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 original | Inglés |
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Título de la publicación alojada | Proceedings - 2021 47th Latin American Computing Conference, CLEI 2021 |
Editorial | Institute of Electrical and Electronics Engineers Inc. |
ISBN (versión digital) | 9781665495035 |
DOI | |
Estado | Publicada - 2021 |
Evento | 47th Latin American Computing Conference, CLEI 2021 - Virtual, Cartago, Costa Rica Duración: 25 oct. 2021 → 29 oct. 2021 |
Serie de la publicación
Nombre | Proceedings - 2021 47th Latin American Computing Conference, CLEI 2021 |
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Conferencia
Conferencia | 47th Latin American Computing Conference, CLEI 2021 |
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País/Territorio | Costa Rica |
Ciudad | Virtual, Cartago |
Período | 25/10/21 → 29/10/21 |
Nota bibliográfica
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