20 March 2019 Semantic region proposals for adaptive license plate detection in open environment
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Abstract
With the development of intelligent transportation and parking, license plate detection in open environments is in great demand. However, due to the clutter of background and variation of license plates, the existing methods could not make a good balance between accuracy and efficiency. A method based on semantic region proposals is presented. By thinking from the pixel level, this method first adopts a semantic segmentation convolutional network for license plate candidate region extraction. To improve accuracy of segmentation, an enhanced loss function is designed. Afterward, a classification and regression network based on the oriented bounding box regression algorithm is used for region verification and refinement. Experiments on three public datasets show that the proposed method can be adapted to license plate images captured under different scenarios and can achieve better performance than the state-of-the-art methods.
© 2019 SPIE and IS&T 1017-9909/2019/$25.00 © 2019 SPIE and IS&T
Jiangmin Tian, Guoyou Wang, and Jianguo Liu "Semantic region proposals for adaptive license plate detection in open environment," Journal of Electronic Imaging 28(2), 023017 (20 March 2019). https://doi.org/10.1117/1.JEI.28.2.023017
Received: 3 December 2018; Accepted: 21 February 2019; Published: 20 March 2019
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CITATIONS
Cited by 4 scholarly publications.
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KEYWORDS
Image segmentation

Classification systems

Laser phosphor displays

Environmental sensing

Feature extraction

Convolution

Lab on a chip

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