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Research on Mask-Wearing Detection Algorithm Based on Improved YOLOv5.

Shuyi Guo1, Lulu Li1, Tianyou Guo1

  • 1School of Mechanical Engineering, North China University of Water Resources and Electric Power, No. 36 Beihuan Road, Zhengzhou 450045, China.

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|July 9, 2022
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Summary
This summary is machine-generated.

This study introduces YOLOv5-CBD, an improved algorithm for detecting mask-wearing in complex scenes. The enhanced model achieves 96.7% average detection accuracy, significantly improving mask detection performance.

Keywords:
BiFPNCoordinate AttentionYOLOv5object detection

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Area of Science:

  • Computer Vision
  • Artificial Intelligence
  • Public Health Technology

Background:

  • COVID-19 transmission is significantly reduced by mask-wearing.
  • Detecting mask compliance in real-world scenarios is challenging due to occlusion, dense crowds, and small targets.
  • Existing detection algorithms often suffer from misdetection and missed detections in complex natural scenes.

Purpose of the Study:

  • To develop an advanced algorithm for accurate mask-wearing detection in complex environments.
  • To improve the robustness and accuracy of object detection models for public health applications.
  • To address limitations in current mask detection systems, particularly concerning missed and misdetections.

Main Methods:

  • Proposed YOLOv5-CBD algorithm integrating Coordinate Attention mechanism for feature enhancement.
  • Replaced the original feature pyramid network with a weighted bidirectional feature pyramid network for improved feature fusion.
  • Combined Distance Intersection over Union (DIoU) with Non-Maximum Suppression (NMS) to mitigate missed detections of overlapping targets.

Main Results:

  • The YOLOv5-CBD model achieved an average detection accuracy of 96.7%.
  • Demonstrated a 2.1% improvement in average detection accuracy compared to the baseline YOLOv5 model.
  • Successfully addressed issues of misdetection and missed detection in complex natural scenes.

Conclusions:

  • YOLOv5-CBD offers a significant advancement in mask-wearing detection accuracy and reliability.
  • The proposed enhancements effectively handle complex environmental factors like occlusion and dense targets.
  • This algorithm holds potential for effective implementation in public health surveillance and compliance monitoring.