摘要
针对学生课堂行为状态识别准确率较低的问题,提出一种基于YOLOv4的改进模型.通过建立学生课堂行为状态数据集,调整YOLOv4算法训练模型的参数,修改卷积块激活函数为ELU函数以优化模型,同时提出将DIoU-Soft-NMS作为非极大值抑制机制,识别分析教室中学生课堂行为状态;根据各状态持续时长及状态变化频率计算学生听课有效时长,并参考山东高考赋分原则,建立学生课堂注意力量化评价准则,同时建立教师课堂授课效果量化评价标准.实验结果表明,以同一评价指标衡量模型,该模型在学生课堂行为检测速率不变的情况下,平均精度均值(mAP)达到98.8%,比原YOLOv4模型提升了3.53%,学生服课堂注意力量化评价准则,有较高的契合度.
Aiming at the low accuracy of students’ classroom behavioral state recognition, an improved model based on YOLOv4 is proposed. We establish a dataset for students’ classroom behavioral states, adjust the YOLOv4 algorithm to train the parameters of the model, and modify the activation function of the convolutional block as an ELU function to optimize the model. Meanwhile, DIoU-soft-NMS is proposed as a non-maximum suppression mechanism to identify and analyze students’ classroom behavioral states. According to the duration of each state and the frequency of state changes,the effective length of students’ attention on lectures is calculated, and by referring to the scoring principle of Shandong college entrance examinations, the quantitative evaluation criteria of students’ classroom attention and the quantitative evaluation criteria of teachers’ classroom teaching effects are established. The experimental results show that when the same evaluation index is used to measure the model, the mean average precision(mAP) of the model reaches 98.8%assumed that the detection rate of students’ classroom behaviors remains unchanged, which is 3.53% higher than that of the original YOLOv4 model. Students approve the quantitative evaluation criteria of classroom attention, and it has a high degree of agreement.
作者
孙绍涵
张运楚
王超
张汉元
SUN Shao-Han;ZHANG Yun-Chu;WANG Chao;ZHANG Han-Yuan(School of Information and Electrical Engineering,Shandong Jianzhu University,Jinan 250101,China;Shandong Provincial Key Laboratory of Intelligent Building Technology,Jinan 250101,China)
出处
《计算机系统应用》
2022年第6期307-314,共8页
Computer Systems & Applications
基金
国家自然科学基金青年项目(62003191)。
关键词
课堂行为识别
YOLOv4
注意力评价
课堂教学效果评价
目标检测
classroom behavior recognition
YOLOv4
attention evaluation
classroom teaching effect evaluation
object detection
