Drone control via deep learning

Drone control via deep learning

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 ap...

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Main Author: Tan, Tony Jun Sheng
Other Authors: Mohamed M. Sabry Aly
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Computer and Information Science
Online Access:https://hdl.handle.net/10356/181124
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1811242024-11-15T11:34:29Z Drone control via deep learning Tan, Tony Jun Sheng Mohamed M. Sabry Aly College of Computing and Data Science Hardware & Embedded Systems Lab (HESL) msabry@ntu.edu.sg Computer and Information Science 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. Bachelor's degree 2024-11-15T11:34:29Z 2024-11-15T11:34:29Z 2024 Final Year Project (FYP) Tan, T. J. S. (2024). Drone control via deep learning. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/181124 https://hdl.handle.net/10356/181124 en application/pdf application/octet-stream Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
spellingShingle Computer and Information Science
Tan, Tony Jun Sheng
Drone control via deep learning
description 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.
author2 Mohamed M. Sabry Aly
author_facet Mohamed M. Sabry Aly
Tan, Tony Jun Sheng
format Final Year Project
author Tan, Tony Jun Sheng
author_sort Tan, Tony Jun Sheng
title Drone control via deep learning
title_short Drone control via deep learning
title_full Drone control via deep learning
title_fullStr Drone control via deep learning
title_full_unstemmed Drone control via deep learning
title_sort drone control via deep learning
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/181124
_version_ 1816858991456681984

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