Cognigun: IoT-integrated firearm safeguarding with YOLOV9 algorithm for forensic analysis

ABSTRACT: Negligent use of firearms has been a rampant problem in the Philippines, may it be the New Year or the unregulated use of loose firearms. Gun control policies do exists but weak enforcement tends to make these policies inefficient. This leads to the extensive use of illegal firearms, with many being unregistered or lacking proper licensing, and improve firearm handling by both civilians and law enforcement. Due to these factors, the researchers designed CogniGun, and IoT-Integrated firearm safeguarding application that utilizes YOLOv9 algorithm for enhanced, real-time forensic analysis. This system aims to address the challenges faced when it comes to firearm safety and investigation by providing real-time object identification and location through a mobile application. CogniGun utilizes the Arduino 33 IoT as the main microcontroller and the NEO M8N Flight Controller GPS Module as its tracking module. These integrated circuits ensure tracking precision and data processing efficiency. CogniGun also utilizes the use of deep learning architectures for firearm recognition. A biometric authentication system using fingerprint technology was successfully implemented in handguns, addressing concerns related to unauthorized access and firearm misuse. These integrations allow law enforcement officers to conduct forensic ballistics analysis on-site with greater speed and precision. CogniGun was able to encompass the real-time object detection, location, and identification using YOLOv9. CogniGun also works smoothly on mobile phones as stated in a survey for user satisfaction and encompasses the different flaws in the current problems with regards to gun policies.

Filipiniana