论文标题
伊朗车牌检测和认可的基于深度学习的框架
Deep Learning Based Framework for Iranian License Plate Detection and Recognition
论文作者
论文摘要
车牌识别系统在许多应用中具有非常重要的作用，例如通行管理，停车控制和交通管理。在本文中，为伊朗车牌识别提供了深度卷积神经网络的框架。第一个CNN是Yolov3网络，该网络在输入图像中检测伊朗车牌，而第二个CNN是更快的R-CNN，可以识别和对检测到的车牌中的字符进行分类。本文还开发了一个由不良条件图像组成的伊朗车牌数据集。 Yolov3网络获得了99.6％的地图，98.26％的召回率，精度为98.08％，平均检测速度仅为23ms。此外，对开发的数据集进行了训练和测试的更快的R-CNN网络，并获得了98.97％的召回率，99.9％的精度和98.8％的精度。拟议的系统可以在具有挑战性的情况下识别车牌，例如车牌上不需要的数据。将该系统与其他伊朗车牌识别系统进行比较表明，它更快，更准确，并且该系统也可以在开放的环境中起作用。
License plate recognition systems have a very important role in many applications such as toll management, parking control, and traffic management. In this paper, a framework of deep convolutional neural networks is proposed for Iranian license plate recognition. The first CNN is the YOLOv3 network that detects the Iranian license plate in the input image while the second CNN is a Faster R-CNN that recognizes and classifies the characters in the detected license plate. A dataset of Iranian license plates consisting of ill-conditioned images also developed in this paper. The YOLOv3 network achieved 99.6% mAP, 98.26% recall, 98.08% accuracy, and average detection speed is only 23ms. Also, the Faster R-CNN network trained and tested on the developed dataset and achieved 98.97% recall, 99.9% precision, and 98.8% accuracy. The proposed system can recognize the license plate in challenging situations like unwanted data on the license plate. Comparing this system with other Iranian license plate recognition systems shows that it is Faster, more accurate and also this system can work in an open environment.
