Application of a Novel and Improved VGG-19 Network in the Detection of Workers Wearing Masks
Jian Xiao, Jia Wang, Shaozhong Cao, Bilong Li
Abstract
In order to work and travel safely during the outbreak of COVID-19, a method of security detection based on deep learning is proposed by using machine vision instead of manual monitoring. To detect the illegal behaviors of workers without masks in workplaces and densely populated areas, an improved convolutional neural network VGG-19 algorithm is proposed under the framework of tensorflow, and more than 3000 images are collected for model training and testing. Using VGG-19 network model, three FC layers are optimized into one flat layer and two FC layers with reduced parameters. The softmax classification layer of the original model is replaced by a 2-label softmax classifier. The experimental results show that the precision of the model is 97.62% and the recall is 96.31%. The precision of identifying the workers without masks is 96.82%, the recall is 94.07%, and the data set provided has a high precision. For the future social health and safety to provide favorable test data.