University Student Control Detection System Based on Machine Learning and Artificial Intelligence

Abstract

The objective of this study was to program a school assistant based on artificial intelligence and integrated with surveillance cameras and loudspeakers in a university classroom. This was done to complement the supervision carried out by university teachers to identify student distractions, guarantee the proper use of masks, and evaluate student behaviors in class. The virtual assistant was developed using Python to generate audio warnings through a graphical interface built in the PyCharm environment. The results demonstrated the desired functionality of the virtual assistant, its ability to meet the requirements of a university classroom, and thereby the effectiveness of the YOLO v5 network and PyCharm used for the training, execution, construction, and implementation of the system.