基于注意力机制的光线昏暗条件下口罩佩戴检测

Detection of Mask Wearing in Dim Light Based on Attention Mechanism

自新冠肺炎疫情爆发以来,口罩佩戴检测成为疫情防控的必备操作。该文针对在光线昏暗条件下口罩佩戴检测准确率较低的问题,提出了将注意力机制引入YOLOv5网络进行口罩佩戴检测的方法。首先对训练集图片使用图像增强算法进行预处理,然后将图片送入到引入了注意力机制的YOLOv5网络中进行迭代训练,完成训练后,将最优权重模型保存并在测试集上测试。实验结果表明,在注意力的加持下,该模型能有效增强人脸和口罩等关键点信息的提取,提高模型的鲁棒性,在光线昏暗条件下对口罩佩戴的检测准确率能达到92%,能够有效满足实际需求。

Since the outbreak of COVID-19,the detection of wearing masks has become a necessary measure for epidemic prevention and control.To solve the problem about low accuracy of mask wearing detection under dim lighting conditions,a method of mask wearing detection combining attention mechanism with YOLOv5 network model is proposed,which uses image enhancement algorithm to pre-process the training set pictures,and then put these pictures to YOLOv5 network with attention mechanism for iterative training.After training,the optimal weight is saved and the best model is used to test the accuracy on the test set.The experimental results show that the YOLOv5 network model with attention mechanism can effectively enhance the extraction of key points such as face and mask and improve the robustness of the model.The accuracy of mask wearing can reach 92%under dim lighting conditions,which can effectively meet the actual needs.