mlsanityData to 7 Oct 2026

Student Classroom Behavior Detection based on Improved YOLOv7

arXiv 2306.03318 ↗First posted 6 Jun 2023cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 24 of its 36 references that are in the map. First posted on arXiv 6 Jun 2023.

Builds on

24 of 36 references.

References · 24

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12 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 0; 2025: 0; 2026: 1.

3 papers.

  1. 2023SCB-Dataset3: A Benchmark for Detecting Student Classroom Behaviorcited by 5
  2. 2023Student Classroom Behavior Detection based on Spatio-Temporal Network and Multi-Model Fusioncited by 0
  3. 2026A Smart Classroom Behavior Analysis Framework with a New Highly Congested Classroom Datasetcited by 0