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Automatic Metallic Surface Defect Detection using ShuffleDefectNet

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2020, 25(3), pp.19-26
  • DOI : 10.9708/jksci.2020.25.03.019
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science
  • Received : December 31, 2019
  • Accepted : February 24, 2020
  • Published : March 31, 2020

Avlokulov Anvar 1 Young Im Cho 1

1가천대학교

Accredited

ABSTRACT

Steel production requires high-quality surfaces with minimal defects. Therefore, the detection algorithms for the surface defects of steel strip should have good generalization performance. To meet the growing demand for high-quality products, the use of intelligent visual inspection systems is becoming essential in production lines. In this paper, we proposed a ShuffleDefectNet defect detection system based on deep learning. The proposed defect detection system exceeds state-of-the-art performance for defect detection on the Northeastern University (NEU) dataset obtaining a mean average accuracy of 99.75%. We train the best performing detection with different amounts of training data and observe the performance of detection. We notice that accuracy and speed improve significantly when use the overall architecture of ShuffleDefectNet.

Citation status

* References for papers published after 2022 are currently being built.

This paper was written with support from the National Research Foundation of Korea.