# Fast Self-Adaptive Digital Camouflage Design Method Based on Deep Learning
## 4 Results
### Standardization of Camouflage Texture  
We standardized the initial camouflage texture using the method described in Section 3.5. As shown in Figure 11b, the camouflage texture we designed corresponded to the rectangular area where the target is located. Note that K is set to 5 when discussed later in this article. At the same time, we needed to design the camouflage texture for the camouflage target's forward view, backward view, left view, right view, and top view, respectively. In practice, the texture of the camouflage area needed to be mapped to the actual target surface. This step could be accomplished with 3D rendering software, such as Maya or OpenGL, which is not described in this article as it focuses on the design approach. In this article, we simply mapped the camouflage texture to the camouflage target surface through a mask to observe its camouflage effect, as shown in Figure 11c. The output camouflage textures in Figure 11c had an overall optical camouflage effect, where textures and semantics were consistent with the environment look very naturally. Figure 11: Adaptive digital camouflage texture. (**a**) Original image; (**b**) Rectangular texture area; (**c**) Camouflaged images using textures. Figure 10: More detailed experimental results. (**a**) airship; (**b**) preliminary camouflage texture of the airship; (**c**) aircraft carrier; (**d**) preliminary camouflage texture of the aircraft carrier; (**e**) tank; (**f**) preliminary camouflage texture of the tank; (**g**) uniformed soldiers; (**h**) preliminary camouflage texture of the uniformed soldiers. The more detailed experimental results are shown in Figure 12. The camouflage texture generation process in this paper is different from the traditional one. The traditional camouflage texture was obtained by random or non-random distribution using finite pattern templates or structural elements of texture. The camouflage texture generation in this paper is to use the method of image completion to generate the texture consistent with the current environment. The texture composition of this paper had no fixed structural elements. It might have been random for the natural environment like forest or regular for the artificial environment like the city, depending on the state of the current environment. The texture features come from a lot of training we did on the places2 [40] data set using the deepfillv1 [39] algorithm. The place2 [40] data set contains more than 400 different types of scene environments and 10 million images. Basically, it covers people's common scenes. The deepfillv1 [39] algorithm, after training on places2 [40], was able to generate a meaningful image consistent with the background texture of the incomplete image. As shown in Figure 12, camouflage textures are designed using this method on both natural and artificial backgrounds. In Figure 12, the first column shows the original images in natural and artificial environments, and the second column shows the camouflaged image corresponding to the first column. The color of the second column camouflage texture was not replaced by the standard color. The third column shows the camouflaged image with the camouflaged texture of the standard color. In Figure 12, the first row shows the camouflage texture in the natural environment using our method, the second row shows the camouflage texture in the artificial environment using our method. As can be seen from Figure 12, the camouflage texture designed using the method we provided has an excellent camouflage effect in both natural and artificial environments. The camouflage texture in the natural environment is irregular, and the camouflage texture in the artificial environment has a certain rule, which is consistent with the current environment. Comparing Figure 12e,f, it is found that the camouflage performance decreased after filling the camouflage texture with the most similar standard color.