UAV intelligent recognition and tracking method based on deep learning

This paper proposes an intelligent recognition and tracking method of unmanned aerial vehicles based on GAN. This method generates high-quality target feature images and performs accurate target recognition through the generator and discriminator in the generative adversarial network, and uses time series information to achieve stable target tracking. Experimental results show that the GAN-based model shows a high accuracy rate in a variety of complex environments (such as dynamic scenes, lighting changes, occlusion, etc.), with a target recognition accuracy of more than 97%, and a tracking accuracy that is about 15% higher than that of traditional methods. While improving the accuracy of target recognition and tracking, this method has strong real-time and anti-interference capabilities, providing strong support for the task execution of unmanned aerial vehicles in dynamic and complex environments.