Design of Fatigue Prediction System for Print Shop Based on OpenCV+Dilb Image Recognition Processing
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.
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