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Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks

Journal Article


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Abstract


  • This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.

UOW Authors


  •   Song, Limei (external author)
  •   Li, Xinyao (external author)
  •   Yang, Yangang (external author)
  •   Zhu, Xinjun (external author)
  •   Guo, Qinghua
  •   Yang, Huaidong (external author)

Publication Date


  • 2018

Citation


  • L. Song, X. Li, Y. Yang, X. Zhu, Q. Guo & H. Yang, "Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks," Sensors, vol. 18, (11) pp. 3709-1-3709-14, 2018.

Scopus Eid


  • 2-s2.0-85055909965

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3064&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2059

Start Page


  • 3709-1

End Page


  • 3709-14

Volume


  • 18

Issue


  • 11

Place Of Publication


  • Switzerland

Abstract


  • This paper proposes a deep convolutional neural network (CNN) -based technique for the detection of micro defects on metal screw surfaces. The defects we consider include surface damage, surface dirt, and stripped screws. Images of metal screws with different types of defects are collected using industrial cameras, which are then employed to train the designed deep CNN. To enable efficient detection, we first locate screw surfaces in the pictures captured by the cameras, so that the images of screw surfaces can be extracted, which are then input to the CNN-based defect detector. Experiment results show that the proposed technique can achieve a detection accuracy of 98%; the average detection time per picture is 1.2 s. Comparisons with traditional machine vision techniques, e.g., template matching-based techniques, demonstrate the superiority of the proposed deep CNN-based one.

UOW Authors


  •   Song, Limei (external author)
  •   Li, Xinyao (external author)
  •   Yang, Yangang (external author)
  •   Zhu, Xinjun (external author)
  •   Guo, Qinghua
  •   Yang, Huaidong (external author)

Publication Date


  • 2018

Citation


  • L. Song, X. Li, Y. Yang, X. Zhu, Q. Guo & H. Yang, "Detection of Micro-Defects on Metal Screw Surfaces Based on Deep Convolutional Neural Networks," Sensors, vol. 18, (11) pp. 3709-1-3709-14, 2018.

Scopus Eid


  • 2-s2.0-85055909965

Ro Full-text Url


  • https://ro.uow.edu.au/cgi/viewcontent.cgi?article=3064&context=eispapers1

Ro Metadata Url


  • http://ro.uow.edu.au/eispapers1/2059

Start Page


  • 3709-1

End Page


  • 3709-14

Volume


  • 18

Issue


  • 11

Place Of Publication


  • Switzerland