Semantic region proposals for adaptive license plate detection in open environment

Jiangmin Tian; Guoyou Wang; Jianguo Liu

doi:10.1117/1.JEI.28.2.023017

20 March 2019 Semantic region proposals for adaptive license plate detection in open environment

Jiangmin Tian, Guoyou Wang, Jianguo Liu

Author Affiliations +

Journal of Electronic Imaging, Vol. 28, Issue 2, 023017 (March 2019). https://doi.org/10.1117/1.JEI.28.2.023017

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.

Citation Download Citation

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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