東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Dual Vision: Enhancing Autonomous Na...

Boadu, Bernard O.

Linked to FindBook

Google Book

Amazon

博客來

Dual Vision: Enhancing Autonomous Navigation With AutoGuardian CNN and YOLO Object Detection Synergy.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Dual Vision: Enhancing Autonomous Navigation With AutoGuardian CNN and YOLO Object Detection Synergy./
Author:	Boadu, Bernard O.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
Description:	149 p.
Notes:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
Subject:	Automotive engineering. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31145077
ISBN:	9798382188607

Dual Vision: Enhancing Autonomous Navigation With AutoGuardian CNN and YOLO Object Detection Synergy.
Boadu, Bernard O.

Dual Vision: Enhancing Autonomous Navigation With AutoGuardian CNN and YOLO Object Detection Synergy. - Ann Arbor : ProQuest Dissertations & Theses, 2024 - 149 p.

Source: Dissertations Abstracts International, Volume: 85-10, Section: B.

Thesis (D.Engr.)--The George Washington University, 2024.

In the growing field of autonomous vehicle technology, the dependability and precision of computer vision systems are crucial. This study compares the performance of two advanced object detection models: the bespoke AutoGuardian Convolutional Neural Network (CNN) and the popular YOLOv8 model. The main goal is to explore the AutoGuardian CNN model's potential as a verification layer to improve the YOLO Object Detection model, thus boosting the effectiveness of computer vision in autonomous vehicles. The study uses a thorough evaluation framework with standard measures such as accuracy, precision, and recall, and it assesses the influence of the AutoGuardian CNN as a verification layer on the YOLO model's output.Initial results indicate that including the AutoGuardian CNN as a verification layer shows great potential in reducing incorrect identifications and improving the overall dependability of the YOLOv8 model in object detection across different environmental settings. This study enhances the scholarly discussion on object detection models in self-driving cars and provides valuable guidance for creating and implementing more robust and dependable computer vision systems in safety-critical scenarios.The study seeks to explore the complex relationship between various object identification models and their ability to operate in the presence of visual noise, with the goal of advancing autonomous vehicle technology. This research has consequences that go beyond autonomous driving, providing vital insights for the wider field of computer vision and its use in safety-critical systems.

ISBN: 9798382188607Subjects--Topical Terms:

2181195
Automotive engineering.
Subjects--Index Terms:

AutoGuardian Convolutional Neural Network

Dual Vision: Enhancing Autonomous Navigation With AutoGuardian CNN and YOLO Object Detection Synergy.
LDR:02946nmm a2200433 4500 001 2401457
005 20241022112620.5
006 m o d
007 cr#unu||||||||
008 251215s2024 ||||||||||||||||| ||eng d
020 $a 9798382188607
035 $a (MiAaPQ)AAI31145077
035 $a AAI31145077
035 $a 2401457
040 $a MiAaPQ $c MiAaPQ
100 1 $a Boadu, Bernard O. $0 (orcid)0009-0008-1521-434X $3 3771553
245 1 0 $a Dual Vision: Enhancing Autonomous Navigation With AutoGuardian CNN and YOLO Object Detection Synergy.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2024
300 $a 149 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
500 $a Advisor: Raghavan, Vijay;Etemadi, Amir.
502 $a Thesis (D.Engr.)--The George Washington University, 2024.
520 $a In the growing field of autonomous vehicle technology, the dependability and precision of computer vision systems are crucial. This study compares the performance of two advanced object detection models: the bespoke AutoGuardian Convolutional Neural Network (CNN) and the popular YOLOv8 model. The main goal is to explore the AutoGuardian CNN model's potential as a verification layer to improve the YOLO Object Detection model, thus boosting the effectiveness of computer vision in autonomous vehicles. The study uses a thorough evaluation framework with standard measures such as accuracy, precision, and recall, and it assesses the influence of the AutoGuardian CNN as a verification layer on the YOLO model's output.Initial results indicate that including the AutoGuardian CNN as a verification layer shows great potential in reducing incorrect identifications and improving the overall dependability of the YOLOv8 model in object detection across different environmental settings. This study enhances the scholarly discussion on object detection models in self-driving cars and provides valuable guidance for creating and implementing more robust and dependable computer vision systems in safety-critical scenarios.The study seeks to explore the complex relationship between various object identification models and their ability to operate in the presence of visual noise, with the goal of advancing autonomous vehicle technology. This research has consequences that go beyond autonomous driving, providing vital insights for the wider field of computer vision and its use in safety-critical systems.
590 $a School code: 0075.
650 4 $a Automotive engineering. $3 2181195
650 4 $a Engineering. $3 586835
650 4 $a Computer engineering. $3 621879
650 4 $a Information technology. $3 532993
653 $a AutoGuardian Convolutional Neural Network
653 $a Autonomous navigation
653 $a Autonomous vehicle
653 $a Contrastive learning
653 $a RetinaNet
653 $a Self-supervised learning
690 $a 0800
690 $a 0540
690 $a 0537
690 $a 0489
690 $a 0464
710 2 $a The George Washington University. $b Cybersecurity Analytics. $3 3707269
773 0 $t Dissertations Abstracts International $g 85-10B.
790 $a 0075
791 $a D.Engr.
792 $a 2024
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31145077