[1]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(06):1-6.[doi:10.3969/ j. issn.1673-629X.2018.06.001]
　WU Yu-zhi,WU Zhi-hong,XIONG Yun-yu.Vehicle Detection and Recognition of a Few Samples Based on Convolutional Neural Network[J].,2018,28(06):1-6.[doi:10.3969/ j. issn.1673-629X.2018.06.001]
点击复制

基于卷积神经网络的小样本车辆检测与识别()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年06期
页码:
1-6
栏目:
智能、算法、系统工程
出版日期:
2018-06-10

文章信息/Info

Title:
Vehicle Detection and Recognition of a Few Samples Based on Convolutional Neural Network
文章编号:
1673-629X(2018)06-0001-06
作者:
吴玉枝1 ; 吴志红2 ; 熊运余2
1. 四川大学 计算机学院,四川 成都 610064;
2. 四川大学 图形图像研究所,四川 成都 610064
Author(s):
WU Yu-zhi 1 ; WU Zhi-hong 2 ; XIONG Yun-yu 2
1. School of Computer Science,Sichuan University,Chengdu 610064,China;
2. Institute of Image and Graphics,Sichuan University,Chengdu 610064,China
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
Keywords:
convolutional neural network; vehicle detection; vehicle recognition; multiple feature maps; phased training
分类号:
TP391.4
DOI:
10.3969/ j. issn.1673-629X.2018.06.001
文献标志码:
A
摘要:
设计了一种快速准确的算法,实现了环境复杂、样本缺少情况下实时车辆检测和车型识别,特别是对三轮车的识别。 利用一种改进的卷积神经网络(convolutional neural network,CNN)快速学习车辆特征,采用微调、分段训练以及多层特征图结合的策略增强网络特征学习能力,在小样本下尽可能全面地学习目标特征。 摒弃繁琐耗时的区域推荐算法和后分类算法,利用单个网络直接预测图片中目标车辆的位置和车型类别,大幅提高了算法性能。 实验结果表明,采用 GeForceGTX 1080 GPU 时,该算法对各类车型识别准确度较为平衡,平均检测准确率高达 72.2%,每秒检测帧数 46.57,在雨天、晴天、夜晚、强光和树荫等各种复杂场景下均有较好的适应性,适用于真实视频监控下智能交通系统精确实时的要求。
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 network,which greatly improves the performance of the algorithm. The experiment shows that when using GeForce GTX 1080 GPU,the vehicle 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.

相似文献/References:

[1]方宏 杜正春.车辆检测线远程监控系统的研制[J].计算机技术与发展,2009,(12):185.
　FANG Hong,DU Zheng-chun.Development of Remote Monitoring System of Vehicle Inspection Lane[J].,2009,(06):185.
[2]张明恒 王华莹 郭烈.基于改进K—Means算法的车辆识别方法[J].计算机技术与发展,2012,(05):53.
　ZHANG Ming-heng,WANG Hua-ying,GUO Lie.Method of Vehicle Detection Based on Improved K-Means Algorithm[J].,2012,(06):53.
[3]崔凤焦.表情识别算法研究进展与性能比较[J].计算机技术与发展,2018,28(02):145.[doi:10．3969/j．issn．1673－629X．2018．02．031]
　CUI Feng-jiao.Ｒesearch and Performance Comparison of Facial Expression Ｒecognition Algorithm[J].,2018,28(06):145.[doi:10．3969/j．issn．1673－629X．2018．02．031]
[4]张丹丹,李雷. 基于PCANet-RF的人脸检测系统[J].计算机技术与发展,2016,26(02):31.
　ZHANG Dan-dan,LI Lei. Face Detection System Based on PCANet-RF[J].,2016,26(06):31.
[5]陈强锐,谢世朋.基于深度学习的肺部肿瘤检测方法[J].计算机技术与发展,2018,28(04):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
　CHEN Qiang-rui,XIE Shi-peng.Lung Cancer Detection Method Based on Deep Learning[J].,2018,28(06):201.[doi:10.3969/ j. issn.1673-629X.2018.04.043]
[6]郭子琰,舒心,刘常燕,等.基于ReLU 函数的卷积神经网络的花卉识别算法[J].计算机技术与发展,2018,28(05):154.[doi:10．3969/j．issn．1673－629X．2018．05．035]
　GUO Ziyan,SHU Xin,LIU Changyan,et al.A Recognition Algorithm of Flower Based on Convolution Neural Network with ReLU Function[J].,2018,28(06):154.[doi:10．3969/j．issn．1673－629X．2018．05．035]
[7]缪宇杰,吴智钧,宫 婧.基于3D 卷积的视频错帧筛选方法[J].计算机技术与发展,2018,28(05):179.[doi:10.3969/ j. issn.1673-629X.2018.05.040]
　MIAO Yu-jie,WU Zhi-jun,GONG Jing.A Wrong Temporal-order Frames Identification Method Based on 3D Convolution[J].,2018,28(06):179.[doi:10.3969/ j. issn.1673-629X.2018.05.040]
[8]李相桥,李晨,田丽华,等.卷积神经网络并行训练的优化研究[J].计算机技术与发展,2018,28(08):12.[doi:10.3969/ j. issn.1673-629X.2018.08.003]
　LI Xiang-qiao,LI Chen,TIAN Li-hua,et al.Research on Optimization of Parallel Training for Convolution Neural Network[J].,2018,28(06):12.[doi:10.3969/ j. issn.1673-629X.2018.08.003]
[9]邓宗平,赵启军,陈虎. 基于深度学习的人脸姿态分类方法[J].计算机技术与发展,2016,26(07):11.
　DEND Zong-ping,ZHAO Qi-jun,CHEN Hu. Face Pose Classification Method Based on Deep Learning[J].,2016,26(06):11.
[10]河海大学 计算机与信息学院,江苏 南京 0098.卷积网络的无监督特征提取对人脸识别的研究[J].计算机技术与发展,2018,28(06):17.[doi:10.3969/ j. issn.1673-629X.2018.06.004]
　DU Bai-sheng.Research on Unsupervised Feature Extraction Based on Convolutional Neural Network for Face Recognition[J].,2018,28(06):17.[doi:10.3969/ j. issn.1673-629X.2018.06.004]
[11]阮航,王立春.基于特征图的车辆检测和分类[J].计算机技术与发展,2018,28(11):39.[doi:10.3969/ j. issn.1673-629X.2018.11.009]
　RUAN Hang,WANG Li-chun.Vehicle Detection and Classification Based on Feature Map[J].,2018,28(06):39.[doi:10.3969/ j. issn.1673-629X.2018.11.009]
[12]陶晓力,刘宁钟,沈家全.基于深度信息融合的航拍车辆检测[J].计算机技术与发展,2019,29(09):117.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 023]
　TAO Xiao-li,LIU Ning-zhong,SHEN Jia-quan.Aerial Vehicle Detection Based on Depth Information Fusion[J].,2019,29(06):117.[doi:10. 3969 / j. issn. 1673-629X. 2019. 09. 023]

更新日期/Last Update: 2018-07-20