Real-Time Weapon Detection and Alerting System Using YOLOv7

With the growing need for advanced security solutions, this study introduces a real-time weapon detection and alerting system leveraging the capabilities of YOLOv7 to improve surveillance and safety in high-risk areas such as schools, hospitals, and airports. Our system is trained using a comprehensive dataset sourced from Google Images, covering a wide range of weapon types and contexts to enhance detection reliability and accuracy. When a weapon is identified in live surveillance footage, the system immediately sends an alert notification to the designated mobile device, enabling rapid response to potential threats. YOLOv7’s efficiency and accuracy in object detection allow our system to operate with high precision, even in complex and dynamic environments. This approach reduces false positives and ensures timely identification, making it suitable for real-world scenarios where quick and reliable responses are essential. Performance metrics, including detection accuracy, processing speed, and resilience across various settings, validate the system’s robustness and practicality for real-time security applications. By integrating this weapon detection and alerting mechanism into existing security frameworks, we aim to enhance situational awareness, streamline responses, and significantly improve the safety of public and private spaces. Future work will explore expanding the dataset and optimizing the system’s performance to accommodate diverse environmental conditions, aiming for broader scalability and adaptability in threat detection applications.