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Proceedings Paper

Object detection based on hierarchical visual perception mechanism
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Paper Abstract

The available high-resolution remote sensing images are growing exponentially in recent years due to the rapid development of remote sensing imaging. However, several problems still exist: 1) How to solve the difficulty caused by the scale and shape of object. 2) How to detect the object quickly and accurately. Inspired by the hierarchical visual perception mechanism, we propose a fusion method combining the low-level feature and high-level feature obtained by convolution neural networks to detect ship target. At the same time, we introduce deformable CNN layer into convolution neural networks to solve the diverse scale and shape of object. Finally, based on the visual attention mechanism, the object contextual information is integrated into the network. The experiment results show that our model can achieve good detection performance and the framework has good expansibility.

Paper Details

Date Published: 14 February 2020
PDF: 11 pages
Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290P (14 February 2020); doi: 10.1117/12.2538265
Show Author Affiliations
Hao Dou, The 38th Research Institute of China Electronics Technology Group Corp. (China)
Huazhong Univ. of Science and Technology (China)
Qianqian Deng, Huazhong Univ. of Science and Technology (China)
Jiaxing Mao, 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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