RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images

doi:10.1109/JSTARS.2023.3289585

CIOMP OpenIR

	RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images
	Y. Zhang, Y. Wang, N. Zhang, Z. Li, Z. Zhao, Y. Gao, C. Chen and H. Feng
	2023
发表期刊	IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
ISSN	19391404
卷号	16 页码:5990-6006
摘要	In remote sensing image (RSI) object detection, the oriented bounding box (OBB) can accurately locate objects with arbitrary orientation and obtain orientation information. The detection based on OBB is still a challenging task. In RSI, the distribution of objects is extremely uneven, which causes aggregation to occur. Some researchers believe that the characteristic of dense distribution is a reason for the difficulty of object detection. However, there are no in-depth experimental studies on this. This paper proposes an OBB-based dense object determination method, which determines the dense objects in datasets by two conditions consisting of interclass distance, intraclass distance, minimum distance between objects, and minimum edge length of objects. The experimental results of dense and non-dense object detection concludes that the characteristics of dense distribution in RSI do not easily cause the objects to be more difficult to detect. To make full use of the object features, we propose a second-stage detection head named RoIF-Net, in which we extract region of interest (RoI) from the input image and fuse it with the RoI extracted from feature maps to add detail features, and construct a feature induction module based on self-attention mechanism to achieve position regression and category classification. This structure can be used in any two-stage network to enhance detection capabilities. Using our method on three credible and challenging datasets, DOTA, DIOR-R, and UCAS-AOD, we obtained 81.80%, 68.49%, and 90.25% mAP, respectively, reaching SOTA based on OBB detection, proving the effectiveness and advancement of our method. © 2008-2012 IEEE.
DOI	10.1109/JSTARS.2023.3289585
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收录类别	sci ; ei
引用统计	被引频次：4[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ciomp.ac.cn/handle/181722/68211
专题	中国科学院长春光学精密机械与物理研究所
推荐引用方式 GB/T 7714	Y. Zhang, Y. Wang, N. Zhang, Z. Li, Z. Zhao, Y. Gao, C. Chen and H. Feng. RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2023,16:5990-6006.
APA	Y. Zhang, Y. Wang, N. Zhang, Z. Li, Z. Zhao, Y. Gao, C. Chen and H. Feng.(2023).RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,16,5990-6006.
MLA	Y. Zhang, Y. Wang, N. Zhang, Z. Li, Z. Zhao, Y. Gao, C. Chen and H. Feng."RoI Fusion Strategy With Self-Attention Mechanism for Object Detection in Remote Sensing Images".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 16(2023):5990-6006.

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