Detección de armas de fuego en videovigilancia mediante aprendizaje profundo: revisión de literatura
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https://doi.org/10.70881/mcj/v4/n4/181Palabras clave:
visión por computadora, redes neuronales convolucionales, detección de objetos, computación en el borde, seguridadResumen
Los sistemas de videovigilancia dependen de observadores humanos con capacidad de atención limitada, lo que impide convertir la cobertura visual en una alerta oportuna ante un arma de fuego. El objetivo de este trabajo fue revisar y organizar la literatura sobre la detección de armas de fuego en videovigilancia mediante aprendizaje profundo, con énfasis en los conjuntos de datos, los modelos, la evaluación y el despliegue. Se recopilaron artículos publicados entre 2016 y 2026 con resultados experimentales sobre escenarios de videovigilancia y conjuntos de datos documentados. Los hallazgos muestran que el problema está condicionado ante todo por los datos: el tamaño reducido del arma dentro del cuadro constituye el principal cuello de botella, junto con la oclusión, la iluminación y los objetos visualmente próximos al arma. Además, los detectores de dos etapas, los de una etapa y los esquemas en cascada ofrecen compromisos distintos entre falsos positivos y velocidad, aunque las cifras publicadas no autorizan una ordenación entre arquitecturas, pues proceden de conjuntos propios y métricas heterogéneas. Se concluye así que estos sistemas identifican la apariencia de un arma y no determinan una amenaza, por lo que el protocolo de verificación humana posterior a la alerta resulta tan relevante como el propio detector
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Derechos de autor 2026 Cristhian Briones-Montalvo, José Steven Cordero-Bazurto, Orlando Erazo, Valeria Torres-Lindao, José Luis Toapanta (Autor/a)
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial 4.0.
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