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Page 16 of 17                        Hu et al. J. Mater. Inf. 2025, 5, 44  https://dx.doi.org/10.20517/jmi.2025.21

                   mechanisms in dual-phase steels. Mater. Genome. Eng. Adv. 2024, 2, e29.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               14.      Wang, H.; Li, M.; Wan, Z. Rail surface defect detection based on improved Mask R-CNN. Comput. Electr. Eng. 2022, 102, 108269.
                   DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               23.      Magar, R.; Wang, Y.; Barati Farimani, A. Crystal twins: self-supervised learning for crystalline material property prediction. npj.
                   Comput. Mater. 2022, 8, 231.  DOI
               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.  DOI
               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.  DOI
               26.      Kim, S.; Ryu, S. Effect of surface and internal defects on the mechanical properties of metallic glasses. Sci. Rep. 2017, 7, 13472.  DOI
               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.  DOI
               28.      Tian, R.; Jia, M. DCC-CenterNet: a rapid detection method for steel surface defects. Measurement 2022, 187, 110211.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
               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.  DOI
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