8 March 2018 Collaborative identification method for sea battlefield target based on deep convolutional neural networks
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091A (2018) https://doi.org/10.1117/12.2285713
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
The target identification of the sea battlefield is the prerequisite for the judgment of the enemy in the modern naval battle. In this paper, a collaborative identification method based on convolution neural network is proposed to identify the typical targets of sea battlefields. Different from the traditional single-input/single-output identification method, the proposed method constructs a multi-input/single-output co-identification architecture based on optimized convolution neural network and weighted D-S evidence theory. The simulation results show that
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Guangdi Zheng, Guangdi Zheng, Mingbo Pan, Mingbo Pan, Wei Liu, Wei Liu, Xuetong Wu, Xuetong Wu, } "Collaborative identification method for sea battlefield target based on deep convolutional neural networks", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091A (8 March 2018); doi: 10.1117/12.2285713; https://doi.org/10.1117/12.2285713
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