{"title":"Detection and analysis of student behavior in college labor education courses based on YOLOv5 network","authors":"Ke Zhang","doi":"10.3233/jcm-247308","DOIUrl":null,"url":null,"abstract":"To improve the application of behavior detection technology in college education, the study proposes a new model built on deep CNN, which is used for student behavior detection and analysis in college labor education courses. The study first analyzed the target detection algorithm, and optimized the selected You Only Look Once version 5 (YOLOv5) algorithm and its network structure with a series of improvements, and based on this, embedded the attention module into the algorithm structure to finally obtain a new model, namely YOLOv5-O. After a series of experiments, YOLOv5-O reached an average accuracy of 90.1% on the test set, while the application test in the actual teaching environment showed that its average accuracy was 86.7%. This result is obviously superior to the existing technology, which proves the validity of the study and provides strong data support for the automatic detection of student behavior. In addition, in the teaching experiment, YOLOv5-O assisted teaching achieved the most significant teaching effect, and students’ achievement improved the most. The feasibility of this method is verified.","PeriodicalId":45004,"journal":{"name":"Journal of Computational Methods in Sciences and Engineering","volume":null,"pages":null},"PeriodicalIF":0.5000,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computational Methods in Sciences and Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3233/jcm-247308","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
Abstract
To improve the application of behavior detection technology in college education, the study proposes a new model built on deep CNN, which is used for student behavior detection and analysis in college labor education courses. The study first analyzed the target detection algorithm, and optimized the selected You Only Look Once version 5 (YOLOv5) algorithm and its network structure with a series of improvements, and based on this, embedded the attention module into the algorithm structure to finally obtain a new model, namely YOLOv5-O. After a series of experiments, YOLOv5-O reached an average accuracy of 90.1% on the test set, while the application test in the actual teaching environment showed that its average accuracy was 86.7%. This result is obviously superior to the existing technology, which proves the validity of the study and provides strong data support for the automatic detection of student behavior. In addition, in the teaching experiment, YOLOv5-O assisted teaching achieved the most significant teaching effect, and students’ achievement improved the most. The feasibility of this method is verified.
为提高行为检测技术在高校教育中的应用,本研究提出了一种基于深度CNN构建的新模型,用于高校劳动教育课程中的学生行为检测与分析。研究首先对目标检测算法进行了分析,对选取的YOLOv5(You Only Look Once version 5)算法及其网络结构进行了一系列优化改进,并在此基础上将注意力模块嵌入到算法结构中,最终得到一个新模型,即YOLOv5-O。经过一系列实验,YOLOv5-O 在测试集上的平均准确率达到了 90.1%,而在实际教学环境中的应用测试表明,其平均准确率为 86.7%。这一结果明显优于现有技术,证明了研究的有效性,为学生行为的自动检测提供了有力的数据支持。此外,在教学实验中,YOLOv5-O 辅助教学取得的教学效果最为显著,学生成绩提高幅度最大。验证了该方法的可行性。
期刊介绍:
The major goal of the Journal of Computational Methods in Sciences and Engineering (JCMSE) is the publication of new research results on computational methods in sciences and engineering. Common experience had taught us that computational methods originally developed in a given basic science, e.g. physics, can be of paramount importance to other neighboring sciences, e.g. chemistry, as well as to engineering or technology and, in turn, to society as a whole. This undoubtedly beneficial practice of interdisciplinary interactions will be continuously and systematically encouraged by the JCMSE.