Teacher–Student Behavior Recognition in Classroom Teaching Based on Improved YOLO-v4 and Internet of Things Technology

Abstract

Based on the classroom teaching scenarios, an improved YOLO-v4 behavior detection algorithm is proposed to recognize the behaviors of teachers and students. With the development of CNN (Convolutional Neural Networks) and IoT (Internet of Things) technologies, target detection algorithms based on deep learning have become mainstream, and typical algorithms such as SSD (Single Shot Detection) and YOLO series have emerged. Based on the videos or images collected in the perception layer of the IoT paradigm, deep learning models are used in the processing layer to implement various intelligent applications. However, none of these deep learning-based algorithms are perfect, and there is room for improvement in terms of detection accuracy, computing speed, and multi-target detection capabilities. In this paper, by introducing the concept of cross-stage local network, embedded connection (EC) components are constructed and embedded at the end of the YOLO-v4 network to obtain an improved YOLO-v4 network. Aiming at the problem that it is difficult to quickly and effectively identify the students’ actions when they are occluded, the Repulsion loss function is connected in series on the basis of the original YOLO-v4 loss function. The newly added loss function consists of two parts: RepGT loss and RepBox loss. The RepGT loss function is used to calculate the loss values between the target prediction box and the adjacent ground truth boxes to reduce false positive detection results; the RepBox loss function is used to calculate the loss value between the target prediction box and other adjacent target prediction boxes to reduce false negative detection results. The training and testing are carried out on the classroom behavior datasets of teachers and students, respectively. The experimental results show that the average precision of identifying various classroom behaviors of different targets exceeds 90%, which verifies the effectiveness of the proposed method. The model performs well in sustainable classroom behavior recognition in educational context, accurate recognition of classroom behaviors can help teachers and students better understand classroom learning and promote the development of intelligent classroom model.