基于战场环境孪生的迷彩伪装动态效果评估方法

doi:10.12382/bgxb.2022.0378

摘要/Abstract

摘要：

现有的迷彩伪装效果评估方法主要针对静态的单幅目标图像,不能真实地响应军事侦察中的动态多视角特性,相应的评估结果也缺乏全面性。为此提出一种基于战场环境孪生的迷彩伪装动态效果评估新方法。新方法以构建三维战场环境数字孪生模型为基础,借助三维仿真计算技术获取战场环境中迷彩伪装目标的动态多视角效果图,采用语义分割模型和目标跟踪模型识别分析场景中的迷彩运动目标;构建新的迷彩伪装评价指标体系及多侦察视角的多属性综合评估模型,实现不同迷彩方案在给定孪生战场环境中的伪装动态效果评价。以仿真军用卡车模型为实验对象,使用Unity 3D建模真实战场环境,以两种对比性迷彩伪装设计方案为例进行评估分析,实验结果表明新方法的合理性和实用性。

关键词: 迷彩伪装, 动态效果评估, 战场环境孪生, 多视角侦察, 多属性综合

Abstract:

The existing evaluation methods for camouflage effectare mainly based on static single target images, which cannot truly respond to the dynamic multi-view characteristics in military reconnaissance, and the corresponding evaluated results are lack of comprehensiveness. A new evaluationmethod fordynamic effect of camouflage based on battlefield environment twinning is proposed. In the new method,a digital twin model of 3D battlefield environment is established, the dynamic multi-view effect map of camouflage targets in the battlefield environment is acquired with the help of 3D simulation and computing technology, the camouflage motion targets in the scene are identified and analyzedby using semantic segmentation and target tracking model, and a new camouflage evaluation index system and a multi-attribute comprehensive evaluation model with multiple reconnaissance views are constructed, finally realizing the dynamic multi-view effect evaluation of different camouflage schemes. The experiments were conducted with simulated military truck model as the experimental object, Unity 3D was used to model the real battlefield environment, and two contrasting camouflage design schemes were evaluated and analyzed. The experimental results show the reasonableness and practicality of the new method.

Key words: camouflage, dynamic effect evaluation, battlefieldenvironment twinning, multi-view reconnaissance, multi-attribute synthesis

中图分类号:

TP391.9

杨迪, 周颖, 谢振平. 基于战场环境孪生的迷彩伪装动态效果评估方法[J]. 兵工学报, 2024, 45(1): 349-362.

YANG Di, ZHOU Ying, XIE Zhenping. Evaluation Method for Dynamic Effect of Camouflage Based on Battlefield Environment Twinning[J]. Acta Armamentarii, 2024, 45(1): 349-362.

