Visual obstacle detection for UAV

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 adva...

Full description

Saved in:
Bibliographic Details
Main Author: Kee, Yi Hao
Other Authors: Xie Lihua
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2022
Subjects:
Online Access:https://hdl.handle.net/10356/159066
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: Nanyang Technological University
Language: English
Description
Summary: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.