Digital camouflage design and camouflage performance evaluation based on background segmentation method
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%.
digital camouflagebackground segmentationcamouflage concealment performancedisguised evaluationneural networkfusion fitnesssimilaritypixel-wise true positive
万方数据
维普
