Objective Driver fatigue is a major factor contributing to traffic accidents especially in low-light environments at night where fatigue detection becomes more challenging. To address the difficulty in extracting the facial features of drivers under low-light conditions a method combining local and global features was proposed for driver fatigue detection in low-light environments. Methods The proposed approach comprised a low-light enhancement module and a dual-stream detection module. The low-light enhancement module incorporated a channel attention mechanism SE-Net into the feature extraction layer of the multi-branch low-light enhancement network MBLLEN . This enhanced the algorithm?? s focus on the driver?? s facial feature information improving the quality of low-light images of drivers at night. The dual- stream detection module employed a dual-branch network structure to extract global and local facial features. First the original nighttime images of drivers were enhanced using the low-light enhancement algorithm. These enhanced images were then input into the dual-stream network where the two-branch structured network used ResNet-34 and ResNet-18 to extract global and local features respectively. Finally the local and global feature information was fused using an ensemble learning method and the final fatigue state prediction was generated with different weight contribution ratios. Results The experimental results show that the proposed method exhibited good performance on the NTHU-DDD dataset, and the final detection accuracy was 90. 10%. Conclusion The method proposed in this paper shows high accuracy in nighttime driver fatigue detection and brings important progress in the field of nighttime driver fatigue detection which is of far-reaching and important significance.