A Target Identification Technique for Unmanned Surface Vessel Based on Deep Learning

At present, China is vigorously developing marine weapons and equipment, and the research on unmanned weapons and equipment has received extensive attention. The intelligentization of unmanned surface vessels is a research hotspot. To meet the detection and high-precision positioning requirements of large and medium-sized targets, this paper focuses on the design of target identification technique for unmanned surface vessel based on deep learning. Firstly, the multi-source and multi-system collaborative sensing architecture design is used to solve the problems of equipment intelligent computing task duplication and resource waste as well as deep learning acceleration. Secondly, multi-level feature extraction, analysis and fusion technique is designed to determine the features that should be selected for single / multi-sensors. Finally, the selected features are used to design multi-feature target detection and identification methods based on deep learning, and a multi-source multi-dimensional joint detection and identification processing method based on deep learning networks is established. The experimental results show that the recognition rate exceeds 99. 7% for visual images, indicating that this technique has good recognition effects. © 2022 China Ordnance Society. All rights reserved.