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Proceedings Paper

Dual efficient self-attention network for multi-target detection in aerial imagery
Author(s): Sikui Wang; Yunpeng Liu; Zhiyuan Lin; Zhongyu Zhang
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Paper Abstract

Aerial imagery target detection has been widely used in the military and economic fields. However, it still faces a variety of challenges. In this paper, we proposed several efficiency improvements based on YOLO v3 framework for getting a better small target detection precision. Firstly, a dual self-attention (DAN) block is embedded in Darknet-53’s ResNet units to refine the feature map adaptively. Furthermore, the deep semantic features are cascaded with the shallow outline features in a feedforward deconvolutional module to obtain context details of small targets. Finally, introducing online hard examples mining and combining Focal Loss to enhance the discriminating ability between classes. The experimental results on the VEDAI aerial dataset show that the proposed algorithm is significantly improved in accuracy compared to the original network and achieves better performance than two-stage algorithms.

Paper Details

Date Published: 31 January 2020
PDF: 7 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114270D (31 January 2020); doi: 10.1117/12.2549196
Show Author Affiliations
Sikui Wang, Shenyang Institute of Automation, Chinese Academy of Sciences (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)
Yunpeng Liu, Shenyang Institute of Automation, Chinese Academy of Sciences (China)
Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences (China)
Key Labs of Opto-Electronic Information Processing/Image Understanding and Computer Vision (China)
Zhiyuan Lin, Shenyang Institute of Automation, Chinese Academy of Sciences (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)
Zhongyu Zhang, Shenyang Institute of Automation, Chinese Academy of Sciences (China)
Univ. of Chinese Academy of Sciences (China)
Key Lab. of Opto-Electronic Information Processing (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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