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

Han Dong; Xinwei Wang; Liang Sun; Yan Zhou

doi:10.1117/12.2553011

31 January 2020 Target recognition for underwater range-gated imaging based on convolutional neural network in fpga

Han Dong, Xinwei Wang, Liang Sun, Yan Zhou

Proceedings Volume 11427, Second Target Recognition and Artificial Intelligence Summit Forum; 114273E (2020) https://doi.org/10.1117/12.2553011
Event: Second Target Recognition and Artificial Intelligence Summit Forum, 2019, Changchun, China

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.

Citation Download Citation

Han Dong, Xinwei Wang, Liang Sun, and Yan Zhou "Target recognition for underwater range-gated imaging based on convolutional neural network in fpga", Proc. SPIE 11427, Second Target Recognition and Artificial Intelligence Summit Forum, 114273E (31 January 2020); https://doi.org/10.1117/12.2553011

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