Face Mask Detection in Real-Time using MobileNetv2

نویسندگان

چکیده

Face mask detection has made considerable progress in the field of computer vision since start Covid-19 epidemic. Many efforts are being to develop software that can detect whether or not someone is wearing a mask. methods and strategies have been used construct face models. A created model for detecting masks described this paper, which uses “deep learning”, “TensorFlow”, “Keras”, “OpenCV”. The MobilenetV2 architecture as foundation classifier perform real-time identification. present dedicates 80 percent training dataset 20% testing, splits into 80% validation, resulting final with 65 training, 15 testing. optimization approach experiment “stochastic gradient descent” momentum (“SGD”), learning rate 0.001 0.85. validation accuracy rose until they reached their maximal peak at epoch 12, 99% 98% accuracy. model's losses both reduced lowest loss 0.050% less than 0.025%. This system allows missing appropriate particularly resource-efficient when it comes deployment, thus be employed safety. So, technique merged embedded application systems public places services airports, trains stations, workplaces, schools ensure subordination guidelines current version compatible IP non-IP cameras. Web desktop apps use live video feed detection. program also linked entrance gates, allowing only those who enter. It shopping malls universities.

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ژورنال

عنوان ژورنال: International journal of engineering and advanced technology

سال: 2021

ISSN: ['2249-8958']

DOI: https://doi.org/10.35940/ijeat.f3050.0810621