Surface Defect Saliency of Magnetic Tile
Abstract
Vision-based detection on surface defects has long postulated in the magnetic tile automation process. In this work, we introduce a real-time and multi-module neural network model called MCuePush U-Net, specifically designed for the image saliency detection of magnetic tile. We show that the model exceeds the state-of-the-art, in which it both effectively and explicitly maps multiple surface defects from low-contrast images. Our model significantly reduces time cost of machinery from 0.5s per image to 0.07s, and enhances saliency accuracy on surface defect detection.
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Existing datasets for anomaly segmentation are limited to the inspection of textured surfaces or focus on novelty detection in multi-class semantic segmentation scenarios.
“Many of them are limited to the inspection of textured surfaces or focus on novelty detection in multi-class semantic segmentation scenarios.”
From The MVTec Anomaly Detection Dataset: A Comprehensive Real-World Dataset for Unsupervised Anomaly Detection (2021) · cites this paper as Huang et al. (2018)
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Fault Detection and Identification Based on Image Processing and Deep Learning
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Bounding Box-Guided Diffusion for Synthesizing Industrial Images and Segmentation Maps
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- Authors
- Huang, Y., Qiu, C., Guo, Y., Wang, X., & Yuan, K.
- Affiliations
- Chinese Academy of Sciences (CN)
- Columbia University (US)
- Venue
- 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE)
- Publisher
- IEEE
- Published
- 2018-08
- Pages
- 612-617
- ISSN
- 2161-8070 (ISSN-L)
- OpenAlex
- W2905065306
- Cited by
- 117
- References
- 36
- Citations by year
- 2026: 10 · 2025: 21 · 2024: 12 · 2023: 24 · 2022: 23 · 2021: 17 · 2020: 7 · 2019: 3
- FWCI
- 10.63
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE International Conference on Automation Science and Engineering (CASE) (publishedVersion)
- Topics
- Physical Sciences › Engineering › Industrial and Manufacturing Engineering › Industrial Vision Systems and Defect Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Visual Attention and Saliency Detection
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- surface defect detection, magnetic tiles, image saliency, U-Net, real-time defect detection, low-contrast images
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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