Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images
文献类型：期刊论文
| 作者 | Yang, Feng1; Li, Wentong1; Hu, Haiwei1; Li, Wanyi2 ; Wang, Peng2
|
| 刊名 | SENSORS
 |
| 出版日期 | 2020-03-01 |
| 卷号 | 20期号:6页码:21 |
| 关键词 | object detection aerial images feature attention convolutional neural networks (CNNs) |
| DOI | 10.3390/s20061686 |
| 通讯作者 | Yang, Feng(yangfeng@nwpu.edu.cn) ; Li, Wanyi(wanyi.li@ia.ac.cn) |
| 英文摘要 | Accurate and robust detection of multi-class objects in very high resolution (VHR) aerial images has been playing a significant role in many real-world applications. The traditional detection methods have made remarkable progresses with horizontal bounding boxes (HBBs) due to CNNs. However, HBB detection methods still exhibit limitations including the missed detection and the redundant detection regions, especially for densely-distributed and strip-like objects. Besides, large scale variations and diverse background also bring in many challenges. Aiming to address these problems, an effective region-based object detection framework named Multi-scale Feature Integration Attention Rotation Network (MFIAR-Net) is proposed for aerial images with oriented bounding boxes (OBBs), which promotes the integration of the inherent multi-scale pyramid features to generate a discriminative feature map. Meanwhile, the double-path feature attention network supervised by the mask information of ground truth is introduced to guide the network to focus on object regions and suppress the irrelevant noise. To boost the rotation regression and classification performance, we present a robust Rotation Detection Network, which can generate efficient OBB representation. Extensive experiments and comprehensive evaluations on two publicly available datasets demonstrate the effectiveness of the proposed framework. |
| 资助项目 | National Natural Science Foundation of China[61771471] ; National Natural Science Foundation of China[91748131] ; Natural Science Foundation of Shaanxi province[2018MJ6048] ; Foundation of CETC Key Laboratory of Data Link Technology[20182316] ; Foundation of CETC Key Laboratory of Data Link Technology[20182203] ; Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University[ZZ2019178] |
| WOS研究方向 | Chemistry ; Engineering ; Instruments & Instrumentation |
| 语种 | 英语 |
| WOS记录号 | WOS:000529139700145 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Shaanxi province ; Foundation of CETC Key Laboratory of Data Link Technology ; Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University |
| 源URL | [http://ir.ia.ac.cn/handle/173211/39393]  |
| 专题 | 智能机器人系统研究 |
| 通讯作者 | Yang, Feng; Li, Wanyi |
| 作者单位 | 1.Northwestern Polytech Univ, Minist Educ, Key Lab Informat Fus Technol, Xian 710100, Peoples R China 2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Feng,Li, Wentong,Hu, Haiwei,et al. Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images[J]. SENSORS,2020,20(6):21. |
| APA | Yang, Feng,Li, Wentong,Hu, Haiwei,Li, Wanyi,&Wang, Peng.(2020).Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images.SENSORS,20(6),21. |
| MLA | Yang, Feng,et al."Multi-Scale Feature Integrated Attention-Based Rotation Network for Object Detection in VHR Aerial Images".SENSORS 20.6(2020):21. |
入库方式： OAI收割
来源：自动化研究所
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