| 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
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| 发表期刊 | REMOTE SENSING
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| 2017-05-01 | |
| 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 |
| DOI | 10.3390/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 | |
| 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 |
| 第一作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 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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| remotesensing-09-004（4065KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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