Date of Award
8-2024
Document Type
Project
Degree Name
Master of Science in Computer Science
Department
School of Computer Science and Engineering
First Reader/Committee Chair
Bilal Khan
Abstract
In this research, we advance the domain of public safety by developing a machine learning model that utilizes the YOLO v8 architecture for real-time detection of firearms in video streams. A diverse and extensive dataset, capturing a range of firearms in varying lighting and backgrounds, was meticulously assembled and preprocessed to enhance the model's adaptability to real-world scenarios. Leveraging the YOLO v8 framework, known for its real-time object detection accuracy, the model was fine-tuned to accurately identify firearms across different shapes and orientations.
The training phase capitalized on GPU computing and transfer learning to expedite the learning process while preserving a high degree of precision, recall, and F1-score in the model’s performance metrics. Through iterative optimization post-evaluation, the model's detection capabilities were further refined.
Deployed in an Online Mode, the model operates on a cloud-based platform, utilizing the scalability and computational prowess of Google Cloud Platform (GCP). A dedicated application, designed with Flutter, delivers a consistent user interface that streamlines interaction, complemented by Google Cloud Functions that manage data communication seamlessly.
This project demonstrates the considerable promise of the YOLO v8 architecture for real-time surveillance and public safety applications. The outcomes are promising, and future endeavors will aim to broaden the validation with more extensive video datasets.
Recommended Citation
Kunchala, Harish Kumar Reddy, "REAL-TIME GUN DETECTION IN VIDEO STREAMS USING YOLO V8" (2024). Electronic Theses, Projects, and Dissertations. 1996.
https://scholarworks.lib.csusb.edu/etd/1996