An automated object detection method for the attention of classroom and conference participants

Abstract

In classroom teaching, teachers pay attention to each student's emotional changes and learning status to regulate teaching and learning to effectively improve the quality of teaching. However, the current classroom has problems such as teachers' lack of energy and delayed teaching feedback, which, to a certain extent, affect the improvement of teaching quality and hinder students' development. In recent years, with the rapid development and widespread application of information technology, new technologies such as image processing and artificial intelligence have brought new ideas and methods for research on improving teaching quality. We propose a face detection algorithm based on Yolov5, which detects the left and right head turn, head up or head down and facial expression by face images; classifies the head posture according to the left and right head turn and head up or head down, and then judges its concentration degree by combining with the detection classification of facial expression. Based on this algorithm, the concentration of classroom and meeting participants can be effectively evaluated based on face detection results, which improves the quality of classroom and meeting.