An Improved Compression Layer Network Structure for VLPR
Available Online July 2019.
- https://doi.org/10.2991/eee-19.2019.42How to use a DOI?
- License plate recognition, CNN, Compression layer network, Parallel structure
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Wen-xuan Wu PY - 2019/07 DA - 2019/07 TI - An Improved Compression Layer Network Structure for VLPR BT - 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 - https://doi.org/10.2991/eee-19.2019.42 ID - Wu2019/07 ER -