Marks Dextre, Oscar Rosas, Jesus Lazo, J. C. Gutiérrez
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引用次数: 2
摘要
从视频监控图像中自动检测武器是一项艰巨的任务，因为:照明，焦点，分辨率等。解决这个问题将对公民安全非常有用。从这个意义上说，本研究工作训练了一个基于YOLOv5 (You Only Look Once)的武器检测系统，针对不同的数据源，在视频监控图像中达到98.56%的准确率，在Nvidia的Jetson AGX Xavier上执行实时推理，达到33 fps，与其他现有的研究相比，这是一个很好的结果。
Gun Detection in Real-Time, using YOLOv5 on Jetson AGX Xavier
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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