Mask-net: Detection of Correct Use of Masks Through Computer Vision

Abstract

This paper focuses on creating a system for recognizing the correct use of a mask through computer vision techniques. Research was carried out with aims of establishing the criteria for the creation of custom datasets, which were used to train, validate and test a pair of deep learning models, Mask-net and I-Mask-net. Both were designed with similar architectures, making use of Transfer Learning Techniques. The results given by training showed that the fine tuning carried out was adequate, while the tests carried out showed that the models have an acceptable level of accuracy, reaching 85.47% for Mask-net and 85.96% for IMask-net, additionally supported by the obtained precision, recall and F1-Score calculations.