Target detection in remote sensing image based on deep learning

被引：0

作者：

Zhao, Lianchen ^{[1
,2
]}

Peng, Yizhun ^{[1
,2
]}

Li, Di ^{[1
,2
]}

Zhang, Yuheng ^{[1
,2
]}

机构：

[1] Tianjin Univ Sci & Technol, Inst Commun & Elect, Tianjin, Peoples R China

[2] Tianjin Univ Sci & Technol, Adv Struct Integr Int Joint Res Ctr, Tianjin, Peoples R China

来源：

PROCEEDINGS OF THE 2021 INTERNATIONAL CONFERENCE ON ARTIFICIAL LIFE AND ROBOTICS (ICAROB 2021) | 2021年

关键词：

Deep learning; Target detection; Residual network; Remote sensing image;

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

For high-resolution optical remote sensing images, there are still many challenges in target detection. In this paper, deep learning algorithm is used to detect the target in remote sensing image. Improve and optimize the deep learning target detection algorithm. When the selected data set is used for target detection, the AP value is improved, which leads to the concept of multi-scale feature fusion feature pyramid and residual network. By improving the selected Yolov3 network model, the detection effect of the two targets of aircraft and ships in remote sensing images has been significantly improved.

引用

页码：542 / 546

页数：5

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