License plate detection based on fully convolutional networks

Han Xiang; Yule Yuan; Yong Zhao; Zufeng Fu

doi:10.1117/1.JEI.26.5.053027

27 October 2017 License plate detection based on fully convolutional networks

Han Xiang, Yule Yuan, Yong Zhao, Zufeng Fu

Author Affiliations +

Journal of Electronic Imaging, Vol. 26, Issue 5, 053027 (October 2017). https://doi.org/10.1117/1.JEI.26.5.053027

Abstract

Fully convolutional networks (FCNs) have shown outstanding performance in image semantic segmentation, which is the key work in license plate detection (LPD). An FCN architecture for LPD is presented. First, a multiscale hierarchical network structure is used to combine multiscale and multilevel features produced by FCN. Then, an enhanced loss structure that contains three loss layers is defined to emphasize the license plates in images. Finally, the FCN generates prediction maps that directly show the location of license plates. Experiments show that our approach is more accurate than many state-of-the-art methods.

Citation Download Citation

Han Xiang, Yule Yuan, Yong Zhao, and Zufeng Fu "License plate detection based on fully convolutional networks," Journal of Electronic Imaging 26(5), 053027 (27 October 2017). https://doi.org/10.1117/1.JEI.26.5.053027

Received: 6 July 2017; Accepted: 3 October 2017; Published: 27 October 2017

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