YOLOv8-Based Firearm Detection for Real-Time Video Surveillance Application

Abstract

Real-time firearms detection is key for ensuring safety in public spaces and security-sensitive environments. In this paper, we present a computer vision approach using the YOLOv8 object detection model to identify whether an individual is carrying a firearm. The model’s architecture allows for rapid and accurate detection, making it suitable for real-time applications. A custom dataset of images was used for training and validation, with preprocessing techniques applied to enhance model generalization. The proposed system was evaluated on key performance metrics, achieving high precision and recall in detecting both exposed and partially concealed firearms. The ability to operate in real-time offers a promising solution for improving security measures in various contexts. Future work will focus on refining the system to reduce false positives and expanding the dataset to include a broader range of scenarios.