Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation

Pei Qi; Lin Chai; Xinyu Ye

doi:10.62762/CJIF.2024.734267

CiteScore

1.66

Impact Factor

Volume 1, Issue 2, Chinese Journal of Information Fusion

Volume 1, Issue 2, 2024

Submit Manuscript Edit a Special Issue

Academic Editor

Gongjian Zhou

Harbin Institute of Technology, China

Article QR Code

Scan the QR code for reading

Popular articles

Research on A Ship Trajectory Classification Method Based on Deep Learning YOLOv7-Bw: A Dense Small Object Efficient Detector Based on Remote Sensing Image Deep Prediction Network Based on Covariance Intersection Fusion for Sensor Data Visual Feature Extraction and Tracking Method Based on Corner Flow Detection A Mimic Fusion Algorithm for Dual Channel Video Based on Possibility Distribution Synthesis Theory Inaugural Editorial of the Chinese Journal of Information Fusion Bridging Modalities: A Survey of Cross-Modal Image-Text Retrieval Simultaneous Spatiotemporal Bias Compensation and Data Fusion for Asynchronous Multisensor Systems A Cyber-Physical System Based on On-Board Diagnosis (OBD-II) for Smart City Extraction of Motion Information from Occupancy Grid Map Using Keystone Transform

Chinese Journal of Information Fusion, 2024, Volume 1, Issue 2: 160-174

Free Access | Research Article | 30 September 2024

Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation

Pei Qi 1

Lin Chai 2 *

Xinyu Ye 1

1 School of Automation, Southeast University, Nanjing 210000, China

2 Key Laboratory of Measurement and Control of Complex Systems of Engineering, Ministry of Education, Nanjing 210096, China

* Corresponding author: Lin Chai, email: chailin1@seu.edu.cn

DOI: 10.62762/CJIF.2024.734267

Received: 31 July 2024, Accepted: 23 September 2024, Published: 30 September 2024

Abstract

In the realm of industrial defect detection, unsupervised anomaly detection methods draw considerable attention as a result of their exceptional accomplishments. Among these, knowledge distillation-based methods have emerged as a prominent research focus, favored for their streamlined architecture, precision, and efficiency. However, the challenge of characterizing the variability in anomaly samples hinders the accuracy of detection. To address this issue, our research presents a novel approach for anomaly detection and localization, leveraging the concept of inverse knowledge distillation as its cornerstone. We employ the encoder as the guiding teacher model and designate the decoder as the learning student model, leveraging the structural disparity between the teacher-student (T-S) models to mitigate the generalization challenge. Additionally, we integrate an attention mechanism into the distillation framework to concentrate on the precise extraction and reconstruction of image features, thereby preventing the loss of nuanced details. To further refine the learning process, we have developed a feature mask generation module that minimizes the impact of spatial redundancy in the teacher's features, thereby enhancing the acquisition of pivotal feature information. Comprehensive experimental evaluations, carried out meticulously on the MVTec AD dataset, convincingly illustrate the superiority of our proposed method over prevalent methodologies in both detecting and pinpointing anomalies across a diverse range of 15 categories. The proposed methodology attains superior outcomes, evinced by the detection AUROC, localization AUROC, and localization PRO metrics achieving respective values of 99.1%, 98.5%, and 95.9%. To substantiate the significance of individual components within the model, we conduct ablation studies, thereby reinforcing both the efficacy and applicability of our approach.

Graphical Abstract

Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation

Keywords

Unsupervised learning

anomaly detection

knowledge distillation

attention mechanism

References

[1] Ruff, L., Kauffmann, J. R., Vandermeulen, R. A., Montavon, G., Samek, W., Kloft, M., Dietterich, T. G., & Müller, K.-R. (2021). A unifying review of deep and shallow anomaly detection. Proceedings of the IEEE, 109(5), 756–795.

[2] Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.-J., & Fei-Fei, L. (2018). Thoracic disease identification and localization with limited supervision. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 8290–8299).

