Position Encoding Enhanced Feature Mapping for Image Anomaly Detection

Wan, Qian; Cao, Yunkang; Gao, Liang; Shen, Weiming; Li, Xinyu

doi:10.1109/case49997.2022.9926547

Cited by 14 publications

(10 citation statements)

References 20 publications

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“…[10] based on hypercoupled spheres for anomaly detection. FastFlow [11], FEFM [12], and CFLOW-AD [13]use normalized flow to summarize distribution of normal features and detect anomalies during inference by computing the likelihood of test features distributed over the normalized flow. However, existing feature matching-based methods have some limitations, in particular, PaDiM calculates anomaly scores using the normal distribution of normal data at the target pixel locations, which may lead to overestimated if the alignment of normal industrial images is not entirely precise.…”

Section: Related Workmentioning

confidence: 99%

Image alignment-based patch distribution framework for anomaly detection

Liu,

Ma,

Jiang

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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Surface quality inspection is a crucial step in industrial manufacturing. However, it is challenging to collect an adequate quantity of abnormal samples in practice. Supervised methods require sample annotation, which is costly, so unsupervised methods that are high-speed and low-cost are more suitable for industrial applications. Among the current unsupervised methods, embedding-similarity methods haven shown excellent performance, but most of them do not preprocess the images and directly use convolutional neural networks to extract image features. While in actual scenarios, image can have some degree of offset or rotation due to machine variations. Therefore, this paper proposes a new patch distribution framework for anomaly detection, specifically a novel image alignment module is proposed to enhance the utility of the model. Image alignment reduces the dense distance between pixels during training, enabling more precise learning of the feature distribution of normal samples and reduce false positives during testing. In addition, in the feature extraction stage, the middle layer of the network is selected to extract features and establish embedding connections. This not only enhances the model’s precision but also reduces its memory requirements. Experiments on the publicly available datasets MVTec AD and BeanTech AD show that our proposed new framework achieves better performance than other baseline models.

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Section: Related Workmentioning

confidence: 99%

Image alignment-based patch distribution framework for anomaly detection

Liu,

Ma,

Jiang

2024

Fourth International Conference on Computer Vision and Data Mining (ICCVDM 2023)

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“…The paper proposes the bidirectional and multi-hierarchical bidirectional pretrained feature mapping based on the vanilla feature mapping. PEFM [42] L2 ResNet The paper introduces position encoding into PFM. FYD [43] L2 ResNet The paper aligns samples at image and feature levels to detect anomalies.…”

Section: Distribution Mapmentioning

confidence: 99%

“…By fitting a multivariate Gaussian to the feature representations of a pre-trained network, [41] proposes bidirectional and multi-hierarchical bidirectional pre-trained feature mapping to enhance the performance of vanilla feature mapping. In addition, Wan et al [42] add position encoding to the PFM framework and propose a novel Position Encoding enhanced Feature Mapping (PEFM) [42] to further enhance PFM. FYD [43] introduce registration to industrial image AD for the first time.…”

Section: Distribution Mapmentioning

confidence: 99%

Deep Industrial Image Anomaly Detection: A Survey

Liu¹,

Xie²,

Wang³

et al. 2023

Preprint

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The recent rapid development of deep learning has laid a milestone in industrial Image Anomaly Detection (IAD). In this paper, we provide a comprehensive review of deep learning-based image anomaly detection techniques, from the perspectives of neural network architectures, levels of supervision, loss functions, metrics and datasets. In addition, we extract the new setting from industrial manufacturing and review the current IAD approaches under our proposed our new setting. Moreover, we highlight several opening challenges for image anomaly detection. The merits and downsides of representative network architectures under varying supervision are discussed. Finally, we summarize the research findings and point out future research directions. More resources are available at https://github.com/M-3LAB/awesome-industrial-anomaly-detection.

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“…In addition, attempts have been made to adapt the weights of the pre-trained model to identify the distribution of nominal data. FastFlow [40], FEFM [37], and CFLOW-AD [15] reported good performances by estimating the distribution of network-based features by normalizing the flow, and CFA [19] implemented feature adaption through Coupled-hypersphere to better ex-plain the distribution of nominal features.…”

Section: Industrial Anomaly Detectionmentioning

confidence: 99%

N-pad : Neighboring Pixel-based Industrial Anomaly Detection

Hwang¹,

Park²

2022

Preprint

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Identifying defects in the images of industrial products has been an important task to enhance quality control and reduce maintenance costs. In recent studies, industrial anomaly detection models were developed using pre-trained networks to learn nominal representations. To employ the relative positional information of each pixel, we present Npad, a novel method for anomaly detection and segmentation in a one-class learning setting that includes the neighborhood of the target pixel for model training and evaluation. Within the model architecture, pixel-wise nominal distributions are estimated by using the features of neighboring pixels with the target pixel to allow possible marginal misalignment. Moreover, the centroids from clusters of nominal features are identified as a representative nominal set. Accordingly, anomaly scores are inferred based on the Mahalanobis distances and Euclidean distances between the target pixel and the estimated distributions or the centroid set, respectively. Thus, we have achieved state-of-the-art performance in MVTec-AD with AUROC of 99.37 for anomaly detection and 98.75 for anomaly segmentation, reducing the error by 34% compared to the next best performing model. Experiments in various settings further validate our model.

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Position Encoding Enhanced Feature Mapping for Image Anomaly Detection

Cited by 14 publications

References 20 publications

Image alignment-based patch distribution framework for anomaly detection

Image alignment-based patch distribution framework for anomaly detection

Deep Industrial Image Anomaly Detection: A Survey

N-pad : Neighboring Pixel-based Industrial Anomaly Detection

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