Abstract:
From the different physical gestures in the examination room, the examinees'
suspicious conduct can be observable. Traditional invigilation methods cannot
perform effective monitoring. Due to the physical constraints of human invigilators,
many illegal acts go unnoticed. The fundamental principle of this research is to
prevent unethical approaches from the examinees by differentiating the correct and
suspicious posture of the candidate. An object detection model was developed by
YOLOv7(You Only Look Once) to identify the movement of the candidates. At
present YOLOv7 has greater accuracy in object detection. During the training, the
model has 97.1% mAP(Mean Average Precision). That indicates the high accuracy of
the model. After that, we deployed that YOLOv7-trained model into the web with the
help of FLASK(Micro Web Framework). With this proposed system, the capability of
a large number of students’ invigilation will be increased. Suspicious behavior can be detected in real-time with the help of CCTV footage. The system is completely
effective in identifying and keeping track of further than 100 participants in a single
frame during assessments. To assess the effectiveness of the Automatic Invigilation
System, many authentic examples are taken into account. Any kind of institution can
apply this model to examine any kind of candidate. To identify and maintain a close
eye on questionable student behavior universities, colleges, and schools might use the
suggested invigilation approach. It will reduce academic dishonesty and cheating
among students. However, by putting the suggested invigilation mechanism into
place, maybe cheating can be stopped and find a remedy for the problem.
