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Student Behavior Detection in the Classroom Based on Improved YOLOv8.
- Source :
- Sensors (14248220); Oct2023, Vol. 23 Issue 20, p8385, 18p
- Publication Year :
- 2023
-
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%. [ABSTRACT FROM AUTHOR]
- Subjects :
- PSYCHOLOGY of students
CLASSROOMS
EFFECTIVE teaching
Subjects
Details
- Language :
- English
- ISSN :
- 14248220
- Volume :
- 23
- Issue :
- 20
- Database :
- Complementary Index
- Journal :
- Sensors (14248220)
- Publication Type :
- Academic Journal
- Accession number :
- 173337559
- Full Text :
- https://doi.org/10.3390/s23208385