复杂背景下显著度表征的车辆伪装效果动态评估

doi:10.12382/bgxb.2022.0181

摘要/Abstract

摘要：

针对作战流程中车辆目标在复杂地貌背景下的伪装效果动态评估需求,提出一种复杂地貌背景超像素分割和综合显著度表征的车辆伪装效果评估方法。基于目标在背景中的显著度与伪装效果的负相关性,方法采用超像素邻接模型,引入本地显著度和目标显著度进行伪装效果综合表征;选取车辆和背景多尺度图像的视觉特征,构建多维显著度矩阵;通过复相关系数法和变异系数法进行有效特征筛选;采用熵权法计算综合显著度对伪装效果进行评估。通过算例验证分析,方法能综合复杂地貌背景、车辆伪装状态和经典显著度视觉特征等多种因素进行伪装效果评估,量化分析车辆伪装状态的显著特征和优势应用环境,为车辆的伪装设计、状态运用和隐蔽区域选择提供建议。

关键词: 复杂背景, 车辆目标, 显著度表征, 伪装评估

Abstract:

To meet the requirement of dynamic evaluation of the vehicle camouflage effect in complex landform background, a vehicle camouflage effect evaluation method based on superpixel segmentation and comprehensive saliency characterization was proposed. The method is based on the negative correlation between the saliency of the target in the background and the camouflage effect, the superpixel adjacency model is adopted, and the local saliency and the target saliency are introduced to comprehensively characterize the camouflage effect. Visual features of the vehicle and multi-scale images of the background are selected to construct multidimensional saliency matrix. Effective feature screening is carried out with the methods of complex correlation coefficient and variation coefficient. The entropy weight method is used to calculate the comprehensive saliency to evaluate the camouflage effect. Through verification and analysis of the numerical examples, the proposed method can evaluate the camouflage effect by integrating the complex landform background, vehicle camouflage state and classical salient visual features, and quantitatively analyze the saliency features and advantageous application environment of the vehicle camouflage state. This work aims to provide suggestions for vehicle camouflage design, state application and concealed area selection.

Key words: complex background, vehicle targets, saliency characterization, camouflage evaluation

王冬, 高钦和, 黄通, 刘志浩, 高蕾. 复杂背景下显著度表征的车辆伪装效果动态评估[J]. 兵工学报, 2023, 44(6): 1620-1631.

WANG Dong, GAO Qinhe, HUANG Tong, LIU Zhihao, GAO Lei. Dynamic Evaluation of Vehicle Camouflage Effect Based on Saliency Characterization in Complex Background[J]. Acta Armamentarii, 2023, 44(6): 1620-1631.

图/表 14

图1 复杂地貌背景下目标显著度表征

Fig.1 Saliency characterization of target in complex landform background

图2 背景图像的超像素分割

Fig.2 Superpixel segmentation of background image

图3 邻接超像素对比模型

Fig.3 Adjacent superpixel comparison model

图4 综合显著度函数

Fig.4 Comprehensive saliency function

表1 经典视觉显著度模型及其表征

Table 1 Classical visual saliency model and its characterization

模型	图像空间	显著度表征
Itti等^[16] 模型	RGB	高斯金字塔法,对图像的亮度、颜色和4个方向的纹理进行“中心周边差分”操作
GBVS模型^[17]	RGB	用稳态平衡作为图像的亮度、颜色和4个方向的纹理的显著度衡量
SR模型^[18]	频域	对图像进行2维离散傅里叶变换,频域中log幅度谱的谱残差
Aho-Corasic 模型^[19]	CIE-Lab	Lab图像三个通道特征值与所有像素平均值的欧氏距离
FT模型^[20]	频域 CIE-Lab	频域滤波取反映图像整体信息的低频部分,计算Lab图像的欧氏距离
CA模型^[21]	CIE-Lab 空间距离	图像Lab三通道欧式距离和像素间的空间距离加权综合表征显著度
MSS模型^[22]	CIE-Lab	Lab图像三个通道与其固定邻域平均值的欧氏距离
SF模型^[23]	CIE-Lab 空间距离	综合分割图像的以空间距离为加权的区域颜色唯一性和以颜色相似度为加权的颜色空间分布

图5 基于复相关系数的循环特征筛选算法

Fig.5 Cyclic feature screening algorithm based on CRR

图6 基于变异系数累积信息的特征筛选算法

Fig.6 Feature screening algorithm based on the cumulative information of CV

图7 复杂背景下车辆迷彩伪装效果评估方法

Fig.7 Evaluation method of vehicle camouflage effect in complex background

表2 背景图像及超像素分割

Table 2 Background image and superpixel segmentation

表3 3种伪装状态在2类背景下9个特征的显著热力图

Table 3 Saliency thermal maps of 9 features in 3 camouflage states under 2 types of background

图8 各特征显著度复相关系和变异系数

Fig.8 CRR and CV of saliency degree with each feature

表4 筛选后有效特征

Table 4 Effective characteristics after screening

背景	伪装方案	有效特征
	1	Lab亮度、Hu矩、颜色矩、SR-LBP、SR-pHash
1	2	HSV颜色、 Hu矩、颜色矩、SR-LBP、SR-pHash
	3	Lab亮度、Hu矩、颜色矩、SR-LBP、SR-pHash
	1	Lab亮度、Hu矩、颜色矩、SR-LBP、SR-pHash
2	2	Lab亮度、Hu矩、颜色矩、SR-LBP、SR-pHash
	3	HSV颜色、 Hu矩、颜色矩、SR-LBP、SR-pHash

表5 最大变异系数和最大平均显著度特征

Table 5 Features of max. CV and max. mean saliency

背景	伪装状态	最大变异系数	最大平均显著度
	1	SR-pHash	SR-LBP
1	2	SR-pHash	SR-LBP
	3	SR-pHash	颜色矩
	1	SR-pHash	SR-LBP
2	2	SR-pHash	颜色矩
	3	HSV颜色	SR-LBP

表6 熵权法计算综合显著度

Table 6 Comprehensive saliency calculated with entropy weight method

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