基于深度学习的车辆分类模型研究  

RESEARCH ON VEHICLE CLASSIFICATION MODEL BASED ON DEEP LEARNING

作　　者：张少巍 殷荣网[2] ZHANG Shao-wei;YIN Rong-wang(Anhui Wenda University of Information Engineering,Hefei Anhui 231201;Hefei University,Hefei Anhui 230601)

机构地区：[1]安徽文达信息工程学院,安徽合肥231201 [2]合肥学院,安徽合肥230601

出　　处：《巢湖学院学报》2018年第6期115-120,共6页Journal of Chaohu University

基　　金：安徽文达信息工程学院校级重点项目(项目编号:XZR2018A04);安徽省高校自然科学基金重点项目(项目编号:KJ2017A649)

摘　　要：提出了一种基于深度学习的车辆分类模型来处理复杂的交通场景。该模型由车辆检测模型和分类模型两部分组成。在车辆检测模型中采用基于区域的卷积神经网络方法 R-CNN(Regions with Convolutional Neural Network),从杂乱背景的图像中提取单个车辆图像,该步骤为下一个分类模型提供数据。在车辆分类模型中,将车辆图像导入CNN模型,并生成特征,然后使用联合贝叶斯网络来实现分类过程。实验表明,该方法可以有效识别交通图像中的车辆构造和模型。A vehicle classification model based on deep learning is proposed to deal with complex traffic scenes.The model consists of two parts:vehicle detection model and classification model.A region-based convolutional neural network(R-CNN)method is used in the vehicle detection model to extract a single vehicle image from a disorderly background image.This step provides data for the next classification model.In the vehicle classification model,an image of a vehicle is included into a CNN model and a feature is produced,and then a joint Bayesian network is used to implement the classification process.Experiments show that the method can effectively identify vehicle structures and models in traffic images.

关 键 词：分类 深度学习 车辆检测 

分 类 号：U495[交通运输工程—交通运输规划与管理]