During the period of 2019-nCoV controlling, to prevent the spread of the virus, it is necessary to regulate the coverage of mask wearing in densely populated places such as airports and stations. In order to effectively monitor the coverage of mask wearing of crowd, this paper proposes a lightweight mask detection algorithm based on improved YOLOv4-tiny. Following the backbone network of YOLOv4-tiny, a spatial pyramid pooling structure is introduced to pool and fuse the input features at multi-scale, which makes the receptive field of the network enhanced. Then, combined with the path aggregation network, multi-scale features are fused and enhanced repeatedly in two paths to improve the expressive ability of feature maps. Finally, label smoothing is utilized to optimize the loss function for modifying the over-fitting problem in the training process. The experimental results show that the proposed algorithm achieves 94.7% AP and 85.7% AP on mask target and face target respectively (at real-time speed of 76.8 FPS on GeForce GTX 1050ti), which is 4.3% and 7.1% higher than that of YOLOv4-tiny. The proposed algorithm meets the accuracy and real-time requirements of mask detection tasks in various scenes.