Abstract
License plate detection plays an important role in intelligent transportation system. However, it is still a challenging task due to plenty of complex scenes. Recent studies show that deep learning approaches achieve prominent results on general object detection. Therefore, in this paper, we propose a deep cascaded convolutional neural network for improving license plate detection in complex scenes. Firstly, we utilize convolutional features to generate candidate vehicles proposals. Then a network is used to detect a license from each vehicle proposal by analyzing the correlation between vehicles and licenses. Finally, we enhance detection performance by processing license boundary. Experimental results on a large dataset demonstrate that our method works effectively in a variety of complex scenes.
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Acknowledgement
This work is partially supported by Shenzhen fundamental research fund (Grant No. JCYJ20170412170438636) and the Natural Science Foundation of China (NSFC) (No. 61772296). We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Tesla K40 GPU used for this research.
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Fu, Q., Shen, Y., Guo, Z. (2017). License Plate Detection Using Deep Cascaded Convolutional Neural Networks in Complex Scenes. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10635. Springer, Cham. https://doi.org/10.1007/978-3-319-70096-0_71
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DOI: https://doi.org/10.1007/978-3-319-70096-0_71
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