To accelerate the digital transformation of education,the precise analysis and empirical application of AI technology integrated into the entire process of teaching and learning behaviors have become a current research hotspot.To address the problems of low detection accuracy,high density of bonding boxes,severe overlap and occlusion,large scale variations,and imbalance of data volume in student classroom behavior detection,this paper establishes a student classroom behavior dataset(DBS Dataset).Additionally,it proposes a student classroom behavior detection algorithm VWE-YOLOv8 based on improved YOLOv8.First,it introduces the CSWin-Transformer attention mechanism to enhance the model's capability to extract global information from images.This improves the network's detection accuracy.Second,it increases the model's recognition capability on multi-scale targets by integrating the Large Separable Kernel Attention(LSKA)module into the SPPF architecture.Additionally,it incorporates an occlusion-aware attention mechanism into the design of the detection head(which modifies the original Head structure to SEAMHead)to effectively detect occluded objects.Finally,it introduces a weight adjustment function(Slide Loss)to address the issue of sample imbalance.The experimental results reveal that compared with YOLOv8,the improved VWE-YOLOv8 achieves increases of 1.16％and 1.70％in mAP@0.50 and 7.36％and 2.13％in mAP@0.50:0.95,on the DBS Dataset and public SCB Dataset.Furthermore,it improves the precision by 4.17％,6.74％and recall rate by 1.96％and 3.13％on these datasets,respectively.These results indicate that the improved algorithm has a higher detection accuracy and stronger generalization capability.Moreover,it is capable of detecting students'classroom behaviors.This can strongly support the application of smart education and aid the digital transformation of education.
维普
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