基于改进VGG-16深度学习网络的防护面罩佩戴识别

Protective Mask Wearing Recognition Based on Improved VGG-16 Deep Learning Network

为高效识别打磨焊接作业人员是否佩戴防护面罩,提出了改进VGG-16的深度学习模型,构建了基于VGG-16的深度特征提取网络挖掘图像的重要信息。为解决VGG-16网络对图像局部特征和全局结构信息捕捉的不足,建立基于坐标注意力的空间位置信息感知机制,增强对图像位置和通道信息的关注。最后,建立基于多层全连接层的分类网络输出识别结果。实验表明,该模型对打磨焊接作业人员是否佩戴防护面罩的识别准确率、精确率、召回率和F1分数分别达到95.88%、96.48%、95.25%和95.86%,具有比传统人工巡检方法更好的效果。

To efficiently identify whether polishing and welding operators are wearing protective masks,an improved deep learning model of VGG-16 network was proposed,and a deep feature extraction network based on VGG-16 was constructed to mine important information of images.To address the shortcomings of the VGG-16 network in capturing local image features and global structural information,a spatial position information perception mechanism based on coordi-nate attention was established to enhance the attention to image position and channel information.Finally,a classification network based on multiple fully connect-ed layers was established to output recognition re-sults.The experimental results showed that the recog-nition accuracy,precision,recall,and F1 score of this model for whether polishing and welding opera-tors wore protective masks reached 95.88%,96.48%,95.25%,and 95.86%,respectively,which had bet-ter performance than traditional manual inspection methods.