Fusing lightweight Retinaface network for fatigue driving detection

Abstract

To address the current issue of slow face detection and the low accuracy of single-feature fatigue detection in drivers, we first introduce a lightweight Retinaface network. This is achieved by replacing the backbone of the Retinaface network with Ghostnet, which accelerates face detection while improving accuracy. We then proceed to locate facial key features. Following this, a comprehensive SSD network is employed for the identification of the driver's ocular and oral conditions. By combining the MAR (Mouth Aspect Ratio) and EAR (Eye Aspect Ratio) values with fatigue detection thresholds, we ultimately determine the driver's condition. The experimental findings reveal that the enhanced Retinaface algorithm surpasses the original Retinaface approach, exhibiting an average accuracy improvement of 2.64%. The final fatigue detection, based on multiple features, achieves an average correctness rate of over 90%.