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徐 莲,任小洪,陈闰雪. 基于眼睛状态识别的疲劳驾驶检测[J]. 科学技术与工程, 2020, 20(20): 8292-8299.
xulian.Fatigue Driving Detection Based on Eye State Recognition[J].Science Technology and Engineering,2020,20(20):8292-8299.
基于眼睛状态识别的疲劳驾驶检测
Fatigue Driving Detection Based on Eye State Recognition
投稿时间:2019-09-28  修订日期:2020-04-03
DOI:
中文关键词:  疲劳驾驶检测 迁移学习 眼睛筛选机制 多任务级联卷积神经网络 眼部状态识别
英文关键词:Fatigue driving test transfer learning eye screening mechanism multi-task cascade convolutional neural network eye state recognition
基金项目:四川省教育厅基金项目(No.17ZB0302)
        
作者单位
徐 莲 Sichuan University of Science and Engineering
任小洪 四川轻化工大学
陈闰雪 四川轻化工大学
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中文摘要:
      由于光照变化、头部姿态等因素,现有的疲劳驾驶检测精度仍有待提高。针对该问题,提出一种基于迁移学习的眼睛状态识别网络GL-CNN(Gabor and LBP-Convolutional Neral Networks),该网络是由Gabor特征和LBP特征通过迁移学习加入卷积神经网络CNN(Convolutional Neral Networks)调制组成的。首先用多任务级联卷积神经网络MTCNN(Multi-Task Convolutional Neral Networks)检测出驾驶员的人脸和双眼,然后经过眼睛筛选机制获取待检测的单只眼睛,通过GL-CNN识别眼睛的睁闭状态,最后根据PERCLOSE准则判断驾驶员的疲劳状态。实验结果表明,该算法具有较高准确率,可以检测多种姿态眼睛的状态,同时满足实时性的要求。
英文摘要:
      Due to factors such as illumination variation and head posture, the accuracy of existing fatigue driving eye state detection needs to be improved. Aiming at this problem, an eye state recognition network GL-CNN (Gabor and LBP-Convolutional Neral Networks) based on transfer learning was proposed. The network was composed of Gabor features and LBP features added to CNN (Convolutional Neral Networks) modulation through transfer learning. First, the driver's face and eyes were detected by the multi-task cascade convolutional neural network MTCNN (Multi-Task Convolutional Neral Networks). Then, through the eye screening mechanism, a single eye to be detected was acquired, and the opening and closing state of the driver's eyes was identified by GL-CNN. Finally, the driver's fatigue state was judged according to the PERCLOSE criterion. The experimental results show that the algorithm has higher accuracy and can detect the state of multiple posture eyes, and meet the requirements of real-time.
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