An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection
This work addresses the need for efficient mask-wearing monitoring in public health during the COVID-19 pandemic, but it is incremental as it builds upon existing YOLOv5 with modifications.
The paper tackles the problem of achieving both high precision and real-time performance in face mask detection by proposing an improved lightweight YOLOv5 model, which increases inference speed by 28.3% and improves precision by 0.58% compared to the original YOLOv5, achieving a mean average precision of 95.2%.
Coronavirus 2019 has brought severe challenges to social stability and public health worldwide. One effective way of curbing the epidemic is to require people to wear masks in public places and monitor mask-wearing states by utilizing suitable automatic detectors. However, existing deep learning based models struggle to simultaneously achieve the requirements of both high precision and real-time performance. To solve this problem, we propose an improved lightweight face mask detector based on YOLOv5, which can achieve an excellent balance of precision and speed. Firstly, a novel backbone ShuffleCANet that combines ShuffleNetV2 network with Coordinate Attention mechanism is proposed as the backbone. Afterwards, an efficient path aggression network BiFPN is applied as the feature fusion neck. Furthermore, the localization loss is replaced with alpha-CIoU in model training phase to obtain higher-quality anchors. Some valuable strategies such as data augmentation, adaptive image scaling, and anchor cluster operation are also utilized. Experimental results on AIZOO face mask dataset show the superiority of the proposed model. Compared with the original YOLOv5, the proposed model increases the inference speed by 28.3% while still improving the precision by 0.58%. It achieves the best mean average precision of 95.2% compared with other seven existing models, which is 4.4% higher than the baseline.