Year: 2025
Type: article
Abstract: Ensuring academic integrity during examinations requires timely and effective detection of unauthorized items and suspicious behaviors. This project focuses on the object detection module within a larger system designed to identify students, invigilators, and prohibited items, such as mobile phones and notes, in classroom environments. A comparative analysis of detection models, including YOLOv8, YOLOv11, Fast R-CNN, and SSD, was conducted to evaluate key metrics. The results of different experiments revealed that YOLOv8 was the most successful model because of its high accuracy, occlusion-handling ability, and real-time processing capabilities. YOLOv8 performed better in real-world classroom scenarios by striking the ideal mix between speed and accuracy, making it a dependable option for object detection in dynamic environments. These results use YOLOv8's potential as the core component of a comprehensive system for real-time malpractice detection, ushering in integrity and fairness in exams. By having such advanced object identification methods embedded within monitoring systems, this approach ensures morality in AI-based solutions that emphasize equity and accountability in education.
Author: Shruti Maria Shibu
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409431442