Detecting Small Objects Using a Channel-Aware Deconvolutional Network
文献类型：期刊论文
| 作者 | Duan, Kaiwen1,2; Du, Dawei3; Qi, Honggang1,2; Huang, Qingming1,2,4 |
| 刊名 | IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY
 |
| 出版日期 | 2020-06-01 |
| 卷号 | 30期号:6页码:1639-1652 |
| 关键词 | Object detection Feature extraction Training Birds Deconvolution Proposals Detectors Small object detection channel-aware deconvolution multi-RPN anchor matching |
| ISSN号 | 1051-8215 |
| DOI | 10.1109/TCSVT.2019.2906246 |
| 英文摘要 | Detecting small objects is a challenging task due to their low resolution and noisy representation even using deep learning methods. In this paper, we propose a novel object detection method based on the channel-aware deconvolutional network (CADNet) for accurate small object detection. Specifically, we develop the channel-aware deconvolution (ChaDeConv) layer to exploit the correlations of feature maps in different channels across deeper layers, improving the recall rate of small objects at low additional computational costs. Following the ChaDeConv layer, the multiple region proposal sub-network (Multi-RPN) is employed to supervise and optimize multiple detection layers simultaneously to achieve better accuracy. The Multi-RPN module is only used in the training phase and does not increase the computation cost of the inference. In addition, we design a new anchor matching strategy based on the center point translation (CPTMatching) of anchors to select more extending anchors as positive samples in the training phase. The extensive experiments on the PASCAL VOC 2007/2012, MS COCO, and UAVDT datasets show that the proposed CADNet achieves state-of-the-art performance compared to the existing methods. |
| 资助项目 | National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[U1636214] ; National Natural Science Foundation of China[61836002] ; National Natural Science Foundation of China[61771341] ; National Natural Science Foundation of China[61472388] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SYS013] |
| WOS研究方向 | Engineering |
| 语种 | 英语 |
| WOS记录号 | WOS:000543144200012 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/15202]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Qi, Honggang; Huang, Qingming |
| 作者单位 | 1.Univ Chinese Acad Sci, Sch Comp Sci & Technol, Beijing 101408, Peoples R China 2.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing 100190, Peoples R China 3.SUNY Albany, Dept Comp Sci, Albany, NY 12222 USA 4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Duan, Kaiwen,Du, Dawei,Qi, Honggang,et al. Detecting Small Objects Using a Channel-Aware Deconvolutional Network[J]. IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,2020,30(6):1639-1652. |
| APA | Duan, Kaiwen,Du, Dawei,Qi, Honggang,&Huang, Qingming.(2020).Detecting Small Objects Using a Channel-Aware Deconvolutional Network.IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY,30(6),1639-1652. |
| MLA | Duan, Kaiwen,et al."Detecting Small Objects Using a Channel-Aware Deconvolutional Network".IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY 30.6(2020):1639-1652. |
入库方式： OAI收割
来源：计算技术研究所
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