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实时疲劳检测算法使用机器学习来检测打和眼睛状态.

Fazliddin Makhmudov1, Dilmurod Turimov1, Munis Xamidov2

  • 1Department of Computer Engineering, Gachon University, Seongnam 1342, Republic of Korea.

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概括

本研究介绍了一种使用卷积神经网络 (CNN) 的实时系统,通过分析面部暗示来检测驾驶员的嗜睡. 该系统实现了96.54%的准确性,为减少因疲劳造成的交通事故提供了有希望的解决方案.

关键词:
头发级联分类器 头发级联分类器在VGG16中,VGG16是VGG16中的一个.卷积神经网络 (CNN) 是一种神经网络.深度学习是一种深度学习.昏昏欲睡的检测检测 昏昏欲睡的检测闭眼检测器可以检测闭眼.面部特征分析 面部特征分析打哈欠检测 检测 打哈欠检测

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 交通安全 交通安全

背景情况:

  • 昏昏欲睡的驾驶是交通事故的重要原因,导致认知功能受损和风险增加.
  • 现有的驾驶员监控系统往往缺乏实时,非侵入性的疲劳检测能力.

研究的目的:

  • 分析基于卷积神经网络 (CNN) 的司机实时,非侵入性嗜睡检测系统.
  • 评估系统在从车载视频数据中识别疲劳指标方面的有效性.

主要方法:

  • 使用卷积神经网络 (CNN) 架构来检测嗜睡.
  • 采用Haar级联分类器用于面部区域提取和用于疲劳诊断的高级图像处理.
  • 在考虑不同的照明和面部角度的不同数据集上训练了系统.

主要成果:

  • 在检测驾驶员昏昏欲睡时,测试准确度达到了96.54%.
  • 证明了行为指标的有效性,如打哈欠频率和眼睛状态检测.
  • 在不同的条件下验证了系统的性能.

结论:

  • 基于CNN的架构有效地解决了公共安全问题,例如昏昏欲睡的驾驶事故.
  • 开发的系统代表了驾驶员监控和道路安全的重大进步.
  • 未来的工作可以结合额外的行为和生理测量,以提高检测.