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Automatic number plate recognition for remote gate automation: An edge computing approach

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dc.contributor.author Le Roux, E
dc.contributor.author Ndiaye, M
dc.contributor.author Abu-Mahfouz, Adnan MI
dc.date.accessioned 2021-11-19T14:28:08Z
dc.date.available 2021-11-19T14:28:08Z
dc.date.issued 2021-06
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
dc.identifier.uri DOI: 10.1109/ISIE45552.2021.9576167
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 - en_ZA
dc.identifier.worklist 25100 en_US


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