[1]阮航,孙涵.基于 Faster R-CNN 的车辆多属性识别[J].计算机技术与发展,2018,28(10):129-134.[doi:10.3969/ j. issn.1673-629X.2018.10.027]
　RUAN Hang,SUN Han.Vehicle Multi-attribute Recognition Based on Faster R-CNN[J].,2018,28(10):129-134.[doi:10.3969/ j. issn.1673-629X.2018.10.027]
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基于 Faster R-CNN 的车辆多属性识别()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年10期
页码:
129-134
栏目:
智能、算法、系统工程
出版日期:
2018-10-10

文章信息/Info

Title:
Vehicle Multi-attribute Recognition Based on Faster R-CNN
文章编号:
1673-629X(2018)10-0129-06
作者:
阮航; 孙涵
南京航空航天大学 计算机科学与技术学院,江苏 南京 211100
Author(s):
RUAN Hang; SUN Han
School of Computer Science and Technology,Nanjing University of Aeronautics and Astronautics,Nanjing 211100,China
关键词:
Faster R-CNN; 多属性识别; 车辆检测; 深度学习; 图像分类
Keywords:
Faster R-CNN; multi-attribute recognition; vehicle detection; deep learning; image classification
分类号:
TP391
DOI:
10.3969/ j. issn.1673-629X.2018.10.027
文献标志码:
A
摘要:
基于 Faster R-CNN 提出一种车辆的多属性识别模型。 首先利用 Faster R-CNN 对车辆数据库进行训练,得到车辆检测网络,对图像中多个车辆目标进行检测。 将检测结果输入改进的车辆属性识别网络中,对检测得到的车辆进行属性推断,包括车辆颜色、品牌和姿态。 为评估车辆检测精度和车辆多属性识别的准确率,采集了 8 000 张真实场景下的图片作为测试集进行测试。 对于车辆检测网络,对比了 R-CNN、Fast R-CNN 等方法的检测精度;对于车辆属性识别,对比了不同网络结构、不同图片分辨率和单属性和多属性等对于识别准确率的影响。 实验结果表明,基于 Faster R-CNN 的车辆多属性识别方法充分学习了不同属性间的特征,具有较高的准确率和检测精度,以及良好的通用性和鲁棒性,适用于车辆多属性分类。
Abstract:
We put forward a vehicle multi-attribute recognition model based on Faster R-CNN. First we use vehicle images to train and obtain vehicle detection network for detection of vehicle targets in image. Then we put detected results into vehicle attribute recognition network and infer attributes including color,type and view. In order to evaluate the accuracy of vehicle detection precision and vehicle multi-attributes detection,we collect 8 000 vehicle images under actual scene as test set for testing. In terms of vehicle detection network,we compare the detection precision of R-CNN and Fast R-CNN,and for vehicle attribute recognition we compare the accuracy of different network,different image definition and different number of attributes. Experiment shows that the proposed vehicle multi-attribute recognition method based on Faster R-CNN can learn more features fully with higher accuracy and precision,as well as better versatility and robustness,which can be used for vehicle multi-attribute classification.

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更新日期/Last Update: 2018-10-10