This paper describes a real-time fatigue sensing and enhanced feedback system designed for video terminal operating groups. This paper analyzes the advantages and disadvantages of various current acquisition devices and various algorithms for fatigue perception. After comparison, this study uses an eye movement instrument to collect user PERCLOS, and then calculates and determines the user’s fatigue state. A detailed fatigue discrimination calculation method is provided in this paper. The fatigue level is divided into three levels: mild fatigue, moderate fatigue and severe fatigue. Finally, this study uses the fatigue method demonstrated above to achieve real-time discrimination of the fatigue level of the user in front of the video operation terminal. This paper elaborates a method for waking up users and enhancing feedback based on their fatigue level and the importance of information. This study provides a solution for avoiding the operational risks caused by fatigue and lays the foundation for the machine to sense the user and provide different service solutions based on the user’s status.
In view of the poor detection effect and low robustness of traditional target detection algorithms, this paper studies target detection algorithms based on deep learning, and designs an embedded real-time target detection evaluation board based on AI chip. It is realized that most of the common deep convolutional neural network models represented by YOLOv3 can be transplanted to the board. Although there is a certain accuracy loss within the acceptable range, this implementation achieves a faster speed to meet the needs of real-time detection. Meanwhile, a complete set of video component calling and network transmission schemes are proposed. By designing a unified standard interface accessed to the program framework, the implementation board can be flexibly extended to meet the needs of various artificial intelligence applications.
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