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All issues Volume 413 (2025) MATEC Web Conf., 413 (2025) 04005 References
Open Access
Issue
MATEC Web Conf.
Volume 413, 2025
International Conference on Measurement, AI, Quality and Sustainability (MAIQS 2025)
Article Number 04005
Number of page(s) 6
Section Artificial Intelligence and Robotics
DOI https://doi.org/10.1051/matecconf/202541304005
Published online 01 October 2025
  1. F. Gerschner et al., “Domain Transfer for Surface Defect Detection using Few-Shot Learning on Scarce Data,” in 2023 IEEE 21st International Conference on Industrial Informatics (INDIN), Lemgo, Germany, Jul. 2023, pp. 1–7. doi:10.1109/INDIN51400.2023.10217859. [Google Scholar]
  2. L. Alzubaidi et al., “Review of deep learning: concepts, CNN architectures, challenges, applications, future directions,” J. Big Data, vol. 8, no. 1, p. 53, Mar. 2021. doi:10.1186/s40537-021-00444-8. [Google Scholar]
  3. S. Ren, K. He, R. Girshick, and J. Sun, “Faster R- CNN: towards real-time object detection with region proposal networks.” arXiv, Jan. 06, 2016. doi:10.48550/arXiv.1506.01497. [Google Scholar]
  4. Y. Tian, Q. Ye, and D. Doermann, “YOLOv12: attention-centric real-time object detectors.” arXiv, Feb. 18, 2025. doi:10.48550/arXiv.2502.12524. [Google Scholar]
  5. X. Tao, X. Hong, X. Chang, S. Dong, X. Wei, and Y. Gong, “Few-shot class-incremental learning.” arXiv, Apr. 24, 2020. doi:10.48550/arXiv.2004.10956. [Google Scholar]
  6. P. Liu, W. Yuan, J. Fu, Z. Jiang, H. Hayashi, and G. Neubig, “Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing,” ACM Comput. Surv., vol. 55, no. 9, pp. 1–35, Sep. 2023. doi:10.1145/3560815. [CrossRef] [Google Scholar]
  7. Q. Sun, Y. Liu, T.-S. Chua, and B. Schiele, “Meta-transfer learning for few-shot learning”. [Google Scholar]
  8. J. Snell, K. Swersky, and R. S. Zemel, “Prototypical networks for few-shot learning.” arXiv, Jun. 19, 2017. doi:10.48550/arXiv.1703.05175. [Google Scholar]
  9. I.J. Goodfellow et al., “Generative adversarial networks.” arXiv, Jun. 10, 2014. doi:10.48550/arXiv.1406.2661. [Google Scholar]
  10. A. Vaswani et al., “Attention is all you need.” arXiv, Aug. 02, 2023. doi:10.48550/arXiv.1706.03762. [Google Scholar]
  11. W. M. Kouw and M. Loog, “A review of domain adaptation without target labels,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 43, no. 3, pp. 766–785, Mar. 2021. doi:10.1109/TPAMI.2019.2945942. [Google Scholar]
  12. Q. Qiao, H. Hu, A. Ahmad, and K. Wang, “A Review of Metal Surface Defect Detection Technologies in Industrial Applications,” IEEE Access, vol. 13, pp. 48380–48400, 2025. doi:10.1109/ACCESS.2025.3544578. [Google Scholar]
  13. H. Di, X. Ke, Z. Peng, and Z. Dongdong, “Surface defect classification of steels with a new semisupervised learning method,” Opt. Lasers Eng., vol. 117, pp. 40–48, Jun. 2019. doi:10.1016/j.optlaseng.2019.01.011. [Google Scholar]
  14. A. M. Nagy and L. Czúni, “Classification and fast few-shot learning of steel surface defects with randomized network,” Appl. Sci., vol. 12, no. 8, p. 3967, Apr. 2022. doi:10.3390/app12083967. [Google Scholar]
