Offloading deep learning powered vision tasks from UAV to 5G edge server with denoising

Name: Offloading deep learning powered vision tasks from UAV to 5G edge server with denoising
Author: Özer, Sedat, Ilhan, H. E., Özkanoğlu, Mehmet Akif, Cirpan, H. A.

İsim	Offloading deep learning powered vision tasks from UAV to 5G edge server with denoising
Yazar	Özer, Sedat, Ilhan, H. E., Özkanoğlu, Mehmet Akif, Cirpan, H. A.
Basım Tarihi:	2023-06
Basım Yeri	- IEEE
Konu	5G, Computational task offloading, Deep learning, Edge computing, Image denoising, Intelligent communication, Noise-removing Net, Object detection, Object segmentation
Tür	Süreli Yayın
Dil	İngilizce
Dijital	Evet
Yazma	Hayır
Kütüphane:	Özyeğin Üniversitesi
Demirbaş Numarası	0018-9545
Kayıt Numarası	d42d9220-a6b3-4c8c-a1da-09b1f2a9c43e
Lokasyon	Computer Science
Tarih	2023-06
Örnek Metin	Offloading computationally heavy tasks from an unmanned aerial vehicle (UAV) to a remote server helps improve battery life and can help reduce resource requirements. Deep learning based state-of-the-art computer vision tasks, such as object segmentation and 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 system reduces the performance of the offloaded deep learning task. Then solutions are introduced to eliminate (or reduce) the negative effect of the noise. Proposed framework starts with introducing many classical techniques as alternative solutions, 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 state-of-the-art results in our experiments.
DOI	10.1109/TVT.2023.3243529
Cilt	72

Kaynağa git Özyeğin Üniversitesi

Aramaya Dön

Özyeğin Üniversitesi

Kaynağa git

Offloading deep learning powered vision tasks from UAV to 5G edge server with denoising

Yazar Özer, Sedat, Ilhan, H. E., Özkanoğlu, Mehmet Akif, Cirpan, H. A.

Basım Tarihi 2023-06

Basım Yeri - IEEE

Konu 5G, Computational task offloading, Deep learning, Edge computing, Image denoising, Intelligent communication, Noise-removing Net, Object detection, Object segmentation

Tür Süreli Yayın

Dil İngilizce

Dijital Evet

Yazma Hayır

Kütüphane Özyeğin Üniversitesi

Demirbaş Numarası 0018-9545

Kayıt Numarası d42d9220-a6b3-4c8c-a1da-09b1f2a9c43e

Lokasyon Computer Science

Tarih 2023-06

Örnek Metin Offloading computationally heavy tasks from an unmanned aerial vehicle (UAV) to a remote server helps improve battery life and can help reduce resource requirements. Deep learning based state-of-the-art computer vision tasks, such as object segmentation and 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 system reduces the performance of the offloaded deep learning task. Then solutions are introduced to eliminate (or reduce) the negative effect of the noise. Proposed framework starts with introducing many classical techniques as alternative solutions, 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 state-of-the-art results in our experiments.

DOI 10.1109/TVT.2023.3243529

Cilt 72