An Improved Lightweight YOLOv5 Model Based on Attention Mechanism for Face Mask Detection

TL;DR

This paper introduces an improved lightweight face mask detection model based on YOLOv5 that balances high precision and real-time speed, utilizing a novel backbone, efficient feature fusion, and training strategies, outperforming existing models.

Contribution

The paper proposes a novel backbone ShuffleCANet with attention mechanism, an efficient feature fusion network BiFPN, and training enhancements to improve face mask detection performance.

Findings

Inference speed increased by 28.3% compared to YOLOv5

Achieved 95.2% mean average precision on AIZOO dataset

Outperformed seven existing models in accuracy and speed

Abstract

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.