  15. H. Wang, Z. Li, and H. Wang, “Few-shot steel surface defect detection,” IEEE Trans. Instrum. Meas., vol. 71, pp. 1–12, 2022. doi:10.1109/TIM.2021.3128208. [CrossRef] [Google Scholar]
  16. W. Zhao, K. Song, Y. Wang, S. Liang, and Y. Yan, “FaNet: Feature-aware network for few shot classification of strip steel surface defects,” Measurement, vol. 208, p. 112446, Feb. 2023. doi:10.1016/j.measurement.2023.112446. [Google Scholar]
  17. G. Duan, Y. Song, Z. Liu, S. Ling, and J. Tan, “Cross-domain few-shot defect recognition for metal surfaces,” Meas. Sci. Technol., vol. 34, no. 1, p. 15202, Jan. 2023. doi:10.1088/1361-6501/ac90de. [Google Scholar]
  18. S. Pang, W. Zhao, S. Wang, L. Zhang, and S. Wang, “Permute-MAML: exploring industrial surface defect detection algorithms for few-shot learning,” Complex Intell. Syst., vol. 10, no. 1, pp. 1473–1482, Feb. 2024. doi:10.1007/s40747-023-01219-9. [Google Scholar]
  19. X. Zhang, X. Zhou, M. Lin, and J. Sun, “ShuffleNet: an extremely efficient convolutional neural network for mobile devices,” in 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, Jun. 2018, pp. 6848–6856. doi:10.1109/CVPR.2018.00716. [Google Scholar]
  20. Cui Kebin and HU Zhenzhen, “Few-shot Insulator Defect Detection Based on Local and Global Feature Representation,” Comput. Sci., pp. 1–17, Jul. 2024. [Google Scholar]
  21. J. Yu et al., “DMnet: A New Few-Shot Framework for Wind Turbine Surface Defect Detection,” Machines, vol. 10, no. 6, p. 487, Jun. 2022. doi:10.3390/machines10060487. [Google Scholar]
  22. P. Zhang, P. Zheng, X. Guo, and E. Chen, “Fewshot defect classification via feature aggregation based on graph neural network,” J. Vis. Commun. Image Represent., vol. 101, p. 104172, May 2024. doi:10.1016/j.jvcir.2024.104172. [Google Scholar]
  23. Y. Deng and Y. Song, “Few-shot steel plate surface defect detection with multi-relation aggregation and adaptive support learning,” ISIJ Int., vol. 63, no. 10, pp. 1727–1737, Oct. 2023. doi:10.2355/isijinternational.ISIJINT-2023-118. [Google Scholar]
  24. S. Pang, L. Zhang, Y. Yuan, W. Zhao, S. Wang, and S. Wang, “Adaptive-MAML: Few-shot metal surface defects diagnosis based on model-agnostic meta-learning,” Measurement, vol. 223, p. 113612, Dec. 2023. doi:10.1016/j.measurement.2023.113612. [Google Scholar]
  25. X. Guo, P. Zhang, P. Zheng, Z. Zhang, and J. Liang, “A Decoupled Few-Shot Defect Detection Approach via Vector Quantization Feature Aggregation,” IEEE Trans. Instrum. Meas., pp. 1–1, 2025. doi:10.1109/TIM.2025.3551992. [Google Scholar]
  26. Z. Huang, Z. Chen, and Y. Liu, “FBINet: Fewshot Semantic Segmentation with Foreground and Background Iteration,” IEEE Trans. Instrum. Meas., pp. 1–1, 2025. doi:10.1109/TIM.2025.3550211. [Google Scholar]
  27. Y. Min, Z. Wang, Y. Liu, and Z. Wang, “FS- RSDD: Few-Shot Rail Surface Defect Detection with Prototype Learning,” Sensors, vol. 23, no. 18, p. 7894, Sep. 2023. doi:10.3390/s23187894. [Google Scholar]
  28. W. Xiao, K. Song, J. Liu, and Y. Yan, “Graph embedding and optimal transport for few-shot classification of metal surface defect,” IEEE Trans. Instrum. Meas., vol. 71, pp. 1–10, 2022. doi:10.1109/TIM.2022.3169547. [CrossRef] [Google Scholar]
