Enhancing Driver Safety with Transfer Learning based Deep Learning Models for Drowsiness Detection

In response to the escalating concern over road accidents due to driver drowsiness, this research delves into enhancing drowsiness detection systems using advanced deep learning techniques and transfer learning. Prior investigations have explored conventional methods, yet they often lack the precision demanded by real-world scenarios. Leveraging convolutional neural networks (CNNs) alongside model fine-tuning and transfer learning, our methodology aims to optimize drowsiness detection accuracy. Through rigorous experimentation, we achieved compelling results, showcasing significant enhancements in accuracy across various CNN architectures. For instance, with Max Pooling and 2 Dense layers, one of our CNN models achieved an accuracy score of 1 at epoch 2, with a remarkably low loss of 6.622934e-12, all while taking only 2.6648 seconds for training. These outcomes underscore its potential to revolutionize driver safety systems, offering promising avenues for future research and implementation in real-world contexts.