O'quv auditoriyalarida shaxslarning holatini chuqur o'rganish orqali baholash uchun dataset yaratish.
Аннотация
O'quv jarayonida talabalarning faoliyati va ishtiroki darajasini baholash ta'lim sifatini oshirishda muhim rol o'ynaydi. Zamonaviy kompyuter ko'rish va sun'iy intellekt texnologiyalari yordamida auditoriyada talabalar holatlarini real vaqt rejimida avtomatik tarzda aniqlash orqali mumkin. Ushbu tadqiqot ishida LabelImg v1.8.1 dasturi yordamida annotatsiya qilish [6] va YOLO (You Only Look Once) chuqur o'rganish algoritmi asosida talabalarning qo'l ko'tarish, uxlash va telefon foydalanish holatlarini aniqlash tizimi tavsiflanadi. Taklif etilgan tizim 90-95 % aniqlik darajasi bilan talaba holatlarini muvaffaqiyatli aniqlash ko’zda tutilgan va bu o'qituvchilarga dars samaradorligini oshirishga yordam beradi.
Об авторах
Список литературы
Nguyen, A., Chen, W., & Rauterberg, M. (2022). Detection of student attention levels in classroom environments using facial expressions analysis. IEEE Transactions on Learning Technologies, 15(3), 323–337.
Yang, L., Jiang, D., & Sahli, H. (2023). Posture analysis for student engagement assessment in classroom environments. International Journal of Human-Computer Studies, 167, 102709.
Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You only look once: Unified, real-time object detection. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 779–788).
Lin, T. Y., Goyal, P., Girshick, R., He, K., & Dollár, P. (2020). Focal loss for dense object detection. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42(2), 318–327.
Jocher, G., Stoken, A., & Borovec, J. (2023). YOLOv5 Documentation. Ultralytics. Retrieved from https://github.com/ultralytics/yolov5
Tzutalin. (2015). LabelImg. Git code. Retrieved from https://github.com/tzutalin/labelImg
Yang, F., & Wang, T. (2024). SCB-Dataset3: A Benchmark for Detecting Student Classroom Behaviour. arXiv:2310.02522v2 [cs.CV]. 9 Sep 2024.