Driver Drowsiness Detection using CNN

Bankar, Jaitee · 2024 · Crossref

DOI: 10.22214/ijraset.2024.62704

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Summary

This paper addresses the critical safety issue of driver drowsiness, a major contributor to road accidents and fatalities worldwide. Motivated by the limitations of traditional detection methods, such as self-reporting and manual observation, which are unreliable for real-time implementation, the authors propose a robust system using Convolutional Neural Networks (CNNs) to detect fatigue. The primary goal is to enhance road safety by monitoring drivers in real-time and providing timely warnings to prevent accidents caused by impaired reaction times and attentiveness. The methodology involves capturing video frames of the driver’s face via a camera installed in the vehicle. The system processes these frames through a pipeline that includes image pre-processing, where images are converted to grayscale to reduce computational complexity and enhance feature visibility. Key facial landmarks and behaviors indicative of drowsiness, such as eye closure duration, yawning frequency, and head position, are extracted. While the title and abstract emphasize CNNs for feature extraction and classification, the detailed system architecture specifically describes the use of the Haar Cascade algorithm for feature detection. This involves using Haar features to detect contrasts, integral images for rapid pixel sum computation, and AdaBoost training to select relevant weak classifiers. The system matches extracted features from the live feed against a pre-labeled dataset containing images of alert and drowsy states to determine the driver's alertness level. Experimental results demonstrate the system's effectiveness in distinguishing between alert and drowsy drivers with high accuracy. The system successfully identifies fatigue through eye closure and yawning detection. Upon detecting drowsiness, the system triggers specific responses, including issuing an audible warning to the driver and sending a notification via email to the vehicle owner. The authors claim the system performs well against various benchmarks, showcasing its potential for integration into modern vehicles as a preventive safety measure. The significance of this work lies in its potential to significantly reduce the risk of fatigue-related accidents. By leveraging computer vision and machine learning, the system offers a proactive mechanism to identify signs of fatigue before they lead to dangerous situations. The authors conclude that while CNN-based detection shows significant promise, ongoing research is necessary to address challenges related to diverse datasets, real-world adaptability, and user acceptance to ensure the continued improvement and effectiveness of such systems in enhancing overall road safety.

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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