To address the challenges of false positives and missed detections in face mask detection within dense environments, we propose the MF-YOLO face mask detection model. First, a feature map convolution approach is introduced to transform the feature map information into the initial weights of convolutional kernels, accelerating the convergence of model training. Additionally, a residual self-attention module is designed to capture fine edge position features by combining global feature extraction with sliding window…