图/表 15

图1 本文方法的技术框架

Fig.1 Technical framework of the proposed method

图2 三维虚拟战场环境及数据集构建过程

Fig.2 Construction process of 3D virtual battlefield environment and dataset

图3 运动目标识别模型

Fig.3 Moving object recognition model

表1 实验迷彩图样与迷彩目标

Table 1 Experimental camouflage patterns and targets

图4 实验构建的虚拟战场场景

Fig.4 Virtual battlefield scene used inthe experiments

图5 分割训练数据集示例

Fig.5 Examples of image segmentation training dataset

图6 评估数据集构建视角图示

Fig.6 Illustration of evaluation dataset construction perspective

图7 跟踪评估数据集

Fig.7 Tracking dataset for evaluation

表2 实验对比性迷彩设计方案

Table 2 Experimental contrast camouflage design schemes

图8 高低空分割干扰率性能结果

Fig.8 Performance results on high and low altitude segmentation jamming rates

图9 高低空伪装形变度性能结果

Fig.9 Performance results on high and lowaltitude camouflage deformations

图10 多视角检测精准率性能结果

Fig.10 Performance results on multi-view detection accuracy

图11 多视角检测干扰率性能结果

Fig.11 Performance results on interference rate of multi-view detection

表3 两个对比迷彩伪装方案的评估决策矩阵

Table 3 Evaluation decision matrix of two compared camouflage strategies

迷彩伪装	A_i	S_i	E_i	L_i	G_i
迷彩伪装1	{ $0.54 (低)$ , $0.66 (高)$ }	{ $0.53 (低)$ , $0.61 (高)$ }	{ $0.61 (低)$ , $0.72 (高)$ , $0.43 (左)$ , $0.54 (右)$ }	{ $0.72 (低)$ , $0.66 (高)$ , $0.45 (左)$ , $0.46 (右)$ }	{ $0.78 (低)$ , $0.65 (高)$ }
迷彩伪装2	{ $0.44 (低)$ , $0.52 (高)$ }	{ $0.49 (低)$ , $0.42 (高)$ }	{ $0.41 (低)$ , $0.48 (高)$ , $0.41 (左)$ , $0.36 (右)$ }	{ $0.35 (低)$ , $0.14 (高)$ , $0.32 (左)$ , $0.23 (右)$ }	{ $0.54 (低)$ , $0.46 (高)$ }

表3 两个对比迷彩伪装方案的评估决策矩阵

Table 3 Evaluation decision matrix of two compared camouflage strategies

迷彩伪装	A_i	S_i	E_i	L_i	G_i
迷彩伪装1	{ $0.54 (低)$ , $0.66 (高)$ }	{ $0.53 (低)$ , $0.61 (高)$ }	{ $0.61 (低)$ , $0.72 (高)$ , $0.43 (左)$ , $0.54 (右)$ }	{ $0.72 (低)$ , $0.66 (高)$ , $0.45 (左)$ , $0.46 (右)$ }	{ $0.78 (低)$ , $0.65 (高)$ }
迷彩伪装2	{ $0.44 (低)$ , $0.52 (高)$ }	{ $0.49 (低)$ , $0.42 (高)$ }	{ $0.41 (低)$ , $0.48 (高)$ , $0.41 (左)$ , $0.36 (右)$ }	{ $0.35 (低)$ , $0.14 (高)$ , $0.32 (左)$ , $0.23 (右)$ }	{ $0.54 (低)$ , $0.46 (高)$ }

表4 两个迷彩方案的伪装效果评价对比

Table 4 Compared results of camouflage effects of two compared camouflage strategies

平衡系数、熵值法和最大偏差法	正理想点	属性权重	方案与理想点相似度	方案排序
α=β=λ= $13$	X⁺={0.66,0.61,0.72,0.72,0.78}	[0.19,0.20,0.14,0.20,0.27]^T	S(X₁)=0.75, S(X₂)=0.48	X₁>X₂
α=β=0,λ=1	X⁺={0.66,0.61,0.72,0.72,0.78}	[0.15,0.19,0.31,0.33,0.20]^T	S(X₁)=0.41, S(X₂)=0.18	X₁>X₂
熵值法	X⁺={0.66,0.61,0.72,0.72,0.78}	[0.18,0.14,0.16,0.21,0.31]^T	S(X₁)=0.61, S(X₂)=0.52	X₁>X₂
最大偏差法	X⁺={0.66,0.61,0.72,0.72,0.78}	[0.19,0.11,0.23,0.32,0.15]^T	S(X₁)=0.68, S(X₂)=0.46	X₁>X₂

表4 两个迷彩方案的伪装效果评价对比

Table 4 Compared results of camouflage effects of two compared camouflage strategies

平衡系数、熵值法和最大偏差法	正理想点	属性权重	方案与理想点相似度	方案排序
α=β=λ= $13$	X⁺={0.66,0.61,0.72,0.72,0.78}	[0.19,0.20,0.14,0.20,0.27]^T	S(X₁)=0.75, S(X₂)=0.48	X₁>X₂
α=β=0,λ=1	X⁺={0.66,0.61,0.72,0.72,0.78}	[0.15,0.19,0.31,0.33,0.20]^T	S(X₁)=0.41, S(X₂)=0.18	X₁>X₂
熵值法	X⁺={0.66,0.61,0.72,0.72,0.78}	[0.18,0.14,0.16,0.21,0.31]^T	S(X₁)=0.61, S(X₂)=0.52	X₁>X₂
最大偏差法	X⁺={0.66,0.61,0.72,0.72,0.78}	[0.19,0.11,0.23,0.32,0.15]^T	S(X₁)=0.68, S(X₂)=0.46	X₁>X₂

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