Marks Dextre, Oscar Rosas, Jesus Lazo, J. C. Gutiérrez
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引用次数: 2
Abstract
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.
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