Application of Cycle Generative Adversarial Network Method in Internal and External Field Mapping

To address the vastly different styles and insufficiently high fidelity of simulation images simulated by the conventional image-generation platform, a cycle generative adversarial network is proposed. This enables the simulated image in the semi-physical simulation test of an image-guided weapon to closely resemble the actual battlefield environment. The proposed network comprises generators and discriminators, and the method involves an unsupervised deep-learning model embedded in existing image-generation platforms. To ensure real-time performance, locking shared memory technology is used to address image transmission timeouts in semi-physical objects. Test results show that the method can ensure real-time performance and improve confidence in semi-physical simulations.