Driver Drowsiness Detection using MobileNetV2 with Transfer Learning Approach

Drowsiness among drivers is one of the leading causes of road accidents and deaths. In this research, we propose a new way to detect driver drowsiness using MobileNet architecture with transfer learning. The goal of the proposed system is to improve the accuracy and effectiveness of Drowsiness Detection in vehicular environments.The first step in the research is to train MobileNet using an existing image classification dataset. Transfer learning is then used to adjust the model for the specific drowsiness detection task. Transfer learning improves the model's capacity to generalize from a limited set of labeled data. Combining MobileNet's deep learning capabilities with transfer learning allows us to leverage the advantages of both techniques, balancing computational efficiency with accuracy. Haar Cascade Classifiers can be used to analyze facial features, including eye tracking, facial expressions, etc. This classic computer vision technique adds an extra layer of detection, increasing the robustness of the overall drowsiness detection solution. Combining the deep learning capabilities of MobileNet with the real-time efficiency of haar Cascade Classifiers provides a complete solution for drowsiness detection in real-time. The proposed system with a validation accuracy of 86% has the potential to be incorporated into advanced driver assistance systems (ADAS) systems to improve road safety by alerting drivers when they are drowsy. This research opens the door to intelligent adaptive systems that could play an important role in reducing the number of drowsiness-related accidents and saving lives.