Classroom Behavior Recognition of Students Based on YOLOv8-OGCA

Behavior detection is a task in computer vision, aiming to promote teachers' professionalism and increase classroom quality by studying students' behavior. Using deep learning methods to automatically detect students' behavior in the classroom can effectively improve the efficiency of analyzing students' classroom performance. This article proposes a method for recognizing students' classroom behavior based on YOLOv8n: YOLOv8-OGCA. First, the module global grouping coordinate attention OGCA is proposed. Then, SPD-Conv is introduced to address the problem of small detection targets for each student in student behavior images, replacing some cross-convolution layers and pooling layers to reduce the loss of fine-grained information. In order to improve the detection accuracy of small targets, this paper replaces the loss function with NWD loss. Compared with the benchmark model YOLOv8n, the method in this article has a slight improvement in both map50 value and accuracy, and performs well in classroom behavior recognition tasks, which can improve the accuracy of student classroom behavior recognition.