A Lightweight End-to-end Network for Wearing Mask Recognition on Low-resolution Images

In realistic scenarios, resolution is still one of the major problems in wearing mask recognition. Due to the large distances between surveillance cameras and human faces, facial images captured by low-power devices usually have low resolution and lead to poor recognition results. To address the above issue, we propose a lightweight end-to-end network to reconstruct Super-resolution (SR) images and achieve wearing mask recognition. Besides, to apply to challenging real applications, we combine hardware devices and software technology to simulate the recognition process of wearing masks in real scenarios. To demonstrate the effectiveness of the method, we comprehensively evaluate our proposed method by comparing it with state-of-the-art methods. The recognition accuracy using super-resolution is 98.44%, outperforming RepVGG-A2 (97.00%) and ResNet34 (93.75%). Moreover, experimental results show that the number of parameters and FLOPs in our recognition model is 9.34 million and 1.85 billion, respectively, both of which outperform traditional CNN methods (20 million+ parameters and 3 billion+ FLOPs). The performance of our recognition system is competitive with state-of-the-art methods in terms of low memory usage and computational complexity, showing that the system can be cost-effectively and widely applied in real-world environments and thus has potential applications in respiratory disease prevention.