Real-Time Mask-wearing Detection on Edge Devices via Lightweight Convolutional Neural Networks

Abstract

Real-time mask-wearing compliance monitoring with autonomous surveillance plays a key role in decision-making in industrial, healthcare, and educational systems. Existing face mask-wearing monitoring systems often rely on computationally intensive or cloud-based models, making them unsuitable for accurate, low-latency, real-time deployment on resource-constrained mobile and edge devices, particularly for multiclass mask-wearing compliance detection. In this regard, the primary focus is on an efficient autonomous edge vision model with a lightweight architecture. The proposed lightweight edge vision framework addresses the limitations of existing cloud-dependent and computationally intensive face mask-wearing monitoring systems by enabling accurate, low-latency, real-time multiclass compliance detection on resource-constrained mobile and edge devices. It consists of real-time data pipeline design, MobileNetV2-based model development, hyperparameter optimization, and real-time performance evaluation. It is implemented with a lightweight CNN architecture optimized for resource-constrained environments, which classifies into three categories: correctly masked, unmasked, and improperly masked. This is demonstrated by deploying the model on real-time devices, such as mobile devices and camera modules, that capture video streams, thereby addressing challenges such as varied lighting conditions and facial orientations. The proposed system achieves a high accuracy of 99.35% while maintaining low latency, making it suitable for public health surveillance in crowded settings. The findings highlight the potential of edge-based AI in enhancing compliance with safety protocols in public spaces.