Drone Controlled by Hand Gestures

The recent surge in interest surrounding drones, also known as unmanned aerial vehicles (UAVs), has been observed in a multitude of applications, including military operations, agricultural practices, industrial processes, and others. One effective method for controlling the flight of a drone is through the use of hand gestures. This approach offers a design at a lower cost. The primary objective of this research is to gain a comprehensive understanding of the functioning of convolutional neural networks and their applications in hand gesture recognition. The model was trained during the implementation phase. Once all the requisite components have been installed, the final user will be able to design a convolutional neural network that can control a drone by pointing the hand toward a laptop or a camera set directly on the drone. Furthermore, it is possible to further train the model with data that is most suited to the environment in question.