Driver Safety Enhancement Using Computer Vision and Embedded for Effective Drowsiness Detection

2026
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Authors: P. Sridhar; R. R. Sathiya; N. A. Gayathri; M. Desigashri; S. Gokileshnavi
Published in: Smart System for Integrated Computing and Communication
Publisher: Springer Nature Switzerland

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Abstract

This chapter delves into the critical issue of driver drowsiness and its role in road accidents. It introduces a cutting-edge system that leverages computer vision and embedded technologies to detect early signs of fatigue. The system employs a lightweight Convolutional Neural Network (CNN) to analyze real-time video streams, focusing on eye closure patterns as a reliable indicator of drowsiness. A vibration motor provides immediate haptic feedback to alert the driver, ensuring proactive intervention. The chapter also discusses the integration of a Wi-Fi-enabled Node MCU for seamless communication and control. Validation using the NTHU-DDD dataset demonstrates the system's high accuracy and robustness, outperforming traditional models like LSTM, InceptionV3, and MLP. The practical implementation of the system, including hardware components and real-time analysis, is thoroughly explored. This innovative approach not only enhances road safety but also contributes to the broader efforts in safety engineering and artificial intelligence.

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Title: Driver Safety Enhancement Using Computer Vision and Embedded for Effective Drowsiness Detection
Authors: P. Sridhar
R. R. Sathiya
N. A. Gayathri
M. Desigashri
S. Gokileshnavi
Publisher: Springer Nature Switzerland
Book: Smart System for Integrated Computing and Communication
Print ISBN: 978-3-031-99938-3

Electronic ISBN: 978-3-031-99939-0

Copyright Year: 2026
DOI: https://doi.org/10.1007/978-3-031-99939-0_12

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