Improved Mask Wearing Detection Algorithm for SSD
Houkang Deng, Jin Zhang, Lingyu Chen, Meiling Cai
Abstract
Abstract Coronavirus disease is seriously affecting the world in 2019. Wearing a mask in public places is a major way to protect people. However, there are few studies on mask detection based on image analysis. In this paper, an improved mask wearing inspection algorithm based on the SSD algorithm is proposed. The SSD algorithm is improved to add a face mask wearing detection task. Based on the original SSD model, the algorithm improves the mask wearing detection capability by introducing inverse convolution and feature fusion in combination with an attention mechanism to filter out the information to be retained. A dataset containing 3656 tensor images was created and manually labeled for network training. Experiments on this dataset show that the algorithm has good accuracy for mask wearing inspection.