Affiliation:
1. School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
2. School of Life Sciences and Technology, 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%.
Funder
Science and Technology Department of Heilongjiang Province
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference30 articles.
1. Smart Education Literature: A Theoretical Analysis;Singh;Educ. Inf. Technol.,2020
2. Classroom Learning Status Assessment Based on Deep Learning;Zhou;Math. Probl. Eng.,2022
3. Hu, M., Wei, Y., Li, M., Yao, H., Deng, W., Tong, M., and Liu, Q. (2022). Bimodal Learning Engagement Recognition from Videos in the Classroom. Sensors, 22.
4. Identifying and Monitoring Students’ Classroom Learning Behavior Based on Multisource Information;Sun;Mob. Inf. Syst.,2022
5. Visual Object Recognition and Pose Estimation Based on a Deep Semantic Segmentation Network;Lin;IEEE Sensors J.,2018
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献