Investigating the capability YOLOv8 object detection framework employing deep learning and machine learning algorithms, to accurately identify objects

Investigating the capability YOLOv8 object detection framework employing deep learning and machine learning algorithms, to accurately identify objects

Ikenna Anyanwu, Criminal Justice, CAHSS, North Carolina Agricultural and Technical State University
Aysia Hammond, Criminal Justice, CAHSS, North Carolina Agricultural and Technical State University
KaMari Smith, Criminal Justice, CAHSS, North Carolina Agricultural and Technical State University
Jalia Brown, Criminal Justice, CAHSS, North Carolina Agricultural and Technical State University
Alexandria Ray, Criminal Justice, CAHSS, North Carolina Agricultural and Technical State University
Carla Coates, Criminal Justice, CAHSS, North Carolina Agricultural and Technical State University

Description

The rapid advancements in artificial intelligence (AI), coupled with the proliferation of the Internet and the Internet of Things (IoT), are transforming the application of AI technologies, particularly in object detection. This research explores the feasibility of using a standard laptop webcam as an input source for real-time object detection. Specifically, it investigates the capability of You Only Look Once version 8 (YOLOv8), a state-of-the-art object detection framework employing deep learning and machine learning algorithms, to accurately identify objects (e.g., weapons) from webcam input. This work establishes a baseline model for object detection using deep convolutional neural networks, aiming to inform future research, development, and training, especially for users with limited technical expertise.