With the decreasing noise level of underwater vehicle, the infrared imaging characteristics of underwater vehicle wake become one of its main detectable sources. Using the infrared characteristics of underwater vehicle wake to remote sensing detect the traces of underwater vehicle has gradually developed into a new detection method. Because of the high contingency and large error in judging underwater vehicle wake artificially, it can be overcome by using deep transfer learning to identify and locate the wake. This paper focuses on the infrared feature recognition of underwater vehicle wake with deep transfer learning, and wake sample sets of different classes are produced by image classification. The training effect of different pre-training networks is compared by using deep transfer learning. The influence of internal parameters of pre-training networks on the training effect of wake is discussed. Finally, combined with Faster-RCNN algorithm, the identification effect of wake is tested. The final recognition accuracy is ideal. It has certain application potential for future research on wake remote sensing detection combined with convolution neural network identification.

Date Published: 18 December 2019
PDF: 10 pages
Proc. SPIE 11333, AOPC 2019: Advanced Laser Materials and Laser Technology, 113330U (18 December 2019); doi: 10.1117/12.2544025

Published in SPIE Proceedings Vol. 11333:
AOPC 2019: Advanced Laser Materials and Laser Technology
Pu Zhou; Jian Zhang; Wenxue Li; Shibin Jiang; Takunori Taira, Editor(s)