YawDD+: Frame-level Annotations for Accurate Yawn Prediction

• URL: http://arxiv.org/abs/2512.11446v2
• Date: Mon, 15 Dec 2025 14:34:32 GMT
• Title: YawDD+: Frame-level Annotations for Accurate Yawn Prediction
• Abstract summary: Driver fatigue is a leading cause of road accidents, with 24% of crashes involving drowsy drivers.<n>We develop a semi-automated labeling pipeline with human-in-the-loop verification, which we apply to YawDD.<n> YawDD+ improves frame accuracy by up to 6% and mAP by 5% over video-level supervision.
• Score: 2.578347047861952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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