Abstract: Mask detection in practical applications is facing the challenges of real-time requirement, multi-target, multi-pose, facial occlusion and complex scene. In this paper, an effective mask detection method based on YOLOv4 is proposed to deal with such challenging factors. In this method, a new neck block RFB-s was designed base on receptive field block (RFB) which replaced the spatial pyramid structure in YOLOv4 and introduced the dilated convolution and asymmetric convolution to expand the receptive field without parameter increase. In addition, an attention module was added to the prediction process to improve the information processing capability of the detection model. Experiments on mask detection dataset and open-source dataset verify the effectiveness of the proposed mask detection method by comparing mAP and running speeds under different network structures and algorithm.