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 mAP@0.5 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.

翻译：准确检测课堂视频中的学生行为有助于分析其课堂表现并提升教学效果。然而，当前行为检测的准确率较低。为应对这一挑战，我们提出了一种基于改进YOLOv7的学生课堂行为检测方法。首先，我们构建了学生课堂行为数据集（SCB-Dataset），该数据集包含18.4k个标注与4.2k张图像，涵盖举手、阅读和书写三种行为。为提升拥挤场景下的检测精度，我们在YOLOv7网络中融合了biformer注意力模块与Wise-IoU损失函数。最终，在SCB-Dataset上进行的实验表明，该模型的mAP@0.5达到79%，较先前结果提升了1.8%。SCB-Dataset与相关代码已发布于：https://github.com/Whiffe/SCB-dataset。