Industrial Product Surface Anomaly Detection with Realistic Synthetic Anomalies Based on Defect Map Prediction Article Swipe
Tao Peng
,
Yu Zheng
,
Lin Zhao
,
Zheng En-rang
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24010264
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/s24010264
The occurrence of anomalies on the surface of industrial products can lead to issues such as decreased product quality, reduced production efficiency, and safety hazards. Early detection and resolution of these problems are crucial for ensuring the quality and efficiency of production. The key challenge in applying deep learning to surface defect detection of industrial products is the scarcity of defect samples, which will make supervised learning methods unsuitable for surface defect detection problems. Therefore, it is a reasonable solution to use anomaly detection methods to deal with surface defect detection. Among image-based anomaly detection, reconstruction-based methods are the most commonly used. However, reconstruction-based approaches lack the involvement of defect samples in the training process, posing the risk of a perfect reconstruction of defects by the reconstruction network. In this paper, we propose a reconstruction-based defect detection algorithm that addresses these challenges by utilizing more realistic synthetic anomalies for training. Our model focuses on creating authentic synthetic defects and introduces an auto-encoder image reconstruction network with deep feature consistency constraints, as well as a defect separation network with a large receptive field. We conducted experiments on the challenging MVTec anomaly detection dataset and our trained model achieved an AUROC score of 99.70% and an average precision (AP) score of 99.87%. Our method surpasses recently proposed defect detection algorithms, thereby enhancing the accuracy of surface defect detection in industrial products.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s24010264
- https://www.mdpi.com/1424-8220/24/1/264/pdf?version=1704188742
- OA Status
- gold
- Cited By
- 7
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390501299
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390501299Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s24010264Digital Object Identifier
- Title
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Industrial Product Surface Anomaly Detection with Realistic Synthetic Anomalies Based on Defect Map PredictionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-01-02Full publication date if available
- Authors
-
Tao Peng, Yu Zheng, Lin Zhao, Zheng En-rangList of authors in order
- Landing page
-
https://doi.org/10.3390/s24010264Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/24/1/264/pdf?version=1704188742Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/24/1/264/pdf?version=1704188742Direct OA link when available
- Concepts
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Anomaly detection, Artificial intelligence, Computer science, Deep learning, Consistency (knowledge bases), Pattern recognition (psychology), Fault detection and isolation, Anomaly (physics), Autoencoder, Machine learning, Physics, Actuator, Condensed matter physicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
7Total citation count in OpenAlex
- Citations by year (recent)
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2025: 5, 2024: 2Per-year citation counts (last 5 years)
- References (count)
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32Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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