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Annotatsiya. Mazkur maqolada konvolyutsion neyron tarmoq (CNN) algoritmlari tahlil qilinib, ularning kompyuter ko‘rish sohasida, xususan, avtomatlashtirilgan yo‘l aniqlash tizimlarida qo‘llanilishi o‘rganiladi. Asosiy e’tibor CNN arxitekturasining tuzilmasi, ishlash tamoyillari va tasvirni segmentatsiya qilishdagi imkoniyatlariga qaratildi. Ushbu ish natijasida sun’iy intellekt texnologiyalariga asoslangan yo‘l aniqlash tizimini ishlab chiqishning nazariy va amaliy jihatlari yoritiladi hamda real vaqt rejimida ishlay oladigan tizimni yaratish bo‘yicha takliflar ishlab chiqiladi.

  • Internet ҳавола
  • DOI
  • UzSCI тизимида яратилган сана 08-11-2025
  • Ўқишлар сони 41
  • Нашр санаси 18-06-2025
  • Мақола тилиO'zbek
  • Саҳифалар сони113-117
Ўзбек

Annotatsiya. Mazkur maqolada konvolyutsion neyron tarmoq (CNN) algoritmlari tahlil qilinib, ularning kompyuter ko‘rish sohasida, xususan, avtomatlashtirilgan yo‘l aniqlash tizimlarida qo‘llanilishi o‘rganiladi. Asosiy e’tibor CNN arxitekturasining tuzilmasi, ishlash tamoyillari va tasvirni segmentatsiya qilishdagi imkoniyatlariga qaratildi. Ushbu ish natijasida sun’iy intellekt texnologiyalariga asoslangan yo‘l aniqlash tizimini ishlab chiqishning nazariy va amaliy jihatlari yoritiladi hamda real vaqt rejimida ishlay oladigan tizimni yaratish bo‘yicha takliflar ishlab chiqiladi.

№ Муаллифнинг исми Лавозими Ташкилот номи
1 Davronov S.R. texnika fanlari bo‘yicha falsafa doktori, dotsent, Qarshi davlat universiteti,
2 Rayimova A.R. magistrant, Qarshi davlat universiteti,
№ Ҳавола номи
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3 Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
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6 He, K., Gkioxari, G., Dollár, P., & Girshick, R. (2017). Mask R-CNN. In Proceedings of the IEEE ICCV.
7 R. Sharma, R. B. Pachori, and P. Sircar, (2020). “Automated emotion recognition based on higher-order statistics and deep learning algorithm,” Biomedical Signal Processing and Control, vol. 58, p. 101867.
8 Zhongqin Bi,·Ling Yu, Honghao Gao, Ping Zhou, Hongyang Yao. (2021) Improved VGG model‑based efficient traffic sign recognition for safe driving in 5G scenarios. International Journal of Machine Learning and Cybernetics 12(3). DOI:10.1007/s13042-020-01185-5
9 A. Almalaq and G. Edwards. (2017). “A review of deep learning methods applied on load forecasting,” in Proceedings - 16th IEEE International Conference on Machine Learning and Applications, ICMLA 2017, pp. 511–516. doi: 10.1109/ICMLA.2017.0-110
10 K. Simonyan and A. Zisserman, (2014) “Very deep convolutional networks for large-scale image recognition,” Computer Science, arXiv preprint arXiv:1409.1556.
11 Alexander Wong, Mohammad Javad Shafiee, Michael St. Jules. (2018) MicronNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-Time Embedded Traffic Sign Classification. DOI:10.1109/ACCESS.2018.2873948
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