Development of Fatigue Detection System using Deep Learning Model

Fatigue is a common issue that affects attention, cognitive performance, and overall well-being, particularly in educational settings. Detecting fatigue is essential where sustained focus and alertness are key to performance and safety, such as in educational, professional, and transportation environments. Traditional methods of detecting fatigue, such as educator observation, are often subjective and ineffective in identifying early signs of fatigue, which can lead to reduced student’s engagement and academic performance. This project proposes a real-time fatigue detection system capable of identifying indicators such as drowsiness and sleepiness across multiple students in a classroom using the YOLOv8 deep learning model. YOLOv8 is a highly efficient object detection model that rapidly and accurately identifies and locates objects in images and videos. The project further evaluates the system’s effectiveness in terms of accuracy and real-time processing within classroom environments. Experimental results demonstrate that the system achieves 92.8% mean average precision (mAP) and 91.4% testing accuracy, outperforming models such as YOLOv5 and Faster R-CNN. By enabling early and reliable detection of fatigue, this project has the potential to significantly enhance classroom engagement and improve learning outcomes.