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A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel

Author

Listed:
  • Xinglong Feng

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China
    These authors contributed equally to this work.)

  • Xianwen Gao

    (College of Information Science and Engineering, Northeastern University, Shenyang 110819, China)

  • Ling Luo

    (Moviebook Technology Co., Ltd., Beijing 100027, China
    These authors contributed equally to this work.)

Abstract

Hot-rolled strip steel is widely used in automotive manufacturing, chemical and home appliance industries, and its surface quality has a great impact on the quality of the final product. In the manufacturing process of strip steel, due to the rolling process and many other reasons, the surface of hot rolled strip steel will inevitably produce slag, scratches and other surface defects. These defects not only affect the quality of the product, but may even lead to broken strips in the subsequent process, seriously affecting the continuation of production. Therefore, it is important to study the surface defects of strip steel and identify the types of defects in strip steel. In this paper, a scheme based on ResNet50 with the addition of FcaNet and Convolutional Block Attention Module (CBAM) is proposed for strip defect classification and validated on the X-SDD strip defect dataset. Our solution achieves a classification accuracy of 94.11%, higher than more than a dozen other compared deep learning models. Moreover, to adress the problem of low accuracy of the algorithm in classifying individual defects, we use ensemble learning to optimize. By integrating the original solution with VGG16 and SqueezeNet, the recognition rate of oxide scale of plate system defects improved by 21.05 percentage points, and the overall defect classification accuracy improved to 94.85%.

Suggested Citation

  • Xinglong Feng & Xianwen Gao & Ling Luo, 2021. "A ResNet50-Based Method for Classifying Surface Defects in Hot-Rolled Strip Steel," Mathematics, MDPI, vol. 9(19), pages 1-15, September.
    • Handle: RePEc:gam:jmathe:v:9:y:2021:i:19:p:2359-:d:641465
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    References listed on IDEAS

    as
    1. Kechen Song & Yunhui Yan, 2013. "Micro Surface Defect Detection Method for Silicon Steel Strip Based on Saliency Convex Active Contour Model," Mathematical Problems in Engineering, Hindawi, vol. 2013, pages 1-13, December.
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    Cited by:

    1. Florin Leon & Mircea Hulea & Marius Gavrilescu, 2022. "Preface to the Special Issue on “Advances in Artificial Intelligence: Models, Optimization, and Machine Learning”," Mathematics, MDPI, vol. 10(10), pages 1-4, May.

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