基于深度学习的光电系统智能目标识别

doi:10.12382/bgxb.2022.A004

摘要/Abstract

摘要： 智能目标识别技术是光电系统多维立体侦察体系的重要支撑，是实现多角度、全方位目标定位、感知分析的基础。为满足复杂环境下光电系统中基于深度学习的目标识别需求，聚焦数据、算法和计算能力三大挑战，提出一种基于多源信息融合的智能化目标识别方法，对多个传感器融合得到的图像进行学习和训练，从而提高目标识别的能力。基于多维图像融合的目标识别技术，将多波段融合图像数据进行标注、训练学习，用来自动识别出图像中的多个目标。实验结果表明，所提算法能够实现对融合目标的精确识别与定位。

关键词: 光电系统, 智能目标识别, 深度学习, 多源数据, 图像融合, 视觉感知

Abstract: Intelligent target recognition technology is an important support of multi-dimensional and three-dimensional reconnaissance system of electro-optical system, which is the basis of multi-angle and omni-directional target positioning and perception analysis. Focusing on the three challenges of data，algorithm and computing power，an intelligent target recognition method based on multi-source information fusion is proposed to meet the needs of deep learning-based target recognition of electro-optical system in complex environment.The proposed method is to learn and train the images fused by multiple sensors so as to improve the ability to recognize targets.The target recognition technology based on multi-dimensional image fusion is used to label，train and learn the multi band fused image data，thus automatically identifying the multiple targets in the image. Experimental results show that the proposed method can be used to accurately identify and locate the fusion target.

Key words: electro-opticalsystem, intelligenttargetrecognition, deeplearning, multi-sourcedata, imagefusion, visualperception

中图分类号:

TP29

李良福，陈卫东，高强，许开銮，刘轩，何曦，钱钧. 基于深度学习的光电系统智能目标识别[J]. 兵工学报, 2022, 43(S1): 162-168.

LI Liangfu， CHEN Weidong， GAO Qiang， XU Kailuan， LIU Xuan， HE Xi， QIAN Jun. Deep Learning-based Intelligent Target Recognition Technology for Electro-optical System[J]. Acta Armamentarii, 2022, 43(S1): 162-168.

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