An Improved Compression Layer Network Structure for VLPR

DOI

10.2991/eee-19.2019.42How to use a DOI?

Keywords

License plate recognition, CNN, Compression layer network, Parallel structure

Abstract

License plate recognition is widely used in road conditions, parking, dredging traffic, etc. The license plate recognition first needs to find the specific location of the license plate and then needs to identify the content of the license plate as an image. During practical applications, main challenges of this recognition technology include the background color, size and specifications of the license plate, weather conditions, background interference, lighting, etc., which may also interfere with license plate recognition. Therefore, complex scenes and moving vehicles are two factors that need to be taken seriously. We propose an improved compression layer network structure for license plate recognition. The main way is to use a parallel structure of the compression layer network connection to alleviate errors caused by illumination, tilt and occlusion in license plate recognition. For reduce the amount of computation often pointed out in the CNN, we have tried to discard some of the parameters. Experiments have shown that this method has a lower error rate than CNN and capsule baseline, also has more advantageous in terms of time consumption.

Copyright

© 2019, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Wen-xuan Wu
PY  - 2019/07
DA  - 2019/07
TI  - An Improved Compression Layer Network Structure for VLPR
BT  - Proceedings of the 2nd International Conference on Electrical and Electronic Engineering (EEE 2019)
PB  - Atlantis Press
SP  - 262
EP  - 267
SN  - 2352-5401
UR  - https://doi.org/10.2991/eee-19.2019.42
DO  - 10.2991/eee-19.2019.42
ID  - Wu2019/07
ER  -