%0 Journal Article
%T 基于深度学习的手势识别研究<br>Research on Gesture Recognition Based on Deep Learning
%A 崔兆文
%A 刘肖飞
%A 夏诗楠
%A 王旭亚
%A 瞿竟
%J Journal of Sensor Technology and Application
%P 570-578
%@ 2331-0243
%D 2024
%I Hans Publishing
%R 10.12677/jsta.2024.124062
%X 本文提出了一种基于深度学习的手势识别系统，该系统利用YOLO捕获手部信息，利用ReXNet捕捉手部21个关键点，利用ResNet-50实现对手势进行分类。试验结果表明，YOLOv5可以准确地将手部信息提取出来，准确度达98.5%；ReXNet捕捉手部21个关键点的准确度达97.2%；ResNet-50手势识别的准确度达98.2%。表明该方法能够检测图像中手势相关信息，对于手势控制有一定的借鉴意义。<br />
This article proposes a gesture recognition system based on deep learning, which uses YOLO to capture hand information, ReXNet to capture 21 key points of the hand, and ResNet-50 to classify gestures. The experimental results show that YOLOv5 can accurately extract hand information with an accuracy of 98.5%; the accuracy of ReXNet in capturing 21 key points of the hand reached 97.2%; the accuracy of ResNet-50 gesture recognition reached 98.2%. This method can detect gesture-related information in images and has certain reference significance for gesture control.
%K 手势识别，YOLOv5，ReXNet，ResNet-50<br>Gesture Recognition
%K YOLOv5
%K ReXNet
%K ResNet-50
%U http://www.hanspub.org/journal/PaperInformation.aspx?PaperID=91044