Design & Manufacturing

  • 제16권4호
  • /
  • Pages.60-66
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  • 2022
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  • 2800-0323(pISSN)
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  • 2733-452X(eISSN)
한국금형공학회 (Korean Society of Die & Mold Engineering)
권호 표지

Deep Learning 기반의 폐기물 선별 Vision 시스템 개발

Development of Deep Learning based waste Detection vision system

  • 투고 : 2022.11.16
  • 심사 : 2022.12.31
  • 발행 : 2022.12.31
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⟨ 이전 논문 다음 논문 ⟩

초록

Recently, with the development of industry and the improvement of living standards, various wastes are generated along with the production of various products. Most of these wastes are used as containers for products, and plastic or aluminum is used. Various attempts are being made to automate the classification of these wastes due to the high labor cost, but most of them are solved by manpower due to the geometrical shape change due to the nature of the waste. In this study, in order to automate the waste sorting task, Deep Learning technology is applied to a robot system for waste sorting and a vision system for waste sorting to effectively perform sorting tasks according to the shape of waste. As a result of the experiment, a Deep Learning parameter suitable for waste sorting was selected. In addition, through various experiments, it was confirmed that 99% of wastes could be selected in individual & group image learning. It is expected that this will enable automation of the waste sorting operation.

키워드

과제정보

본 논문은 해양수산부 "해양 플라스틱 쓰레기 저감을 위한 기술개발"사업(20200584)의 연구과제로 수행되었음.

참고문헌

  1. H. S. Jang "Research on plastic regulation trends and innovation business models in major countries", Institute for international trand No.13 pp. 2093-3118, 2019.
  2. J. H. Choi, B. C. Jeon, M. W. Cho. " Development of Web Based Mold Discrimination System using the Matching Process for Vision Information and CAD DB" Transactions of the Korean Society of Machine Tool Engineers, Vol.15 No.5, pp 37-43 2006.
  3. H. Y. Bae, H. J. Kim, J. I. Paeng, H. S. Sim, S. H. Han, J. C. Moon " A Study on Shape Recognition Technology of Die Casting and Forging Parts Based on Robot Vision for Inspection Process Automation in Limit Environment" Journal of The Korean Society of Industry Convergence Vol.21, No.6 pp. 369-378 2018.
  4. M. Y. Cho1, M. S. Jang, C. S. Jang, D. H. Lee "Evaluation of Object Recognition Intelligence of Social Robots" 19th International Conference on Control, Automation and Systems, 2019.
  5. K. K. Kim, S. S. Kang, J. B. Kim, J. Y. Lee, H. M. Do, T. Y. Choi, J. H. Kyung " Object Recognition Method for Industrial Intelligent Robot" J. Korean Soc. Precis. Eng., Vol. 30, No. 9, pp. 901-908 2013. https://doi.org/10.7736/KSPE.2013.30.9.901
  6. Y. Bengio, A. Courville, P. Vincent, "Representation Learning: AReview and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol35, issue8, pp. 1798-1828. 2013. https://doi.org/10.1109/TPAMI.2013.50
  7. Y. Bengio, Y. LeCun, G. Hinton, "Deep Learning". Nature. Vol.521 pp. 436-444, 2015. https://doi.org/10.1038/nature14539
  8. L. Deng, D. Yu, "Deep Learning: Methods and Applications" Foundations and Trends in Signal Processing, Vol. 7, 2014.
  9. Y. LeCun, Y. Bengio, G. Hinton, "Deep learning". Nature. Vol. 521, pp. 436-444. 2015. https://doi.org/10.1038/nature14539
  10. J. Schmidhuber, J. "Deep Learning in Neural Networks: An Overview", Neural Networks. Vol. 61, pp. 85-117, 2015. https://doi.org/10.1016/j.neunet.2014.09.003
  11. Y. Bengio, A. Courville, P. Vincent, "Representation Learning: AReview and New Perspectives". IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, issue. 8, pp. 1798-1828. 2013. https://doi.org/10.1109/TPAMI.2013.50
  12. https://en.wikipedia.org/wiki/Deep_learning