Driver drowsiness shield (DDSH): a real-time driver drowsiness detection system

Abstract Detecting drowsiness is crucial for improving traffic safety and preventing fatigue-related accidents. This paper aims to develop an advanced real-time drowsiness detection system using deep learning algorithms. For this purpose, we utilized an eye image dataset from the MRL Eye Dataset and performed extensive feature engineering and preprocessing to prepare the data for analysis. An algorithm has been proposed to classify eye states as open or closed using Transfer Learning based on the MobileNet architecture. Using a balanced dataset, the model was trained to distinguish between open and closed eyes accurately. The experimental results demonstrate the effectiveness of our approach, achieving 90% accuracy, 100% precision, 83.3% recall, and an F1-score of 90.9%. To further validate the system, we integrated the trained model into a real-time camera application that monitors the eye conditions of drivers. The application analyzes real-time video streams, detects faces and eyes, and uses instances of closed eyelids to predict signs of drowsiness. The efficiency of the model is evaluated using metrics such as accuracy, precision, recall, and F1-score. The results indicate that our approach accurately identifies fatigue indicators, presenting a viable solution for real-time drowsiness monitoring to help prevent accidents caused by exhaustion. Future studies will explore incorporating additional physiological information and applying advanced deep-learning techniques to enhance detection accuracy and system robustness.