Students' Classroom Behavior Detection System Incorporating Deformable DETR with Swin Transformer and Light-Weight Feature Pyramid Network

Artificial intelligence (AI) and computer vision technologies have gained significant prominence in the field of education. These technologies enable the detection and analysis of students’ classroom behaviors, providing valuable insights for assessing individual concentration levels. However, the accuracy of target detection methods based on Convolutional Neural Networks (CNNs) can be compromised in classrooms with multiple targets and varying scales, as convolutional operations may result in the loss of location information. In contrast, transformers, which leverage attention mechanisms, have the capability to learn global features and mitigate the information loss caused by convolutional operations. In this paper, we propose a students’ classroom behavior detection system that combines deformable DETR with a Swin Transformer and light-weight Feature Pyramid Network (FPN). By employing a feature pyramid structure, the system can effectively process multi-scale feature maps extracted by the Swin Transformer, thereby improving the detection accuracy for targets of different sizes and scales. Moreover, the integration of the CARAFE lightweight operator into the FPN structure enhances the network’s detection accuracy. To validate the effectiveness of our approach, extensive experiments are conducted on a real dataset of students’ classroom behavior. The experimental results demonstrate a significant 6.1% improvement in detection accuracy compared to state-of-the-art methods. These findings highlight the superiority of our proposed network in accurately detecting and analyzing students’ classroom behaviors. Overall, this research contributes to the field of education by addressing the limitations of CNN-based target detection methods and leveraging the capabilities of transformers to improve accuracy. The proposed system showcases the benefits of integrating deformable DETR, Swin Transformer, and the lightweight FPN in the context of students’ classroom behavior detection. The experimental results provide compelling evidence of the system’s effectiveness and its potential to enhance classroom monitoring and assessment practices.