Drowsiness Detection using CNN

Bhosale, Ashwini · 2024 · Crossref

DOI: 10.22214/ijraset.2024.59959

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Summary

This paper addresses the critical safety issue of driver drowsiness, a major contributor to road accidents globally. The authors propose a Driver Drowsiness Detection system that leverages Convolutional Neural Networks (CNNs) to analyze real-time facial features from in-vehicle cameras. The research is motivated by the limitations of traditional detection methods, which often rely on simple thresholding or basic image processing and lack the robustness required for complex, real-world driving scenarios. By utilizing CNNs, the study aims to create a more accurate and adaptable system capable of distinguishing between alert and drowsy states through the automatic learning of hierarchical features from facial expressions. The methodology involves capturing real-time video streams of the driver’s face using in-vehicle cameras. Computer vision techniques are applied to extract specific facial landmarks and expressions, such as eye movements, blinking patterns, head pose, and overall facial expressions. These extracted features undergo pre-processing to enhance data quality and remove noise. The core of the system is a CNN model trained on a diverse dataset comprising both drowsy and alert facial expressions. This training ensures the model’s adaptability to various driving conditions and individual driver characteristics. The system operates in real-time, continuously monitoring the driver and classifying their state. When signs of drowsiness exceed a certain threshold, the system triggers an alert via sound, visual warning, or haptic feedback. The results indicate that the proposed CNN-based system achieves high accuracy, precision, and recall rates in identifying signs of driver drowsiness. The model demonstrates strong capability in processing real-time data, making it suitable for practical, on-road deployment. The extensive experiments conducted across various datasets and scenarios confirm the system's effectiveness in detecting fatigue indicators reliably. The authors highlight that the CNN’s ability to learn complex patterns from facial data offers a significant improvement over traditional methods, providing a robust solution for continuous driver monitoring. The significance of this research lies in its potential to enhance road safety by providing timely interventions to prevent accidents caused by driver fatigue. The study contributes to the integration of artificial intelligence into vehicle safety systems, offering a proactive mechanism for risk mitigation. While the results are promising, the authors conclude that ongoing research is necessary to address challenges related to diverse datasets, real-world adaptability, and user acceptance. Future work must also consider ethical aspects, such as privacy concerns, to ensure the widespread and effective implementation of CNN-based driver detection systems.

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Topics

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• drowsiness detection algorithms
• distraction detection algorithms
• drowsiness
• truck driver fatigue

Information type

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• Empirical Findings: physiological data
• Methodological Resource: tool software
• Theoretical Contribution: theory or model