High precision anomaly detection based on pre-trained features enhanced by only large amount of normal samples

Hiroki Kobayashi; Manabu Hashimoto

doi:10.1117/12.3018802

2 May 2024 High precision anomaly detection based on pre-trained features enhanced by only large amount of normal samples

Hiroki Kobayashi, Manabu Hashimoto

Author Affiliations +

Proceedings Volume 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024; 1316422 (2024) https://doi.org/10.1117/12.3018802
Event: International Workshop on Advanced Imaging Technology (IWAIT) 2024, 2024, Langkawi, Malaysia

Abstract

As improvement of superior method called PaDiM in anomaly detection, we propose a method based on pre-trained features enhanced by training with consolidating normal samples to its centroid in feature space. PaDiM pre-trains the model with only ImageNet and parameterizes the features of target normal images by normal distribution. However, this method pre-trains the model while ignoring normal images that follow a normal distribution, which leads to performance degradation. In contrast, our method centralizes the features of normal images during pre-training, and as a result, the mean Image/Pixel AUROC of the proposed method was higher than that of PaDiM (94.2/96.2 and 93.6/95.7, respectively) in experiments with the MVTec AD dataset.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hiroki Kobayashi and Manabu Hashimoto "High precision anomaly detection based on pre-trained features enhanced by only large amount of normal samples", Proc. SPIE 13164, International Workshop on Advanced Imaging Technology (IWAIT) 2024, 1316422 (2 May 2024); https://doi.org/10.1117/12.3018802

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KEYWORDS

Education and training

Image classification

Feature extraction

Batch normalization

Statistical analysis

Data modeling

Defect detection

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