REFERENCES

1. Ren, B.; Wang, C.; Zhang, Y.; Wei, X.; Xu, W. Industrial big data analysis strategy based on automatic data classification and interpretable knowledge graph. J. Mater. Inf. 2025, 5, 2.

2. Zhou, C.; Lu, Z.; Lv, Z.; et al. Metal surface defect detection based on improved YOLOv5. Sci. Rep. 2023, 13, 20803.

3. Park, J. K.; Kwon, B. K.; Park, J. H.; Kang, D. J. Machine learning-based imaging system for surface defect inspection. Int. J. Precis. Eng. Manuf. Green. Tech. 2016, 3, 303-10.

4. Han, S.; Wang, C.; Zhang, Y.; Xu, W.; Di, H. Employing deep learning in non-parametric inverse visualization of elastic–plastic mechanisms in dual-phase steels. Mater. Genome. Eng. Adv. 2024, 2, e29.

5. Qiao, Z.; Shi, D.; Yi, X.; Shi, Y.; Zhang, Y.; Liu, Y. UEFPN: unified and enhanced feature pyramid networks for small object detection. ACM. Trans. Multimed. Comput. Commun. Appl. 2023, 19, 1-21.

6. Liu, Y.; Sun, P.; Wergeles, N.; Shang, Y. A survey and performance evaluation of deep learning methods for small object detection. Expert. Syst. Appl. 2021, 172, 114602.

7. Zhu, X.; Wang, Q.; Zhang, B.; Sun, Z.; Yu, J.; Qian, S. An improved feature enhancement CenterNet model for small object defect detection on metal surfaces. Adv. Theory. Simul. 2024, 7, 2301230.

8. Yu, J.; Cheng, X.; Li, Q. Surface defect detection of steel strips based on anchor-free network with channel attention and bidirectional feature fusion. IEEE. Trans. Instrum. Meas. 2022, 71,1-10.

9. He, Q.; Li, Z.; Yang, W. LMFE-RDD: a road damage detector with a lightweight multi-feature extraction network. Multimed. Syst. 2024, 30, 176.

10. Sun, L.; Cai, Z.; Liang, K.; Wang, Y.; Zeng, W.; Yan, X. An intelligent system for high-density small target pest identification and infestation level determination based on an improved YOLOv5 model. Expert. Syst. Appl. 2024, 239, 122190.

11. Yuan, Y.; Wu, Y.; Zhao, L.; Chen, H.; Zhang, Y. Multiple object detection and tracking from drone videos based on GM-YOLO and multi-tracker. Image. Vis. Comput. 2024, 143, 104951.

12. Zhu, Y.; Ai, Z.; Yan, J.; Li, S.; Yang, G.; Yu, T. NATCA YOLO-based small object detection for aerial images. Information 2024, 15, 414.

13. Wang, Y.; Xia, H.; Yuan, X.; Li, L.; Sun, B. Distributed defect recognition on steel surfaces using an improved random forest algorithm with optimal multi-feature-set fusion. Multimed. Tools. Appl. 2017, 77, 16741-70.

14. Wang, H.; Li, M.; Wan, Z. Rail surface defect detection based on improved Mask R-CNN. Comput. Electr. Eng. 2022, 102, 108269.

15. Liu, L. J.; Zhang, Y.; Karimi, H. R. Resilient machine learning for steel surface defect detection based on lightweight convolution. Int. J. Adv. Manuf. Technol. 2024, 134, 4639-50.

16. Shi, X.; Zhou, S.; Tai, Y.; Wang, J.; Wu, S.; Liu, J. An improved faster R-CNN for steel surface defect detection. In 2022 IEEE 24th International Workshop on Multimedia Signal Processing (MMSP), Shanghai, China. Sep 26-28, 2022. IEEE; 2022. p. 1-5.

17. Akhyar, F.; Liu, Y.; Hsu, C. Y.; Shih, T. K.; Lin, C. Y. FDD: a deep learning-based steel defect detectors. Int. J. Adv. Manuf. Technol. 2023, 126, 1093-107.

18. Hong, Y.; Wang, Z.; Wu, W.; et al. Steel surface defect detection based on denoising diffusion implicit models with data augmentation. In 2024 8th International Conference on Imaging, Signal Processing and Communications (ICISPC), Fukuoka, Japan. Jul 19-21, 2024. IEEE; 2024. pp. 15-9.

19. Kadam, S. Advancements in image detection: a comprehensive approach to object localization and classification using deep learning techniques. Int. J. Multidiscip. Res. 2024, 6, 27133.

20. Fang, Z.; Roy, K.; Xu, J.; Dai, Y.; Paul, B.; Lim, J. B. P. A novel machine learning method to investigate the web crippling behaviour of perforated roll-formed aluminium alloy unlipped channels under interior-two flange loading. J. Build. Eng. 2022, 51, 104261.

21. Balestriero, R.; Ibrahim, M.; Sobal, V.; et al. A cookbook of self-supervised learning. arXiv 2023, arXiv:2304.12210. https://doi.org/10.48550/arXiv.2304.12210. (accessed 10 Jul 2025).

22. Wang, Y.; Li, T.; Zong, H.; et al. Self-supervised probabilistic models for exploring shape memory alloys. npj. Comput. Mater. 2024, 10, 185.

23. Magar, R.; Wang, Y.; Barati Farimani, A. Crystal twins: self-supervised learning for crystalline material property prediction. npj. Comput. Mater. 2022, 8, 231.

