Proceedings of the 2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017)

A Detection Model of Granary Storage Quantity Based on Deep Learning

Authors
Xin Zhang, Dexian Zhang
Corresponding Author
Xin Zhang
Available Online August 2017.
DOI
10.2991/mseee-17.2017.21How to use a DOI?
Keywords
Storage Quantity Monitoring; Pressure Sensor; Deep Learning; Advantage.
Abstract

According to the special significance of grain and all the detection methods of granary storage quantity, this paper select detection methods of granary storage quantity based on pressure sensors figured with high universality, practicability and reliability, analyze them and put forward a improved version of detection method based on deep learning. This paper then analyze its feasibility and introduce its implementation method and finally summarize the advantage of deep learning in detection methods of granary storage quantity.

Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the 2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017)
Series
Advances in Engineering Research
Publication Date
August 2017
ISBN
978-94-6252-377-7
ISSN
2352-5401
DOI
10.2991/mseee-17.2017.21How to use a DOI?
Copyright
© 2017, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Xin Zhang
AU  - Dexian Zhang
PY  - 2017/08
DA  - 2017/08
TI  - A Detection Model of Granary Storage Quantity Based on Deep Learning
BT  - Proceedings of the 2017 International Conference on Material Science, Energy and Environmental Engineering (MSEEE 2017)
PB  - Atlantis Press
SP  - 110
EP  - 113
SN  - 2352-5401
UR  - https://doi.org/10.2991/mseee-17.2017.21
DO  - 10.2991/mseee-17.2017.21
ID  - Zhang2017/08
ER  -