A Triple-Stage CNN Framework For Early Driver Drowsiness Detection Using Facial Positioning
DOI:
https://doi.org/10.29284/hhs41209Keywords:
Convolutional Neural Networks; Accuracy; Detection; Deep Learning; Drowsiness.Abstract
In general, we need powerful detection technologies to fight driver fatigue, which is becoming a global issue. Researchers have experimented with Deep Learning (DL) and Machine Learning (ML) techniques, such as various Convolutional Neural Networks (CNNs), but they have not been able to be applied in real-time scenarios because they are too difficult to implement due to their high computational complexity and poor test results. In light of these limitations, a multistage adaptive multi-level CNN model has been created especially for Driver Drowsiness Detection (DDD), with a focus on resolving system complexity and performance issues. For the purpose of detecting driver sleepiness, the proposed system uses a multi-tier methodology that incorporates innovations from several distinct research phases. The results show how well the proposed model works on different datasets. When it comes to bounding box regression and facial point localisation accuracy, the suggested model performs better than earlier models like YOLOv7-Face, MTCNN, AAMS, and Hyperface-Resnet. The outcomes show how effective the suggested model is at identifying early driver drowsiness and face positioning, which makes it useful for enhancing traffic safety.
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This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
