Term of Award

Spring 2025

Degree Name

Master of Science, Information Technology

Document Type and Release Option

Thesis (open access)

Copyright Statement / License for Reuse

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.

Department

Department of Information Technology

Committee Chair

Atef Shalan

Committee Member 1

Lei Chen

Committee Member 2

Jongyeop Kim

Abstract

In recent years, AI-driven automation has revolutionized the field of object detection and computer vision, enabling sophisticated and efficient solutions across various industries. This research explores the latest advances and techniques in improving AI-driven automation for object detection and computer vision applications. We examine state-of-the-art deep learning models and frameworks that have contributed to significant improvements in accuracy and speed and highlight the generative results. The focus is on exploring the real-time processing capabilities that have expanded the applicability of these technologies in real-world scenarios. Furthermore, we investigate image integration and video data to improve precision detection and contextual understanding. Challenges such as model interpretability and computational resource requirements are discussed, along with potential strategies to mitigate these issues. Through comprehensive case studies and experimental results, this paper demonstrates the transformative impact of AI-enhanced automation in domains such as smart surveillance and educational automation. Finally, we propose future research directions that address current limitations and unlock new possibilities for AI-driven object detection and computer vision systems.

Research Data and Supplementary Material

Yes

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