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Erschienen in:
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2018 | OriginalPaper | Buchkapitel

Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline

verfasst von : Zhenbo Xu, Wei Yang, Ajin Meng, Nanxue Lu, Huan Huang, Changchun Ying, Liusheng Huang

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

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Abstract

Most current license plate (LP) detection and recognition approaches are evaluated on a small and usually unrepresentative dataset since there are no publicly available large diverse datasets. In this paper, we introduce CCPD, a large and comprehensive LP dataset. All images are taken manually by workers of a roadside parking management company and are annotated carefully. To our best knowledge, CCPD is the largest publicly available LP dataset to date with over 250k unique car images, and the only one provides vertices location annotations. With CCPD, we present a novel network model which can predict the bounding box and recognize the corresponding LP number simultaneously with high speed and accuracy. Through comparative experiments, we demonstrate our model outperforms current object detection and recognition approaches in both accuracy and speed. In real-world applications, our model recognizes LP numbers directly from relatively high-resolution images at over 61 fps and 98.5% accuracy.

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Metadaten
Titel
Towards End-to-End License Plate Detection and Recognition: A Large Dataset and Baseline
verfasst von
Zhenbo Xu
Wei Yang
Ajin Meng
Nanxue Lu
Huan Huang
Changchun Ying
Liusheng Huang
Copyright-Jahr
2018
Buch
Computer Vision – ECCV 2018

Print ISBN: 978-3-030-01260-1

Electronic ISBN: 978-3-030-01261-8

Copyright-Jahr: 2018

DOI
https://doi.org/10.1007/978-3-030-01261-8_16