现代计算机2024,Vol.30Issue(1) :110-115.DOI:10.3969/j.issn.1007-1423.2024.01.019

基于YOLOv5的智慧监考模型设计与研究

Design and application of smart proctoring model based on YOLOv5

王秋美茜 王鹏涛 张晓宽 刘经纬 纪佳琪
现代计算机2024,Vol.30Issue(1) :110-115.DOI:10.3969/j.issn.1007-1423.2024.01.019

基于YOLOv5的智慧监考模型设计与研究

Design and application of smart proctoring model based on YOLOv5

王秋美茜 1王鹏涛 1张晓宽 1刘经纬 1纪佳琪2
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作者信息

  • 1. 河北民族师范学院数学与计算机科学学院,承德 067000
  • 2. 河北民族师范学院数学与计算机科学学院,承德 067000;河北省文化旅游大数据技术创新中心,承德 067000
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摘要

针对传统监考模式存在的监考人员工作量大、主观性强等问题,构建基于目标检测的智慧监考模型.该模型能够应用于国家标准化考场,自动实时监测考生的作弊行为.采用YOLOv5算法训练出智慧监考模型,对考生回头的作弊行为进行检测,并使用深度学习方法对其作弊行为进行判定.在实验过程中,通过模拟真实的考场环境,该智慧监考模型对考生作弊行为检测的准确率较高,检测精确度可达96.3%,并能在GPU支持下实现实时检测.同时,实验对不同光线和像素下的识别准确度进行了比较分析,证明光线和像素会对准确度造成一定影响.实验结果表明,该模型能有效降低监考人员工作成本,实现考场监考公平性.

Abstract

Aiming at the problems of large workload and strong subjectivity of invigilators in the traditional invigilation mode,an intelligent proctoring model based on human motion detection is constructed.The model can be applied to the national standard-ized examination room to automatically monitor the cheating behavior of candidates in real time.The YOLOv5 algorithm is used to train a smart proctoring model to detect the cheating behavior of the candidates,and use the deep learning method to determine the cheating behavior.In the process of experimentation,by simulating the real examination room environment,the intelligent proctor-ing model has a high accuracy rate of detecting candidates'cheating behavior,with an accuracy of 96.3%on the training set,and real-time detection can be achieved with GPU support.At the same time,the recognition accuracy under different light and pixels is compared and analyzed,and it is proved that light and pixels will have a certain impact on accuracy.Experimental results show that the model can effectively reduce the work cost of invigilators and realize the fairness of invigilation in the examination room.

关键词

目标检测/智慧监考/标准化考场/YOLOv5模型/深度学习

Key words

object detection/smart proctoring/standardized examination rooms/YOLOv5 model/deep learning

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基金项目

2023年省级大学生创新创业训练计划项目(S202310098028)

承德市科技计划项目(202201A059)

出版年

2024
现代计算机
中大控股

现代计算机

影响因子:0.292
ISSN:1007-1423
参考文献量10
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