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Modeling and recognition of steel-plate surface defects based on a new backward boosting algorithm

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Abstract

The surface quality of steel plates that have been widely used in the manufacturing industry directly affects the final product performance. The existing inspection system of steel-plate surface defects has some drawbacks: (1) an unbalance problem in the steel-plate surface defect dataset, and (2) the number of classifiers used in the recognition process is insufficient for identifying stains, dirtiness, and other non-common defects. It is imperative to develop a new method to identify steel-plate surface defects. In this paper, the normalization technique and the synthetic minority over-sampling technique (SMOTE) are used to establish a steel-plate surface defect dataset with complete categories, balanced quantities, and normalized features. Based on the existing boosting algorithms in the literature, a new backward AdaBoost (AdaBoost.BK) algorithm is proposed for defect recognition. AdaBoost.BK selects the most suitable weak classifier by the filtering mechanism, thus increasing the number of weak classifiers that can be combined. Experiments show that the model not only improves the recognition accuracy of non-common defects, but also improves the accuracy of the whole classification.

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (NSFC) (Project No.51505350) and Teaching Research Project of Hubei Province (Project No.2013236 and 2014237) in China.

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Authors and Affiliations

  1. Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Ministry of Education, Wuhan, China

    Lianting Hu, Min Zhou, Feng Xiang & Qianmei Feng

  2. Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan, Hubei, China

    Lianting Hu, Min Zhou, Feng Xiang & Qianmei Feng

  3. Department of Industrial Engineering, University of Houston, Houston, TX, 77204, USA

    Qianmei Feng

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  1. Lianting Hu
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  2. Min Zhou
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  3. Feng Xiang
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Correspondence to Qianmei Feng.

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Hu, L., Zhou, M., Xiang, F. et al. Modeling and recognition of steel-plate surface defects based on a new backward boosting algorithm. Int J Adv Manuf Technol 94, 4317–4328 (2018). https://doi.org/10.1007/s00170-017-1113-4

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  • DOI: https://doi.org/10.1007/s00170-017-1113-4

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