Offloading Deep Learning Powered Vision Tasks from UAV to 5G Edge Server with Denoising

TL;DR

This paper investigates the impact of 5G communication noise on offloaded deep learning vision tasks from UAVs and proposes a novel transformer-based denoising method to improve task accuracy.

Contribution

It introduces a comprehensive analysis of communication noise effects on offloaded deep learning tasks and proposes a new deep transformer-based denoising algorithm, NR-Net, for enhanced performance.

Findings

5G noise significantly reduces offloaded vision task accuracy.

Classical denoising techniques are less effective than deep learning methods.

NR-Net achieves state-of-the-art denoising performance in experiments.

Abstract

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.