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The outbreak of contagious diseases has emphasized the need for preventive measures, notably the use of face masks. In this context, a reliable face mask detection system becomes crucial to ensure compliance with safety protocols. This paper presents a novel approach utilizing deep learning techniques for automated face mask detection.
Dataset Collection and Preprocessing:
Collected and pre-processed a dataset containing images of individuals both with and without masks across different settings. This dataset formed the foundation for training and evaluating the proposed deep learning model.
Deep Learning Model Architecture:
Designed and implemented a Convolutional Neural Network (CNN) architecture to accurately detect whether face masks are present or absent in images. The model leverages state-of-the-art techniques to achieve high accuracy and robustness.
Training and Evaluation:
Trained the model using the prepared dataset, employing techniques like data augmentation and transfer learning to enhance its performance. We rigorously assessed the model’s accuracy, precision, recall, and F1 score using evaluation metrics.
Results and Discussion:
The experimental results demonstrate the effectiveness of the proposed system in accurately detecting face masks in real-time scenarios. The system shows robustness against variations in lighting, angles, and mask types, making it suitable for diverse environments.
Conclusion and Future Work:
The developed face mask detection system using deep learning proves to be a promising solution for ensuring adherence to safety measures. Then, a comprehensive examination of additional improvements and strategic assessments for practical deployment are deliberated, so laying the groundwork for future explorations in this area.