Proceedings Paper
Collaborative identification method for sea battlefield target based on deep convolutional neural networks| Format | Member Price | Non-Member Price |
|---|---|---|
| $17.00 | $21.00 |
Paper 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
Paper Details
Date Published: 8 March 2018
PDF: 8 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091A (8 March 2018); doi: 10.1117/12.2285713
Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)
PDF: 8 pages
Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091A (8 March 2018); doi: 10.1117/12.2285713
Show Author Affiliations
Guangdi Zheng, Wuhan National Lab. for Optoelectronics (China)
Mingbo Pan, Wuhan National Lab. for Optoelectronics (China)
Mingbo Pan, Wuhan National Lab. for Optoelectronics (China)
Wei Liu, Wuhan National Lab. for Optoelectronics (China)
Xuetong Wu, Wuhan National Lab. for Optoelectronics (China)
Xuetong Wu, Wuhan National Lab. for Optoelectronics (China)
Published in SPIE Proceedings Vol. 10609:
MIPPR 2017: Pattern Recognition and Computer Vision
Zhiguo Cao; Yuehuang Wang; Chao Cai, Editor(s)
© SPIE. Terms of Use
