Self-Supervised Learning for Industrial Image Anomaly Detection by Simulating Anomalous Samples
-
Published:2023-09-20
Issue:1
Volume:16
Page:
-
ISSN:1875-6883
-
Container-title:International Journal of Computational Intelligence Systems
-
language:en
-
Short-container-title:Int J Comput Intell Syst
Author:
Pei MingjingORCID, Liu Ningzhong, Zhao Bing, Sun Han
Abstract
AbstractIndustrial image anomaly detection (AD) is a critical issue that has been investigated in different research areas. Many works have attempted to detect anomalies by simulating anomalous samples. However, how to simulate abnormal samples remains a significant challenge. In this study, a method for simulating anomalous samples is designed. First, for the object category, patch extraction and patch paste are designed to ensure that the extracted image patches come from the objects and are pasted to the objects in the image. Second, based on the statistical analysis of various anomalies’ presence, a combination of data augmentation is proposed to cover various anomalies as much as possible. The method is evaluated on MVTec AD and BTAD datasets; the experimental results demonstrate that our method achieves an overall detection AUC of 97.6% in MVTec AD datasets, outperforming the baseline by 1.5%, and the improvement over VT-ADL method is 4.3% on the BTAD datasets, demonstrating our method’s effectiveness and generalization.
Funder
National Natural Science Foundation of China Natural Science Foundation of Jiangsu Province of China Guangxi Science and Technology Project Natural Science Key Project of Anhui Provincial Education Department
Publisher
Springer Science and Business Media LLC
Subject
Computational Mathematics,General Computer Science
Reference51 articles.
1. Pang, G., Shen, C., Cao, L., Hengel, A.V.D.: Deep learning for anomaly detection: a review. ACM Comput. Surv. (CSUR) 54, 1–38 (2021) 2. Liang, X., Song, X., Qi, K., Li, J., Liu, J., Jian, L.: Anomaly detection aided budget online classification for imbalanced data streams. IEEE Intell. Syst. 36, 14–22 (2021) 3. Zhang, J., Xie, Y., Pang, G., Liao, Z., Verjans, J., Li, W., Sun, Z., He, J., Li, Y., Shen, C., et al.: Viral pneumonia screening on chest X-rays using confidence-aware anomaly detection. IEEE Trans. Med. Imaging 40, 879–890 (2020) 4. Tian, Y., Pang, G., Liu, F., Chen, Y., Shin, S.H., Verjans, J.W., Singh, R., Carneiro, G.: Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in medical images. In: Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 128–140. Springer (2021) 5. Togay, C., Kasif, A., Catal, C., Tekinerdogan, B.: A firewall policy anomaly detection framework for reliable network security. IEEE Trans. Reliab. 71, 339–347 (2021)
|
|