SqueezeMaskNet: Real-Time Mask-Wearing Recognition for Edge Devices

Abstract

This paper presents SqueezeMaskNet, a lightweight convolutional neural network designed for real-time recognition of proper and improper mask usage. The model classifies four categories: masks worn correctly, masks covering only the mouth, masks not covering, and no mask. SqueezeMaskNet integrates seamlessly with existing face detection systems, removing the need for retraining. We propose using Fire modules for efficiency, along with attention mechanisms like efficient channel attention (ECA) and squeeze-and-excitation (SE) blocks for improved feature refinement. SqueezeMaskNet achieved 96.7% accuracy on the challenging FineFM test set and ran at 297 FPS on a GPU and up to 96 FPS on edge devices like a Jetson Orin NX. We also introduced ImproperTFM, a subset of real-world images focusing on improper mask usage, which enhanced the model accuracy when combined with FineFM data. Comparative experiments demonstrated SqueezeMaskNet’s superior performance, efficiency, and adaptability compared to MobileNet and EfficientNet, making it a practical solution for mask-wearing recognition across various devices and settings.