Mr. Hiroki Kobayashi Profile

Hiroki Kobayashi

at Chukyo Univ

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Publications (2)

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Proceedings Article | 2 May 2024 Paper

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

Hiroki Kobayashi, Manabu Hashimoto

Proceedings Volume 13164, 1316422 (2024) https://doi.org/10.1117/12.3018802

KEYWORDS: Education and training, Image classification, Feature extraction, Statistical analysis, Batch normalization, Data modeling, Performance modeling, Inspection, Defect detection, Visual inspection

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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.

Proceedings Article | 13 March 2021 Paper

Scratch detection based on image reconstruction using ideal-image-pair dataset of just before/after defect occurrence

Hiroki Kobayashi, Ryo Miyoshi, Manabu Hashimoto

Proceedings Volume 11766, 117662R (2021) https://doi.org/10.1117/12.2591009

Read Abstract +

When inspecting defects such as scratches due to image processing, if we can obtain an image before defect occurrence, the defect can be detected by simply comparing the image pair before and after defect occurrence. However, this idea is generally unrealistic because it is impossible to obtain an image before defect occurrence from an image after the defect occurred unless we go back in time. Therefore, we propose a method of training a generation-based model to detect scratches based on subtraction between an input image and output image during testing. We obtained a large amount of ideal image pairs before and after defect occurrence (i.e., image pairs in which only defect regions are different and the others are almost completely the same) using an image-capturing device. An image with a background texture of high reconstruction performance was generated with our method by using this dataset. Although we used image pairs of only an aluminum plate for training, the area under the curve of the receiver operating characteristic (ROC-AUC) measure was 0.9973 for a copper plate and 0.9904 for a stainless steel plate. This shows that our method is also effective for background textures different from those used for training. We also compared our generation-based method with a method of training a classificationbased model (ResNet18). We confirmed that the ROC-AUC measure with the proposed method was 0.9824 for an acrylic plate and 0.6156 for a carbon fiber sheet, which is better than those with the classification-based method (0.9650 and 0.5000, respectively). The proposed method has much higher reconstruction performance than previous training of defect-free images only, and is effective for scratch detection of background texture similar to those used for training.

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