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Proceedings Paper

Target recognition for underwater range-gated imaging based on convolutional neural network in fpga
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Paper Abstract

Aiming at the practical application requirements of small dark target recognition for underwater unmanned aerial vehicles, a underwater laser gating imaging target recognition network based on convolutional neural network is designed to classify and identify underwater multiple targets. The integrated tool HLS transplants the network into the FPGA for circuit implementation. Firstly, the algorithm is designed to verify the realization of the convolutional neural network. Then the underwater target recognition experiment is carried out on the implemented convolutional neural network circuit. The network identification accuracy rate is 94% for the three types of underwater target used in the experiment, which verifies the feasibility of convolutional neural network implementation in FPGA.

Paper Details

Date Published: 31 January 2020
PDF: 6 pages
Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114273E (31 January 2020); doi: 10.1117/12.2553011
Show Author Affiliations
Han Dong, Institute of Semiconductors, Chinese Academy of Sciences (China)
Xinwei Wang, Institute of Semiconductors, Chinese Academy of Sciences (China)
Univ. of Chinese Academy of Sciences (China)
Liang Sun, Institute of Semiconductors, Chinese Academy of Sciences (China)
Yan Zhou, Institute of Semiconductors, Chinese Academy of Sciences (China)
Univ. of Chinese Academy of Sciences (China)


Published in SPIE Proceedings Vol. 11427:
Second Target Recognition and Artificial Intelligence Summit Forum
Tianran Wang; Tianyou Chai; Huitao Fan; Qifeng Yu, Editor(s)

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