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 compliance, occupational safety, and public health monitoring, even in post-pandemic environments where real-time and non-intrusive surveillance is required. Traditional deep learning models provide strong recognition performance but are often impractical for deployment on embedded and edge devices due to their computational and energy demands. Recent research has therefore emphasized lightweight and hybrid architectures that seek to preserve detection accuracy while reducing model complexity, inference latency, and power consumption. This review presents an architecture-centered synthesis of face mask detection systems, examining conventional convolutional models, lightweight convolutional networks such as the MobileNet family, and hybrid frameworks that integrate efficient backbones with optimized detection heads. Comparative analysis of reported results highlights key trade-offs between accuracy, efficiency, and deployment feasibility under heterogeneous datasets, evaluation protocols, and hardware settings. Open challenges, including improper mask detection, domain adaptation, model compression, and the extension of mask detection toward broader Personal Protective Equipment (PPE) compliance monitoring, are discussed to outline a forward-looking research agenda. Overall, this review consolidates current understanding of architectural design strategies for face mask detection and provides guidance for developing scalable, robust, and real-time deep learning solutions suitable for embedded and mobile platforms.