Dongsuk Lee, Sook Yoon, Jaehwan Lee, Dong Sun Park

KIPS Transactions on Software and Data Engineering, Vol. 5, No. 11, pp. 511-520, Nov. 2016
10.3745/KTSDE.2016.5.11.511,   PDF Download:
Keywords: License Plate, Convolutional Neural Network, Faster Region Based Convolutional Neural Network
Abstract

Automatic License Plate Detection (ALPD) is a key technology for a efficient traffic control. It is used to improve work efficiency in many applications such as toll payment systems and parking and traffic management. Until recently, the hand-crafted features made for image processing are used to detect license plates in most studies. It has the advantage in speed. but can degrade the detection rate with respect to various environmental changes. In this paper, we propose a way to utilize a Faster Region based Convolutional Neural Networks (Faster R-CNN) and a Conventional Convolutional Neural Networks (CNN), which improves the computational speed and is robust against changed environments. The module based on Faster R-CNN is used to detect license plate candidate regions from images and is followed by the module based on CNN to remove False Positives from the candidates. As a result, we achieved a detection rate of 99.94% from images captured under various environments. In addition, the average operating speed is 80ms/image. We implemented a fast and robust Real-Time License Plate Detection System.

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