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Deep ensemble transfer learning-based approach for classifying hot-rolled steel strips surface defects

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Abstract

Over the last few years, advanced deep learning-based computer vision algorithms are revolutionizing the manufacturing field. Thus, several industry-related hard problems can be solved by training these algorithms, including flaw detection in various materials. Therefore, identifying steel surface defects is considered one of the most important tasks in the steel industry. In this paper, we propose a deep learning-based model to classify six of the most common steel strip surface defects using the NEU-CLS dataset. We investigate the effectiveness of two state-of-the-art CNN architectures (MobileNet-V2 and Xception) combined with the transfer learning approach. The proposed approach uses an ensemble of two pre-trained state-of-the-art Convolutional Neural Networks, which are MobileNet-V2 and Xception. To perform a comparative analysis of the proposed architectures, several evaluation metrics are adopted, including loss, accuracy, precision, recall, F1-score, and execution time. The experimental results show that the proposed deep ensemble learning approach provides higher performance achieving an accuracy of 99.72% compared to MobileNet-V2 (98.61%) and Xception (99.17%) while preserving fast execution time and small models’ size.

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References

  1. Ashour M, Fatimah K, Abdul Halin A, Abdullah L, Darwish S (2019) Surface defects classification of hot-rolled steel strips using multi-directional Shearlet features. Arab J Sci Eng 44:2925–2932. https://doi.org/10.1007/s13369-018-3329-5

    Article  Google Scholar 

  2. Tang W-P, Liong S-T, Chen C-C, Tsai M-H, Hsieh P-C, Tsai Y-T, Chen S-H, Wang K-C (2021) Design of multi-receptive field fusion-based network for surface defect inspection on hot-rolled steel strip using lightweight dataset. Appl Sci 11(20). https://doi.org/10.3390/app11209473

  3. Song K, Yan Y (2013) A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl Surf Sci 285:858–864. https://doi.org/10.1016/j.apsusc.2013.09.002

    Article  Google Scholar 

  4. Mentouri Z, Moussaoui A, Boudjehem D, Hakim D (2018) Steel strip surface defect identification based on binarized statistical features. UPB Sci Bull Ser B Chem Mater Sci 80:145–156

    Google Scholar 

  5. Zaghdoudi R, Seridi H, Boudiaf A, Ziani S (2020) Binary Gabor pattern (BGP) descriptor and principal component analysis (PCA) for steel surface defects classification. In: 2020 international conference on advanced aspects of software engineering (ICAASE). https://doi.org/10.1109/ICAASE51408.2020.9380108, pp 1–7

  6. Aslan MF, Sabanci K, Durdu A, Unlersen MF (2022) COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization. Comput Biol Med 142:105244. https://doi.org/10.1016/j.compbiomed.2022.105244

    Article  Google Scholar 

  7. Bouguettaya A, Zarzour H, Kechida A, Taberkit AM (2021) Vehicle detection from UAV imagery with deep learning: a review. IEEE Trans Neural Netw Learn Syst, 1–21. https://doi.org/10.1109/TNNLS.2021.3080276

  8. Kheradmandi N, Mehranfar V (2022) A critical review and comparative study on image segmentation-based techniques for pavement crack detection. Construct Build Mater 321:126162. https://doi.org/10.1016/j.conbuildmat.2021.126162

    Article  Google Scholar 

  9. Wan X, Zhang X, Liu L (2021) An improved VGG19 transfer learning strip steel surface defect recognition deep neural network based on few samples and imbalanced datasets. Appl Sci 11(6). https://doi.org/10.3390/app11062606

  10. Jain S, Seth G, Paruthi A, Soni U, Kumar G (2022) Synthetic data augmentation for surface defect detection and classification using deep learning. J Intell Manuf 33. https://doi.org/10.1007/s10845-020-01710-x

  11. Wang W, Lu K, Wu Z, Long H, Zhang J, Chen P, Wang B (2021) Surface defects classification of hot rolled strip based on improved convolutional neural network. ISIJ Int 61(5):1579–1583. https://doi.org/10.2355/isijinternational.ISIJINT-2020-451

