Feature Rescaling and Fusion for Tiny Object Detection
文献类型：期刊论文
| 作者 | Liu, Jingwei1,2; Gu, Yi3; Han, Shumin3; Zhang, Zhibin1; Guo, Jiafeng1; Cheng, Xueqi1 |
| 刊名 | IEEE ACCESS
 |
| 出版日期 | 2021 |
| 卷号 | 9页码:62946-62955 |
| ISSN号 | 2169-3536 |
| 关键词 | Feature extraction Object detection Semantics Task analysis Training Spatial resolution Shape Tiny object detection nonparametric adaptive selection feature fusion feature pyramid network ensemble model |
| DOI | 10.1109/ACCESS.2021.3074790 |
| 英文摘要 | Recent years have witnessed rapid developments on computer vision, however, there are still challenges in detecting tiny objects in a large-scale background. The tiny objects knowledge become sparse and weak due to their tiny size, which makes the tiny objects difficult to be detected with the common approaches. In this paper, a new network named Specific Characteristics based Feature Rescaling and Fusion (SFRF) is designed to detect tiny persons in a broad horizon and massive background. Different from the methods in general, a Nonparametric Adaptive Dense Perceiving Algorithm (NADPA) is designed to automatically select and generate a new resized feature map with the high density distribution of tiny objects. Then, a method called Many-For-One strategy is used for feature fusion of the feature pyramid network (FPN) layers to improve the feature representation and detection. Finally, an ensemble model method named hierarchical Coarse-to-fine mechanism is designed based on the proposed methods to further improve the performance. The experiments demonstrate that the proposed approach achieves an obvious performance improvement on tiny object detection than the existing approaches, and our approach has been awarded as the 1st-place in the first large-scale Tiny Object Detection (TOD) challenge. |
| WOS研究方向 | Computer Science ; Engineering ; Telecommunications |
| 语种 | 英语 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| WOS记录号 | WOS:000645861200001 |
| 源URL | [http://119.78.100.204/handle/2XEOYT63/16675]  |
| 专题 | 中国科学院计算技术研究所期刊论文_英文 |
| 通讯作者 | Han, Shumin; Cheng, Xueqi |
| 作者单位 | 1.Chinese Acad Sci, Inst Comp Technol, CAS Key Lab Network Data Sci & Technol, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Baidu Online Network Technol Beijing Co Ltd, Beijing 100085, Peoples R China |
| 推荐引用方式 GB/T 7714 | Liu, Jingwei,Gu, Yi,Han, Shumin,et al. Feature Rescaling and Fusion for Tiny Object Detection[J]. IEEE ACCESS,2021,9:62946-62955. |
| APA | Liu, Jingwei,Gu, Yi,Han, Shumin,Zhang, Zhibin,Guo, Jiafeng,&Cheng, Xueqi.(2021).Feature Rescaling and Fusion for Tiny Object Detection.IEEE ACCESS,9,62946-62955. |
| MLA | Liu, Jingwei,et al."Feature Rescaling and Fusion for Tiny Object Detection".IEEE ACCESS 9(2021):62946-62955. |
入库方式： OAI收割
来源：计算技术研究所
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