Videokuzatuv ma’lumotlari asosida xodimlarning xavfsizlik qoidalariga rioya etishini avtomatik nazorat qilishning ahamiyati
Abstract
Mazkur maqolada videokuzatuv ma’lumotlari asosida ishlab chiqarish jarayonida xodimlarning xavfsizlik qoidalariga rioya etishini avtomatik nazorat qilish tizimlarining ahamiyati tahlil qilinadi. Zamonaviy sanoat korxonalarida inson omili bilan bog‘liq xavflar yuqori bo‘lib, an’anaviy nazorat usullari ularni to‘liq bartaraf eta olmaydi. Shu sababli kompyuter ko‘rish va sun’iy intellekt texnologiyalariga asoslangan videoanalitika tizimlari dolzarb yechim sifatida qaralmoqda. Tadqiqotda real vaqt rejimida ishlovchi algoritmlar yordamida xodimlarning himoya vositalaridan foydalanishi, xavfli zonalarga kirishi va mehnat muhofazasi talablariga rioya etishi aniqlanishi mumkinligi asoslab beriladi. Shuningdek, bunday tizimlar yordamida avariya holatlarining oldini olish, ishlab chiqarish samaradorligini oshirish va nazorat jarayonini avtomatlashtirish imkoniyatlari yoritiladi. Natijada, videokuzatuvga asoslangan intellektual monitoring tizimlari sanoatda xavfsizlikni ta’minlashning muhim komponenti ekanligi ko‘rsatib beriladi.
Kalit so‘zlar. videokuzatuv, sun’iy intellekt, kompyuter ko‘rish, ishlab chiqarish xavfsizligi, mehnat muhofazasi, videoanalitika, real vaqt monitoring, xavfli holatlarni aniqlash
References
1. Fang, Q., Li, H., Luo, X., Ding, L., Luo, H., Rose, T. M., & An, W. (2018). Detecting non-hardhat-use by a deep learning method from far-field surveillance videos. Automation in Construction, 85, 1–9. https://doi.org/10.1016/j.autcon.2017.09.018
2. Zhang, C., Li, H., & Wang, X. (2020). Automated detection of personal protective equipment using deep learning in construction sites. Advanced Engineering Informatics, 46, 101156. https://doi.org/10.1016/j.aei.2020.101156
3. Nath, N. D., Chaspari, T., & Behzadan, A. H. (2020). Automated detection of unsafe equipment and workers’ behavior using computer vision in construction. Journal of Construction Engineering and Management, 146(2), 04019094. https://doi.org/10.1061/(ASCE)CO.1943-7862.0001739
4. Kim, K., Cho, Y. K., & Zhang, S. (2016). Vision-based detection of unsafe actions of construction workers using wearable inertial sensors and cameras. Automation in Construction, 68, 125–135. https://doi.org/10.1016/j.autcon.2016.04.003
5. Wu, J., Cai, N., Chen, W., Wang, H., & Wang, Y. (2019). Automatic detection of hardhat wearing based on deep learning. IEEE Access, 7, 171600–171611. https://doi.org/10.1109/ACCESS.2019.2956106
6. Cheng, T., Migliaccio, G. C., Teizer, J., & Gatti, U. C. (2013). Data fusion of real-time location sensing and physiological status monitoring for ergonomics analysis of construction workers. Automation in Construction, 29, 55–65. https://doi.org/10.1016/j.autcon.2012.09.017
7. Zhou, Z., Irizarry, J., & Li, Q. (2013). Applying advanced technology to improve safety management in construction projects: A review. Automation in Construction, 34, 1–10. https://doi.org/10.1016/j.autcon.2012.09.008
8. Feng, Y., Zhang, S., & Wu, P. (2021). Factors influencing workplace safety: A review and future research directions using artificial intelligence. Safety Science, 134, 105097. https://doi.org/10.1016/j.ssci.2020.105097
9. Redmon, J., & Farhadi, A. (2018). YOLOv3: An incremental improvement. arXiv preprint arXiv:1804.02767. https://arxiv.org/abs/1804.02767
10. Bochkovskiy, A., Wang, C. Y., & Liao, H. Y. M. (2020). YOLOv4: Optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934. https://arxiv.org/abs/2004.10934
