Classroom Anomalous Behavior Detection Based On Improved YOLOv5

In recent years, there has been an increasing interest in using machine learning methods to improve deep learning-based classroom abnormal behaviour detection tasks, and the theory of fractional order calculus is beginning to be used to enhance the model's ability to describe the features of the data. In this paper, we propose a classroom abnormal behaviour detection method based on fractional order calculus for YOLOv5 to monitor and analyse students' classroom behaviour immediately. A fractional order coordinate attention mechanism is designed in the YOLOv5 feature learning stage to capture the long-range relationship of features in combination with position information, while a hybrid convolutional layer is introduced to achieve computational lightness, and finally an improved loss function is used for training to improve the detection accuracy robustness. In this paper, we use the improved YOLOv5 model based on fractional order differentiation for classroom abnormal behaviour detection. It is experimentally shown that the method proposed in this paper achieves significant performance improvement in classroom behaviour detection, with 5.4 Spf improvement in detection speed and 4.1% improvement in classroom abnormal behaviour accuracy, which is highly observable and provides more targeted intervention and management tools for the education industry.