Research on Digital Camouflage Pattern Generation Algorithm Based on Adversarial Autoencoder Network

Abstract

In the past, most of the digital camouflage used textural features to extract the configuration features of spots in gray images, unable to effectively utilize the position relationship between color information. In order to overcome this shortcoming, a new digital camouflage pattern design model was proposed based on the model of adversarial autoencoder network. Firstly, the complexity and performance of several main color extraction algorithms were analyzed and compared, and combined with AFK-MC2 algorithm and color similarity coefficient, a fast camouflage main color clustering method was proposed. Then a deep convolution adversarial autoencoder network was designed to extract and describe the configuration features of the spots in background pattern. In order to diffuse pixel spot and achieve the effect of spatial color blending, a morphological processing algorithm was proposed to process the generated camouflage patterns. Finally, two sets of grassland and woodland datasets were established, respectively. The influence of the number of latent variables of network on the training process was tested on the dataset, and the number of camouflage feature descriptions was determined to be greater than or equal to 10. In order to verify the effectiveness of the generated camouflage, the spots in background region and target region were randomly selected, and the Euclidean distance between the feature parameters of these spots was calculated. Both the visual and experimental results demonstrate that the generated spots have high fusion with the background.