# Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning
## 1 Introduction  
Camouflage is the most common and effective means to combat military reconnaissance [1; 2]. It can conceal military equipment in natural environments. With the development of camouflage technology, optical camouflage has evolved from deformable camouflage to digital camouflage [3]. However, traditional digital camouflage is mainly designed for a single background and state [4]. In the traditional digital camouflage design method, the colors are only the main color of a specific environment, and the texture is formed by the non-random algorithm arrangement of finite pattern templates [5]. The traditional way of realizing digital camouflage is to coat the equipment surface with camouflage paint according to the designed camouflage texture or to wear or cover the fabric with camouflage texture. There is a limitation that the camouflage performance of a specified time and place is appealing, and the camouflage performance is greatly weakened when the location, season, and time changes. Cross-region, multi-season, multi-period camouflage has become a new military demand for modern weapons and equipment. Therefore, the camouflage technology, which can change with the environment in real-time has become the inevitable direction of the development of the military camouflage field. During the last decades, much effort has been directed toward achieving this goal. To realize multi-region adaptive camouflage, texture and color must not be fixed, real-time camouflage texture is designed according to the changes in the environment. Different fromthe traditional camouflage, the implementation way needs to use a controllable multi-color variable material. By fabricating the controllable color-changing material into color-changing units embedded on the surface of the camouflaged target, just like the cells that make up the chameleon's skin, we can refer to the whole device as camouflage skin. The external control system controls the camouflage skin to display the design's camouflage texture. Researchers find inspiration from nature. It is well known that there are loads of animals in nature with excellent camouflage abilities, such as cephalopods (like squid and cuttlefish) [6; 7; 8; 9], chameleons [10], some insects [11], and so on. These animals have amazing control over their appearance (such as color, contrast, pattern). The principle of animal camouflage is to use the nervous system to sense changes in the environment and control the cells on the skin to change into different colors and textures according to the environment. Inspired by the principle of animal camouflage, researchers have designed various types of color-changing devices or camouflage samples [12; 13; 14], to mimic cells on the surface of the animal's skin. Single material for all colors has been reported [15; 16; 17]; it is an inverse polymer-gel opal which is prepared from an electroactive material. It can be stimulated by an electric field to change colors. In addition, devices that use magnetic field stimulation to achieve color changes have also been reported [18]. A mechanical chameleon based on dynamic plasmonic-nanostructures has been designed [14]. At present, most researchers focus on the technology of controllable color change, but there are few reports on the design method of new generation self-adaptive camouflage texture [19]. As one of the key technologies of adaptive optical camouflage, the study of adaptive camouflage texture design has important theoretical and practical significance. In this paper, the design method of adaptive camouflage texture for typical military targets in the natural environment is studied. With the development of computer vision technology, deep learning has been applied to various image processing scenarios. It has been used for image classification [20; 21; 22], object detection [23; 24; 25], image segmentation [26; 27; 28; 25], image completion [29; 30; 31], and so on, and has achieved a series of amazing results.