Abstract

This research develops a computer vision-based system to detect face masks in nuclear power plants, ensuring compliance with safety regulations . The system employs image processing techniques to enhance and preprocess images from surveillance cameras, which are then fed into a Convolutional Neural Network (CNN) model for classification . The CNN model is trained on a large dataset of images collected from various power plant scenarios, achieving high accuracy in detecting individuals with or without face masks . The system detects mask-wearing individuals in real-time, enabling prompt action to ensure personnel safety and compliance . This automated system reduces manual monitoring efforts, enhances overall safety, and supports compliance with regulations . The proposed system demonstrates the effectiveness of CNN-based image processing in face mask detection, offering a reliable solution for nuclear power plants and potential applications in other industries

References

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