现代信息科技2024，Vol.8Issue(10)：17-20,4.DOI:10.19850/j.cnki.2096-4706.2024.10.004
基于深度学习的高空坠落危险行为识别方法
A Method for Identifying High-altitude Falling Hazard Behavior Based on Deep Learning
摘要
Abstract
Deep Learning algorithms represented by Convolutional Neural Networks can extract human behavior features more accurately and effectively,applying Deep Learning to human behavior recognition and prediction has become a research hotspot.On the basis of the classic HRnet network structure,this paper proposes a new network model L-HRnet by improving the L-Swish activation function and introducing the Squeeze-and-Excitation module,which is used to determine whether the behavioral actions of construction worker during high-altitude operations are dangerous.Behavioral classification and recognition experiments are conducted on the public dataset HMDB51,and the results show that the improved network structure L-HRnet had significantly better recognition accuracy than HRnet,effectively improving the protection level of high-altitude workers.关键词
神经网络/深度学习/高空坠落/动作识别Key words
neural network/Deep Learning/high-altitude falling/action recognition分类
信息技术与安全科学引用本文复制引用
聂程,叶翔,方百里,孙嘉兴,张滔..基于深度学习的高空坠落危险行为识别方法[J].现代信息科技,2024,8(10):17-20,4.基金项目
南方电网科技项目(KJXM20222024) （KJXM20222024）
