摘要
Abstract
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.关键词
打磨焊接作业/防护面罩/坐标注意力机制/VGG-16网络/深度学习/卷积神经网络(CNN)/智能识别Key words
grinding and welding work/protective face mask/coordinate attention mechanism/VGG-16 network/deep learning/convolutional neural network(CNN)/intelligent recognition分类
信息技术与安全科学
