| Abstrakt: |
A new report from Tun Hussein Onn University of Malaysia discusses the importance of automating face mask detection in public areas to maintain public health, particularly during the COVID-19 pandemic. The study proposes a You Only Look Once (YOLO) based deep learning C-Mask model for real-time face mask detection and recognition via drone surveillance in public spaces. The model utilizes algorithms such as Convolutional Neural Network (CNN), Cross-Stage Partial (CSP) DarkNet53, Path Aggregation Network (PANet), and Spatial Pyramid Pooling Network (SPPNet) to enhance the efficiency and accuracy of face mask detection. The model achieved an overall accuracy of 92.20% in detecting face masks in real-time video streams under various conditions. [Extracted from the article] |
