Empirical Analysis of Face Mask Detection Using Deep Learning
- Authors: Arunima Jaiswal1, Khushboo Kem2, Aruna Ippili3, Lydia Nenghoithem Haokip4, Nitin Sachdeva5
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View Affiliations Hide Affiliations1 Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University For Women, India 2 Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University For Women, India 3 Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University For Women, India 4 Department of Computer Science & Engineering, Indira Gandhi Delhi Technical University For Women, India 5 IT Department , Galgotias College of Engineering , Greater Noida , India
- Source: Demystifying Emerging Trends in Green Technology , pp 166-180
- Publication Date: February 2025
- Language: English
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Covid-19 has been highly destructive to human health across the globe. Ever since it was discovered in 2019, the pandemic has continued to take the lives of millions. Global efforts like wearing a face mask in public areas have led to the decline of infection, which has given rise to many face mask detection models to ensure that individuals are wearing their masks properly. In this paper, we aim to compare five deep learning models for face mask detection on two different datasets namely the face mask detection dataset (DS1) and the face mask 12k images dataset (DS2). The different models that we have implemented are YOLOV3, YOLOV5, ResNet 50, MobileNet V2, and VGG-16 . The results are evaluated on the grounds of precision, recall, mean average precision (mAP), and accuracy.
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