Weapon D - A Hybrid Approach for Detecting Weapons in Dark Environments Using Deep Learning Techniques

Abstract

Weapon detection, a crucial part of modern security is vital for public safety and strengthening security measures. Accurately spotting weapons in different places helps law enforcement, surveillance, and security. The ongoing improvements in weapon detection technologies not only boost preventive actions but also help respond quickly in emergencies, reducing risks and improving readiness. These technologies greatly assist law enforcement in identifying threats early and taking action promptly to keep the public safe and protect important places. Our proposed system suggests YOLOv7 with brightening algorithms, specially designed to detect weapons in low-light or nighttime situations. This shift from the existing to the proposed system marks a substantial improvement, addressing the challenges of nighttime weapon detection. This breakthrough not only enhances the scope of security measures but also underscores the adaptability of technology to real-world challenges. By catering to challenging dark settings, this advancement strengthens the foundation of public safety initiatives, offering a proactive approach to mitigating potential threats in diverse environments.