旅行追踪器:带有深度学习方法的驾驶员警报系统
N L Yashaswini1, Vanishri Arun1, B M Shashikala2
1Department of Information Science and Engineering, JSS Science and Technology University, Mysuru, Karnataka, India.
Frontiers in robotics and AI
|October 21, 2024
概括
一个新的行程追踪系统使用EfficientNet来检测驾驶员的昏昏欲睡,提高公共交通安全. 这个人工智能系统实现了95%的准确性,提醒司机,并允许对提高专业性的处罚.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 运输安全运输安全
背景情况:
- 司机昏昏欲睡是公共交通中的重大安全风险.
- 现有的系统可能缺乏准确性或实时监控能力.
研究的目的:
- 开发和评估一个人工智能驱动的行程追踪系统,用于检测司机的昏昏欲睡.
- 加强公共交通安全和司机问责制.
主要方法:
- 在媒体研究实验室 (MRL) 的眼睛数据集上训练了一种定制的EfficientNet模型.
- 眼部区域被隔离,反射被过,并建立了基线行为.
- 使用了"swish"激活功能和从头开始训练的模型.
主要成果:
- 该系统在检测驾驶员昏昏欲睡时达到95%的准确率.
- "切换"激活功能表现优于其他测试的功能.
- 从头开始训练的模型比预先训练的模型表现更好.
结论:
- 开发的系统有效监测驾驶员的警觉性,并检测昏昏欲睡.
- 这项技术促进了更安全的公共交通和专业的驾驶员行为.
- 个性化数据和学生检测增强了警报和惩罚触发器.
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