The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
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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“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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Cites
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Saliency Detection via Graph-Based Manifold Ranking
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Li, C., Yan, H., Zhu, S., Hong, Y., Zhu, P., Wen, Y., Tian, H., Liao, C., Li, X., Wang, X., et al. (2022) · Optoelectronic Imaging and Multimedia Technology · doi:10.1117/12.2644099
Self-citationProduct Defect Identification System
Rutu, Rakesh, M. R., Pooja, L., & Hema, C. (2023) · International Journal for Research in Applied Science and Engineering Technology · doi:10.22214/ijraset.2023.51485
Rathnakumar, R., & Liu, Y. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4583105
GACNet: Defect Detection with Graph Attention Mechanism Convolutional Network Model
Guo, Y., Yao, Y., & Jie, L. (2023) · Advances in Engineering Technology Research · doi:10.56028/aetr.9.1.176.2024
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Knowledge-Guided Neural Architecture Search for Anomaly Localization
Qian, C., Lao, X., & Li, C. (2024) · International Conference on Signal Processing · doi:10.1109/icsp62129.2024.10846006
MSAM: a multi-scale attention mechanism for improving industrial defect segmentation
Han, M., Ji, Z., Yang, Y., Feng, Q., & Yin, S. (2025) · Neural Computing and Applications · doi:10.1007/s00521-025-11362-8
Position-Aware Network for Concealed Defect Detection
Bi, H., Fu, X., Zhang, C., & Li, S. (2025) · International Conference on Advanced Algorithms and Control Engineering (ICAACE) · doi:10.1109/icaace65325.2025.11019067
Zhang, Y., Zhou, J., Ji, D., & Wen, X. (2025) · Communications in computer and information science · doi:10.1007/978-981-96-6736-9_10
FC2P: Feature Cross-Channel Projection for Unsupervised Anomaly Segmentation
Chen, Y., Xian, W., Wang, J., Tao, X., & Chen, B. (2025) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2025.3608319
An Evaluation of Diffusion-Based Anomaly Detection in Metal Can Products
Huynh, X.-V., Pham, Q.-D., Pham, V.-N., Tran, D.-V., & Nguyen, M.-H. (2025) · Technical Education Science/Giáo dục Kỹ thuật · doi:10.54644/jte.2025.1831
Luo, L., Hu, Y., & Zhang, M. (2025) · International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) · doi:10.1109/icicml67980.2025.11333502
Novel Design of Machine Learning Based Surface Defect Detection Systems
Kim, S.-K., & Su, R. (2026) · International Conference on Electronics, Information, and Communication (ICEIC) · doi:10.1109/iceic69189.2026.11386041
Dual-branch contrastive learning with domain-aware augmentation for industrial anomaly detection
Liu, Y., He, W., & Wang, H. (2026) · International Conference on Machine Learning and Artificial Intelligence Applications (MLAIA) · doi:10.1117/12.3110574
Adaptive Attention Distillation for Robust Few-Shot Segmentation Under Environmental Perturbations
Guo, Q., Wu, J., Ren, J., Ge, W., & Zhang, W. (2026) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2026.3690158
Industrial Visual Anomaly Synthesis: A Deployment-Oriented Survey
Zhaong, R., Yin, J., Li, Z., Liu, Q., & Qi, Z. (2026) · IEEE Access · doi:10.1109/access.2026.3706950
LGCG-Net: Sequential Local-Global Refinement for Imbalanced Industrial Defect Classification
Zhou, H., Chen, J., & Yang, H. (2026) · Lecture notes in computer science · doi:10.1007/978-981-92-3429-5_49
Cross-Domain Few-shot Surface Defect Detection with Memorable Contrastive Learning
Guo, B., Wang, X. B., & Yu, R. (2026) · Youth Academic Annual Conference of Chinese Association of Automation (YAC) · doi:10.1109/yac71005.2026.11616174
Self-citationJežek, Š., Jonák, M., Skotak, M., Kolařík, M., Bürget, R., & Havlicek, L. (2026) · IEEE Access · doi:10.1109/access.2026.3736478
Metadata
- 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
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