Browse > Article

Development of Deep Learning based waste Detection vision system  

Bong-Seok Han (NHC Inc.)
Hyeok-Won Kwon (R&D Team, NHC Inc.)
Bong-Cheol Shin (Department of Mechanical Engineering, Inha National University)
Publication Information
Design & Manufacturing / v.16, no.4, 2022 , pp. 60-66 More about this Journal
Abstract
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.
Keywords
Deep Learning; Detection; Robot; Vision; Waste;
Citations & Related Records
연도 인용수 순위
  • Reference
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.   DOI
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.   DOI
7 Y. Bengio, Y. LeCun, G. Hinton, "Deep Learning". Nature. Vol.521 pp. 436-444, 2015.   DOI
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.   DOI
10 J. Schmidhuber, J. "Deep Learning in Neural Networks: An Overview", Neural Networks. Vol. 61, pp. 85-117, 2015.   DOI
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.   DOI
12 https://en.wikipedia.org/wiki/Deep_learning