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Surface Defects Classification of Hot Rolled Strip Based on Improved Convolutional Neural Network

机译：基于改进的卷积神经网络的热轧带材的表面缺陷分类

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摘要

Surface defect classification of hot-rolled strip based on machine vision is a challenge task caused by the diversity of defect morphology, high inter-class similarity, and the real-time requirements in actual production. In this work, VGG16-ADB, an improved VGG16 convolution neural network, is proposed to address the problem of defect identification of hot-rolled strip. The improved network takes VGG16 as the benchmark model, reduces the system consumption and memory occupation by reducing the depth and width of network structure, and adds the batch normalization layer to accelerate the convergence speed of the model. Based on a standard dataset NEU, the proposed method can achieve the classification accuracy of 99.63% and the recognition speed of 333 FPS, which fully meets the requirements of detection accuracy and speed in the actual production line. The experimental results also show the superiority of VGG16-ADB over existing classification models for surface defect classification of hot-rolled strip.
机译：基于机器视觉的热轧带材的表面缺陷分类是缺陷形态，高级相似性的多样性和实际生产中的实时要求引起的挑战任务。在这项工作中，提出了一种改进的VGG16卷积神经网络，以解决热轧带材的缺陷识别问题。改进的网络将VGG16作为基准模型，通过降低网络结构的深度和宽度来降低系统消耗和存储器占用，并添加批量归一化层以加速模型的收敛速度。基于标准数据集Neu，该方法可以达到99.63％的分类精度，识别速度为333 FPS，这完全满足了实际生产线中检测精度和速度的要求。实验结果还显示了热轧带材表面缺陷分类的现有分类模型的VGG16-adb的优越性。

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  • 来源
    《ISIJ international》 |2021年第5期|1579-1583|共5页
  • 作者

    Wenyan WANG; Kun LU; Ziheng WU; Hongming LONG; Jun ZHANG; Peng CHEN; Bing WANG;

  • 作者单位

    School of Metallurgical Engineering Anhui University of Technology Ma'anshan Anhui 243032 China Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui University of Technology) Ministry of Education Ma'anshan 243002 China School of Electrical and Information Engineering Anhui University of Technology Ma'anshan Anhui 243032 China;

    School of Metallurgical Engineering Anhui University of Technology Ma'anshan Anhui 243032 China Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui University of Technology) Ministry of Education Ma'anshan 243002 China School of Electrical and Information Engineering Anhui University of Technology Ma'anshan Anhui 243032 China;

    School of Electrical and Information Engineering Anhui University of Technology Ma'anshan Anhui 243032 China Key Laboratory of Power Electronics and Motion Control Anhui Education Department Anhui University of Technology Ma'anshan Anhui 243032 China;

    School of Metallurgical Engineering Anhui University of Technology Ma'anshan Anhui 243032 China Key Laboratory of Metallurgical Emission Reduction & Resources Recycling (Anhui University of Technology) Ministry of Education Ma'anshan 243002 China;

    Co-Innovation Center for Information Supply & Assurance Technology Anhui University Hefei Anhui 230032 China;

    Co-Innovation Center for Information Supply & Assurance Technology Anhui University Hefei Anhui 230032 China;

    School of Metallurgical Engineering Anhui University of Technology Ma'anshan Anhui 243032 China Key Laboratory of Power Electronics and Motion Control Anhui Education Department Anhui University of Technology Ma'anshan Anhui 243032 China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);美国《生物学医学文摘》(MEDLINE);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    hot rolled strip; surface defect; convolutional neural network; defect classification;

    机译：热轧带材;表面缺陷;卷积神经网络;缺陷分类;

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