Aiming at the problems of complex network structure, difficult training and difficult deployment in mobile , and embedded devices of single-stage target detection in deep learning, a lightweight target detection algorithm based on adaptive spatial feature fusion is proposed. The proposed algorithm takes YOLOv4 as the basic framework of the network and uses lightweight MobileNet as the feature extraction network to reduce the network depth and training difficulty and improve the detection speed; an adaptive spatial feature fusion (ASFF) method is used to improve the poor effect of PANet on multi-scale feature fusion; by adding the output dimension of the network, the Gaussian algorithm is used to model the new dimension and output the uncertainty of the position of the prediction box; finally, the position loss function is redefined to improve the accuracy of position regression. The proposed algorithm takes the mask wearing detection robot during the epidemic as the deployment carrier to test the face mask wearing. The experimental results show that the detection accuracy of the proposed algorithm reaches 95.92% and the detection speed reaches 19 frame/s. Compared with the original algorithm and other mainstream detection algorithms, the proposed algorithm is more suitable for deployment in mobile and embedded devices to realize real-time detection.