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Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images
Li, Feimo1,2; Li, Shuxiao1,2; Zhu, Chengfei1,2; Lan, Xiaosong1,2; Chang, Hongxing1,2; 李非墨,李书晓
2017-05-01
发表期刊REMOTE SENSING
卷号9期号:5页码:494
文章类型Article
摘要Joint vehicle localization and categorization in high resolution aerial images can provide useful information for applications such as traffic flow structure analysis. To maintain sufficient features to recognize small-scaled vehicles, a regions with convolutional neural network features (R-CNN) -like detection structure is employed. In this setting, cascaded localization error can be averted by equally treating the negatives and differently typed positives as a multi-class classification task, but the problem of class-imbalance remains. To address this issue, a cost-effective network extension scheme is proposed. In it, the correlated convolution and connection costs during extension are reduced by feature map selection and bi-partite main-side network construction, which are realized with the assistance of a novel feature map class-importance measurement and a new class-imbalance sensitive main-side loss function. By using an image classification dataset established from a set of traditional real-colored aerial images with 0.13 m ground sampling distance which are taken from the height of 1000 m by an imaging system composed of non-metric cameras, the effectiveness of the proposed network extension is verified by comparing with its similarly shaped strong counter-parts. Experiments show an equivalent or better performance, while requiring the least parameter and memory overheads are required.
关键词Vehicle Localization Vehicle Classification High Resolution Aerial Image Convolutional Neural Network (Cnn) Class Imbalance
WOS标题词Science & Technology ; Technology
DOI10.3390/rs9050494
关键词[WOS]SCENE CLASSIFICATION ; FEATURES
收录类别SCI
语种英语
项目资助者National Science Foundation of China (NSFC)(61302154 ; 61573350)
WOS研究方向Remote Sensing
WOS类目Remote Sensing
WOS记录号WOS:000402573700097
引用统计
被引频次:1[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/14579
专题综合信息系统研究中心
通讯作者李非墨,李书晓
作者单位1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
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GB/T 7714
Li, Feimo,Li, Shuxiao,Zhu, Chengfei,et al. Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images[J]. REMOTE SENSING,2017,9(5):494.
APA Li, Feimo,Li, Shuxiao,Zhu, Chengfei,Lan, Xiaosong,Chang, Hongxing,&李非墨,李书晓.(2017).Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images.REMOTE SENSING,9(5),494.
MLA Li, Feimo,et al."Cost-Effective Class-Imbalance Aware CNN for Vehicle Localization and Categorization in High Resolution Aerial Images".REMOTE SENSING 9.5(2017):494.
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