With the ongoing development of smart homes, cities, and campuses, there is growing awareness of fall risk safety concerns. Existing models often struggle with false positives and missed detections in complex scenarios such as strong lighting, nighttime conditions, small distant objects, and obstructive backgrounds, as well as privacy invasion issues. To address these challenges, this research proposes an improved YOLOv8n object detection method for complex scenarios, named YOLOv8-MING (MA-Iou-ResNet-GSConv). The method integrates the ResNet residual network to enhance accuracy through increased network depth while maintaining low computational complexity. It introduces a novel multimodal attention mechanism (MA) to effectively extract crucial channel and spatial information, improving the understanding of postural motion in images. For lightweight deployment, GSConv lightweight convolution is incorporated to accelerate detector head construction, significantly reducing model parameters. Additionally, the loss function is optimized through reparameterization to improve detection performance and convergence speed. To address privacy concerns in fall detection, an Autoblurface masking model is integrated with a visual heatmap method, effectively protecting facial privacy while handling complex data, significantly enhancing the privacy security of detection results. The proposed fall detection model shows broad application potential.