Gun Detection in Real-Time, using YOLOv5 on Jetson AGX Xavier

Marks Dextre, Oscar Rosas, Jesus Lazo, Juan C. Gutiérrez

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Resumen

Automating the detection of weapons from video surveillance images is a difficult task due to: lighting, focus, resolution, among others. Solving this problem would be very useful for citizen security purposes. In this sense, this research work trains a weapon detection system based on YOLOv5 (You Only Look Once) for different data sources, reaching an accuracy of 98.56 % in video surveillance images, performing Real-Time inferences reaching 33 fps on Nvidia’s Jetson AGX Xavier which is a good result compared to other existing research in the state of the art.

Idioma originalInglés
Título de la publicación alojadaProceedings - 2021 47th Latin American Computing Conference, CLEI 2021
EditorialInstitute of Electrical and Electronics Engineers Inc.
ISBN (versión digital)9781665495035
DOI
EstadoPublicada - 2021
Evento47th Latin American Computing Conference, CLEI 2021 - Virtual, Cartago, Costa Rica
Duración: 25 oct. 202129 oct. 2021

Serie de la publicación

NombreProceedings - 2021 47th Latin American Computing Conference, CLEI 2021

Conferencia

Conferencia47th Latin American Computing Conference, CLEI 2021
País/TerritorioCosta Rica
CiudadVirtual, Cartago
Período25/10/2129/10/21

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Publisher Copyright:
©2021 IEEE

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