ABSTRACT
To solve the problem of missing model detection for small targets, occluded targets, and crowded targets scenarios in mask detection, we propose an end-to-end mask-wearing detection model based on a bidirectional feature fusion network. Firstly, to improve the ability of the model to extract features, we introduce the modified EfficientNet as the backbone network in the model. Secondly, for the prediction network, we introduce depth-wise separable convolution to reduce the amount of model parameters. Lastly, to improve the performance of the model on small targets and occluded targets, we propose a bidirectional feature fusion network and introduce a spatial pyramid pooling network. We evaluate our proposed method on a real-world data set. The mean average precision of the model is 87.54%. What’s more, our proposed method achieves better performance than the comparison approaches in most cases.