Object detection based on hierarchical visual perception mechanism

Hao Dou; Qianqian Deng; Jiaxing Mao

doi:10.1117/12.2538265

14 February 2020 Object detection based on hierarchical visual perception mechanism

Hao Dou, Qianqian Deng, Jiaxing Mao

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Proceedings Volume 11429, MIPPR 2019: Automatic Target Recognition and Navigation; 114290P (2020) https://doi.org/10.1117/12.2538265
Event: Eleventh International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2019), 2019, Wuhan, China

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.

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Hao Dou, Qianqian Deng, and Jiaxing Mao "Object detection based on hierarchical visual perception mechanism", Proc. SPIE 11429, MIPPR 2019: Automatic Target Recognition and Navigation, 114290P (14 February 2020); https://doi.org/10.1117/12.2538265

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KEYWORDS

Target detection

Convolution

Remote sensing

Feature extraction

Visualization

Image fusion

Image processing

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