Enhancing Web-Based Object Recognition: Employing Pretrained Models for Accurate and Efficient Visual Recognition in Real-Time Scenarios

Verma, Ankit (2024) Enhancing Web-Based Object Recognition: Employing Pretrained Models for Accurate and Efficient Visual Recognition in Real-Time Scenarios. Masters thesis, Dublin, National College of Ireland.

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Abstract

This study examines the conception, creation, and evaluation of an online object detection system using the Flask framework and the YOLOv8 paradigm. The system offers real-time object identification, an easy-to-use user interface, and robust management for a variety of automation, monitoring, and surveillance applications. The YOLOv8 model’s accuracy is demonstrated by measures of precision and recall, and its utility is demonstrated by statistical evaluations of system responsiveness and user interface interactions. When the database is full and retrieval performance is strong, the system is more dependable. Scalability assessments make ensuring that the system works under different loads. The protection of sensitive data and system resilience are given top priority in robustness and security considerations. The experiment’s successful completion lays the groundwork for additional advancements and real-world uses.

Item Type:	Thesis (Masters)
Supervisors:	Name Email Trinh, Anh Duong UNSPECIFIED
Subjects:	Q Science > QA Mathematics > Electronic computers. Computer science T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science Q Science > QA Mathematics > Computer software > Computer Security T Technology > T Technology (General) > Information Technology > Computer software > Computer Security Q Science > QH Natural history > QH301 Biology > Methods of research. Technique. Experimental biology > Data processing. Bioinformatics > Artificial intelligence > Computer vision Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Artificial intelligence > Computer vision
Divisions:	School of Computing > Master of Science in Artificial Intelligence
Depositing User:	Ciara O'Brien
Date Deposited:	30 May 2025 14:31
Last Modified:	30 May 2025 14:31
URI:	https://norma.ncirl.ie/id/eprint/7719

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