The use of deep learning methods to automatically detect students' classroom behavior is a promising approach for analyzing their class performance and improving teaching effectiveness. However, the lack of publicly available datasets on student behavior poses a challenge for researchers in this field. To address this issue, we propose the Student Classroom Behavior dataset (SCB-dataset3), which represents real-life scenarios. Our dataset comprises 5686 images with 45578 labels, focusing on six behaviors: hand-raising, reading, writing, using a phone, bowing the head, and leaning over the table. We evaluated the dataset using the YOLOv5, YOLOv7, and YOLOv8 algorithms, achieving a mean average precision (map) of up to 80.3$\%$. We believe that our dataset can serve as a robust foundation for future research in student behavior detection and contribute to advancements in this field. Our SCB-dataset3 is available for download at: https://github.com/Whiffe/SCB-dataset

翻译：利用深度学习方法自动检测学生课堂行为，是分析其课堂表现并提升教学效果的一种有前景的途径。然而，缺乏公开可用的学生行为数据集对该领域的研究者构成了挑战。为解决此问题，我们提出了学生课堂行为数据集（SCB-dataset3），该数据集模拟现实场景。数据集包含5686张图像及45578个标注，重点关注六种行为：举手、阅读、书写、使用手机、低头和伏桌。我们使用YOLOv5、YOLOv7和YOLOv8算法对该数据集进行了评估，平均精度均值（map）最高达到80.3%。我们相信，该数据集可为学生行为检测的未来研究提供坚实基础，并推动该领域的进步。我们的SCB-dataset3可于以下链接下载：https://github.com/Whiffe/SCB-dataset