Student Classroom Behavior Detection Algorithm Based on Improved YOLOv8 in Smart Education
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.
万方数据
维普
