The evaluation index of camouflage patterns is important in the field of military application. It is the goal that researchers have always pursued to make the computable evaluation indicators more in line with the human visual mechanism. In order to make the evaluation method more computationally intelligent, a Multi-Feature Camouflage Fused Index (MF-CFI) is proposed based on the comparison of grayscale, color and texture features between the target and the background. In order to verify the effectiveness of the proposed index, eye movement experiments are conducted to compare the proposed index with existing indexes including Universal Image Quality Index (UIQI), Camouflage Similarity Index (CSI) and Structural Similarity (SSIM). Twenty-four different simulated targets are designed in a grassland background, 28 observers participate in the experiment and record the eye movement data during the observation process. The results show that the highest Pearson correlation coefficient is observed between MF-CFI and the eye movement data, both in the designed digital camouflage patterns and large-spot camouflage patterns. Since MF-CFI is more in line with the detection law of camouflage targets in human visual perception, the proposed index can be used for the comparison and parameter optimization of camouflage design algorithms.

迷彩图案的评价指标在军事应用领域具有重要意义。让可计算的评价指标更符合人类视觉机制是研究人员一直追求的目标。为了使评估方法具有更高的计算智能性，基于目标和背景之间灰度、颜色和纹理特征的比较，提出了一种多特征伪装融合指数（MF-CFI）。为了验证所提出的指标的有效性，通过眼动实验将所提出的指标与现有的指标进行比较，包括通用图像质量指数（UIQI）、伪装相似度指数（CSI）和结构相似度（SSIM）。在草原背景下设计了二十四个不同的模拟目标，28名观察者参与实验并记录观察过程中的眼动数据。结果表明，在设计的数码迷彩图案和大斑点迷彩图案中，MF-CFI与眼动数据之间的Pearson相关系数最高。由于MF-CFI更符合人类视觉感知中伪装目标的检测规律，提出的指标可用于伪装设计算法的比较和参数优化。