Knowledge Distillation With Fast CNN for License Plate Detection
Chunwei Tian, Xuanyu Zhang, Xu Liang, Bo Li, Yougang Sun, Shichao Zhang
Abstract
Deep convolutional neural networks (CNNs) can improve recognition rate in license plate to improve traffic. However, these methods may refer to big computational costs and a lot of parameters. In this paper, we propose a knowledge distillation with a fast CNN for license plate detection (KDNet). KDNet uses knowledge distillation to guide a CNN to optimize parameters and quickly obtain a detector for license plate. To overcome naive effect of local information, a non-local similarity mechanism is used into a CNN to enhance effect of global information for extracting salient information in license plate detection. Experimental results that this proposed KDNet is superior to detection speed for license plate. The code of KDNet can be obtained at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/hellloxiaotian/KDNet</uri> .