Abnormal behavior recognition of experimental process based on improved YOLOV4

In order to strengthen the management of experimental teaching process and improve the accuracy of abnormal behavior detection of students, the existing YOLOv4 algorithm is improved. By adding channel attention mechanism SE module in the main feature extraction network, the algorithm ensures fast and effective extraction of student behavior effective features. A soft pooled SPP network based on CSP structure is used to assist optimization and feature extraction to reduce information loss in the pooling process. By introducing Focal loss function to deal with uneven number of plus and minus samples and difficult sample training in data, the effect of student behavior classification is improved. The algorithm is verified by experiment on the self-made behavior data set. The results show that the improved YOLOv4 algorithm has a better detection effect on students' behavior, and the average accuracy rate reaches 86.41%. Compared with the YOLOv4 algorithm, the recognition accuracy rate is improved by about 7%.