Mask detection for COVID-19 through of Deep Learning using OpenCV and Cascade Trainer GUI
DOI:
https://doi.org/10.26423/rctu.v8i1.572Keywords:
Machine Learning, Deep Learning, convolutional networks, false positivesAbstract
The covid-19 pandemic caused a health crisis worldwide, one of the recommendations of scientists and governments to avoid contagion is the use of a mask, therefore this article was focused on which software is developed that allows the mask to be detected in different scenarios using the Python programming language through the cv2, os, numpy and imutils libraries, using convolutional neural networks that are more efficient than common neural networks, which were trained with the Cascade Trainer GUI software, using Different amounts of databases from 400 to 1400 images to compare different types of mask detection system accuracy. However, the first database did not obtain a good pressure due to a low number of false positives, so as more data is used, the precision increased considerably until obtaining a precision of 92% with a mask and a 100% no mask.
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