  Offloading computationally heavy tasks from an unmanned aerial vehicle (UAV)
to a remote server helps improve the battery life and can help reduce resource
requirements. Deep learning based state-of-the-art computer vision tasks, such
as object segmentation and object detection, are computationally heavy
algorithms, requiring large memory and computing power. Many UAVs are using
(pretrained) off-the-shelf versions of such algorithms. Offloading such
power-hungry algorithms to a remote server could help UAVs save power
significantly. However, deep learning based algorithms are susceptible to
noise, and a wireless communication system, by its nature, introduces noise to
the original signal. When the signal represents an image, noise affects the
image. There has not been much work studying the effect of the noise introduced
by the communication system on pretrained deep networks. In this work, we first
analyze how reliable it is to offload deep learning based computer vision tasks
(including both object segmentation and detection) by focusing on the effect of
various parameters of a 5G wireless communication system on the transmitted
image and demonstrate how the introduced noise of the used 5G wireless
communication system reduces the performance of the offloaded deep learning
task. Then solutions are introduced to eliminate (or reduce) the negative
effect of the noise. The proposed framework starts with introducing many
classical techniques as alternative solutions first, and then introduces a
novel deep learning based solution to denoise the given noisy input image. The
performance of various denoising algorithms on offloading both object
segmentation and object detection tasks are compared. Our proposed deep
transformer-based denoiser algorithm (NR-Net) yields the state-of-the-art
results on reducing the negative effect of the noise in our experiments.
