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Published on: December 15, 2023
Student Behavior Detection in the Classroom Based on Improved YOLOv8
Haiwei Chen1, Guohui Zhou1, Huixin Jiang2
1School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China.
Abstract:
Accurately detecting student classroom behaviors in classroom videos is beneficial for analyzing students' classroom performance and consequently enhancing teaching effectiveness. To address challenges such as object density, occlusion, and multi-scale scenarios in classroom video images, this paper introduces an improved YOLOv8 classroom detection model. Firstly, by combining modules from the Res2Net and YOLOv8 network models, a novel C2f_Res2block module is proposed. This module, along with MHSA and EMA, is integrated into the YOLOv8 model. Experimental results on a classroom detection dataset demonstrate that the improved model in this paper exhibits better detection performance compared to the original YOLOv8, with an average precision (mAP@0.5) increase of 4.2%.
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