Detection of face mask with convolutional neural network models to reduce COVID19 spread

In recent years, Covid-19, which has become a reality of our lives and has become a pandemic for the whole world, increases the rate of infection and even variants of it begin to appear if the necessary precautions are not strictly followed. As the measures published by WHO and necessary to be taken are taken, the fight against the disease may become easier. Although it is difficult to comply with the measures, if care is taken to comply, the disease is either milder or the disease is not easily caught. One of the most important of these measures is to pay attention to the use of masks in crowded areas. After the importance of mask use was supported by research, inspections for the use of masks in crowded places such as some shopping malls, health institutions and schools began. However, since it is difficult for a human to perform these inspections, mask detection studies have begun to be carried out with deep learning methods, which are frequently used today. In this thesis, it is aimed to perform mask detection using transfer learning based models. DenseNet121, EfficientNetV2M, NasNetMobile, InceptionV3 and VGG19 deep learning models were used with a total of 906 images with the data set available on the Kaggle website. As a result of the experimental evaluations, it was seen that the best success rate was obtained with the NasNetMobile model, with 99.35% accuracy, 99% precision, 99% recall and 99% f1 scores.