Misoperation caused by fatigue of mine workers is an important reason for coal mine accidents.To address the problems of the low quality of collected mine operation images,the singleness of fatigue characteristics,and individual differences,this study proposed an improved RetinaFace-PFLD lightweight adaptive facial fatigue detection algorithm(RPLA).Specifically,median filtering and gamma correction were used to preprocess real-time video data to improve image quality.Based on the RetinaFace model,MobileNetv3 network extracted features were improved,and feature pyramid network was simplified to reduce the complexity of the recognition algorithm.Facial key points and fatigue characteristics were obtained through the practical facial landmark detector PFLD framework,and fatigue was detected by using an adaptive fatigue detection method.Tested on the face data set,self-collected miner data set and driving data set,the fatigue detection accuracy reached 97.73%.The algorithm is transplanted to Jetson Nano,and the frame rate per second is 16.13,faster than the sampling speed,which shows that this algorithm is suitable for real-time monitoring and early warning in mobile terminal devices.
维普
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