Fatigue Detection and Early Warning System for Drivers Based on Deep Learning

this paper introduces a deep learning-based driver fatigue detection system using a convolutional neural network (CNN). Unlike traditional methods, it directly extracts visual features from in-vehicle camera images. The dataset, annotated with severe, mild, and normal fatigue levels, undergoes data augmentation to avoid overfitting. A customized CNN, trained with dropout and auxiliary classifiers, achieves a high accuracy of 92.4% on unseen test images. The system offers real-time, non-intrusive fatigue monitoring, enhancing driving safety by alerting distracted drivers and preventing hazardous behaviors due to drowsiness. This advancement represents a significant step forward in intelligent vehicle safety technologies.