计算机技术与发展2018，Vol.28Issue(1)：134-137,4.DOI:10.3969/j.issn.1673-629X.2018.01.029
基于级联卷积神经网络的车牌定位
License Plate Location Based on Cascaded Convolution Neural Network
摘要
Abstract
Aiming at the problem of license plate positioning in complex environments such as multi-vehicle and low resolution,we present a license plate recognition method based on human vision. By imitating the visual principle of human eyes,the precise positioning of license plate is realized by the approach of extraction of target region characteristics through cascade convolution neural network and gradually narro-wing the search area. Firstly,the target motion region which we are interested in is located by the motion detection. Then,the vehicle identifi-cation is performed on the hot spot region by convolutional neural network. Finally,license plates are located in vehicle picture. Training pic-tures are collected in 20 different traffic junctions of the skynet camera images,as well as nearly 5000 images and about 15000 targets la-beled by manual. At the same time,the labeled images are transformed randomly to improve the effectiveness of the training. According to the experiments,the extraction of motion region enhances the speed and recognition precision of convolutional neural network,and greatly improves the license plate recognition rate in complex scenes compared to the traditional license plate recognition algorithm. Moreover,it per-forms better in dealing with high-resolution pictures.关键词
车牌定位/运动目标检测/视觉特性/卷积神经网络Key words
license plate location/moving target detection/human visual characteristics/convolution neural network分类
信息技术与安全科学引用本文复制引用
傅鹏,谢世朋..基于级联卷积神经网络的车牌定位[J].计算机技术与发展,2018,28(1):134-137,4.基金项目
江苏省科技重点研发计划-产业前瞻与共性关键技术(BE2016001-4) （BE2016001-4）
教育部-中国移动科研基金(MCM20150504) （MCM20150504）
