Multi-Component Fusion Network for Small Object Detection in Remote Sensing Images
文献类型：期刊论文
| 作者 | Liu, Jing1,2,3; Yang, Shuojin1,2; Tian, Liang1,2; Guo, Wei2; Zhou, Bingyin2; Jia, Jianqing2; Ling, Haibin4 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2019 |
| 卷号 | 7页码:128339-128352 |
| 关键词 | Small object remote sensing multi-component dual pyramid fusion occlusion complex scene |
| ISSN号 | 2169-3536 |
| DOI | 10.1109/ACCESS.2019.2939488 |
| 英文摘要 | Small object detection is a major challenge in the field of object detection. With the development of deep learning, many methods based on deep convolutional neural networks (DCNNs) have greatly improved the speed of detection while ensuring accuracy. However, due to the contradiction between the spatial details and semantic information of DCNNs, previous deep learning methods often meet problems when detecting small objects. The challenge can be more serious in complex scenes involving similar background objects and/or occlusion, such as in remote sensing imagery. In this paper, we propose an end-to-end DCNN called the multi-component fusion network (MCFN) to improve the accuracy of small object detection in such cases. First, we propose a dual pyramid fusion network, which densely concatenates spatial information and semantic information to extract small object features via encoding and decoding operations. Then we use a relative region proposal network to adequately extract the features of small objects samples and parts of objects. Finally, to achieve robustness against background disturbance, we add contextual information to the proposal regions before final detection. Experimental evaluations demonstrate that the proposed method significantly improves the accuracy of object detection in remote sensing images compared with other state-of-the-art methods, especially in complex scenes with the conditions of occlusion. |
| 资助项目 | National Natural Science Foundation of China[61802109] ; Science and Technology Foundation of Hebei Province Higher Education[QN2019166] ; Natural Science Foundation of Hebei Province[F2017205066] ; Science Foundation of Hebei Normal University[L2017B06] ; Science Foundation of Hebei Normal University[L2018K02] ; Science Foundation of Hebei Normal University[L2019K01] |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| WOS记录号 | WOS:000487233800014 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/4652]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Tian, Liang; Guo, Wei |
| 作者单位 | 1.Hebei Normal Univ, Coll Comp & Cyber Secur, Shijiazhuang 050024, Hebei, Peoples R China 2.Hebei Normal Univ, Coll Math & Informat Sci, Key Lab Augmented Real, Shijiazhuang 050024, Hebei, Peoples R China 3.Chinese Acad Sci, Inst Comp Technol, Beijing Key Lab Mobile Comp & Pervas Device, Beijing 100190, Peoples R China 4.Temple Univ, Dept Comp & Informat Sci, Ctr Data Analyt & Biomed Informat, Philadelphia, PA 19122 USA |
| 推荐引用方式 GB/T 7714 | Liu, Jing,Yang, Shuojin,Tian, Liang,et al. Multi-Component Fusion Network for Small Object Detection in Remote Sensing Images[J]. IEEE ACCESS,2019,7:128339-128352. |
| APA | Liu, Jing.,Yang, Shuojin.,Tian, Liang.,Guo, Wei.,Zhou, Bingyin.,...&Ling, Haibin.(2019).Multi-Component Fusion Network for Small Object Detection in Remote Sensing Images.IEEE ACCESS,7,128339-128352. |
| MLA | Liu, Jing,et al."Multi-Component Fusion Network for Small Object Detection in Remote Sensing Images".IEEE ACCESS 7(2019):128339-128352. |
入库方式： OAI收割
来源：计算技术研究所
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