To address the challenges in mask detection for faces in dense crowd scenarios, particularly due to information loss from crowd occlusion, small detection targets, and low resolution, improved YOLOv8 algorithm for dense crowd mask detection is proposed. This approach replaces the FPN structure in YOLOv8 with a GD mechanism to solve the issue of missing cross-layer information transmission. The GD mechanism uses a unified module to collect and integrate information from all layers, enabling lossless cross-layer information transmission and enhancing the network′s feature extraction capabilities. The ODconv module is inserted into the GD mechanism to learn the information transmitted by GD along four dimensions, improving the model′s detection accuracy for low-resolution images and small targets. Additionally, a SCSBD is introduced to lighten the YOLOv8 detection head, which occupies a significant proportion, thereby balancing the model size. Experimental results show that the improved network has increased precision, recall, and mean average precision by 13.6%、1.5% and 6.9%, respectively, with an 81.1% accuracy in mask detection on faces. The model′s weight file is only 25 MB, and its size is between YOLOv8s and Gold-YOLO-S, achieving a balance between size and accuracy.