Logit Inducing With Abnormality Capturing for Semi-Supervised Image Anomaly Detection
Abstract
Image Anomaly Detection is a significant stage for visual quality inspection in intelligent manufacturing systems. According to the assumption that only normal images are available during the training stage, unsupervised methods have been studied recently for image anomaly detection. But anomalous images of small scale can be collected for training in many real-world industrial scenarios, and the unsupervised methods make no use of them to improve the detection accuracy. This leads to a semi-supervised image anomaly detection with an unbalanced detection challenge. In this paper, a Logit Inducing with Abnormality Capturing (LIAC) method is proposed to address semi-supervised image anomaly detection. Firstly, a Logit Inducing Loss is proposed to train a classifier for dealing with unbalanced detection. And secondly, an Abnormality Capturing Module is proposed to address anomaly detection. With labeling only 40 anomalous images for training, the proposed LIAC method achieves a 98.8% f1-score on the image anomaly detection of the printed circuit board, compared with the state-of-the-art methods. More, the proposed LIAC method is experimentally compared with the state-of-the-art methods on MTD, ROCT, and ELPV three open-source datasets, respectively achieves f1-score of 85.2%, 96.8%, and 66.6% with given 40 anomalous images for training.
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“[123] propose a Logit Inducing Loss (LIS) for training with imbalanced data distribution and an Abnormality Capturing Module (ACM) for characterizing anomalous features in order to effectively utilize a small amount of anomalous information.”
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- Authors
- Wan, Q., Gao, L., & Li, X.
- Affiliations
- State Key Laboratory of Digital Manufacturing Equipment and Technology (CN)
- Venue
- IEEE Transactions on Instrumentation and Measurement
- Published
- 2022
- Volume
- 71
- Pages
- 1-12
- ISSN
- 0018-9456 (ISSN-L)
- 1557-9662
- OpenAlex
- W4295308550
- Cited by
- 22
- References
- 35
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 10 · 2023: 8
- FWCI
- 2.41
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- Locations
- IEEE Transactions on Instrumentation and Measurement (publishedVersion)
- Funders
- National Natural Science Foundation of China (U21B2029, 52188102)
- Major Special Science and Technology Project of Hubei Province (2020AEA009)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
- Keywords
- image anomaly detection, semi-supervised learning, class imbalance, printed circuit board inspection, visual quality inspection, industrial defect detection
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