Abstract

Firearm violence in educational settings represents a critical challenge to institutional security globally. This study proposes an innovative system for early detection of firearms using advanced computer vision techniques, convolutional neural networks (CNN), and computational intelligence in educational institutions. The main objective was to develop a deep learning model based on CNN architectures capable of identifying and alerting the presence of weapons in real time, improving traditional security protocols. The methodology implemented a systematic approach comprising three fundamental stages: data collection, model training, and experimental validation. For model training, a set of 9,000 rigorously labeled images processed using the Roboflow tool was used. The YOLOv5 algorithm, implemented in Google Colab, allows training a detection model with high-precision parameters. The system was integrated directly with the security camera infrastructure of the university campus. Experimental results demonstrated remarkable efficiency: 99% accuracy, 93% detection rate and 97% overall performance. The system can continuously monitor spaces, triggering instant alerts upon identification of potential threats, with a processing capacity that significantly exceeds traditional human monitoring. The findings suggest that machine vision is a promising strategy for strengthening preventive security in educational institutions. Future research will focus on optimizing the model's adaptability to diverse environments and environmental conditions, potentially expanding its implementation to different types of institutions.