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English(EN) Improving Driver Drowsiness Detection via Personalized EAR/MAR Thresholds and CNN-Based Classification

CNN和个性化阈值提高了驾驶员困倦检测的准确性

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种新的驾驶员困倦检测系统，该系统使用个性化的眼部纵横比（EAR）和口部纵横比（MAR）阈值来考虑个体差异。该系统将这些个性化指标与卷积神经网络（CNN）模型相结合，以提高在各种条件下的准确性。评估显示，个性化阈值将检测准确率提高了2-3％，而CNN组件在眼部状态和打哈欠检测方面实现了超过98.8％的准确率。 AI

影响 通过改进个性化AI模型的疲劳检测准确性，增强了驾驶员安全系统。

排序理由 详细介绍驾驶员困倦检测新方法的学术论文。

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

CNN和个性化阈值提高了驾驶员困倦检测的准确性

报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · G\"okdeniz Ersoy, Mehmet Alper Tatar, Eray Tonbul, Serap K{\i}rb{\i}z ·

    Improving Driver Drowsiness Detection via Personalized EAR/MAR Thresholds and CNN-Based Classification

    arXiv:2604.22479v1 Announce Type: new Abstract: Driver drowsiness is a major cause of traffic accidents worldwide, posing a serious threat to public safety. Vision-based driver monitoring systems often rely on fixed Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) thresholds; …

  2. arXiv cs.CV TIER_1 English(EN) · Serap Kırbız ·

    Improving Driver Drowsiness Detection via Personalized EAR/MAR Thresholds and CNN-Based Classification

    Driver drowsiness is a major cause of traffic accidents worldwide, posing a serious threat to public safety. Vision-based driver monitoring systems often rely on fixed Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) thresholds; however, such fixed values frequently fail to ge…

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