Please use this identifier to cite or link to this item:
Title: Visual obstacle detection for UAV
Authors: Kee, Yi Hao
Keywords: Engineering::Electrical and electronic engineering::Computer hardware, software and systems
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Kee, Y. H. (2022). Visual obstacle detection for UAV. Final Year Project (FYP), Nanyang Technological University, Singapore.
Project: A1148-211
Abstract: Recently, a great deal of computer vision's most innovative and state-of-the-art object detection algorithms have evolved around deep learning. With the rise of Deep Learning (DL) from Machine Learning (ML), it has emerged among the greatest technological advancements and inventions in the advancing age of our technological inventions. In the context of Deep Learning (DL), Convolutional Neural Networks (CNN) are regarded as one of the most critical components. Recognizing images and detecting objects is something that CNN has achieved significant success in. Nonetheless, CNN can be very large in size, and it carries an extremely high load of logical computations. As a result, a new type of CNN, called You Only Look Once (YOLO), was developed to detect and classify objects. Additionally, it provides a smaller overall architecture and faster computing capabilities.
Schools: School of Electrical and Electronic Engineering 
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
A1148-211 Final Report.pdf
  Restricted Access
5.18 MBAdobe PDFView/Open

Page view(s)

Updated on Sep 30, 2023

Download(s) 50

Updated on Sep 30, 2023

Google ScholarTM


Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.