  29. C. Liang and S. Bai, “Multiple prototype guided enhanced network for few-shot steel surface defect segmentation,” in 2024 5th International Conference on Computer Vision, Image and Deep Learning (CVIDL), Zhuhai, China, Apr. 2024, pp. 1216–1219. doi:10.1109/CVIDL62147.2024.10604061. [Google Scholar]
  30. T. Kim, J. Lee, S. Gong, J. Lim, D. Kim, and J. Jeong, “A Novel FS-GAN-Based Anomaly Detection Approach for Smart Manufacturing,” Machines, vol. 13, no. 1, p. 21, Dec. 2024. doi:10.3390/machines13010021. [Google Scholar]
  31. K. Zhang et al., “DP-GAN: A Transmission Line Bolt Defects Generation Network Based on Dual Discriminator Architecture and PseudoEnhancement Strategy,” IEEE Trans. Power Deliv., vol. 39, no. 3, pp. 1622–1633, Jun. 2024. doi:10.1109/TPWRD.2024.3373130. [Google Scholar]
  32. Z. Ye, M. Liu, S. Zhang, and P. Wei, “Dual-Path GAN: A Method for Enhancing Small-scale Defect Detection on Metal Images,” in 2022 41st Chinese Control Conference (CCC), Hefei, China, Jul. 2022, pp. 6292–6297. doi:10.23919/CCC55666.2022.9902599. [Google Scholar]
  33. L. Hao, P. Shen, Z. Pan, and Y. Xu, “Multi-level semantic information guided image generation for few-shot steel surface defect classification,” Front. Phys., vol. 11, p. 1208781, May 2023. doi:10.3389/fphy.2023.1208781. [Google Scholar]
  34. Y.-Z. Hu, R.-X. Liu, J.-P. He, G.-W. Zhou, and D.-Y. Li, “Deep learning methods for roping defect analysis in aluminum alloy sheets: prediction and grading,” Adv. Manuf., vol. 12, no. 3, pp. 576–590, Sep. 2024. doi:10.1007/s40436-024-00499-9. [Google Scholar]
  35. Y. Li et al., “DSRF: few-shot PCB surface defect detection via dynamic selective regulation fusion,” J. Supercomput., vol. 81, no. 4, p. 529, Feb. 2025. doi:10.1007/s11227-025-07071-7. [Google Scholar]
  36. Y. Gong, X. Wang, C. Zhou, M. Ge, C. Liu, and X. Zhang, “Human–machine knowledge hybrid augmentation method for surface defect detection based few-data learning,” J. Intell. Manuf., vol. 36, no. 3, pp. 1723–1742, Mar. 2025. doi:10.1007/s10845-023-02270-6 [Google Scholar]
  37. Y. Dong, C. Xie, L. Xu, H. Cai, W. Shen, and H. Tang, “Generative and Contrastive Combined Support Sample Synthesis Model for Few-/Zero- Shot Surface Defect Recognition,” IEEE Trans. Instrum. Meas., vol. 73, pp. 1–15, 2024. doi:10.1109/TIM.2023.3329163 [Google Scholar]
  38. Z. Tian, H. Zhao, M. Shu, Z. Yang, R. Li, and J. Jia, “Prior Guided Feature Enrichment Network for Few-Shot Segmentation,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 44, no. 2, pp. 1050–1065, Feb. 2022. doi:10.1109/TPAMI.2020.3013717 [Google Scholar]
  39. Z. Liu, Y. Song, R. Tang, G. Duan, and J. Tan, “Few-shot defect recognition of metal surfaces via attention-embedding and self-supervised learning,” J. Intell. Manuf., vol. 34, no. 8, pp. 3507–3521, Dec. 2023. doi:10.1007/s10845-022-02022-y. [Google Scholar]
  40. Z. He, S. Ge, Y. He, J. Liu, and X. An, “An Improved Feature Pyramid Network and Metric Learning Approach for Rail Surface Defect Detection,” Appl. Sci., vol. 13, no. 10, p. 6047, May 2023. doi:10.3390/app13106047. [Google Scholar]
