Abstract
Driver fatigue remains a leading cause of road accidents, with 24\% of crashes involving drowsy drivers. While yawning serves as an early behavioral indicator of fatigue, existing machine learning approaches face significant challenges due to video-annotated datasets that introduce systematic noise from coarse temporal annotations. We develop a semi-automated labeling pipeline with human-in-the-loop verification, which we apply to YawDD, enabling more accurate model training. 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. The resulting approach deliver up to 59.8 FPS on edge AI hardware (NVIDIA Jetson Nano), confirming that enhanced data quality alone supports on-device yawning monitoring without server-side computation.
Abstract (translated)
驾驶员疲劳仍然是导致道路事故的主要原因之一，24%的交通事故涉及嗜睡驾驶者。虽然打哈欠是疲劳早期的行为表现之一，但现有的机器学习方法在处理视频注释数据集时面临系统性噪声问题，这些噪声来自于粗糙的时间标注。为此，我们开发了一种半自动化的标签生成流程，并引入了人类验证环节（human-in-the-loop verification），并将此应用于YawDD数据集中，以实现更准确的模型训练。 通过这种改进的数据处理方式，在YawDD+上对已确立的MNasNet分类器和YOLOv11检测架构进行训练后，帧精度提高了多达6%，mAP（平均精度均值）则提升了5%。与视频级别的监督相比，这一方法达到了99.34%的分类准确率和95.69%的目标检测mAP。 该技术在边缘AI硬件（如NVIDIA Jetson Nano）上运行时，可实现每秒最多59.8帧的速度，这表明通过提高数据质量，可以在不依赖服务器端计算的情况下，在设备端实现实时监测驾驶员打哈欠。
URL
https://arxiv.org/abs/2512.11446
