G-YOLOv5: A Face Mask Detector That Balances Effectiveness and Real-Time

In crowded scenarios, face mask detection algorithms still suffer from the target misdetection and omission, and the difficulty of reconciling real time and effectiveness. To address these issues, we present an G-YOLOv5 face mask detection algorithm. First, we adopt a weighted bidirectional feature pyramid network as the feature fusion network to enhance multiscale feature fusion; second, we utilize the WIoU loss function to strengthen the impact of good anchor frames while weakening the detrimental impact of poor quality anchor frames. Finally, considering the real-time and effectiveness issues of detector, we designed the Ghostv2-C3 module instead of the C3 module of the backbone to improve the inference speed of the model. The results of the experiment demonstrates that compared with other detectors, our detector plays a positive role in solving the target misdetection and omission and balancing the real-time nature of the model.