VIDEO TASVIRLARDA AVTOTRANSPORT YO‘LAKCHALARINI ANIQLASH

Authors

  • Azizbek Ruzmetov ,Yetmishboyev Shaxzodbek Ma’murjon o‘g‘li Toshkent Kimyo xalqaro universiteti Author

Keywords:

Yo‘l belgilari, kompyuter, video tasvir, avtotransport, xavfsizlik, yo‘lakchalar.

Abstract

 Ushbu maqola video tasvirlarda avtotransport yo‘lakchalarini aniqlash usullari to‘g‘risida. Bundan tashqari xorijiy tadqiqotchilarning tajribalaridan kelib chiqqan holda mazkur mavzu doirasida keltirilgan fikrlari umumlashtirilib, taklif va tavsiyalar berilgan.

References

World Health Organization. Save Lives: A Road Safety Technical Package; World Health Organization: Geneva, Switzerland, 2017; 60.

World Health Organization. Global Status Report on Road Safety 2023; World Health Organization: Geneva, Switzerland, 2023.

Forum, I.T. Monitoring Progress in Urban Road Safety; International Traffic Forum: Paris, France, 2018.

Caruntu, C.F.; Ferariu, L.; Pascal, C.; Cleju, N.; Comsa, C.R. Connected cooperative control for multiple-lane automated vehicle flocking on highway scenarios. Proceedings of the 23rd International Conference on System Theory, Control and Computing; Sinaia, Romania, 9–11 October 2019; pp. 791-796. [DOI: https://dx.doi.org/10.1109/ICSTCC.2019.8885496]

Sun, Y.; Song, J.; Li, Y.; Li, Y.; Li, S.; Duan, Z. IVP-YOLOv5: An intelligent vehicle-pedestrian detection method based on YOLOv5s. Connect. Sci.; 2023; 35, 2168254. [DOI: https://dx.doi.org/10.1080/09540091.2023.2168254]

Ćorović, A.; Ilić, V.; Ðurić, S.; Marijan, M.; Pavković, B. The Real-Time Detection of Traffic Participants Using YOLO Algorithm. Proceedings of the 2018 26th Telecommunications Forum (TELFOR); Belgrade, Serbia, 20–21 November 2018; pp. 1-4. [DOI: https://dx.doi.org/10.1109/TELFOR.2018.8611986]

Joshi, R.; Rao, D. AlexDarkNet: Hybrid CNN architecture for real-time Traffic monitoring with unprecedented reliability. Neural Comput. Appl.; 2024; 36, pp. 1-9. [DOI: https://dx.doi.org/10.1007/s00521-024-09450-2]

Mandal, V.; Mussah, A.R.; Jin, P.; Adu-Gyamfi, Y. Artificial Intelligence-Enabled Traffic Monitoring System. Sustainability; 2020; 12, 9177. [DOI: https://dx.doi.org/10.3390/su12219177]

Sultan, F.; Khan, K.; Shah, Y.A.; Shahzad, M.; Khan, U.; Mahmood, Z. Towards Automatic License Plate Recognition in Challenging Conditions. Appl.Sci.; 2023; 13, 3956. [DOI: https://dx.doi.org/10.3390/app13063956]

Rafique, S.; Gul, S.; Jan, K.; Khan, G.M. Optimized real-time parking management framework using deep learning. Expert Syst. Appl.; 2023; 220, 119686. [DOI: https://dx.doi.org/10.1016/j.eswa.2023.119686].

Zhang, H.C.; Zhou, H. GPS positioning error analysis and outlier elimination method in forestry. Trans. Chin. Soc. Agric. Mach.; 2010; 41, pp.143-147.[DOI:https://dx.doi.org/10.3969/j.issn.10001298.2010.05.029]

van Diggelen, F.; Enge, P.K. The World’s first GPS MOOC and Worldwide Laboratory using Smartphones. Proceedings of the 28th International Technical Meeting of the Satellite Division of the Institute of Navigation (ION GNSS+ 2015); Tampa, FL, USA, 14–18 September 2015.

