1. Facial Drowsiness Detection
- Input: The system starts with a live feed or a captured image of the driver.
- Base Model: A baseline model is initially developed to detect facial features and signs of drowsiness. This model might not be highly accurate and can overfit the training data.
- Transfer Learning: To improve performance, pre-trained models like VGG16, ResNet18, and MobileNetV2 are employed. These models are fine-tuned with our specific dataset to leverage their pre-learned features for better accuracy.
- Best Model: Among the models used in transfer learning, the one with the best performance metrics (such as MobileNetV2) is selected as the final model for facial drowsiness detection. Detection: The best model is used to detect if the driver shows signs of facial drowsiness. If Facial Drowsiness Detected: An alert is triggered indicating that the driver is drowsy.
2. Eye Closure Drowsiness Detection
- Input: Parallel to facial analysis, another model focuses specifically o…
