采用Faster-RCNN的学生课堂行为检测方法

Detection of Student Classroom Behavior using the Faster-RCNN

学生课堂行为检测对了解学生学习情况、提高教学质量具有重要意义。随着互联网技术的发展进步,在教育领域中应用深度学习算法已成为学生课堂行为识别的一个重要研究方向。本文提出了一种采用快速区域卷积神经网络Faster-RCNN检测学生课堂行为的方法,实现对学生课堂行为数据集中的“阅读”、“写作”、“举手”行为的检测分析。实验结果表明,本文算法能够有效识别并定位教学背景下的学生课堂行为。

The detection of student classroom behavior is of great significance for understanding students'learning situation and improving teaching quality.With the development of Internet technology,the application of deep learning algorithms in the field of education has become an important research direction for students'classroom behavior recognition.A method of using faster regional convolutional neural network to detect students'classroom behavior is proposed in this paper,achieving detection and analysis of"reading","writing",and"raising hands"behavior in the student classroom behavior datasets.The experimental results show that the algorithm proposed in this paper can effectively identify and locate students'classroom behavior in the teaching environment.