江汉大学学报(自然科学版) ›› 2021, Vol. 49 ›› Issue (5): 79-87.doi: 10.16389/j.cnki.cn42-1737/n.2021.05.012

• 人工智能 • 上一篇    下一篇

基于YOLOv3 的手势识别技术

凌利a,陶俊*a,吴瑰b   

  1. 江汉大学 a. 人工智能学院;b. 工程训练中心,湖北 武汉 430056
  • 发布日期:2021-10-12
  • 通讯作者: 陶俊
  • 作者简介:凌利(1998— ),男,硕士生,研究方向:深度学习,计算机视觉。
  • 基金资助:
    武汉市教育科学“十三五”规划重点课题(2017A071);武汉市教育局教学研究项目(2019068);江汉大学研究生科研创新基金项目(Jhdxyjs17kz003)

Gesture Recognition Technology Based on YOLOv3

LING Lia,TAO Jun*a,WU Guib   

  1. a. School of Artificial Intelligence;b. Engineering Training Center,Jianghan University,Wuhan 430056,Hubei,China
  • Published:2021-10-12
  • Contact: TAO Jun

摘要: 基于YOLOv3 的手势识别检测系统使用darknet53.conv.74 模型进行训练与学习,通过对输入图像进行平滑以及二值化处理分离不必要图像信息,提高识别准确率,实现视频实时手势识别模型,然后利用Python Tkinter 模块开发出图形交互界面。结果表明,模型在识别精确度上能达到76. 76%,有着目前主流深度学习目标检测算法相当的精确度,在识别速度上优于其他目前主流深度学习目标检测算法,在处理自然交互信息方面具有优势,为人机交互提供有效手段。

关键词: 深度学习, 卷积神经网络, 手势识别, TensorFlow, YOLO

Abstract: The gesture recognition system based on YOLOv3 uses darknet53.conv.74 model to train and learn,and separates input images unnecessary information by smoothing and binarizing algorithm,so as to improve the recognition accuracy and realize a video real-time gesture recognition model. The graphical interface is developed by Python Tkinter. The results show that the recognition accuracy of the model can reach 76. 76%, which is comparable with the current mainstream deep learning target detection algorithm. The model is superior to other current mainstream deep learning target detection algorithms in recognition rate,and has advantages in dealing with natural interactive information,which provides an effective means for human-computer interaction.

Key words: deep learning, convolutional neural network, gesture recognition, TensorFlow, YOLO

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