Earticle

초록

영어

Recently, cloud computing technology has been offering cloud-based plate number classification applications with lower latency. In this paper, we design and implement a new distributed plate number classification system (DPNC). The proposed DPNC system absorbs a more significant number of input sensor data from autonomous cars with a lightweight model that provides high accuracy. In addition, our model has employed the entire convolution network – Long Short-term Memory (FCN-LSTM) to predict a total of 3 classes such as image plate, boundary, and number detection. We evaluate the proposed system using an existing Iranian plate dataset containing a collection of plate images using an autonomous car. We used various Amazon cloud services for deploying the proposed DPNC architecture. The experimental results show that the proposed architecture improves end-to-end latency by 2.1 times compared to the traditional architecture.

목차

Abstract
1. Introduction
2. Methods
2.1. architecture
3. Experimental Section
4. Conclusions
Acknowledgement
References

저자

• Mehdi Pirahandeh [ Integrated System Engineering (ISE) Department Inha University ] Corresponding author
• Deok Hwan Kim [ Electronic and Computer Engineering Department Inha University ]

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