Lightweight Deep Learning Models for Face Mask Detection in Real-Time Edge Environments: A Review and Future Research Directions

Automated face mask detection remains an important component of hygiene compli-ance, occupational safety, and public health monitoring, even in post-pandemic envi-ronments where real-time, non-intrusive surveillance is required. Traditional deep learning models offer strong recognition performance but are often impractical for de-ployment on embedded and edge devices due to their computational complexity. Re-cent research has therefore emphasized lightweight and hybrid architectures that maintain high detection accuracy while reducing model size, inference latency, and energy consumption. This review provides an architecture-centered examination of face mask detection systems, analyzing conventional convolutional models, light-weight convolutional networks such as the MobileNet family, and hybrid frameworks that integrate efficient backbones with optimized detection heads. A comparative per-formance analysis highlights key trade-offs between accuracy and computational effi-ciency, emphasizing the constraints of real-world and edge-oriented deployments. Open challenges, including improper mask detection, domain adaptation, model com-pression, and extending detection systems toward broader compliance-monitoring ap-plications, are discussed to outline a forward-looking research agenda. This work con-solidates current understanding of architectural strategies for mask detection and of-fers guidance for developing scalable, robust, and real-time deep learning solutions suitable for embedded and mobile platforms.