A deep-learning-based approach for fast and robust steel surface defects classification

Guizhong Fu, Peize Sun, Wenbin Zhu, Jiangxin Yang, Yanlong Cao, Michael Ying Yang, Yanpeng Cao*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

47 Citations (Scopus)
60 Downloads (Pure)

Abstract

Automatic visual recognition of steel surface defects provides critical functionality to facilitate quality control of steel strip production. In this paper, we present a compact yet effective convolutional neural network (CNN)model, which emphasizes the training of low-level features and incorporates multiple receptive fields, to achieve fast and accurate steel surface defect classification. Our proposed method adopts the pre-trained SqueezeNet as the backbone architecture. It only requires a small amount of defect-specific training samples to achieve high-accuracy recognition on a diversity-enhanced testing dataset of steel surface defects which contains severe non-uniform illumination, camera noise, and motion blur. Moreover, our proposed light-weight CNN model can meet the requirement of real-time online inspection, running over 100 fps on a computer equipped with a single NVIDIA TITAN X Graphics Processing Unit (12G memory). Codes and a diversity-enhanced testing dataset will be made publicly available.

Original languageEnglish
Pages (from-to)397-405
Number of pages9
JournalOptics and Lasers in Engineering
Volume121
Early online date15 May 2019
DOIs
Publication statusPublished - 1 Oct 2019

Keywords

  • Convolutional neural network
  • Defect classification
  • Feature extraction
  • Multi-receptive field
  • Surface inspection
  • ITC-ISI-JOURNAL-ARTICLE

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