Aiming to address the issues of complex detection processes, numerous parameters, low accuracy, and slow execution speed in current driver fatigue detection algorithms, we propose a lightweight model based on an improved YOLOv8n-Pose. This model optimizes the structure of YOLOv8n-Pose. Firstly, Ghost convolution is introduced into the backbone network to reduce the number of model parameters and unnecessary convolution computations. Secondly, a Slim-neck is introduced to fuse features of different sizes extracted by the backbone network, accelerating network prediction calculations.Additionally, an occlusion-aware attention module (SEAM) is added to the neck part to emphasize the facial region in images and weaken the background, improving keypoint localization accuracy. Finally, a GNSC-Head structure is proposed in the detection head part, which incorporates shared convolution and optimizes the BN layers of traditional convolution with more stable GN layers, effectively saving model parameter space and computational resources. Experimental results show that compared with the original algorithm, the improved YOLOv8n-Pose increases mAP@0.5 by 0.9%, reduces parameter count and computational cost by 50%, and increases FPS by 8%. The final fatigue driving recognition rate reaches 93.5%. Verified through experiments, this algorithm maintains high detection accuracy while being lightweight and effectively recognizes driver status, providing strong support for deployment on vehicle edge devices.