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基于深度神经网络的迷彩目标发现仿真学习方法

Simulation Learning Method for Discovery of Camouflage Targets Based on Deep Neural Networks

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摘要

针对自动化迷彩目标发现学习中有效样本严重不足的问题,借鉴AlphaGo的技术思想,提出了一种基于样本模拟的深度神经网络仿真训练方法。建立了迷彩场景仿真合成模型,通过设计图像空间的复合算法、场景图像深度特征提取策略、目标融合度测量策略,以及图聚类采样算法,批量化地生成了可用于深度神经网络训练和学习的具有代表性的迷彩场景仿真样本; 设计了基于深度残差神经网络的迷彩目标发现模型,并引入了多尺度网络训练方法。模拟样本和真实场景图像的实验结果表明,所提方法可有效应用于迷彩目标的自动化识别与评估。

Abstract

Aiming at the problem of serious lack of effective samples in the automatic discovery of camouflage targets, a simulation training method is proposed based on the sample simulation of a deep neural network and the technical idea of AlphaGo. A simulation synthesis model of camouflage scenes is established. The compound algorithm in the image space, the deep feature extraction strategy of scene images, the measurement strategy of target fusion degree, and the sampling algorithm for graph clustering are designed, respectively. Thus the representative samples for camouflage scene simulation are batch generated, which can be used for the deep neural network training and learning. Moreover, a discovery model of camouflage targets is designed based on a deep residual neural network, in which a multi-scale network training strategy is considered. The experimental results on the simulated samples and real scene images show that the proposed method can be effectively used for the automatic discovery and evaluation of camouflage targets.

Newport宣传-MKS新实验室计划
补充资料

中图分类号:TP391.9

DOI:10.3788/lop56.071102

所属栏目:成像系统

基金项目:中央高校基本科研业务费(JUSRP41808)、国家自然科学基金(61872166)

收稿日期:2018-09-29

修改稿日期:2018-10-17

网络出版日期:2018-10-22

作者单位    点击查看

卓刘:江南大学数字媒体学院, 江苏 无锡 214122江苏省媒体设计与软件技术重点实验室, 江苏 无锡 214122
陈晓琪:江南大学数字媒体学院, 江苏 无锡 214122江苏省媒体设计与软件技术重点实验室, 江苏 无锡 214122
谢振平:江南大学数字媒体学院, 江苏 无锡 214122江苏省媒体设计与软件技术重点实验室, 江苏 无锡 214122
蒋晓军:近地面探测与感知技术国防科技重点实验室, 江苏 无锡 214035
毕道鹍:近地面探测与感知技术国防科技重点实验室, 江苏 无锡 214035

联系人作者:谢振平(xiezhenping@hotmail.com)

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引用该论文

Zhuo Liu,Chen Xiaoqi,Xie Zhenping,Jiang Xiaojun,Bi Daokun. Simulation Learning Method for Discovery of Camouflage Targets Based on Deep Neural Networks[J]. Laser & Optoelectronics Progress, 2019, 56(7): 071102

卓刘,陈晓琪,谢振平,蒋晓军,毕道鹍. 基于深度神经网络的迷彩目标发现仿真学习方法[J]. 激光与光电子学进展, 2019, 56(7): 071102

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