Driver fatigue remains a leading cause of road accidents, responsible for 24% of crashes. While yawning serves as an early behavioral indicator of fatigue, existing approaches face significant challenges due to the presence of systematic noise in video-annotated datasets arising from coarse temporal annotations. Training robust machine learning (ML) models requires rich supervisory labels that help learn salient features from the training data. Moreover, efficient on-device training and inference of models on edge devices is crucial in driver fatigue detection tasks to enable accurate real-time decisions on vehicles without reliance on cloud infrastructure. To address this issue, we develop a semi-automated labeling pipeline with human-in-the-loop verification to annotate YawDD videos to YawDD+ frame-level annotations, enabling more accurate model training on edge platforms such as NVIDIA Jetson NANO. Training the established MNasNet classifier and YOLOv11 detector architectures on YawDD+ improves frame accuracy by up to 6% and mAP by 5% over video-level supervision, achieving 99.34% classification accuracy and 95.69% detection mAP on Jetson NANO and AGX. Moreover, MNasNet completed the epoch time in just 8.69 min/epoch while delivering up to 115 frames-per-second (FPS) inference time on AGX, confirming that enhanced data quality alone supports on-device driver fatigue monitoring systems without server-side computation. The YawDD+ dataset and trained models are available online.

翻译：驾驶员疲劳仍是导致道路事故的主要原因，占碰撞事故的24%。尽管打哈欠可作为疲劳的早期行为指标，但现有方法因视频标注数据集中粗粒度时间标注引入的系统噪声而面临显著挑战。训练鲁棒的机器学习模型需要丰富的监督标签，以帮助从训练数据中学习显著特征。此外，在边缘设备上实现高效模型训练与推理对于驾驶员疲劳检测任务至关重要，这能在无需依赖云端基础设施的情况下，于车辆上做出准确的实时决策。为解决此问题，我们开发了一种结合人工在环验证的半自动标注流水线，将YawDD视频标注升级为YawDD+帧级标注，从而在NVIDIA Jetson NANO等边缘平台上实现更精确的模型训练。基于YawDD+数据集训练既有的MNasNet分类器与YOLOv11检测器架构后，帧级准确率提升达6%，平均精度提升5%，相较于视频级监督方法，在Jetson NANO与AGX上分别实现了99.34%的分类准确率与95.69%的检测平均精度。此外，MNasNet每轮训练仅需8.69分钟，且在AGX上推理速度达115帧/秒，证实仅通过提升数据质量即可支持无需服务器端计算的车上驾驶员疲劳监测系统。YawDD+数据集及训练好的模型已在网上公开。