Abstract
Background At present, the new crown virus is spreading around the world, causing all people in the world to wear masks to prevent the spread of the virus. Problem. People with masks have found a lot of trouble for face recognition. Finding a feasible method to recognize faces wearing masks is a problem that needs to be solved urgently. Method This paper proposes a mask recognition algorithm based on improved YOLO-V4 neural network and the integrated SE-Net and DenseNet network and introduces deformable convolution. Conclusion Compared with other target detection networks, the improved YOLO-V4 neural network used in this paper improves the accuracy of face recognition and detection with masks to a certain extent.
Highlights
In 2020, the new crown epidemic broke out globally. is sudden epidemic caught countries all over the world by surprise
The pandemic has been more than a year judging from the current global epidemic situation, the situation is still not optimistic
In the current epidemic situation, the wearing of masks can reduce the chance of infection and has a positive effect on personal protection and global epidemic control
Summary
The new crown virus is spreading around the world, causing all people in the world to wear masks to prevent the spread of the virus. People with masks have found a lot of trouble for face recognition. Finding a feasible method to recognize faces wearing masks is a problem that needs to be solved urgently. Is paper proposes a mask recognition algorithm based on improved YOLO-V4 neural network and the integrated SE-Net and DenseNet network and introduces deformable convolution. Compared with other target detection networks, the improved YOLO-V4 neural network used in this paper improves the accuracy of face recognition and detection with masks to a certain extent
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