YOLOv5-based Student Counting Software Design

Abstract

To address the issues of students' campus safety as well as campus management, this paper combines computer vision technology and deep learning algorithms to design a counting software for observing the number of students in a classroom, which can provide accurate headcount and data analysis by monitoring and counting the classroom crowd in real time in order to solve the problem of classroom crowd counting. The classroom crowd counting software based on YOLOv5 has a wide application potential in the field of education. It provides real-time and accurate headcount statistics for classroom management and supports them in making decisions on staff scheduling and resource management. In this paper, we adopt YOLOv5 algorithm as the main target detection framework, which is capable of fast and accurate target detection and localization. Then, this paper designs a crowd counting software based on Qt Designer, which can monitor the number of people in the classroom in real time and perform accurate headcount. In addition, we added data visualization and analysis functions to the software for more in-depth analysis of the headcount results. Finally, experiments on the publicly available benchmark dataset CUHK Occlusion show that the algorithms in this paper exhibit significant advantages in terms of accuracy and real-time performance.