  41. F. Deng, Z. Huang, R. Hao, X. Gu, and S. Yang, “An inception transformer-based weighted prototype network for few-shot defect recognition of wheelset bearing,” J. Comput. Des. Eng., vol. 12, no. 3, pp. 36–50, Mar. 2025. doi:10.1093/jcde/qwaf019. [Google Scholar]
  42. T. Wang et al., “Few-Shot Steel Surface Defect Recognition via Self-Supervised Teacher–Student Model With Min–Max Instances Similarity,” IEEE Trans. Instrum. Meas., vol. 72, pp. 1–16, 2023. doi:10.1109/TIM.2023.3315404. [Google Scholar]
  43. X. Liu et al., “Few-Shot Steel Defect Detection Based on a Fine-Tuned Network with Serial Multi-Scale Attention,” Appl. Sci., vol. 14, no. 13, p. 5823, Jul. 2024. doi:10.3390/app14135823. [Google Scholar]
  44. J. Zhao et al., “A knowledge distillation-based multi-scale relation-prototypical network for cross-domain few-shot defect classification,” J. Intell. Manuf., vol. 35, no. 2, pp. 841–857, Feb. 2024. doi:10.1007/s10845-023-02080-w. [Google Scholar]
  45. Y. Cang and X. Zhang, “Feature enhancementbased few-shot bearing surface defect image classification method,” Neural Process. Lett., vol. 57, no. 1, p. 8, Jan. 2025. doi:10.1007/s11063-025-11720-6. [Google Scholar]
  46. R. Chen et al., “Patch matching for few-shot industrial defect detection,” IEEE Trans. Instrum. Meas., vol. 73, pp. 1–11, 2024. doi:10.1109/TIM.2024.3413170. [Google Scholar]
  47. X. Huang, Y. Li, Y. Bao, and X. Zhu, “Sparse cross-transformer network for surface defect detection,” Sci. Rep., vol. 14, no. 1, p. 24731, Oct. 2024. doi:10.1038/s41598-024-75680-y [Google Scholar]
  48. K. Wu, J. Tan, and C. Liu, “Cross-domain fewshot learning approach for lithium-ion battery surface defects classification using an improved siamese network,” IEEE Sens. J., vol. 22, no. 12, pp. 11847–11856, Jun. 2022. doi:10.1109/JSEN.2022.3161331. [Google Scholar]
  49. Y. Cao, W. Zhu, J. Yang, G. Fu, D. Lin, and Y. Cao, “An effective industrial defect classification method under the few-shot setting via two-stream training,” Opt. Lasers Eng., vol. 161, p. 107294, Feb. 2023. doi:10.1016/j.optlaseng.2022.107294. [Google Scholar]
  50. H. Zhang et al., “An extending interclass distance real-time network using positional orientation transformation for few-shot strip steel surface defect classification,” IEEE Sens. J., vol. 24, no. 24, pp. 42523–42537, Dec. 2024. doi:10.1109/JSEN.2024.3488000. [Google Scholar]
  51. Z. Ma, Y. Li, M. Huang, and N. Deng, “Online visual end-to-end detection monitoring on surface defect of aluminum strip under the industrial fewshot condition,” J. Manuf. Syst., vol. 70, pp. 31–47, Oct. 2023. doi:10.1016/j.jmsy.2023.06.016. [Google Scholar]
  52. H. Yao et al., “Few-shot unseen defect segmentation for polycrystalline silicon panels with an interpretable dual subspace attention variational learning framework,” Adv. Eng. Inform., vol. 62, p. 102613, Oct. 2024. doi:10.1016/j.aei.2024.102613. [Google Scholar]
  53. Feng Hu, Song Kechen, Cui Wenqi, Zhou Zhenbo, and Yan Yunhui, “A Suspected Defect Screening- Guided Lightweight Network for Few-Shot Aviation Steel Tube Surface Defect Segmentation,” IEEE Sens. J., vol. 24, no. 22, pp. 38113–38124, Nov. 2024. doi:10.1109/JSEN.2024.3449918. [Google Scholar]

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