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Dual Vision: Enhancing Autonomous Na...

Boadu, Bernard O.

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

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dual Vision: Enhancing Autonomous Navigation With AutoGuardian CNN and YOLO Object Detection Synergy./
作者:	Boadu, Bernard O.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2024,
面頁冊數:	149 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-10, Section: B.
Contained By:	Dissertations Abstracts International85-10B.
標題:	Automotive engineering. -
電子資源:	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.
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