Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Development of Checker-Switch Error Detection System using CNN Algorithm

CNN 알고리즘을 이용한 체커스위치 불량 검출 시스템 개발

  • Suh, Sang-Won (Dual Mechanics Co., Ltd.) ;
  • Ko, Yo-Han (Department of Electronic Engineering, Chonbuk National University) ;
  • Yoo, Sung-Goo (Saemangeum Eenterprise Development Agency, Kunsan National University) ;
  • Chong, Kil-To (Advance Electronics & Information Research Center, Chonbuk National University)
  • 서상원 (두얼메카닉스(주)) ;
  • 고요한 (전북대학교 전자공학부) ;
  • 유성구 (군산대학교 새만금중소기업진흥원) ;
  • 정길도 (전북대학교 전자정보신기술연구센터)
  • Received : 2019.09.26
  • Accepted : 2019.10.20
  • Published : 2019.12.31
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Abstract

Various automation studies have been conducted to detect defective products based on product images. In the case of machine vision-based studies, size and color error are detected through a preprocessing process. A situation may arise in which the main features are removed during the preprocessing process, thereby decreasing the accuracy. In addition, complex systems are required to detect various kinds of defects. In this study, we designed and developed a system to detect errors by analyzing various conditions of defective products. We designed the deep learning algorithm to detect the defective features from the product images during the automation process using a convolution neural network (CNN) and verified the performance by applying the algorithm to the checker-switch failure detection system. It was confirmed that all seven error characteristics were detected accurately, and it is expected that it will show excellent performance when applied to automation systems for error detection.

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References

  1. Oh, S. C., "Prediction of Machining Performance using ANN and Training using ACO" Journal of the Korean Society of Manufacturing Process Engineers, Vol. 16, No. 6, pp. 125-132, 2017. https://doi.org/10.14775/ksmpe.2017.16.6.125
  2. Park, K. S., Shin, D. W., “Defect Inspection of the Pixels in OLED Type Display Device by Image Processing,” Journal of the Korean Society of Manufacturing Process Engineers, Vol. 8, No. 2, pp. 25-31, 2009.
  3. Viola, P., Jones, M. J., Snow, D., “Detecting Pedestrians using Patterns of Motion and Appearance,” International Journal of Computer Vision, Vol. 63, No. 2, pp. 153-161, 2005. https://doi.org/10.1007/s11263-005-6644-8
  4. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., Ramanan, D., “Object Detection with Discriminatively Trained Part-based Models,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 32, No. 9, pp. 1627-1645, 2010. https://doi.org/10.1109/TPAMI.2009.167
  5. Punuhsingon, C. S., Oh, S. C., "Prediction of Surface Roughness and Electric Current Consumption in Turning Operation using Neural Network with Back Propagation and Particle Swarm Optimization," Journal of the Korean Society of Manufacturing Process Engineers, Vol. 14, No. 3 pp. 65-73, 2015. https://doi.org/10.14775/ksmpe.2015.14.3.065
  6. Jeong, Y. H., "Tool Breakage Detection Using Feed Motor Current," Journal of the Korean Society of Manufacturing Process Engineers, Vol. 14, No. 6 pp. 1-6, 2015. https://doi.org/10.14775/ksmpe.2015.14.6.001
  7. Pyo, C. R., “Development of Auto Positioning Laser System by using Image Measurement Data,” Journal of the Korean Society of Manufacturing Process Engineers, Vol. 12, No. 3, pp. 36-40, 2013.
  8. Huh, Y. M.. Eom, J. J., "Development of automatic ALC Block Measurement Algorithm using Image Processing," Journal of the Institute of Electronics and Information Engineers, Vol 41, No. 5, pp 1-8, September 2004.
  9. Cheon, M., Lee, W., Yoon, C., Park, M., “Vision-based Vehicle Detection System with Consideration of the Detecting Location,” IEEE transactions on intelligent transportation systems, Vol. 13, No. 3, pp. 1243-1252, 2012. https://doi.org/10.1109/TITS.2012.2188630
  10. Alessandretti, G., Broggi, A., Cerri, P., “Vehicle and Guard Rail Detection using Radar and Vision Data Fusion,” IEEE Transactions on Intelligent Transportation Systems, Vol. 8, No. 1, pp. 95-105, 2007. https://doi.org/10.1109/TITS.2006.888597
  11. Pyo, C. R., “Development of Auto Positioning Laser System by using Image Measurement Data,” Journal of the Korean Society of Manufacturing Process Engineers, Vol. 12, No. 3, pp. 36-40, 2013.
  12. Stauffer, C., Grimson, W. E. L., “Learning Patterns of Activity using Real-time Tracking,” IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 8, pp. 747-757, 2000. https://doi.org/10.1109/34.868677
  13. Krizhevsky, A., Sutskever, I., Hinton, G. E., "Imagenet Classification with Deep Convolutional Neural Networks," In Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.
  14. Yang, H., Jang, H., Jeong, J., Lee, B., Jeong, D., "A Real-time People Counting Algorithm Using Background Modeling and CNN," Journal of the Institute of Electronics and Information Engineers, Vol 54, No. 3, pp 70-77, 2017. https://doi.org/10.5573/ieie.2017.54.1.070
  15. Zeiler, M. D., Fergus, R., "Visualizing and Understanding Convolutional Networks," In European Conference on Computer Vision, pp. 818-833, 2014.
  16. Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Rabinovich, A., "Going Deeper with Convolutions," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1-9, 2014.