Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks

Maverick Poma Rosales, Ciro Rodriguez, Yuri Pomachagua, Carlos Navarro

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

Resumen

The purpose of the research is to develop a study of models of Convolutional Neural Networks using YOLOv3 and YOLOv5s (Only look once) for the detection of firearms trained with images of weapons obtained from the research database (Soft Computing and Intelligent Information Systems A University of Granada Research Group) in order to test the effectiveness of the algorithm and its training in real images of video cameras in an accessible database, to demonstrate that although the images are of low quality, the chances of identifying the firearm are high.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
Páginas61-65
Número de páginas5
ISBN (versión digital)9781728176956
DOI
EstadoPublicada - 22 sep 2021
Evento13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021 - Lima, Perú
Duración: 22 sep 202123 sep 2021

Serie de la publicación

NombreProceedings - 2021 IEEE 13th International Conference on Computational Intelligence and Communication Networks, CICN 2021

Conferencia

Conferencia13th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2021
País/TerritorioPerú
CiudadLima
Período22/09/2123/09/21

Nota bibliográfica

Publisher Copyright:
© 2021 IEEE.

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