Development of a Robust Real-Time Hand Gesture Recognition System Tailored for Drone Navigation to Facilitate Efficient Military Applications

Real-time hand gesture recognition technologies revolutionize drone control, making it more responsive and efficient in dynamic military settings. This research develops a robust hand gesture recognition system specifically for drone navigation in military contexts. Using computer vision and machine learning, the system enables seamless, reliable UAV control via hand gestures. It employs residual neural networks (ResNet20v2) for gesture recognition, converting gesture photos into precise commands. Extensive flight testing validates the system's resilience and dependability, achieving an impressive 99.73% accuracy rate in gesture detection. Integrating this system into military UAV operations enhances navigation success and efficiency, delivering unprecedented operational reliability. Developing user-friendly, adaptive control systems is crucial for meeting growing military demands, ensuring operational precision, versatility, and secure UAV navigation in challenging environments.