Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/159074
Title: Deep learning based tools for drone surveillance and detection in adverse environmental conditions
Authors: Chia, Wei Fong
Keywords: Engineering::Aeronautical engineering::Accidents and air safety
Issue Date: 2022
Publisher: Nanyang Technological University
Source: Chia, W. F. (2022). Deep learning based tools for drone surveillance and detection in adverse environmental conditions. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/159074
Project: C034
Abstract: This study explores the design of an automated Machine pipeline comprising state-of-theart image enhancement and object detection algorithms as an aid for air traffic controllers to quickly spot and identify drone incursions in the surrounding airspace. Experiments were conducted to evaluate the drone detection performance of the Machine pipeline by itself, of human capabilities by themselves and a Human-Machine collaboration with human operators and the Machine pipeline. Results suggest that by human effort alone, drone detectable range and spatial awareness were lacking for effective detection. By machine effort alone, presence of errors limits use of the Machine pipeline in actual air traffic management where there is a low tolerance for errors for safety reasons. Rather, a Human-Machine collaboration is shown to be optimal as the Human and Machine components compensate for each other’s shortcomings while complementing in strong points, leading to improved drone detection performance.
URI: https://hdl.handle.net/10356/159074
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Student Reports (FYP/IA/PA/PI)

Files in This Item:
File Description SizeFormat 
Chia Wei Fong_C034_FYP Submission_DR-NTU.pdf
  Restricted Access
Final Year Report1.49 MBAdobe PDFView/Open

Page view(s)

29
Updated on Dec 1, 2022

Download(s)

3
Updated on Dec 1, 2022

Google ScholarTM

Check

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