重庆大学学报2017，Vol.40Issue(7)：32-36,5.DOI:10.11835/j.issn.1000-582X.2017.07.005
Faster-RCNN的车型识别分析
Vehicle detection based on faster-RCNN
摘要
Abstract
As one of the object detection,vehicle detection,which has been a hot research area in recent years,is one of the important application in intelligent transportation system.To figure out the problem that vehicle detection is lack of the ability of vehicle category recognition,we adopted the strategy of integrating the Faster-RCNN (region-based convolutional neural networks)model with 3 different convolutional neural networks (ZF,VGG-16 and ResNet-101)respectively.By comparing the vehicle category recognition results of the 3 integrating strategies on BIT-Vehicle database and CompCars database,the strategy integrating the Faster-RCNN model with ResNet-101 shows the best result among the 3 models and recognition accuracy reaches 91.3％ on BIT-Vehicle database.On the migration test CompCars database,3 strategy models show good generalization ability.关键词
车型识别/目标检测/Faster RCNN/卷积神经网络Key words
vehicle detection/object detection/faster-RCNN/convolutional neural network分类
信息技术与安全科学引用本文复制引用
桑军,郭沛,项志立,罗红玲,陈欣..Faster-RCNN的车型识别分析[J].重庆大学学报,2017,40(7):32-36,5.基金项目
高等学校博士学科点专项科研基金博导类资助项目(20130191110027).Supported by the Research Fund for the Doctoral Program of Higher Education of China under Grant (20130191110027). （20130191110027）
