Abstract: In order to address a series of problems such as blurred lighting and unclear occlusions that may occur in face detection in practical application scenarios, and promote the detection accuracy, the network model for face mask object detection was engineered based on the YOLOv5s algorithm, and through the following strategies: Adding an attention mechanism to the backbone network part of YOLOv5s and different network levels of the neck; Replacing the loss function of the YOLOv5s model for the bounding box regression task to accelerate convergence and improve regression accuracy. The experimental results show that when the Coordinate attention mechanism is added after the P5 layer of the backbone part in YOLOv5s and the P4 and P5 layers of the Neck part, and the original loss function is replaced with SIoU Loss, the accuracy rendered by the engineered YOLOv5s algorithm is increased by 1.5% compared with that of the benchmark model.