Camouflage pattern is a commonly used camouflage method, and improving the camouflage ability can effectively ensure the safety of personnel and equipment. In the context of current military information construction, camouflage has evolved from simple irregular large color blocks or biomimetic designs to the current digital camouflage. Digital camouflage pixelates the colors, textures, and distribution in the background images, making it more aligned with the imaging principle of the human eyes, thereby achieving favorable camouflage effectiveness at different reconnaissance distances. However, the traditional digital camouflage generation technology is cumbersome and fails to realize real-time generation. Additionally, the generation conditions generally rely on computer equipment, which is bulky and not portable, resulting in low efficiency in practical camouflage application scenes. Therefore, it is necessary to establish an integrated adaptive digital camouflage system to solve the limitations of traditional technology. The system is deployed in embedded devices to meet the needs of actual scenes.

This study designed an integrated system based on the improved cycle generative adversarial network (CycleGAN) model, combining modules such as digital camouflage generation, dynamic color change control, real-time evaluation of camouflage effectiveness, and feedback control, thereby realizing a full-process integrated design. The generation model combined the perceptual loss function based on pre-trained visual geometry group (VGG) network and the perceptual loss function based on Sobel operator to optimize the ability of the model for extracting digital camouflage structural information and high-level semantics. Additionally, Wasserstein generative adversarial network with gradient penalty (WGAN-GP) was used to ensure training stability. The original WGAN may cause the gradient vanishing or explosion through weight clipping, while WGAN-GP directly constrained the discriminator gradient through gradient penalty. The evaluation method used the comprehensive similarity indicator to assess the camouflage effectiveness from three dimensions, namely structure, texture, and color. The obtained indicator could indicate the level of camouflage effectiveness. By analyzing the comprehensive similarity indicator, the system further performed the feedback processing on the camouflage generated by the model, and further improved the similarity indicator by iterating the feedback algorithm. Through model generation, camouflage effectiveness evaluation, and feedback processing, the system was capable of handling digital camouflage throughout the whole process. Furthermore, the system was deployed on embedded devices to meet the requirements of low power consumption and portability in practical uses.

After lightweight model design and the development of the user interface, the system was deployed on the embedded device Raspberry Pi 4B, with the total power consumption limited to less than 8 W. The improved model could generate camouflage patterns within 5 s on the device. In the field scene camouflage experiment (Fig.9 and Fig.10) and the edge detection algorithm experiment of Canny operator (Fig.14), compared with the traditional design, the digital camouflage effectiveness generated by the system exhibited a 32% reduction in contour, a 28% improvement in color similarity, and a 50% increase in comprehensive similarity (Table 1). These data proved that the system could accurately capture background features and convert them into camouflage features, while overcoming traditional limitations including real-time generation of environment-adaptive digital camouflage, miniaturization for portability, and closed-loop feedback to enhance adaptability, avoiding the performance degradation of fixed-mode camouflage in cross-scene applications.

An integrated adaptive digital camouflage system based on an improved CycleGAN model is developed, forming a complete closed-loop workflow for camouflage of three modules—real-time generation, online camouflage evaluation, and feedback control. This system overcomes the limitations of traditional technologies, such as weak real-time performance, low portability, and poor adaptability. It also provides an innovative solution for the transformation of military camouflage technologies toward intelligent and miniaturized designs, with a solid foundation in the practical application of military camouflage. Future development directions can focus on fields related to infrared or multispectral capabilities in complex scenes.