Digital Classroom: Automated Visual Counting System in Political Education Classes

This study aims to develop a deep learning-based visual counting system for automatically counting students in political education classes, enhancing the efficiency of classroom attendance monitoring. The system employs the FamNet model, which utilizes multi-scale feature extraction and density map prediction techniques to achieve high-precision student counting. Through automated image acquisition and analysis, this system replaces traditional manual attendance methods. Experimental results demonstrate that FamNet outperforms GMN in counting accuracy, precision, and generalizability, making it suitable for complex classroom settings. This system not only improves classroom management efficiency but also shows potential for broader applications in educational contexts.