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Performance evaluation of license plate detection using deep neural networks on NPU VIM3 hardware platform
The detection of license plates (LPs) is a crucial step to develop the intelligent traffic management systems. Several challenges exist for the detection of LPs such as the high variation of the geometry of LPs or the frequent variation in the conditions of LP image acquisition. The paper presents a...
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| creator | Phong, Bui Hai Trong, Nguyen Huu Hoang, Manh- Thang Le, Thi-Lan |
| description | The detection of license plates (LPs) is a crucial step to develop the intelligent traffic management systems. Several challenges exist for the detection of LPs such as the high variation of the geometry of LPs or the frequent variation in the conditions of LP image acquisition. The paper presents an end-to-end framework for the detection of LPs. The framework consists of two steps. The first one is the application and optimization of YOLOv4 network to detect LPs accurately. The second one is the strategy of the deployment and testing of the neural network on the NPU VIM3 tool kit. We have performed the evaluation on the large public dataset (Vietnamese license plate detection dataset). The performance comparison (the detection accuracy and execution time) with existed methods on various hardware platforms shows the effectiveness of the proposed method. |
| doi_str_mv | 10.1109/ATC55345.2022.9943007 |
| format | conference_proceeding |
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The performance comparison (the detection accuracy and execution time) with existed methods on various hardware platforms shows the effectiveness of the proposed method.</description><identifier>EISSN: 2162-1039</identifier><identifier>EISBN: 9781665451888</identifier><identifier>EISBN: 1665451882</identifier><identifier>DOI: 10.1109/ATC55345.2022.9943007</identifier><language>eng</language><publisher>IEEE</publisher><subject>Deep learning ; Deep neural network ; Geometry ; Hardware ; license plate detection ; License plate recognition ; Neural networks ; NPU VIM3 kit ; Optimization ; Performance evaluation</subject><ispartof>2022 International Conference on Advanced Technologies for Communications (ATC), 2022, p.92-97</ispartof><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/9943007$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>309,310,780,784,789,790,27925,54555,54932</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/9943007$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc></links><search><creatorcontrib>Phong, Bui Hai</creatorcontrib><creatorcontrib>Trong, Nguyen Huu</creatorcontrib><creatorcontrib>Hoang, Manh- Thang</creatorcontrib><creatorcontrib>Le, Thi-Lan</creatorcontrib><title>Performance evaluation of license plate detection using deep neural networks on NPU VIM3 hardware platform</title><title>2022 International Conference on Advanced Technologies for Communications (ATC)</title><addtitle>ATC</addtitle><description>The detection of license plates (LPs) is a crucial step to develop the intelligent traffic management systems. 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The performance comparison (the detection accuracy and execution time) with existed methods on various hardware platforms shows the effectiveness of the proposed method.</description><subject>Deep learning</subject><subject>Deep neural network</subject><subject>Geometry</subject><subject>Hardware</subject><subject>license plate detection</subject><subject>License plate recognition</subject><subject>Neural networks</subject><subject>NPU VIM3 kit</subject><subject>Optimization</subject><subject>Performance evaluation</subject><issn>2162-1039</issn><isbn>9781665451888</isbn><isbn>1665451882</isbn><fulltext>true</fulltext><rsrctype>conference_proceeding</rsrctype><creationdate>2022</creationdate><recordtype>conference_proceeding</recordtype><sourceid>6IE</sourceid><recordid>eNotkN1OwkAQhVcTEwnyBMZkX6A4u7Pbbi8J8YcElQv0lgzdWS2WlmyLxLenCufmy8nJzGSOEHcKxkpBfj9ZTq1FY8catB7nuUGA7EKM8sypNLXGKufcpRholepEAebXYtS2G-iVGUDIBmKz4BiauKW6YMk_VO2pK5taNkFWZcF1y3JXUcfSc8fFf7Rvy_qz97yTNe8jVT26QxO_W9mnr4t3-TF7QflF0R8onub_TtyIq0BVy6Mzh2L5-LCcPifzt6fZdDJPSuMgoeDXWhvMAlkHUCjl1ToDSlEFy4hoSbHGYEwRyLOjUCCYNHee-499ikNxe1pbMvNqF8stxd_VuRw8AsJtWiM</recordid><startdate>20221020</startdate><enddate>20221020</enddate><creator>Phong, Bui