摘要
疲劳状态检测对于保护电力工作人员的安全具有重要作用。深度学习虽然已经成为面部疲劳状态检测方面的重要方法,但是其检测结果的准确性还需要提高。针对这一问题,提出了一种新的电力工作人员疲劳状态识别系统。该系统通过对采集到的视频图像使用人脸检测算法确定电力场景中工作人员的面部位置,利用YOLOv4 tiny目标检测算法提取眼睛部位的视觉特征,建立检测模型,以提升视觉特征的辨别能力;采用PERCLOSE准则分析眨眼频率并结合打哈欠的频率和瞌睡点头频率来判断电力场景中工作人员是否疲劳。实验结果证明了所提的基于深度学习的疲劳状态识别方法的有效性。
Fatigue state detection plays an important role in protecting the safety of electric power workers.Although deep learning has become an important method for facial fatigue detection,the accuracy of its detection results still needs to be improved.To solve this problem,a new fatigue status recognition system for electric power workers is proposed.The system uses face detection algorithmon the collected video images to determine the facial position of workers in power scenes,uses YOLOv4 tiny target detection algorithm to extract the visual features of eyes,establishes detection model to improve the discriminative ability of visual features,and uses PERCLOSE criterion to analyze blink frequency and combine yawn frequency and doze nod frequency to determine whether the workers in the scene are tired or not.The experimental results prove the effectiveness of the proposed fatigue state detection method based on deep learning.
作者
赵倩
郭彤
王成龙
ZHAO Qian;GUO Tong;WANG Chenglong(School of Electronics and Information Engineering,Shanghai University of Electric Power,Shanghai200090,China)
出处
《上海电力大学学报》
CAS
2021年第6期567-572,共6页
Journal of Shanghai University of Electric Power
关键词
人脸检测
卷积神经网络
疲劳检测
face detection
convolution neural network
fatigue detection
