A scalable and efficient multi-label cnn-based license plate recognition on spark
Zhang, Weishan; Xue, Bing; Zhou, Jiehan; Liu, Xin; Lv, Hao (2018-12-06)
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, https://doi.org/10.1109/SmartWorld.2018.00294
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https://urn.fi/URN:NBN:fi-fe2020042922897
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Abstract
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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