Development of a machine-learning-based detection and tracking algorithm for drones

The proliferation of drones around the world was sparked by the reduction in the cost of owning a drone. As such, numerous drone research, worldwide, began to sprout. Majority of these researches focused on the applications of drones using computer vision as well as artificial intelligence. Many res...

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Main Author: Er, Hwa Ming
Other Authors: Low Kin Huat
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2021
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Online Access:https://hdl.handle.net/10356/150863
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Institution: Nanyang Technological University
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spelling sg-ntu-dr.10356-1508632021-06-03T07:20:43Z Development of a machine-learning-based detection and tracking algorithm for drones Er, Hwa Ming Low Kin Huat School of Mechanical and Aerospace Engineering MKHLOW@ntu.edu.sg Engineering::Aeronautical engineering The proliferation of drones around the world was sparked by the reduction in the cost of owning a drone. As such, numerous drone research, worldwide, began to sprout. Majority of these researches focused on the applications of drones using computer vision as well as artificial intelligence. Many researchers developed detections and tracking algorithms that focused on bounding boxes. Some researchers dedicated their work to semantic segmentation using aerial imagery or street views. Few had tried integrating detections and tracking for drones. However, fewer had tried using semantic segmentation for detection and tracking using drones [1], [2]. It is a challenge to achieve real time semantic segmentation for detection and tracking on drones as the drones had a limited amount of computation power. Since edge devices, often, did not have high processing power [3], steps must be taken to ensure that the algorithm was efficient in accommodating edge devices. After comparing several algorithms, DeepLabv3+ with Mobilenetv2 backbone was the fastest algorithm and was tested using one of the best edge devices available, NVIDIA Jetson TX2, for detections and tracking. Drones were able to conduct real time detections and tracking operations. However, the frame rate of this operation was lower than expected due to hardware limitations. This project indicated that efficient semantic segmentation detections and tracking using drones would be possible with future improvements in the processing power of hardware and/or improvement in the efficiency of algorithms. Bachelor of Engineering (Aerospace Engineering) 2021-06-03T07:20:43Z 2021-06-03T07:20:43Z 2021 Final Year Project (FYP) Er, H. M. (2021). Development of a machine-learning-based detection and tracking algorithm for drones. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150863 https://hdl.handle.net/10356/150863 en application/pdf 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 Engineering::Aeronautical engineering
spellingShingle Engineering::Aeronautical engineering
Er, Hwa Ming
Development of a machine-learning-based detection and tracking algorithm for drones
description The proliferation of drones around the world was sparked by the reduction in the cost of owning a drone. As such, numerous drone research, worldwide, began to sprout. Majority of these researches focused on the applications of drones using computer vision as well as artificial intelligence. Many researchers developed detections and tracking algorithms that focused on bounding boxes. Some researchers dedicated their work to semantic segmentation using aerial imagery or street views. Few had tried integrating detections and tracking for drones. However, fewer had tried using semantic segmentation for detection and tracking using drones [1], [2]. It is a challenge to achieve real time semantic segmentation for detection and tracking on drones as the drones had a limited amount of computation power. Since edge devices, often, did not have high processing power [3], steps must be taken to ensure that the algorithm was efficient in accommodating edge devices. After comparing several algorithms, DeepLabv3+ with Mobilenetv2 backbone was the fastest algorithm and was tested using one of the best edge devices available, NVIDIA Jetson TX2, for detections and tracking. Drones were able to conduct real time detections and tracking operations. However, the frame rate of this operation was lower than expected due to hardware limitations. This project indicated that efficient semantic segmentation detections and tracking using drones would be possible with future improvements in the processing power of hardware and/or improvement in the efficiency of algorithms.
author2 Low Kin Huat
author_facet Low Kin Huat
Er, Hwa Ming
format Final Year Project
author Er, Hwa Ming
author_sort Er, Hwa Ming
title Development of a machine-learning-based detection and tracking algorithm for drones
title_short Development of a machine-learning-based detection and tracking algorithm for drones
title_full Development of a machine-learning-based detection and tracking algorithm for drones
title_fullStr Development of a machine-learning-based detection and tracking algorithm for drones
title_full_unstemmed Development of a machine-learning-based detection and tracking algorithm for drones
title_sort development of a machine-learning-based detection and tracking algorithm for drones
publisher Nanyang Technological University
publishDate 2021
url https://hdl.handle.net/10356/150863
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