Hai</creator><creator>Trong, Nguyen Huu</creator><creator>Hoang, Manh- Thang</creator><creator>Le, Thi-Lan</creator><general>IEEE</general><scope>6IE</scope><scope>6IL</scope><scope>CBEJK</scope><scope>RIE</scope><scope>RIL</scope></search><sort><creationdate>20221020</creationdate><title>Performance evaluation of license plate detection using deep neural networks on NPU VIM3 hardware platform</title><author>Phong, Bui Hai ; Trong, Nguyen Huu ; Hoang, Manh- Thang ; Le, Thi-Lan</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-i480-afdb22437fa5800c11d1b70a631f5e3335a1e23f44cfade8afc304698de654d63</frbrgroupid><rsrctype>conference_proceedings</rsrctype><prefilter>conference_proceedings</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Deep learning</topic><topic>Deep neural network</topic><topic>Geometry</topic><topic>Hardware</topic><topic>license plate detection</topic><topic>License plate recognition</topic><topic>Neural networks</topic><topic>NPU VIM3 kit</topic><topic>Optimization</topic><topic>Performance evaluation</topic><toplevel>online_resources</toplevel><creatorcontrib>Phong, Bui Hai</creatorcontrib><creatorcontrib>Trong, Nguyen Huu</creatorcontrib><creatorcontrib>Hoang, Manh- Thang</creatorcontrib><creatorcontrib>Le, Thi-Lan</creatorcontrib><collection>IEEE Electronic Library (IEL) Conference Proceedings</collection><collection>IEEE Proceedings Order Plan All Online (POP All Online) 1998-present by volume</collection><collection>IEEE Xplore All Conference Proceedings</collection><collection>IEEE Xplore</collection><collection>IEEE Proceedings Order Plans (POP All) 1998-Present</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Phong, Bui Hai</au><au>Trong, Nguyen Huu</au><au>Hoang, Manh- Thang</au><au>Le, Thi-Lan</au><format>book</format><genre>proceeding</genre><ristype>CONF</ristype><atitle>Performance evaluation of license plate detection using deep neural networks on NPU VIM3 hardware platform</atitle><btitle>2022 International Conference on Advanced Technologies for Communications (ATC)</btitle><stitle>ATC</stitle><date>2022-10-20</date><risdate>2022</risdate><spage>92</spage><epage>97</epage><pages>92-97</pages><eissn>2162-1039</eissn><eisbn>9781665451888</eisbn><eisbn>1665451882</eisbn><abstract>The detection of license plates (LPs) is a crucial step to develop the intelligent traffic management systems. Several challenges exist for the detection of LPs such as the high variation of the geometry of LPs or the frequent variation in the conditions of LP image acquisition. The paper presents an end-to-end framework for the detection of LPs. The framework consists of two steps. The first one is the application and optimization of YOLOv4 network to detect LPs accurately. The second one is the strategy of the deployment and testing of the neural network on the NPU VIM3 tool kit. We have performed the evaluation on the large public dataset (Vietnamese license plate detection dataset). The performance comparison (the detection accuracy and execution time) with existed methods on various hardware platforms shows the effectiveness of the proposed method.</abstract><pub>IEEE</pub><doi>10.1109/ATC55345.2022.9943007</doi><tpages>6</tpages></addata></record> |
| fulltext | fulltext_linktorsrc |
| identifier | EISSN: 2162-1039 |
| ispartof | 2022 International Conference on Advanced Technologies for Communications (ATC), 2022, p.92-97 |
| issn | 2162-1039 |
| language | eng |
| recordid | cdi_ieee_primary_9943007 |
| source | IEEE Xplore All Conference Series |
| subjects | Deep learning Deep neural network Geometry Hardware license plate detection License plate recognition Neural networks NPU VIM3 kit Optimization Performance evaluation |
| title | Performance evaluation of license plate detection using deep neural networks on NPU VIM3 hardware platform |
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