Abstract: Fatigue driving is one of the leading causes of traffic accidents.To address the issues of insufficient feature extraction caused by factors such as camera angles and environmental lighting,as well as poor model adaptability across different datasets,this paper proposes a novel transfer learning-based fatigue driving detection framework.The framework employs a dual-branch feature extraction and fusion architecture combining CNN and Transformer,leveraging their complementary strengths to enhance feature representation and comprehensively capture both local and global facial features of drivers.To improve the model’s adaptive capability between source and target domains,a segmented domain adapt-ation strategy is adopted.Adversarial domain adaptation and multi-kernel maximum mean discrepancy(MK-MMD) are applied during the feature extraction stage,while MK-MMD and minimum class confusion(MCC) loss are further introduced during the feature fusion stage to enhance cross-domain adapt-ability.Experimental results on two datasets with significant feature disparities demonstrate that the proposed framework achieves6 detection accuracies of 93.3%(A→B) and 75.1%(B→A) on the target domain,significantly improving the model’s adaptability and robustness.

Key words: Deep learning, Domain adaptation, Transfer learning, Fatigue detection, Convolutional network, Transformer