Anomalib: A Deep Learning Library for Anomaly Detection

Akçay, Samet; Ameln, Dick; Vaidya, Ashwin; Lakshmanan, Barath; Ahuja, Nilesh; Genc, Utku

doi:10.1109/icip46576.2022.9897283

Cited by 43 publications

(15 citation statements)

References 12 publications

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“…In the training process, "m" refers to the defect sample used. It is worth mentioning that the unsupervised algorithms were implemented using the open-source industrial defect detection library anomalib [46]. Combining the data from Tables 1 and 2, it can be observed that our proposed method outperforms other unsupervised industrial defect detection algorithms in terms of imagelevel classification ROC, except for PatchCore.…”

Section: Comparison With Unsupervised-based Algorithmmentioning

confidence: 84%

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

Min,

Wang,

Liu

et al. 2023

Sensors

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As an important component of the railway system, the surface damage that occurs on the rails due to daily operations can pose significant safety hazards. This paper proposes a simple yet effective rail surface defect detection model, FS-RSDD, for rail surface condition monitoring, which also aims to address the issue of insufficient defect samples faced by previous detection models. The model utilizes a pre-trained model to extract deep features of both normal rail samples and defect samples. Subsequently, an unsupervised learning method is employed to learn feature distributions and obtain a feature prototype memory bank. Using prototype learning techniques, FS-RSDD estimates the probability of a test sample belonging to a defect at each pixel based on the prototype memory bank. This approach overcomes the limitations of deep learning algorithms based on supervised learning techniques, which often suffer from insufficient training samples and low credibility in validation. FS-RSDD achieves high accuracy in defect detection and localization with only a small number of defect samples used for training. Surpassing benchmarked few-shot industrial defect detection algorithms, FS-RSDD achieves an ROC of 95.2% and 99.1% on RSDDS Type-I and Type-II rail defect data, respectively, and is on par with state-of-the-art unsupervised anomaly detection algorithms.

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Section: Comparison With Unsupervised-based Algorithmmentioning

confidence: 84%

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

Min,

Wang,

Liu

et al. 2023

Sensors

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“…Furthermore, we also compared our method to those that utilize external priors, such as S-T, SPADE, etc. All methods were reproduced based on the official implementation or AnomaLib [1]. For fair comparisons, we adapted the 1 × 1 convolution W to the pre-trained encoder, where the pre-trained encoder is the first two layers of ResNet18 for extracting the image into features of original 1/4 size and with 64 channels.…”

Section: Anomaly Localizationmentioning

confidence: 99%

PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

Lei¹,

Wang²,

Liu³

2023

Preprint

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During industrial processing, unforeseen defects may arise in products due to uncontrollable factors. Although unsupervised methods have been successful in defect localization, the usual use of pre-trained models results in lowresolution outputs, which damages visual performance. To address this issue, we propose PyramidFlow, the first fully normalizing flow method without pre-trained models that enables high-resolution defect localization. Specifically, we propose a latent template-based defect contrastive localization paradigm to reduce intra-class variance, as the pre-trained models do. In addition, PyramidFlow utilizes pyramid-like normalizing flows for multi-scale fusing and volume normalization to help generalization. Our comprehensive studies on MVTecAD demonstrate the proposed method outperforms the comparable algorithms that do not use external priors, even achieving state-of-the-art performance in more challenging BTAD scenarios.

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“…For the TILDA dataset, we restructured the data to conform to the MVTEC nomenclature. To ensure a fair comparison with state-of-the-art methods, the anomalib library 46 was employed. The results are shown in Table 4 and Figure 7.…”

Section: State Of the Art Auroc Comparisonmentioning

confidence: 99%

Distillation-based fabric anomaly detection

Thomine,

Snoussi

2023

Textile Research Journal

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Unsupervised texture anomaly detection has been a concerning topic in a vast number of industrial processes. Patterned textures inspection, particularly in the context of fabric defect detection, is indeed a widely encountered use case. This task involves handling a diverse spectrum of colors and textile types, encompassing a wide range of fabrics. Given the extensive variability in colors, textures, and defect types, fabric defect detection poses a complex and challenging problem in the field of patterned textures inspection. In this article, we propose a knowledge distillation-based approach tailored specifically for addressing the challenge of unsupervised anomaly detection in textures resembling fabrics. Our method aims to redefine the recently introduced reverse distillation approach, which advocates for an encoder-decoder design to mitigate classifier bias and to prevent the student from reconstructing anomalies. In this study, we present a new reverse distillation technique for the specific task of fabric defect detection. Our approach involves a meticulous design selection that strategically highlights high-level features. To demonstrate the capabilities of our approach both in terms of performance and inference speed, we conducted a series of experiments on multiple texture datasets, including Machine Vision Technology Development Corporation (MVTEC) anomaly detection, Asociación de Investigación de la Industria Textil (AITEX), and TILDA, alongside conducting experiments on a dataset acquired from a textile manufacturing facility. The main contributions of this paper are the following: a robust texture anomaly detector utilizing a reverse knowledge-distillation technique suitable for both anomaly detection and domain generalization and a novel dataset encompassing a diverse range of fabrics and defects.

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Anomalib: A Deep Learning Library for Anomaly Detection

Cited by 43 publications

References 12 publications

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

FS-RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning

PyramidFlow: High-Resolution Defect Contrastive Localization using Pyramid Normalizing Flow

Distillation-based fabric anomaly detection

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