Harnessing Deep Learning in Computer Vision for Effective Face Mask Detection in the Global COVID-19 Crisis

• Autor(a) principal: Regone, Wiliam
• Data de Publicação: 2023
• Outros Autores: Henrique de Oliveira, Luciel
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: GEPROS. Gestão da Produção. Operações e Sistemas
• Texto Completo: https://revista.feb.unesp.br/gepros/article/view/2988
• topic: Computer vision; Convolutional Neural Network; Face mask; Computer Vision; Convolutional Neural Network; Face Mask Detection

Purpose: This study aims to scrutinize the integration of deep learning algorithms within the sphere of computer vision, with a concentrated focus on proficiently detecting face mask usage amidst the global COVID-19 pandemic. Theoretical Framework: The research is grounded in the theoretical underpinnings of deep learning, a branch of artificial intelligence, and its application in computer vision. It explores the advancements in machine learning algorithms capable of complex image processing and pattern recognition, essential for identifying face mask usage in various settings. Methodology/Approach: The research adopts a methodological approach involving the design and development of a deep learning model. This model is trained on a diverse dataset encompassing images of individuals with and without face masks. Python, along with libraries such as OpenCV, Keras, and TensorFlow, forms the backbone of the implementation, facilitating the processing and analysis of image data. Findings: The study's findings reveal that the developed model demonstrates a high degree of accuracy, with a 99% success rate in test image predictions, showcasing the effectiveness of deep learning in image recognition tasks. This underscores the model's proficiency in identifying face mask usage, a critical factor in controlling the spread of airborne viruses like COVID-19. Research, Practical & Social Implications: This research contributes significantly to the field of computer vision, offering practical applications in public health monitoring and societal well-being. The model's ability to accurately detect face mask usage paves the way for enhanced pandemic management strategies and reinforces the role of technology in public health initiatives. Originality/Value: This study innovates within existing research by applying deep learning in computer vision for addressing the COVID-19 crisis. It uniquely focuses on developing technological solutions for efficient and cost-effective monitoring of face mask usage, emphasizing prevention. Keywords: Computer Vision, Convolutional Neural Network, Face Mask Detection.