24. Fu, N.; Wei, L.; Hu, J. Physics-guided dual self-supervised learning for structure-based material property prediction. J. Phys. Chem. Lett. 2024, 15, 2841-50.

25. Zhang, S.; Wang, W. Y.; Wang, X.; et al. Large language models enabled intelligent microstructure optimization and defects classification of welded titanium alloys. J. Mater. Inf. 2024, 4, 34.

26. Kim, S.; Ryu, S. Effect of surface and internal defects on the mechanical properties of metallic glasses. Sci. Rep. 2017, 7, 13472.

27. Masci, J.; Meier, U.; Ciresan, D.; Schmidhuber, J.; Fricout, G. Steel defect classification with max-pooling convolutional neural networks. In The 2012 International Joint Conference on Neural Networks (IJCNN), Brisbane, Australia. Jun 10-15, 2012. IEEE; 2012. p. 1-6.

28. Tian, R.; Jia, M. DCC-CenterNet: a rapid detection method for steel surface defects. Measurement 2022, 187, 110211.

29. Zabin, M.; Kabir, A. N. B.; Kabir, M. K.; Choi, H. J.; Uddin, J. Contrastive self-supervised representation learning framework for metal surface defect detection. J. Big. Data. 2023, 10, 145.

30. Xu, R.; Hao, R.; Huang, B. Efficient surface defect detection using self-supervised learning strategy and segmentation network. Adv. Eng. Inform. 2022, 52, 101566.

31. Zhang, S.; Zhang, Q.; Gu, J.; Su, L.; Li, K.; Pecht, M. Visual inspection of steel surface defects based on domain adaptation and adaptive convolutional neural network. Mech. Syst. Signal. Process. 2021, 153, 107541.

32. Sirotkin, K.; Escudero-Viñolo, M.; Carballeira, P.; García-Martín, Á. Improved transferability of self-supervised learning models through batch normalization finetuning. Appl. Intell. 2024, 54, 11281-94.

33. Geng, X.; Wang, F.; Wu, H. H.; et al. Data-driven and artificial intelligence accelerated steel material research and intelligent manufacturing technology. Mater. Genome. Engi. Adv. 2023, 1, e10.

34. Bachman, P.; Hjelm, R. D.; Buchwalter, W. Learning representations by maximizing mutual information across views. arXiv 2019, arXiv:1906.00910. https://doi.org/10.48550/arXiv.1906.00910. (accessed 10 Jul 2025).

35. Pöppelbaum, J.; Chadha, G. S.; Schwung, A. Contrastive learning based self-supervised time-series analysis. Appl. Soft. Comput. 2022, 117, 108397.

36. He, K.; Fan, H.; Wu, Y.; Xie, S.; Girshick, R. Momentum contrast for unsupervised visual representation learning. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Seattle, USA. Jun 13-19, 2020. IEEE; 2020. pp. 9726-35.

37. Chen, T.; Kornblith, S.; Norouzi, M.; Hinton, G. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning. PMLR; 2020. pp. 1597-607. https://proceedings.mlr.press/v119/chen20j.html. (accessed 10 Jul 2025).

38. Chen, X.; He, K. Exploring simple siamese representation learning. In 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashiville, USA. Jun 20-25, 2021. IEEE; 2021. pp. 15745-53.

39. Song, K.; Yan, Y. A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 2013, 285, 858-64.

40. Gui, J.; Chen, T.; Zhang, J.; Cao, Q.; Sun, Z.; Luo, H. A survey on self-supervised learning: algorithms, applications, and future trends. IEEE. Trans. Pattern. Anal. Mach. Intell. 2024, 46, 9052-71.

41. Zhao, Z.; Alzubaidi, L.; Zhang, J.; Duan, Y.; Gu, Y. A comparison review of transfer learning and self-supervised learning: definitions, applications, advantages and limitations. Expert. Syst. Appl. 2024, 242, 122807.

42. Huang, J.; Rathod, V.; Sun, C.; et al. Speed/accuracy trade-offs for modern convolutional object detectors. arXiv 2016, arXiv:1611.10012. https://doi.org/10.48550/arXiv.1611.10012. (accessed 10 Jul 2025).

43. Lin, T. Y.; Dollár, P.; Girshick, R.; He, K.; Hariharan, B.; Belongie, S. Feature pyramid networks for object detection. In 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, USA. Jul 21-26, 2017. IEEE; 2017. pp. 936-44.

44. Lin, T. Y.; Maire, M.; Belongie, S.; et al. Microsoft COCO: common objects in context. arXiv 2014, arXiv:1405.0312. https://doi.org/10.48550/arXiv.1405.0312. (accessed 10 Jul 2025).

45. Li, Z.; Wei, X.; Jiang, X. SSDD-Net: a lightweight and efficient deep learning model for steel surface defect detection. In Pattern Recognition and Computer Vision: 6th Chinese Conference, PRCV 2023, Xiamen, China. Oct 13-15, 2023. Springer-Verlag; 2023. pp. 237-48.

46. Li, M.; Wei, L.; Zheng, B. Steel surface defect detection based on improved YOLOv7. In 2024 4th International Conference on Computer, Control and Robotics (ICCCR), Shanghai, China. Apr 19-21, 2024. IEEE; 2024. pp. 51-5.

Journal of Materials Informatics
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