YOLO Based Deep Learning Network for Metal Surface Inspection System

Author:

Aein Shwe Lamin,Thu Theint Theint,Htun Phyu Phyu,Paing Aung,Htet Hay Thar Myo

Publisher

Springer Singapore

Reference9 articles.

1. Kholief, E.A., Darwish, S.H., Fors, M.N.: Detection of steel surface defect based on machine learning using deep auto-encoder network. In: Proceedings of the 7th International Conference on Industrial Engineering and Operations Management (IEOM), Rabat, Morocco, 11–13 April (2017)

2. Martins, L.A.O., Pádua, F.L.C., Almeida. E.M.P.: Automatic detection of surface defects on rolled steel using computer vision and artificial neural networks. In: IECON 2010 - 36th Annual Conference on IEEE Industrial Electronics Society, 7–10 November (2010)

3. Zaghdoudi, R.: Detection and Classification of steel defects using machine vision and SVM classifier. In: F., Editor, S. (eds.) Conference 2016, LNCS, vol. 9999, pp. 1–13. Springer, Heidelberg (2016)

4. Li, Z., Zhang, J., Zhuang, T.: Metal surface defect detection based on MATLAB. In: 2018 IEEE 3rd Advanced Information Technology, Electronic and Automation Control Conference (IAEAC) (2018)

5. Ma, Y., Li, Q., He, F., Yan, L., Xi, S.: Adaptive segmentation algorithm for metal surface defects. Chin. J. Sci. Instrum. 38(1), 245–251 (2017)

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