University of Oulu

W. Zhang, B. Xue, J. Zhou, X. Liu and H. Lv, "A Scalable and Efficient Multi-Label CNN-Based License Plate Recognition on Spark," 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Guangzhou, 2018, pp. 1738-1744,

A scalable and efficient multi-label cnn-based license plate recognition on spark

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Author: Zhang, Weishan1; Xue, Bing1; Zhou, Jiehan2;
Organizations: 1Department of Software Engineering, China University of Petroleum Qingdao, China
2Oulu University, Finland
3School Computer and Communication Engineering, China University of Petroleum Qingdao, China
4College Computer and Communication Engineering, China University of Petroleum Qingdao, China
Format: article
Version: accepted version
Access: open
Online Access: PDF Full Text (PDF, 0.6 MB)
Persistent link:
Language: English
Published: Institute of Electrical and Electronics Engineers, 2018
Publish Date: 2020-04-29


Surveillance cameras are being rapidly deployed for facilitating smart transportation. Recognizing the vehicle license plate from massive videos becomes a challenge in context of system scalability and efficiency. This paper proposes a novel algorithm for scalable and efficient license plate recognition (SELPR). The SELPR algorithm first locates the license plate using a YOLO (You Look Only Once) network and recognizes the license plate using multi-label convolutional neural network (Multi-label CNN). We deploy the SELPR algorithm to the Apache Spark framework to evaluate its scalability and efficiency in parallel processing. The results demonstrates that SELPR can achieve synthesized performance with 95% recognition accuracy, better processing efficiency and scalability on a Spark cluster.

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ISBN: 978-1-5386-9380-3
ISBN Print: 978-1-5386-9381-0
Pages: 1738 - 1744
Article number: 8560272
DOI: 10.1109/SmartWorld.2018.00294
Host publication: 4th IEEE SmartWorld, 15th IEEE International Conference on Ubiquitous Intelligence and Computing, Advanced and Trusted Computing, Scalable Computing and Communications, Cloud and Big Data Computing, Internet of People and Smart City Innovations, SmartWorld/UIC/ATC/ScalCom/CBDCom/IoP/SCI 2018
Host publication editor: Loulergue, F.
Wang, G.
Bhuiyan, M. Z. A.
Ma, X.
Li, P.
Roveri, M.
Han, Q.
Chen, L.
Conference: IEEE International Conference on Cloud and Big Data Computing
Type of Publication: A4 Article in conference proceedings
Field of Science: 213 Electronic, automation and communications engineering, electronics
Funding: This work was supported in part by the Key Research Program of Shandong Province under Grant 2017GGX10140 and in part by the Fundamental Research Funds for the Central Universities(15CX08015A), National Natural Science Foundation of China (No. 61309024).
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