Year: 2025
Type: article
Abstract: The escalation of security threats has highlighted the implementation of intelligent surveillance systems with the ability to identify threats in advance. This study suggests a real-time deep learning-based weapon detection system with the YOLOv8, an object detection deep model. The system inspects real-time video streams for identifying and locating weapons such as guns and knives at high accuracy and low latency. By leveraging the improved-optimized architecture of YOLOv8, the model achieves high accuracy of detection with computational performance maintained high enough for real-time applications. Deep training on weapon dataset annotations enables strong performance in a broad surveillance setting. The developed methodology enhances situational awareness and provides secure autonomous response, and thus it is a great candidate for use in safety- critical environments like public transportation centers, schools, and critical infrastructure facilities. Index Terms — Real-Time Surveillance, Weapon Detection, Deep Learning, YOLOv8, Object Detection, Smart Surveillance, Public Safety, Threat Detection.
DOI: 10.55041/ijsrem50726
Author: Dr.Shalima Sulthana
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4411395883