dc.contributor.author |
Le Roux, E
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dc.contributor.author |
Ndiaye, M
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|
dc.contributor.author |
Abu-Mahfouz, Adnan MI
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|
dc.date.accessioned |
2021-11-19T14:28:08Z |
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dc.date.available |
2021-11-19T14:28:08Z |
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dc.date.issued |
2021-06 |
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dc.identifier.citation |
Le Roux, E., Ndiaye, M. & Abu-Mahfouz, A.M. 2021. Automatic number plate recognition for remote gate automation: An edge computing approach. http://hdl.handle.net/10204/12166 . |
en_ZA |
dc.identifier.isbn |
978-1-7281-9022-8 |
|
dc.identifier.isbn |
978-1-7281-9023-5 |
|
dc.identifier.issn |
2163-5145 |
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dc.identifier.uri |
DOI: 10.1109/ISIE45552.2021.9576167
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|
dc.identifier.uri |
http://hdl.handle.net/10204/12166
|
|
dc.description.abstract |
The article proposes a gate automation system based on automatic number plate recognition (ANPR). The system uses a convolutional neural network (CNN) to identify and classify each number plate which is then compared to a database of authorized numbers before a gate is opened. Once the gate is opened an extra feature of alerting the farm settlement owner via SMS and LoRa is added. What this work tries to do is demonstrate the ability of an IoT-edge device to perform complex image processing computations simply by adding more computing resources at the edge. Edge computing suggests adding a powerful edge device to support edge devices however, we envision the extra computing power can be extended and embedded in the edge devices themselves. To demonstrate this we equip our edge device system with a single board computer to provide the needed computing resources for ANPR. |
en_US |
dc.format |
Abstract |
en_US |
dc.language.iso |
en |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/document/9576167 |
en_US |
dc.relation.uri |
https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9576167 |
en_US |
dc.source |
2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), Kyoto, Japan, 20-23 June 2021 |
en_US |
dc.subject |
Automatic number plate recognition |
en_US |
dc.subject |
ANPR |
en_US |
dc.subject |
Convolutional neural network |
en_US |
dc.subject |
CNN |
en_US |
dc.title |
Automatic number plate recognition for remote gate automation: An edge computing approach |
en_US |
dc.type |
Conference Presentation |
en_US |
dc.description.pages |
6pp |
en_US |
dc.description.note |
© 2021 IEEE. Due to copyright restrictions, the attached PDF file only contains the abstract of the full text item. For access to the full text item, please consult the publisher's website: https://ieeexplore.ieee.org/document/9576167 |
en_US |
dc.description.cluster |
Next Generation Enterprises & Institutions |
en_US |
dc.description.impactarea |
EDT4IR Management |
en_US |
dc.identifier.apacitation |
Le Roux, E., Ndiaye, M., & Abu-Mahfouz, A. M. (2021). Automatic number plate recognition for remote gate automation: An edge computing approach. http://hdl.handle.net/10204/12166 |
en_ZA |
dc.identifier.chicagocitation |
Le Roux, E, M Ndiaye, and Adnan MI Abu-Mahfouz. "Automatic number plate recognition for remote gate automation: An edge computing approach." <i>2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), Kyoto, Japan, 20-23 June 2021</i> (2021): http://hdl.handle.net/10204/12166 |
en_ZA |
dc.identifier.vancouvercitation |
Le Roux E, Ndiaye M, Abu-Mahfouz AM, Automatic number plate recognition for remote gate automation: An edge computing approach; 2021. http://hdl.handle.net/10204/12166 . |
en_ZA |
dc.identifier.ris |
TY - Conference Presentation
AU - Le Roux, E
AU - Ndiaye, M
AU - Abu-Mahfouz, Adnan MI
AB - The article proposes a gate automation system based on automatic number plate recognition (ANPR). The system uses a convolutional neural network (CNN) to identify and classify each number plate which is then compared to a database of authorized numbers before a gate is opened. Once the gate is opened an extra feature of alerting the farm settlement owner via SMS and LoRa is added. What this work tries to do is demonstrate the ability of an IoT-edge device to perform complex image processing computations simply by adding more computing resources at the edge. Edge computing suggests adding a powerful edge device to support edge devices however, we envision the extra computing power can be extended and embedded in the edge devices themselves. To demonstrate this we equip our edge device system with a single board computer to provide the needed computing resources for ANPR.
DA - 2021-06
DB - ResearchSpace
DP - CSIR
J1 - 2021 IEEE 30th International Symposium on Industrial Electronics (ISIE), Kyoto, Japan, 20-23 June 2021
KW - Automatic number plate recognition
KW - ANPR
KW - Convolutional neural network
KW - CNN
LK - https://researchspace.csir.co.za
PY - 2021
SM - 978-1-7281-9022-8
SM - 978-1-7281-9023-5
SM - 2163-5145
T1 - Automatic number plate recognition for remote gate automation: An edge computing approach
TI - Automatic number plate recognition for remote gate automation: An edge computing approach
UR - http://hdl.handle.net/10204/12166
ER -
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en_ZA |
dc.identifier.worklist |
25100 |
en_US |