YawDD+: Frame-level Annotations for Accurate Yawn Prediction
This addresses driver safety by enhancing on-device yawning monitoring, though it is incremental as it builds on existing methods with improved data quality.
The paper tackled the problem of inaccurate yawn prediction for driver fatigue detection by developing a semi-automated labeling pipeline to create YawDD+, which improved frame accuracy by up to 6% and mAP by 5%, achieving 99.34% classification accuracy and 95.69% detection mAP.
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.