Authors - Bhavanam Sruthi, Mettu Sai Preethi, Krishna Reddy Abstract - Driver drowsiness is a major reason for accidents on the road, hence it is important to detect it early to increase safety on the road. A driver drowsiness detection system based on deep learning algorithms is proposed and it uses images captured through a camera installed inside a car. Various deep learning algorithms, namely CNN, VGG16, DenseNet121, MobileNet, LeNet, AlexNet, RNN, patchTST,Vision Transformer and Swin Transformer are implemented and compared to assess their performance.The system detects the conditions of the driver, whether eyes are open, closed, yawning, or not yawning. Among all these algorithms, the highest accuracy of 97.61% was obtained by using the MobileNet model, which proves that deep learning can play a vital role in detecting drowsiness. In addition, an alert can also be sent to warn the driver.
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