[3] Lin, D., Li, Y., Prasad, S., Nwe, T. L., Dong, S., & Oo, Z. M. (2021). CAM-guided Multi-Path Decoding U-Net with Triplet Feature Regularization for defect detection and segmentation. Knowledge-Based Systems, 228, 107272.

[4] Luo, J., Yang, Z., Li, S., & Wu, Y. (2021). FPCB surface defect detection: A decoupled two-stage object detection framework. IEEE Transactions on Instrumentation and Measurement, 70, 1–11.

[5] Chen, F., Wang, W., Yang, H., Pei, W., & Lu, G. (2022). Multiscale feature fusion for surveillance video diagnosis. Knowledge-Based Systems, 240, 108103.

[6] Niu, S., Li, B., Wang, X., & Peng, Y. (2021). Region-and strength-controllable GAN for defect generation and segmentation in industrial images. IEEE Transactions on Industrial Informatics, 18(7), 4531–4541.

[7] Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020). Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 4183–4192).

[8] Atlason, H. E., Love, A., Sigurdsson, S., Gudnason, V., & Ellingsen, L. M. (2019). Unsupervised brain lesion segmentation from MRI using a convolutional autoencoder. In Medical Imaging 2019: Image Processing (Vol. 10949, pp. 372–378). SPIE.

[9] Zhao, R., Yan, R., Chen, Z., Mao, K., Wang, P., & Gao, R. X. (2019). Deep learning and its applications to machine health monitoring. Mechanical Systems and Signal Processing, 115, 213–237.

[10] Kingma, D. P., & Welling, M. (2013). Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114.

[11] Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.

[12] Kwon, G., Prabhushankar, M., Temel, D., & AlRegib, G. (2020). Backpropagated gradient representations for anomaly detection. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXI 16 (pp. 206–226). Springer International Publishing.

[13] Chu, W.-H., & Kitani, K. M. (2020). Neural batch sampling with reinforcement learning for semi-supervised anomaly detection. In Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXVI 16 (pp. 751–766). Springer International Publishing.

[14] Kim, D., Jeong, D., Kim, H., Chong, K., Kim, S., & Cho, H. (2022). Spatial contrastive learning for anomaly detection and localization. IEEE Access, 10, 17366–17376.

[15] Schlegl, T., Seeböck, P., Waldstein, S. M., Schmidt-Erfurth, U., & Langs, G. (2017). Unsupervised anomaly detection with generative adversarial networks to guide marker discovery. In International conference on information processing in medical imaging (pp. 146–157). Cham: Springer International Publishing.

[16] Akcay, S., Atapour-Abarghouei, A., & Breckon, T. P. (2019). Ganomaly: Semi-supervised anomaly detection via adversarial training. In Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth, Australia, December 2–6, 2018, Revised Selected Papers, Part III 14 (pp. 622–637). Springer International Publishing.

[17] Schlegl, T., Seeböck, P., Waldstein, S. M., Langs, G., & Schmidt-Erfurth, U. (2019). f-AnoGAN: Fast unsupervised anomaly detection with generative adversarial networks. Medical image analysis, 54, 30–44.

[18] Salehi, M., Sadjadi, N., Baselizadeh, S., Rohban, M. H., & Rabiee, H. R. (2021). Multiresolution knowledge distillation for anomaly detection. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (CVPR) (pp. 14902–14912).

[19] Deng, H., & Li, X. (2022). Anomaly detection via reverse distillation from one-class embedding. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 9737–9746).

[20] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 770–778).

[21] Zagoruyko, S., & Komodakis, N. (2016). Wide residual networks. arXiv preprint arXiv:1605.07146.

[22] Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database. In Proceedings of the 2009 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 248–255). IEEE.

[23] Li, J., Wen, Y., & He, L. (2023). Scconv: Spatial and channel reconstruction convolution for feature redundancy. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 6153–6162).

[24] Yang, L., Zhang, R.-Y., Li, L., & Xie, X. (2021). Simam: A simple, parameter-free attention module for convolutional neural networks. In International conference on machine learning (pp. 11863–11874). PMLR.

