Fatigue driving detection is critical for traffic safety. Real-time monitoring and accurate identification of a driver’s fatigue level, coupled with an early warning system, can significantly reduce the risk of accidents caused by fatigue. Addressing the challenges of small micro-expression targets and complex background environments in current driving fatigue detection, this paper proposes an improved driving fatigue detection model—YOLOv10-GMF. The model incorporates an enhanced global grouped coordinate attention (GGCA) module, which improves feature representation by weighting feature maps with global information and generating attention maps, thereby enhancing the model’s ability to capture micro-expression features under fatigue conditions. Additionally, a multi-dimension fusion attention (MDFA) module is integrated, which combines multi-scale dilated convolutions with spatial and channel attention mechanisms in parallel to strengthen the model’s recognition ability for image features in complex driving environments. To further optimize the training process, a feedback-driven loss function (FDL) is introduced, effectively accelerating model convergence and improving prediction accuracy. Ablation experiments demonstrate that the YOLOv10-GMF model achieves a detection accuracy of 98.1%, a 14.5% improvement over YOLOv10, with a detection speed of 64.3 fps. Through real vehicle embedded deployment tests, the average fatigue detection process takes 19.0 ms, and the model fully meets the real-time monitoring needs for fatigue driving.