轻量化二维人体骨骼关键点检测算法综述

随着移动设备和嵌入式设备的发展，对二维人体骨骼关键点检测网络提出了更高的要求。设计轻量化神经网络是解决网络参数量大、计算量大的重要方法。首先，介绍了基于神经网络的二维人体骨骼关键点检测中常用的数据集、主流方法和轻量级神经网络；然后，对近几年基于神经网络的轻量化人体姿态估计方法进行了分类和总结，根据神经网络轻量化方式将二维骨骼关键点检测方法归成四类：轻量化特征提取网络、深度可分离卷积、Dense连接机制和Lightweight瓶颈结构，并分析了它们的优缺点和轻量化手段；最后，介绍了常用的评价指标，并对改进后的轻量化方法进行了实验数据对比，结合当前研究所面临的问题及未来的发展趋势进行了总结。

A Survey of Lightweight Two-dimensional Human Skeleton Keypoint Detection Algorithms

With the development of mobile and embedded devices, higher demands are placed on 2D human skeletal keypoint detection networks. Designing lightweight neural networks is an important approach to solve the problem of large number of network parameters and large computational effort. First, the mainstream methods and lightweight neural networks for 2D human skeletal keypoint detection based on neural networks are introduced; then, the lightweight human pose estimation methods based on neural networks in recent years are classified and summarized, and the 2D skeletal keypoint detection methods are grouped into four categories according to the lightweight way of neural networks: lightweight backbone networks, deep separable convolution, Dense connection mechanism and Lightweight Bottleneck, and analyzed their advantages and disadvantages and lightweighting means; finally, the common data sets and corresponding evaluation metrics are introduced, and the improved lightweighting methods are compared with experimental data. A summary and outlook are given in relation to the current challenges and future development trends of the research.