关键词: 无人艇, 目标识别, 特征提取, 深度学习

Abstract: 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.

Key words: unmannedsurfacevessel, targetidentification, featureextraction, deeplearning