计算机技术与发展2018，Vol.28Issue(6)：1-6,6.DOI:10.3969/j.issn.1673-629X.2018.06.001
基于卷积神经网络的小样本车辆检测与识别
Vehicle Detection and Recognition of a Few Samples Based on Convolutional Neural Network
摘要
Abstract
We design a quick and accurate algorithm to achieve the detection and recognition of vehicles,especially the tricycles,in the complex environments of lacking of samples. Firstly,an improved convolutional neural network is used to learn vehicle features rapidly, then many methods such as fine-tuning neural network,combining predictions from multiple feature maps and phased training are used to enhance network' s learning with a few samples. By eliminating the tedious and time-consuming regional recommendation algorithm and the post-classification algorithm,the position and category of the target vehicle in the image are directly predicted by using a single net-work,which greatly improves the performance of the algorithm. The experiment shows that when using GeForce GTX 1080 GPU,the ve-hicle recognition accuracy of the proposed algorithm is relatively balanced,with an average detection accuracy by 72. 2％,and the number of frames per second is 46. 57. It owns better adaptability in all kinds of complicated scenarios such as rainy day,sunny day,night,light and shade and so on,which is suitable for the precisely real-time requirements of intelligent transportation system under the real video monitoring.关键词
卷积神经网络/车辆检测/车型识别/多特征结合/分段训练Key words
convolutional neural network/vehicle detection/vehicle recognition/multiple feature maps/phased training分类
信息技术与安全科学引用本文复制引用
吴玉枝,吴志红,熊运余..基于卷积神经网络的小样本车辆检测与识别[J].计算机技术与发展,2018,28(6):1-6,6.基金项目
国家高技术研究发展计划(2015AA016405) （2015AA016405）
