Abstract: In order to solve the problems of large scale variations,serious occlusions,and large computational burden that makes it difficult to popularize on a wide scale for student behavior detection in classroom scenarios,this paper proposes a lightweight student classroom behavior detection method BDEO-YOLO based on the improved YOLOv8.Firstly,dynamic convolution is introduced on the basis of YOLOv8n C2f module in YOLOv8,which enhances the model’s adaptability to complex classroom scenarios and feature expression ability.Secondly,the multi-scale feature fusion ability of the model is optimized by combining Bi-FPN and GLSA,and ELA mechanism is introduced in the Backbone part of the model,which enhances the model’s ability to detect small targets and detailed features.Finally,a lightweight detection head one13 structure is designed to simplify the feature extraction process and significantly reduce the computational burden of the model.Experimental results on the public dataset STBD-08 show that the mAP of the BDEO-YOLO model reaches 92.2%,which is 1.3 percentage points higher than that of the ori-ginal YOLOv8n,and the computational burden is reduced from 8.1 GFLOPs to 4.8 GFLOPs,which is 40.7% lower than the ori-ginal model,and the model size is only 5.7 MB,which verifies the effectiveness of the lightweight design.Validation on the public datasets SCB-Dataset3 and VOC2007 shows that the improved algorithm improves in all performance metrics,verifies the genera-lization ability of the model,and exhibits high robustness in dealing with occlusion,scale change,and illumination change in the classroom.

Key words: Student behavior detection, Lightweight, Dynamic convolution, BiFPN, Attention mechanism