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...
| Main Author: | |
|---|---|
| Other Authors: | |
| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
Nanyang Technological University
2024
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/181124 |
| _version_ | 1826121980992028672 |
|---|---|
| author | Tan, Tony Jun Sheng |
| author2 | Mohamed M. Sabry Aly |
| author_facet | Mohamed M. Sabry Aly Tan, Tony Jun Sheng |
| author_sort | Tan, Tony Jun Sheng |
| collection | NTU |
| 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. |
| first_indexed | 2025-03-09T13:07:11Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/181124 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-03-09T13:07:11Z |
| publishDate | 2024 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Computer and Information Science Tan, Tony Jun Sheng Drone control via deep learning |
| title | 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_short | Drone control via deep learning |
| title_sort | drone control via deep learning |
| topic | Computer and Information Science |
| url | https://hdl.handle.net/10356/181124 |
| work_keys_str_mv | AT tantonyjunsheng dronecontrolviadeeplearning |