• 대한전기학회
Mobile QR Code QR CODE : The Transactions of the Korean Institute of Electrical Engineers
  • COPE
  • kcse
  • 한국과학기술단체총연합회
  • 한국학술지인용색인
  • Scopus
  • crossref
  • orcid
The Transactions of the Korean Institute of Electrical Engineers

The Transactions of the Korean Institute of Electrical Engineers

ISO Journal TitleTrans. Korean. Inst. Elect. Eng.
SCImago Journal & Country Rank

Journal Search

  1. Home
  2. Archive
  3. 2018-07 (Vol.67 No.07)
  4. 10.5370/KIEE.2018.67.7.865
XML PDF INFO REF

References

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