Lightweight YOLOv8 Networks for Driver Profile Face Drowsiness Detection

Abstract

Vision-based driver monitoring, a non-invasive method designed to identify potentially dangerous operations, has attracted increasing attention in recent years. In this study, a head pitch angle detection method was established to evaluate the driver’s drowsiness. Rather than employing the front facial landmarks to estimate head pitch angle, the proposed method measure this angel directly from driver’s profile face. To meet the requirement of real-time detection, the method applies the YOLOv8 network of single-stage detection and utilizes MobileNetV3 and FasterNet for lightweight improvement. The detector is trained with re-labeled CFP datasets, and real-time speed tests have been performed. Results demonstrate that the non-improved detector can achieve an mAP50 of 97.3% of the keypoints in a single frame, meanwhile realizing the frame rate of 30.41 FPS. After improvement, parameters of the model have been reduced by 21.3% and 40.9% respectively, while the frame rate can be increased to 37.13 FPS and 52.70 FPS, and the mAP50 of keypoints is increased by 0.41% and 0.51%. The results during the in-car experiment have proved that the developed detection method can effectively evaluate the head pitch angle, thus detect the driver’s drowsiness. We provide open-access to the annotated data and pre-trained models in this study.