Ornamental Portable Battlefield Surveillance using Generative Adversarial Networks

This article investigates the sole use of generative adversarial networks (GANs) for portable battlefield surveillance equipment optimisation. High-resolution and clear pictures are produced by the strategic application of GANs for accurate object recognition, anomaly detection, and real-time video quality enhancement. In military applications, the resultant GAN-centric surveillance system exhibits increased accuracy and efficiency. We report major progress in object detection, anomalous activity detection, including video quality improvement. This GAN-centric approach significantly enhances situational awareness, allowing military personnel to make quick and accurate decisions in a variety of dynamic and varied operational settings. The study demonstrates how GANs may effectively advance military technology for surveillance on mobile platforms, strengthening national security standards in the process. The results highlight how GANs are uniquely adept in powerful improvements in military surveillance capabilities, which in turn improves their operational efficiency. In order to improve both the visual appeal and the efficacy of surveillance in a variety of military environments, this research paper suggests a novel way to improve portable battlefield surveillance systems through the integration of Generative Adversarial Networks (GANs). The technique emphasises discrete design and real-time processing capabilities for portable devices, with a focus on the creation and implementation of a GAN-based system.