    Article  Google Scholar 

  12. Li S, Wu C, Xiong N (2022) Hybrid architecture based on CNN and transformer for strip steel surface defect classification. Electronics 11(8). https://doi.org/10.3390/electronics11081200

  13. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791

    Article  Google Scholar 

  14. Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Commun ACM 60(6):84–90. https://doi.org/10.1145/3065386

    Article  Google Scholar 

  15. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556

  16. Szegedy C, Liu W, Jia Y, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A (2015) Going deeper with convolutions. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 1–9. https://doi.org/10.1109/CVPR.2015.7298594

  17. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778. https://doi.org/10.1109/CVPR.2016.90

  18. Sandler M, Howard A, Zhu M, Zhmoginov A, Chen L-C (2018) Mobilenetv2: Inverted residuals and linear bottlenecks. In: 2018 IEEE/CVF conference on computer vision and pattern recognition, pp 4510–4520. https://doi.org/10.1109/CVPR.2018.00474

  19. Howard A, Sandler M, Chen B, Wang W, Chen L-C, Tan M, Chu G, Vasudevan V, Zhu Y, Pang R, Adam H, Le Q (2019) Searching for MobileNetv3. In: 2019 IEEE/CVF international conference on computer vision (ICCV), pp 1314–1324. https://doi.org/10.1109/ICCV.2019.00140

  20. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, Andreetto M, Adam H (2017) MobileNets: efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861

  21. Iandola F, Han S, Moskewicz M, Ashraf K, Dally W, Keutzer K (2016) SqueezeNet: Alexnet-level accuracy with 50x fewer parameters and < 0.5mb model size

  22. Szegedy C, Ioffe S, Vanhoucke V, Alemi AA (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the 31st AAAI conference on artificial intelligence. AAAI’17. AAAI Press, pp 4278–4284

  23. Chollet F (2016) Xception: deep learning with depthwise separable convolutions. arXiv. https://doi.org/10.48550/ARXIV.1610.02357

  24. Chollet F (2017) Xception: Deep learning with depthwise separable convolutions. In: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1800–1807. https://doi.org/10.1109/CVPR.2017.195

  25. Yi L, Li G, Jiang M (2017) An end-to-end steel strip surface defects recognition system based on convolutional neural networks. Steel Res Int 88(2):176–187. https://doi.org/10.1002/srin.201600068

    Article  Google Scholar 

  26. Hao Z, Li Z, Ren F, Lv S, Ni H (2022) Strip steel surface defects classification based on generative adversarial network and attention mechanism. Metals 12(2). https://doi.org/10.3390/met12020311

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Funding

Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

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Authors and Affiliations

  1. Research Centre in Industrial Technologies (CRTI), P.O. Box 64, Cheraga, 16014, Algiers, Algeria

    Abdelmalek Bouguettaya & Zoheir Mentouri

  2. LIM Research, Department of Mathematics and Computer Science, Souk Ahras University, 41000, Souk Ahras, Algeria

    Hafed Zarzour

Authors
  1. Abdelmalek Bouguettaya
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  2. Zoheir Mentouri
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  3. Hafed Zarzour
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Correspondence to Hafed Zarzour.

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Author contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Abdelmalek Bouguettaya, Zoheir Mentouri, and Hafed Zarzour. The first draft of the manuscript was written by Abdelmalek Bouguettaya and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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The dataset used in this study is publicly available at https://doi.org/10.1016/j.apsusc.2013.09.002.

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Abdelmalek Bouguettaya, Zoheir Mentouri and Hafed Zarzour contributed equally to this work.

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Bouguettaya, A., Mentouri, Z. & Zarzour, H. Deep ensemble transfer learning-based approach for classifying hot-rolled steel strips surface defects. Int J Adv Manuf Technol 125, 5313–5322 (2023). https://doi.org/10.1007/s00170-023-10947-8

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  • DOI: https://doi.org/10.1007/s00170-023-10947-8

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