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

The Korean Institute of Electrical Engineers (대한전기학회)
권호 표지
DOI QR코드

DOI QR Code

Comparison of Region-based CNN Methods for Defects Detection on Metal Surface

금속 표면의 결함 검출을 위한 영역 기반 CNN 기법 비교

  • Lee, Minki (Dept. of Electronics Engineering, Seokyeong University) ;
  • Seo, Kisung (Dept. of Electronics Engineering, Seokyeong University)
  • Received : 2018.05.05
  • Accepted : 2018.05.22
  • Published : 2018.07.01
Download PDF
⟨ Previous Next ⟩

Abstract

A machine vision based industrial inspection includes defects detection and classification. Fast inspection is a fundamental problem for many applications of real-time vision systems. It requires little computation time and localizing defects robustly with high accuracy. Deep learning technique have been known not to be suitable for real-time applications. Recently a couple of fast region-based CNN algorithms for object detection are introduced, such as Faster R-CNN, and YOLOv2. We apply these methods for an industrial inspection problem. Three CNN based detection algorithms, VOV based CNN, Faster R-CNN, and YOLOv2, are experimented for defect detection on metal surface. The results for inspection time and various performance indices are compared and analysed.

Keywords

References

  1. Y. Park, and I. S. Kweon, "Ambiguous Surface Defect Image Classification of AMOLED Displays in Smartphones," IEEE Trans. on Industrial Informatics, vol. 12, no. 2, pp. 597-607, 2016 https://doi.org/10.1109/TII.2016.2522191
  2. S. Ghorai, A. Mukherjee, M. Gangadaran, P. K. Dutta, "Automatic Defect Detection on Hot-Rolled Flat Steel Products," IEEE Trans. on Instrumentation and Measurement, vol. 62, Iss. 3, pp. 612-621, 2013. https://doi.org/10.1109/TIM.2012.2218677
  3. LeCun, Yann, et al. "Gradient based learning applied to document recognition," Proceedings of the IEEE, pp. 2278-2324, 1998
  4. J-K. Park, N. Kwon, J-H. and D. Kang, "Machine Learning-Based Imaging System for Surface Defect Inspection," International Journal of Precision Engineering and Manufacturing-Green Technology, vol. 3, no. 3, pp. 303-310, 2016 https://doi.org/10.1007/s40684-016-0039-x
  5. H. Choi and K. Seo, "CNN Based Detection of Surface Defects for Electronic Parts," Journal of Korean Institute of Intelligent Systems, vol. 27, no. 3, pp. 195-200, 2017. https://doi.org/10.5391/JKIIS.2017.27.3.195
  6. H. Choi and K. Seo, "Comparison of CNN Structures for Detection of Surface Defects," The Transactions of the Korean Institute of Electrical Engineers, vol. 66, no. 7, pp. 1100-1104, 2017 https://doi.org/10.5370/KIEE.2017.66.7.1100
  7. B.-K Kwon, J.-S. Won, D.-J. Kang. "Fast defect detection for various types of surfaces using random forest with VOV features," International Journal of Precision Engineering and Manufacturing, vol. 16, no. 5, pp. 965-970, 2015 https://doi.org/10.1007/s12541-015-0125-y
  8. R. Girshick, J. Donahue, T. Darrell, J. Malik, "Region-Based Convolutional Networks for Accurate Object Detection and Segmentation," IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 38, no. 1. pp. 142-158. 2016 https://doi.org/10.1109/TPAMI.2015.2437384
  9. R. Girshick, "Fast R-CNN," ICCV 2015, pp. 1440-1448. 2015
  10. Shaoqing Ren, Kaiming He, Ross Girshick, Jian Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," Advances in neural information processing systems, pp. 91-99, 2015
  11. M. Lee, K, Seo, "Using Deep Learning Based Machine Vision for Defects Detection," Proceedings of Information and Control Symposium CICS'2017, pp. 54-55, 2017.
  12. Joseph Redmon, Santosh Divvala, Ross Girshick, Ali Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," Proceedings of the IEEE conference on computer vision and pattern recognition, 2016
  13. M. Lee, K, Seo, "Comparison of CNN Methods for Defects Detection," Proceedings of Information and Control Symposium ICS'2018, pp. 101-102, 2018.
  14. Joseph Redmon, Ali Farhadi, "YOLO9000: better, faster, stronger," arXiv:1612.08242, 2017.