基于深度学习的学生课堂行为识别研究

Research on Classroom Behavior Recognition of Students Based on Deep Learning

智慧课堂面临学校硬件设备难以普适、课堂数据缺失等问题,给学生课堂行为识别研究带来了一定的难度和挑战。为了降低对课堂场景部署设备的要求,提高识别精度与速度,在已有识别模型基础上提出了一种基于深度学习的学生课堂行为识别模型。首先融合重影模块(Ghost)实现轻量化,其次加入坐标注意力机制(Coordinate Attention)提升检测精度。实验结果表明,改进后的模型识别精度(m AP)达到了86.2%,与原模型相比提高了3.5%,推理时间减少了16.7%,参数量降低了35.5%,速度与精度均有一定提升,符合智慧课堂的基本要求。

Smart classrooms face problems such as difficulty in adapting school hardware equipment and missing classroom data,which poses certain difficulties and challenges to the research of student classroom behavior recognition.In order to reduce the requirements for deploying equipment in classroom scenarios and improve recognition accuracy and speed,this paper proposes a deep learning-based student classroom behavior recognition model based on existing recognition models.Firstly,the Ghost module is integrated to achieve lightweight,and secondly,a Coordinate Attention mechanism is added to improve detection accuracy.The experimental results show that the improved model has a recognition accuracy(mAP)of 86.2%,which is 3.5%higher than the original model.The inference time is reduced by 16.7%,and the parameter quantity is reduced by 35.5%.The speed and accuracy are both improved to a certain extent,which meets the basic requirements of smart classrooms.