一个具有注意力机制的生成性自适应卷积神经网络,用于检测驾驶员疲劳,与类不平衡和数据不足有关
1State Key Laboratory of Power Transmission Equipment Technology, School of Electrical Engineering, Chongqing University, Chongqing 400044, People's Republic of China.
Behavioural brain research
|February 21, 2024
概括
两个新的深度学习模型,ARMFCN-LSTM和GARMFCN-LSTM,增强了驾驶员疲劳检测. 这些系统自动提取特征并解决数据挑战,通过精确的疲劳识别提高交通安全.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 生物医学工程 生物医学工程
背景情况:
- 司机疲劳是交通事故的主要原因,需要先进的检测系统.
- 现有的驾驶员疲劳检测方法通常需要手动调整,并与不平衡或不足的数据作斗争.
- 开发强大的疲劳识别系统,能够应对现实世界的数据挑战仍然是一个重大障碍.
研究的目的:
- 为自动驾驶员疲劳检测提出新的深度学习模型.
- 解决现有方法的局限性,特别是在手动参数调整和数据不平衡方面.
- 提高驾驶员疲劳识别系统的准确性和效率.
主要方法:
- 引入了基于注意力的残余自适应多尺度全卷积网络长期短期内存网络 (ARMFCN-LSTM) 用于自动多尺度特征提取和时间依赖性分析.
- 开发了生成ARMFCN-LSTM (GARMFCN-LSTM) 通过将瓦瑟斯坦GAN与梯度惩罚 (WGAN-GP) 集成来缓解数据稀缺和阶级失衡问题.
- 使用脑电图 (EEG) 和眼电图 (EOG) 数据集用于模型评估.
主要成果:
- 在用于二元疲劳分类的类平衡EEG数据集上,ARMFCN-LSTM实现了95.84%的分类准确性.
- GARMFCN-LSTM在一个不平衡类的EOG数据集上以84.70%的分类准确度表现出更好的性能,用于三重疲劳分类.
- 两种拟议的模型在各自的评估中都超过了竞争对手的方法.
结论:
- 拟议的ARMFCN-LSTM和GARMFCN-LSTM模型为驾驶员疲劳检测提供了有效的解决方案,即使使用具有挑战性的数据集.
- 这些模型在实时,在线驾驶员疲劳监测系统的实际实施方面显示出重大前景.
- 开发的深度学习架构提供了一个强大的方法来克服疲劳识别研究中的数据限制.
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