摘要
针对现有汽车车型识别方法计算量大、提取特征复杂等问题,提出一种基于深度卷积神经网络的汽车车型识别方法。该方法借助于深度学习,对经典的卷积神经网络做出改进并得到由多个卷积层和次抽样层构成的深度卷积神经网络。根据五种车型的分类结果,表明该方法在识别率方面较传统方法有明显的提高。实验还研究了网络层数、卷积核大小、特征维数对深度卷积神经网络的性能和识别率的影响。
Aiming at problems of excessive calculation and complex feature extraction of existing vehicle model recognition methods,a vehicle model recognition method is proposed based on deep convolutional neural network（ DCNN）. With the aid of deep learning,improvement is made on classic convolutional neural network and DCNN made of multiple convolutional layers and time sampling layers is gained. According to classification results of the five models,it shows that this method has obvious increase than traditional methods in terms of recognition rates.The experiments also study on influences of number of network layer,size of convolutional kernel,characteristic dimension on performance of DCNN and recognition rates.
出处
《传感器与微系统》
CSCD
2016年第11期19-22,共4页
Transducer and Microsystem Technologies
基金
天津市科技计划基金资助项目(13ZXCXGX40400)
关键词
深度学习
深度卷积神经网络
汽车车型识别
特征提取
deep learning
deep convolutional neural network（DCNN）
vehicle model recognition
feature extraction
