A digital camouflage design method based on background segmentation was proposed to address the camouflage concealment issues of personnel and military equipments in natural environment.Firstly,by analyzing the characteristics of environmental scenes and employing feature extraction techniques,the optimal elements of camouflage pattern were determined.Secondly,the color distribution ratio of digital camouflage pattern was further refined using background segmentation,ensuring that the digital camouflage pattern could maintain effective camouflage concealment regardless of its position in the environmental scene.Finally,the deep neural networks model ResNet-50 and camouflage target segmentation model Mask R-CNN were utilized to evaluate the camouflage concealment performance of digital camouflage patterns designed based on background segmentation.The results indicated a significant improvement in the concealment effectiveness of digital camouflage compared to traditional camouflage generation method.The mean fusion fitness exceeded 99.00%,the environmental scene similarity increased by 20.41%,and the mean pixel-wise true positive decreased by 82.21%.
关键词
数码迷彩/背景分割/伪装隐蔽性能/伪装评价/神经网络/隐蔽融合度/相似度/分割识出率
Key words
digital camouflage/background segmentation/camouflage concealment performance/disguised evaluation/neural network/fusion fitness/similarity/pixel-wise true positive
维普
万方数据