Drone control via deep learning - Tan, Tony Jun Sheng (Nanyang Technological University, 2024)
The deployment of deep learning algorithms on embedded devices has the potential to unlock a wide range of applications in fields such as robotics, healthcare, and autonomous systems. However, the limited computational resources of these devices present a challenge, particularly for real-time applications where both speed and accuracy are crucial. This paper investigates the feasibility of running state-of-the-art deep learning models on resource-constrained embedded devices, using drone control via hand gestures as a case study for real-time, interactive applications.