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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Dual Vision :/
其他題名:	Enhancing Autonomous Navigation With AutoGuardian CNN and YOLO Object Detection Synergy.
作者:	Boadu, Bernard O.
面頁冊數:	1 online resource (149 pages)
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:	click for full text (PQDT)
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. - 1 online resource (149 pages)

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

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

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798382188607Subjects--Topical Terms:

1104081
Automotive engineering.
Subjects--Index Terms:

AutoGuardian Convolutional Neural NetworkIndex Terms--Genre/Form:

554714
Electronic books.

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