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A CNN-based Deep Learning Framework for Driver’s Drowsiness Detection
... Three main areas of research are commonly used to analyze driver fatigue: 1) biological signal-based approaches utilizing sensors; 2) vehicle behavior-based methods; and 3) image processing methods employing computer vision to analyze changes in face features, which are important markers of sleepiness. Since Convolutional Neural Networks (CNN) have high computational efficiency and performance, we have opted for our recent deep learning research on drowsiness detection [4][5][6]. This model selection guarantees great accuracy during the testing, training, and assessment stages while saving time. ...
Introduction: Two major factors leading to traffic accidents are driver fatigue and distraction. The World Health Organization (WHO) reports that 7% of fatal and severe traffic accidents are caused by sleepy driving. Advances in machine learning, artificial intelligence, and neural networks have paved new avenues for real-time sleepiness detection, providing practical ways that mitigate the number of mishaps Objectives: The primary objective of this research is : to develop a new, real-time system for detecting driver drowsiness. The technology evaluates facial expressions and detects indicators of exhaustion using deep learning algorithms, which eventually improves road safety by reducing drowsiness-related accidents. Methods: The proposed strategy uses facial cues, such as the eyes, lips, head, and pupil, to detect driver fatigue. The MediaPipe method, which is renowned for its great accuracy and resilience, is used to extract these properties. The InceptionV3, VGG19, and ResNet50V2 deep learning neural networks were assessed using a dataset captured in real time at NTHU. The drivers in the dataset show varying degrees of tiredness under different settings. To evaluate the models' efficacy, performance criteria like accuracy, recall, precision, and F1-score were applied. Results: In detecting fatigue, all three convolutional neural networks performed admirably. The ResNet50V2 model performed more effectively than the other two, with an overall accuracy of 98.51%. This suggests that it can distinguish between fatigue and non-fatigue states with greater accuracy. Conclusions: The efficacy of deep learning models in real-time fatigue detection has been proven by the research. More specifically, the ResNet50V2 model exhibits remarkable accuracy, making it a viable option for reducing accidents caused by drowsy driving. To improve road safety, future work can entail more system optimization and practical implementation.
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