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Proceedings Paper

Aircraft detection in remote sensing imagery with lightweight feature pyramid network
Author(s): Zhenrui Wang; Gongyan Wang; Weidong Yang
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Paper Abstract

Object detection is an important part of remote sensing image processing and analysis. Traditional object detection methods in remote sensing imagery encounter with tough challenges when detecting small objects such as aircrafts and automobiles, due to complex background clutter, small target size, variation of visual angle, etc. We propose a targets detection network to detect the aircrafts in large-format remote sensing imagery based on deep convolutional neural network. Our method utilizes the Feature Pyramid Network (FPN [1]) to extract and inosculate multi-scale convolutional features to model the characteristics of targets and background. Moreover, in order to reduce the computational complexity of convolutional neural network, we utilize MobileNet [2] as backbone network and propose a computational efficient region proposal structure. We collect and manually annotate a dataset for aircrafts detection in remote sensing imagery in order to evaluate the proposed method. We achieve an average precision (AP) of 0.91 on the dataset, which is superior to other state-of-the-art methods, while our model is still faster and more compact than other models.

Paper Details

Date Published: 14 February 2020
PDF: 9 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290W (14 February 2020); doi: 10.1117/12.2539372
Show Author Affiliations
Zhenrui Wang, Huazhong Univ. of Science and Technology (China)
Gongyan Wang, Huazhong Univ. of Science and Technology (China)
Weidong Yang, Huazhong Univ. of Science and Technology (China)


Published in SPIE Proceedings Vol. 11429:
MIPPR 2019: Automatic Target Recognition and Navigation
Jianguo Liu; Hanyu Hong; Xia Hua, Editor(s)

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