  Micro-drones can be integrated into various industrial applications but are
constrained by their computing power and expert pilots, a secondary challenge.
This study presents a computationally-efficient deep convolutional neural
network that utilizes Gabor filters and spatial separable convolutions with low
computational complexities. An attention module is integrated with the model to
complement the performance. Further, perception-based action space and
trajectory generators are integrated with the model's predictions for intuitive
navigation. The computationally-efficient model aids a human operator in
controlling a micro-drone via gestures. Nearly 18% of computational resources
are conserved using the NVIDIA GPU profiler during training. Using a low-cost
DJI Tello drone for experiment verification, the computationally-efficient
model shows promising results compared to a state-of-the-art and conventional
computer vision-based technique.
