Automatic License Plate Recognition Based on Faster R-CNN Algorithm

3Citations
Citations of this article
6Readers
Mendeley users who have this article in their library.
Get full text

Abstract

This paper proposed a method based on Faster R-CNN algorithm to locate and recognize Chinese license plate. Faster R-CNN is composed by Region Proposal Network (RPN) and fast R-CNN. To make Faster R-CNN locate and recognize license plate more effective, we optimize the training process. To validate performance of the proposed method, two datasets (standard dataset and real scene dataset) are created. Faster R-CNN with three different model are used. The experimental results show that the proposed method achieve better performance contrasting six traditional methods. In standard dataset (simple situation), three modes achieve similar recognition results. However, in real scene dataset, more deeper model achieve better recognition performance.

Cite

CITATION STYLE

APA

Yang, Z., Du, F. L., Xia, Y., Zheng, C. H., & Zhang, J. (2018). Automatic License Plate Recognition Based on Faster R-CNN Algorithm. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10956 LNAI, pp. 319–326). Springer Verlag. https://doi.org/10.1007/978-3-319-95957-3_35

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free