Application of deep learning algorithm in detecting and analyzing classroom behavior of art teaching

Abstract

Regarding the problem of automatic detection in art teaching classroom behavior, the research combines the YOLOv5 algorithm in the deep learning algorithm and adds a two-way feature information pyramid function with weighting capability to the neck part of the algorithm to achieve performance-based algorithm improvement. This research prunes and optimizes the model for the campus technology implementation problem to improve the robustness and ease of implementation of the model. The model is designed in line with the model of the art teaching classroom behavior training set, and the applied experimental method is adopted for analysis. The results show that the average accuracy of all classes of state classification is 0.973 level after model improvement, the average accuracy of all classes of state classification is 0.970 level after model pruning, and the realizability of the model is significantly enhanced while the performance and efficiency are improved. Therefore, the research-designed classroom behavior detection and analysis model for art teaching can effectively detect the types of classroom behaviors of students in the process of art teaching with excellent performance, providing an effective way to ensure the quality of student learning in classroom teaching.