Cover Image

Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism

Examinations are among the most widely used and effective methods for assessing knowledge mastery, both domestically and internationally, and are extensively used in various talent-selection processes. Currently, offline exam venues usually rely on on-site manual invigilation combined with exam-mon...

Full description

Saved in:
Bibliographic Details
Main Authors: Yan, Zuo, Chai, Soo See, Goh, Kok Luong
Format: Article
Language:English
Published: Science Publications 2024
Subjects:
QA76 Computer software
Online Access:http://ir.unimas.my/id/eprint/47234/1/Cheating%20Detection.pdf
http://ir.unimas.my/id/eprint/47234/
https://thescipub.com/abstract/jcssp.2024.1668.1680
https://doi.org/10.3844/jcssp.2024.1668.1680
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.unimas.ir-47234
record_format eprints
spelling my.unimas.ir-472342025-01-03T02:25:09Z http://ir.unimas.my/id/eprint/47234/ Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism Yan, Zuo Chai, Soo See Goh, Kok Luong QA76 Computer software Examinations are among the most widely used and effective methods for assessing knowledge mastery, both domestically and internationally, and are extensively used in various talent-selection processes. Currently, offline exam venues usually rely on on-site manual invigilation combined with exam-monitoring videos to further strengthen invigilation efforts. However, this invigilation method not only utilizes large amounts of human and material costs but also cannot comprehensively detect cheating behavior during exam processes and thus fairness cannot be guaranteed. To improve the efficiency of video reviews during invigilation, save labor costs, and strengthen invigilation efforts, this study proposes the use of target detection algorithms to achieve automatic detection of cheating actions in the exam room. To improve the speed of video detection, a student's abnormal-behavior detection method was proposed based on improved YOLOv8 and attention mechanism to achieve real-time detection of cheating actions in an exam room on a regular performance computer. The results showed that the detection accuracy of the improved YOLOv8 model reached 82.71%, thus meeting the application requirements. Science Publications 2024 Article PeerReviewed text en http://ir.unimas.my/id/eprint/47234/1/Cheating%20Detection.pdf Yan, Zuo and Chai, Soo See and Goh, Kok Luong (2024) Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism. Journal of Computer Science, 20 (12). pp. 1668-1680. ISSN 1552-6607 https://thescipub.com/abstract/jcssp.2024.1668.1680 https://doi.org/10.3844/jcssp.2024.1668.1680
institution Universiti Malaysia Sarawak
building Centre for Academic Information Services (CAIS)
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sarawak
content_source UNIMAS Institutional Repository
url_provider http://ir.unimas.my/
language English
topic QA76 Computer software
spellingShingle QA76 Computer software
Yan, Zuo
Chai, Soo See
Goh, Kok Luong
Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism
description Examinations are among the most widely used and effective methods for assessing knowledge mastery, both domestically and internationally, and are extensively used in various talent-selection processes. Currently, offline exam venues usually rely on on-site manual invigilation combined with exam-monitoring videos to further strengthen invigilation efforts. However, this invigilation method not only utilizes large amounts of human and material costs but also cannot comprehensively detect cheating behavior during exam processes and thus fairness cannot be guaranteed. To improve the efficiency of video reviews during invigilation, save labor costs, and strengthen invigilation efforts, this study proposes the use of target detection algorithms to achieve automatic detection of cheating actions in the exam room. To improve the speed of video detection, a student's abnormal-behavior detection method was proposed based on improved YOLOv8 and attention mechanism to achieve real-time detection of cheating actions in an exam room on a regular performance computer. The results showed that the detection accuracy of the improved YOLOv8 model reached 82.71%, thus meeting the application requirements.
format Article
author Yan, Zuo
Chai, Soo See
Goh, Kok Luong
author_facet Yan, Zuo
Chai, Soo See
Goh, Kok Luong
author_sort Yan, Zuo
title Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism
title_short Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism
title_full Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism
title_fullStr Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism
title_full_unstemmed Cheating Detection in Examinations Using Improved YOLOv8 with Attention Mechanism
title_sort cheating detection in examinations using improved yolov8 with attention mechanism
publisher Science Publications
publishDate 2024
url http://ir.unimas.my/id/eprint/47234/1/Cheating%20Detection.pdf
http://ir.unimas.my/id/eprint/47234/
https://thescipub.com/abstract/jcssp.2024.1668.1680
https://doi.org/10.3844/jcssp.2024.1668.1680
_version_ 1821007927520002048
score 13.232432

Similar Items