An artificial intelligence approach to smart exam supervision using YOLOv5 and siamese network

<p>Artificial intelligence has introduced revolutionary and innovative solutions to many complex problems by automating processes or tasks that used to require human power. The limited capabilities of human efforts in real-time monitoring have led to artificial intelligence becoming increasingly popular. Artificial intelligence helps develop the monitoring process by analysing data and extracting accurate results. Artificial intelligence is also capable of providing surveillance cameras with a digital brain that analyses images and live video clips without human intervention. Deep learning models can be applied to digital images to identify and classify objects accurately. Object detection algorithms are based on deep learning algorithms in artificial intelligence. Using the deep learning algorithm, object detection is achieved with high accuracy. In this paper, a combined model of the YOLOv5 model and network Siames technology is proposed, in which the YOLOv5 algorithm detects cheating tools in classrooms, such as a cell phone or a book, in such away that the algorithm detects the student as an object and cannot recognize his face. Using the Siames network, we compare the student’s face against the data base of students in order to identify the student with cheating tools.</p>