基于改进YOLOv4-tiny算法的手势识别

Gesture Recognition Based on Improved YOLOv4-tiny Algorithm

随着人机交互的发展,手势识别越来越重要。同时,移动端应用发展迅速,将人机交互技术在移动端实现是一个发展趋势。该文提出一种改进YOLOv4-tiny的手势识别算法。首先,在YOLOv4-tiny网络基础上,添加空间金字塔池化(SPP)模块,融合了图像的局部和全局特征,增强网络的准确定位能力。其次,在YOLOv4-tiny原网络的3个最大池化层和新增SPP模块后各添加一个1×1的卷积模块,减少了网络的参数,提高网络的预测速度。在此基础上,利用K-means++算法生成适合检测手势的先验框,加快网络检测手势。在手势数据集NUSII上,与YOLOv3-tiny算法和YOLOv4-tiny算法进行对比,改进算法平均精度均值(mAP)为100%,每秒传输帧数(fps)为377,可以快速准确地检测识别手势。将该文改进算法部署在安卓(Android)移动端,实现了移动端实时的手势检测与识别,对人机交互的发展有很大的研究意义。

With the development of human-computer interaction,gesture recognition is becoming more and more important.At the same time,mobile terminal applications are developing rapidly,it is a development trend to implement human-computer interaction technology on the mobile terminal.An improved YOLOv4-tiny gesture recognition algorithm is proposed.Firstly,on the basis of YOLOv4-tiny network,the Spatial Pyramid Pooling(SPP)module is added to integrate the local and global features of the image to enhance the accurate positioning ability of the network.Secondly,a 1×1 convolution is added after the 3 maximum pooling layers of the original YOLOv4-tiny network and the newly added SPP module,which reduces the network parameters and improves the prediction speed of the network.On this basis,the K-means++algorithm is used to generate an anchor box suitable for detecting gestures to speed up the network detection of gestures.In the gesture dataset NUS-II,compared with the YOLOv3-tiny algorithm and the YOLOv4-tiny algorithm,the improved algorithm mean Average Precision(mAP)is 100%,frames per second(fps)is 377,which can detect and recognize gestures quickly and accurately.The improved algorithm of this paper is deployed on the Android mobile terminal to realize the real-time gesture detection and recognition on the mobile terminal,which has great research significance for the development of human-computer interaction.