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Exploring CNN for Driver Drowsiness Detection Towards Smart Vehicle Development
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Author(s): Pushpa Singh (GL Bajaj Institute of Technology and Management, Greater Noida, India), Raghav Sharma (KIET Group of Institutions, India), Yash Tomar (KIET Group of Institutions, India), Vivek Kumar (KIET Group of Institutions, India)and Narendra Singh (GL Bajaj Institute of Technology and Management, India)
Copyright: 2023
Pages: 16
Source title:
Revolutionizing Industrial Automation Through the Convergence of Artificial Intelligence and the Internet of Things
Source Author(s)/Editor(s): Divya Upadhyay Mishra (ABES Engineering College, Ghaziabad, India)and Shanu Sharma (ABES Engineering College, Ghaziabad, India)
DOI: 10.4018/978-1-6684-4991-2.ch011
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Abstract
Driver drowsiness is one of the major problems that every country is facing. The ICT sector is continuously investing in the automaker industry worldwide to bring about digital transformation in existing vehicles and driving. The smart behavior of vehicles is becoming possible with the convergence of intelligent manufacturing, AI, and IoT. In this chapter, the authors are presenting a framework for efficient detection of driver's drowsiness by utilizing the power of deep learning technology. The use of convolution neural network (CNN) is explored, and the system is developed and tested using different activation functions. The proposed driver drowsiness framework is able to signify the drowsiness state of the driver and to automatically alert the driver. The accuracy of the proposed model is compared at different activation functions such as ReLu, SeLu, Sigmoidal, Tanh, and SoftPlus, and higher accuracy is achieved with ReLu as 98.21%.
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