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Comparative analysis of deep learning models for detecting face mask.

M Vickya Ramadhan¹, Kahlil Muchtar^1,2, Yudha Nurdin¹

¹Department of Electrical and Computer Engineering, Universitas Syiah Kuala, Banda Aceh, 23111, Indonesia.

Procedia Computer Science

|January 16, 2023

Summary

This summary is machine-generated.

EfficientNetB4 achieved the highest accuracy (95.77%) in automated face mask detection, outperforming other computer vision models. This advancement aids in enforcing health protocols to curb COVID-19 spread.

Keywords:

Binary Classification Deep Learning Face mask detection

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Area of Science:

Computer Vision
Artificial Intelligence
Public Health

Background:

COVID-19 transmission remains high in Indonesia due to inconsistent mask-wearing.
Automated face mask detection systems are crucial for enforcing health protocols.
Computer vision algorithms offer advanced solutions for real-time mask detection.

Purpose of the Study:

To evaluate and compare the performance of various deep learning architectures for face mask detection.
To identify the most effective computer vision model for accurate and reliable face mask detection.
To inform the selection of optimal algorithms for public health surveillance.

Main Methods:

Comparative analysis of ResNet50, VGG11, InceptionV3, EfficientNetB4, and YOLO architectures.
Utilized the MaskedFace-Net dataset for training and evaluation.
Employed transfer learning with pre-trained weights from the COCO dataset for YOLO models.

Main Results:

EfficientNetB4 demonstrated superior accuracy at 95.77%.
YOLOv4 achieved 93.40% accuracy, followed by InceptionV3 (87.30%) and YOLOv3 (86.35%).
ResNet50 (84.41%) and VGG11 (84.38%) showed comparable, lower performance, while YOLOv2 yielded 78.75%.

Conclusions:

EfficientNetB4 is the recommended architecture for high-accuracy face mask detection.
The study provides valuable insights for developing effective automated systems to monitor mask compliance.
Accurate face mask detection technology can significantly contribute to controlling infectious disease outbreaks.