Hu, M.; Li, Y.; Bai, L. Multi-Color Vehicle Tracking Based on Lightweight Neural Network. Proceedings of the 2022 4th International Conference on Natural Language Processing (ICNLP); Xi’an, China, 25–27 March 2022; pp. 272-276.

Riaz, Z.; Khan, B.; Abdullah, S.; Khan, S.; Islam, M.S. Lung Tumor Image Segmentation from Computer Tomography Images Using MobileNetV2 and Transfer Learning. Bioengineering; 2023; 10, 981.

Wu, S.; Hadachi, A.; Vivet, D.; Prabhakar, Y. This Is the Way: Sensors Auto-Calibration Approach Based on Deep Learning for Self-Driving Cars. IEEE Sens. J.; 2021; 21, pp. 27779-27788. [DOI: https://dx.doi.org/10.1109/JSEN.2021.3124788]

Hannes, S.; Tibor, K. A Driver Behavior Monitoring System for Sustainable Traffic and Road Construction. Sustainability; 2023; 15, 12305.

Kim, W.; Yang, H.; Kim, J. Blind Spot Detection Radar System Design for Safe Driving of Smart Vehicles. Appl. Sci.; 2023; 13, 6147. [DOI: https://dx.doi.org/10.3390/app13106147]

Kim, J.B. Detecting the Turn on of Vehicle Brake Lights to Prevent Collisions in Highway Tunnels. Sustainability; 2022; 14, 14322. [DOI: https://dx.doi.org/10.3390/su142114322]

Oh, G.; Lim, S. One-Stage Brake Light Status Detection Based on YOLO v8. Sensors; 2023; 23, 7436. [DOI: https://dx.doi.org/10.3390/s23177436]

Li, X.; Lin, K.Y.; Meng, M.; Li, X.; Li, L.; Hong, Y.; Chen, J. A Survey of ADAS Perceptions with Development in China. IEEE Trans. Intell. Transp. Syst.; 2022; 23, pp. 14188-14203. [DOI: https://dx.doi.org/10.1109/TITS.2022.3149763]

Sun, Q.; Zhang, H.; Li, Z.; Wang, C.; Du, K. ADAS Acceptability Improvement Based on Self-Learning of Individual Driving Characteristics: A Case Study of Lane Change Warning System. IEEE Access; 2019; 7, pp. 81370-81381. [DOI: https://dx.doi.org/10.1109/ACCESS.2019.2923822]

Kim, J.B. Vehicle Detection Using Deep Learning Technique in Tunnel Road Environments. Symmetry; 2020; 12, 2012. [DOI: https://dx.doi.org/10.3390/sym12122012]

Zakaria, N.J.; Shapiai, M.I.; Ghani, R.A.; Yassin, M.N.M.; Ibrahim, M.Z.; Wahid, N. Lane Detection in Autonomous Vehicles: A Systematic Review. IEEE Access; 2023; 11, pp. 3729-3765. [DOI: https://dx.doi.org/10.1109/ACCESS.2023.3234442]

Park, S.; Yun, S. Analysis of LDWS Recognition Rate According to the Aging of Road Marking. Eng. Proc.; 2023; 36, 34.

Hedeya, M.A.; Eid, A.H.; Abdel-Kader, R.F. A Super-Learner Ensemble of Deep Networks for Vehicle-Type Classification. IEEE Access; 2020; 8, pp. 98266-98280. [DOI: https://dx.doi.org/10.1109/ACCESS.2020.2997286]

Pillai, U.K.K.; Valles, D. Vehicle Type and Color Classification and Detection for Amber and Silver Alert Emergencies Using Machine Learning. Proceedings of the 020 IEEE International IOT, Electronics and Mechatronics Conference (IEMTRONICS); Vancouver, BC, Canada, 9–12 September 2020; pp. 1-5.

Published

2024-09-10