摘要
基于深度学习框架Caffe和具有强大计算能力的GPU,运用深度神经网络AlexNet和GoogleNet,对具有不同背景的汽车图像进行网络训练,以达到车辆自动识别的目的。分别对4类车标进行网络训练与测试,实验结果表明,在图像识别分类方面,与传统识别方法相比,深度卷积神经网络更具优势。
Based on Caffe of deep learning frame and GPU with power computing capacity, using deep neural network AlexNet and GoogleNet, network training is carried out on vehicle images with differ-ent background for automatic recognition of vehicle. In this paper, network training and tests on are car-ried out on four types of auto logo. The experiments show that in terms of image recognition and classifi-cation, deep convolutional neural network has more advantages compared to conventional method.
出处
《工业仪表与自动化装置》
2017年第5期75-78,共4页
Industrial Instrumentation & Automation
基金
国家自然科学基金项目(41601344
61601059)
中央高校基本科研业务费专项资金项目(310832163402
310832161001
310832171006)
关键词
深度学习
神经网络
车标分类
图像识别
deep learning
neural network
vehicle classification
image recognition