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Face Mask-Wearing Detection Model Based on Loss Function and Attention Mechanism.

Zhong Wang1, Wu Sun1, Qiang Zhu1

  • 1School of Computer Science and Technology, Hefei Normal University, Hefei 230601, China.

Computational Intelligence and Neuroscience
|July 22, 2022
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Summary
This summary is machine-generated.

This study enhances face mask detection accuracy in complex environments using a YOLOv5s model with an attention mechanism and CIoU loss. The improved model achieves 90.96% mAP, significantly outperforming traditional methods for real-world safety applications.

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Deep Learning

Background:

  • Face mask detection is crucial for public health and safety, especially during epidemics.
  • Existing methods struggle with accuracy due to occlusion, varied lighting, and crowded scenes.

Purpose of the Study:

  • To develop a robust neural network model for accurate face mask detection in challenging environments.
  • To improve feature utilization and positioning accuracy in mask detection systems.

Main Methods:

  • An enhanced YOLOv5s model incorporating attention mechanisms (CBAM, SE, CA) and CIoU loss function was proposed.
  • A custom dataset of 7,958 mask-wearing images and unlabeled images was curated.
  • The model's performance was evaluated using mean Average Precision (mAP) on a validation set.

Main Results:

  • The proposed model achieved a mAP of 90.96% on the validation set, surpassing traditional deep learning approaches.
  • Integration of attention mechanisms and CIoU loss significantly boosted detection accuracy.
  • The model demonstrated effectiveness in real-world scenarios, meeting daily detection needs.

Conclusions:

  • The novel YOLOv5s-based model offers superior performance for face mask detection in complex conditions.
  • The findings suggest the model's viability for real-time safety monitoring and epidemic control.
  • Attention mechanisms and advanced loss functions are key to improving object detection accuracy.