基于卷积神经网络的视频监控摄像机图像中的枪支检测

Maverick Poma Rosales, Ciro Rodríguez, Yuri Pomachagua, Carlos Navarro
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引用次数: 0

摘要

研究的目的是开发一项使用YOLOv3和YOLOv5s(只看一次)的卷积神经网络模型的研究,用于检测从研究数据库(格拉纳达大学研究小组的软计算和智能信息系统)获得的武器图像进行训练的枪支,以便测试该算法及其在可访问数据库中摄像机真实图像训练的有效性。为了证明虽然图像质量很低,但识别枪支的几率很高。
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Firearm Detection in Images of Video Surveillance Cameras with Convolutional Neural Networks
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.
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