Student Classroom Behavior Detection based on Improved YOLOv7

Fan Yang
6/6/2023
cs.CV

Official code: Whiffe/SCB-dataset (496 stars, last commit 2025-09-18).

Abstract

Accurately detecting student behavior in classroom videos can aid in analyzing their classroom performance and improving teaching effectiveness. However, the current accuracy rate in behavior detection is low. To address this challenge, we propose the Student Classroom Behavior Detection method, based on improved YOLOv7. First, we created the Student Classroom Behavior dataset (SCB-Dataset), which includes 18.4k labels and 4.2k images, covering three behaviors: hand raising, reading, and writing. To improve detection accuracy in crowded scenes, we integrated the biformer attention module and Wise-IoU into the YOLOv7 network. Finally, experiments were conducted on the SCB-Dataset, and the model achieved an [email protected] of 79%, resulting in a 1.8% improvement over previous results. The SCB-Dataset and code are available for download at: https://github.com/Whiffe/SCB-dataset.

View on arXivView PDF

Code Implementations(1)

Whiffe/SCB-datasetOfficial100%

Student Classroom Behavior dataset

49652Apr 5, 20231 years ago

Similar Papers

Student Classroom Behavior Detection based on YOLOv7-BRA and Multi-Model Fusion

98% match1 repo4962023

A Spatio-Temporal Attention-Based Method for Detecting Student Classroom Behaviors

92% match2023

Student Classroom Behavior Detection based on Spatio-Temporal Network and Multi-Model Fusion

89% match1 repo4962023

Towards Student Actions in Classroom Scenes: New Dataset and Baseline

88% match1 repo292024

Exploring the Application of Visual Question Answering (VQA) for Classroom Activity Monitoring

86% match2025

Explainable Artificial Intelligence for Quantifying Interfering and High-Risk Behaviors in Autism Spectrum Disorder in a Real-World Classroom Environment Using Privacy-Preserving Video Analysis

85% match2024

Cite this paper

@article{yang2023student,
  title  = {Student Classroom Behavior Detection based on Improved YOLOv7},
  author = {Fan Yang},
  year   = {2023},
  eprint = {2306.03318},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2306.03318v2}
}

Add a badge to your README

Shows whether official code is linked for this paper.

Code of Paper badge
[![Code of Paper](https://codeofpaper.com/badge/2306.03318.svg)](https://codeofpaper.com/paper/2306.03318)

Discussion