Object detection is a pivotal technology in computer vision that involves identifying and locating objects within images or videos. During online examinations, object detection is used to enhance proctoring by monitoring the exam environment for unauthorized materials or suspicious behavior.
Core Elements:
Detection Models: Techniques like R-CNN (Region-Based Convolutional Neural Networks), YOLO (You Only Look Once), and SSD (Single Shot MultiBox Detector) ensure accurate and efficient detection.
Bounding Boxes: These are visual markers drawn around detected objects, such as books, mobile phones, or multiple faces.
Multi-Class Detection: Identifies and classifies various objects simultaneously within the exam setting.
Technological Basis
Advanced deep learning methods and convolutional neural networks (CNNs) extract features and enhance detection accuracy.
Applications
Object detection helps maintain the integrity of online exams by ensuring a secure and monitored environment, deterring cheating, and ensuring compliance with examination protocols.
Challenges
The proctoring platform must reliably detect objects across varying lighting conditions, angles, and in real-time to be effective in an online examination scenario
Enhance security with object detection in online exams
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