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Robust face mask detection in complex scenarios using YOLOv8 and context-aware convolutions
Yingjie Wei1, Huili Li2, Yuanfei He1
1College of Information Engineering, Zhoukou Vocational College of Arts and Science, Zhoukou, 466000, China.
Scientific Reports
|July 2, 2025
Summary
This study introduces an advanced face mask detection algorithm designed for challenging conditions, significantly improving accuracy and real-time performance in complex environments.
Area of Science:
- Computer Vision
- Artificial Intelligence
Background:
- Face mask detection faces challenges like occlusion, lighting variations, and distance.
- Existing algorithms struggle with accuracy in complex environments.
Purpose of the Study:
- To develop a robust face mask detection algorithm for complex environments.
- To enhance detection accuracy and real-time performance.
Main Methods:
- A comprehensive face mask dataset was constructed.
- The YOLOv8 architecture was enhanced with depth-separable convolutions and SENet attention.
- Context-aware convolutions and a DAM-Head were integrated for improved feature extraction and detection.
Main Results:
- The proposed algorithm achieved 98.11% mean Average Precision (mAP).
- A Frames Per Second (FPS) rate of 135.61 was recorded.
- Superior performance compared to mainstream algorithms was demonstrated.
Conclusions:
- The enhanced algorithm effectively addresses challenges in face mask detection.
- The method offers high accuracy and real-time capabilities for practical applications.
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