The Transactions of The Korean Institute of Electrical Engineers (전기학회논문지)

The Korean Institute of Electrical Engineers (대한전기학회)
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Comparison of CNN Structures for Detection of Surface Defects

표면 결함 검출을 위한 CNN 구조의 비교

  • Choi, Hakyoung (Dept. of Electronics and Computer Engineering, Seokyeong University) ;
  • Seo, Kisung (Dept. of Electronics Engineering, Seokyeong University)
  • Received : 2017.06.12
  • Accepted : 2017.06.19
  • Published : 2017.07.01
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Abstract

A detector-based approach shows the limited performances for the defect inspections such as shallow fine cracks and indistinguishable defects from background. Deep learning technique is widely used for object recognition and it's applications to detect defects have been gradually attempted. Deep learning requires huge scale of learning data, but acquisition of data can be limited in some industrial application. The possibility of applying CNN which is one of the deep learning approaches for surface defect inspection is investigated for industrial parts whose detection difficulty is challenging and learning data is not sufficient. VOV is adopted for pre-processing and to obtain a resonable number of ROIs for a data augmentation. Then CNN method is applied for the classification. Three CNN networks, AlexNet, VGGNet, and mofified VGGNet are compared for experiments of defects detection.

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References

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