基于OpenCV+Dilb图像识别处理的印刷车间疲劳预测系统设计
Design of Fatigue Prediction System for Print Shop Based on OpenCV+Dilb Image Recognition Processing
熊晖 1齐元胜 1来永爱 1阮西东 1张占伟 2李翠娟 2王荣2
作者信息
- 1. 北京印刷学院印刷与包装工程学院,北京 102600
- 2. 云南嘉科包装科技股份有限公司,玉溪 653100
- 折叠
摘要
精确预测工人疲劳状态,有助于维护工人的安全与健康.本研究采用OpenCV和Dlib图像处理技术,创新设计了一种印刷车间疲劳预测系统.通过OpenCV进行人脸识别和目标检测,结合Dlib进行检测眼睛长宽比、嘴巴长宽比以及头部姿态等面部特征识别.此系统通过EAR、MAR及头部姿态估计算法实时监测印刷车间工人工作时的眨眼频率、打哈欠行为和头部点头等姿态和表情,及时捕捉工人的生理状态,实现对工人疲劳状态的准确监控和预测.此系统还可以利用深度学习及大量数据的训练和优化,构建专属数据集,更好地提升模型准确率,匹配在不同工作环境下车间工人疲劳状态的识别和预测能力,并通过预警提示提醒工人及时休息,为印刷生产车间工人提供更安全、更高效的工作环境,有效预防工伤事故的发生.
Abstract
Accurate prediction of workers' fatigue status is helpful to maintain the safety and health of workers.In this study,OpenCV and Dlib image processing technologies were utilized to design an innovative fatigue prediction system for printing workshops.The system employs OpenCV for facial recognition and object detection,in conjunction with Dlib to detect facial features such as eye aspect ratio (EAR),mouth aspect ratio (MAR),and head pose estimation.By integrating these algorithms,the system can monitor workers' blink frequency,yawning behavior,and head nodding in real-time,precisely capturing their physiological state to predict fatigue.Furthermore,the system incorporates deep learning and extensive data training to construct a proprietary dataset,significantly enhancing model accuracy and adaptability to various working environments.This facilitates the precise identification and prediction of worker fatigue states.The system also provides alert notifications to prompt workers to take breaks,thereby ensuring a safer and more efficient working environment in the printing workshop and effectively mitigating the risk of workplace accidents.
关键词
疲劳预测系统/OpenCV/Dlib图像处理/深度学习/人脸识别Key words
Fatigue prediction system/OpenCV/Dlib image processing/Deep learning/Facial recognition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据