关键词: 关键词：课堂行为检测, YOLOv8, Swin Transformer, 注意力机制, Slim-Neck

Abstract:
Abstract: Aiming at the problems of low detection accuracy of student classroom behavior under monitoring and difficulty in deploying models， an improved YOLOv8 algorithm is proposed for detecting student behavior. Firstly， the YOLOv8 backbone network is improved by introducing the Swin Transformer network as the backbone feature extraction network to reduce information loss and improve the effectiveness of feature extraction. Secondly， to enhance the model’s attention to the features of distant targets， a flexible dual channel attention mechanism EMA is introduced， which makes the model focus more on targets with fewer pixels at long distances and improves detection accuracy. Finally， in the Neck section， the Slim Neck design paradigm containing GSConv is used to make lightweight improvements to the model. The experimental results on the SCB-Dataset3 dataset show that the improved model has a parameter count of 3.3 M and a computational load of 11.1 GFLOPs， respectively， with a detection accuracy of 88.75%. Compared with the original model， the parameter count is reduced by 40.7%， the computational load is reduced by 15.9%， and the detection accuracy is improved by 7.7 percentage points. This achieves good detection accuracy while achieving model lightweighting.

Key words: Key words: classroom behavior detection, YOLOv8, Swin Transformer, attention mechanism, Slim-Neck
Backbone