[25] Yang, Z., Li, Z., Shao, M., Shi, D., Yuan, Z., & Yuan, C. (2022). Masked generative distillation. In European Conference on Computer Vision (pp. 53–69). Cham: Springer Nature Switzerland.

[26] Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2019). MVTec AD–A comprehensive real-world dataset for unsupervised anomaly detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 9592–9600).

[27] Aytekin, C., Ni, X., Cricri, F., & Aksu, E. (2018). Clustering and unsupervised anomaly detection with l2 normalized deep auto-encoder representations. In 2018 International Joint Conference on Neural Networks (IJCNN) (pp. 1–6). IEEE.

[28] Cohen, N., & Hoshen, Y. (2020). Sub-image anomaly detection with deep pyramid correspondences. arXiv preprint arXiv:2005.02357.

[29] Yi, J., & Yoon, S. (2020). Patch svdd: Patch-level svdd for anomaly detection and segmentation. In Proceedings of the Asian conference on computer vision.

[30] Defard, T., Setkov, A., Loesch, A., & Audigier, R. (2021). Padim: A patch distribution modeling framework for anomaly detection and localization. In International Conference on Pattern Recognition (pp. 475–489). Cham: Springer International Publishing.

[31] Wang, G., Han, S., Ding, E., & Huang, D. (2021). Student-teacher feature pyramid matching for anomaly detection. arXiv preprint arXiv:2103.04257.

[32] Li, C.-L., Sohn, K., Yoon, J., & Pfister, T. (2021). Cutpaste: Self-supervised learning for anomaly detection and localization. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (pp. 9664–9674).

[33] Zavrtanik, V., Kristan, M., & Skočaj, D. (2021). Draem-a discriminatively trained reconstruction embedding for surface anomaly detection. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 8330–8339).

[34] Perera, P., Nallapati, R., & Xiang, B. (2019). Ocgan: One-class novelty detection using gans with constrained latent representations. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition (pp. 2898-2906).

[35] Abati, D., Porrello, A., Calderara, S., & Cucchiara, R. (2019). Latent space autoregression for novelty detection. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 481-490).

[36] Venkataramanan, S., Peng, K. C., Singh, R. V., & Mahalanobis, A. (2020, August). Attention guided anomaly localization in images. In European Conference on Computer Vision (pp. 485-503). Cham: Springer International Publishing.

[37] Golan, Izhak & El-Yaniv, Ran. (2018). Deep Anomaly Detection Using Geometric Transformations. In Proceedings of the 32nd International Conference on Neural Information Processing Systems (NIPS’18) (pp.9781–9791).

[38] Xu, Haohao & Xu, Shuchang & Yang, Wenzhen. (2023). Unsupervised industrial anomaly detection with diffusion models. Journal of Visual Communication and Image Representation.

[39] Yang, Qiying & Guo, Rongzuo. (2024). An Unsupervised Method for Industrial Image Anomaly Detection with Vision Transformer-Based Autoencoder. Sensors.

[40] Haoyuan, Shen & Baolei, Wei & Yizhong, Ma & Xiaoyu, Gu. (2023). Unsupervised industrial image ensemble anomaly detection based on object pseudo-anomaly generation and normal image feature combination enhancement. Computers and Industrial Engineering.

Cite This Article

APA Style

Qi P., Chai L., & Ye X. (2024). Unsupervised Industrial Anomaly Detection Based on Feature Mask Generation and Reverse Distillation. Chinese Journal of Information Fusion, 1(2), 160–173. https://doi.org/10.62762/CJIF.2024.734267

Article Metrics

Citations:

Google Scholar

Crossref

Scopus

Web of Science

Article Access Statistics:

PDF Downloads: 52

Publisher's Note

IECE stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

IECE or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Chinese Journal of Information Fusion

ISSN: 2998-3371 (Online) | ISSN: 2998-3363 (Print)

Email: fusion001@csif.org.cn

Portico

All published articles are preserved here permanently:
https://www.portico.org/publishers/iece/

Google Scholar

Crossref

Scopus

Web of Science

We use cookies