联合驱动状态分类方法:面部分类模型开发和面部特征分析改进
Farkhod Akhmedov1, Halimjon Khujamatov1, Mirjamol Abdullaev2
1Department of Computer Engineering, Gachon University, Seongnam 13120, Gyeonggi-Do, Republic of Korea.
Sensors (Basel, Switzerland)
|March 17, 2025
概括
本研究引入了一种用于检测驾驶员昏昏欲睡的双框架系统. 通过整合卷积神经网络 (CNN) 和面部地标分析,它显著提高了识别昏昏欲睡的驾驶员的准确性,以提高道路安全.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 道路安全工程 道路安全工程
背景情况:
- 司机昏昏欲睡是道路交通事故的主要原因之一.
- 现有的检测方法在现实条件下往往缺乏稳定性.
研究的目的:
- 开发和评估一个双框架系统,以准确检测驾驶员的昏昏欲睡.
- 通过减轻与昏昏欲睡相关的车辆事故,提高道路安全.
主要方法:
- 一种双框架方法,集成一个卷积神经网络 (CNN) 和基于深度学习的面部地标分析模型.
- 先进的图像预处理技术包括正常化,照明校正和面部幻觉.
- 分析主要的昏昏欲睡指标:闭眼动态,打哈欠模式和头部运动.
主要成果:
- 该CNN模型实现了92.5%的驾驶员状态分类准确度 (清醒/沉睡).
- 通过预处理增强的面部地标分析模型达到97.33%的分类准确度.
- 集成的双模型架构表现出更好的稳定性,特别是在低光条件下.
结论:
- 拟议的双模型架构有效地提高了驾驶员昏昏欲睡检测的准确性和稳定性.
- 这种综合方法显示出在车辆安全系统中实际实施的巨大潜力.
- 多模型策略对于可靠的嗜睡检测和事故预防至关重要.
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