceptive network to address the consistency and identity shortcut problems that may arise from small sampling steps, which further improves the reconstruction quality. Experiments on the most popular benchmark MVTec-AD dataset demonstrate that our TimeNet competes with the current state-of-the-art methods in terms of accuracy while requiring less computational effort and achieving faster speeds. The high accuracy and real-time performance of TimeNet satisfy the requirements for industrial anomaly detection and localization.
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