AI-based Yolo v4 intelligent traffic light control system

• Autorzy: Prathap Boppuru Rudra , Kumar Kukatlapalli Pradeep , Chowdary Cherukuri Ravindranath , Hussain Javid

Identyfikatory

DOI

10.14313/JAMRIS/4-2022/33

• Języki publikacji: EN

Abstrakty

EN

With the growing number of city vehicles, traffic management is becoming a persistent challenge. Traffic bottlenecks cause significant disturbances in our everyday lives and raise stress levels, negatively impacting the environment by increasing carbon emissions. Due to the population increase, megacities are experiencing severe challenges and significant delays in their day-to-day activities related to transportation. An intelligent traffic management system is required to assess traffic density regularly and take appropriate action. Even though separate lanes are available for various vehicle types, wait times for commuters at traffic signal points are not reduced. The proposed methodology employs artificial intelligence to collect live images from signals to address this issue in the current system. This approach calculates traffic density, utilizing the image processing technique YOLOv4 for effective traffic congestion management. The YOLOv4 algorithm produces better accuracy in the detection of multiple vehicles. Intelligent monitoring technology uses a signal-switching algorithm at signal intersections to coordinate time distribution and alleviate traffic congestion, resulting in shorter vehicle waiting times.

Słowa kluczowe

EN

traffic jam; traffic light system; traffic management; intelligent monitoring; signal switching algorithm; artificial intelligence

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2022
• Tom: Vol. 16, No. 4
• Strony: 53--61
• Opis fizyczny: Bibliogr. 28 poz., rys.

Twórcy

autor

Prathap Boppuru Rudra

• Department of Computer Science and Engineering, CHRIST University, Bangalore-560074, Karnataka, India

• https://orcid.org/0000-0002-5161-4972

autor

Kumar Kukatlapalli Pradeep

• Department of Computer Science and Engineering, CHRIST University, Bangalore-560074, Karnataka, India

autor

Chowdary Cherukuri Ravindranath

• Department of Computer Science and Engineering, CHRIST University, Bangalore-560074, Karnataka, India

autor

Hussain Javid

• Department of Computer Science and Engineering, CHRIST University, Bangalore-560074, Karnataka, India

Bibliografia

• [1] A. A. Zaid, Y. Suhweil and M. A. Yaman, “Smart controlling for traffic light time,” 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), Aqaba, 2017, pp. 1–5. doi:10.1109/AEECT.2017.8257768.
• [2] Adarsh P., Rathi, P., Kumar, M. (2020). “YOLO v3-Tiny: Object detection and recognition using one stage improved model.” 2020 6th International Conference on Advanced Computing and Communication Systems (ICACCS). doi:10.1109/icaccs48705.2020.9074315
• [3] Chattaraj, A., Bansal, S., Chandra, A. (2009). “Implementation of image processing in real-time IEEE Potentials,” 28(3), 40–43. doi:10.1109/mpot.2009.932094
• [4] Chong HF, Ng DWK. (2016). “Development of IoT device for traffic management system.” 2016 IEEE Student Conference on Research and Development (SCOReD). doi:10.1109/scored.2016.7810059
• [5] Choudhury S, Chattopadhyay SP, Hazra TK. (2017). “Vehicle detection and counting using Haar feature-based classifier.” 2017 8th Annual Industrial Automation and Electromechanical Engineering Conference (IEMECON). doi:10.1109/iemecon.2017.8079571
• [6] D.Y. Huang, Chao-Ho chen ,Wu-chin hu “Reliable moving vehicle detection based on the filtering of swinging tree leaves and raindrops.” doi:10.1109/student.2019.6089337
• [7] Kanungo A, Sharma A, Singla C. (2014). “Smart traffic lights switching and traffic density calculation using video processing.” 2014 Recent Advances in Engineering and Computational Sciences (RAECS). doi:10.1109/raecs.2014.6799542
• [8] Khushi. (2017). “Smart Control of Traffic Light System Using Image Processing.” 2017 International Conference on Current Trends in Computer, Electrical, Electronics and Communication (CTCEEC). doi:10.1109/ctceec.2017.8454966
• [9] Li J, Zhang Y, Chen Y. (2016). “A Self-Adaptive Traffic Light Control System Based on Speed of Vehicles.” 2016 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C). doi:10.1109/qrs-c.2016.58
• [10] Muhammad Fachrie. “A Simple Vehicle Counting System Using Deep Learning with YOLOv3 Model.” (ICCSNT), 2017. doi:10.1109/iccsnt.2017.8343709
• [11] Manikonda P, Yerrapragada AK, Annasamudram SS. (2011). “Intelligent traffic management system.” 2011 IEEE Conference on Sustainable Utilization and Development in Engineering and Technology (STUDENT). doi:10.1109/student.2011.6089337
• [12] K. Sangeetha, Kavibharathi,Gnanasoundari, andKishorekumar. “Implementation of image processing in real-time traffic light control.” 2019, 3rd International Conference on Electronics Computer Technology. doi:10.1109/icectech.2019.5941662
• [13] Osman T, Psyche SS, Ferdous JMS, Zaman Hu. “Intelligent traffic management system for cross-sections of roads using computer vision.” 2017 IEEE 7th Annual Computing and Communication Workshop and Conference (CCWC). 2017. doi:10.1109/ccwc.2017.7868350
• [14] Pranav Shinde, Srinand Yadav, Shivani Rudrake, Pravin Kumbhar. “Smart Traffic Control System using YOLO.” 2019 IEEE 8th Data-Driven Control and Learning Systems Conference (DDCLS). doi:10.1109/ddcls.2019.8908873
• [15] Rani LJ, Kumar M, Naresh KS, Vignesh, S. “Dynamic traffic management system using infrared (IR) and Internet of Things (IoT).” 2017, Third International Conference on Science Technology Engineering Management (ICON- STEM). doi:10.1109/iconstem.2017.8261308
• [16] Rizwan P., Suresh K.,Babu MR. “Real-time smart traffic management system for smart cities by using the Internet of Things and big data.” 2016, International Conference on Emerging Technological Trends (ICETT). doi:10.1109/icett.2016.7873660
• [17] Tao J, Wang H, ZhangX , Li, X., Yang, H. “An object detection system based on YOLO in traffic scene.” 2017, 6th International Conference on Computer Science and Network Technology (ICCSNT). doi:10.1109/iccsnt.2017.8343709
• [18] Tai Huu - Phuong Tran, Jae Wook Jeon. “Accurate Real-Time Traffic Light Detection Using YOLOv4.” 2020. DOI: 10.1109/ICCE-Asia49877.2020.9277063
• [19] Corovic, A., Ilic, V., Duric, S., Marijan, M., Pavkovic, B. “The Real-Time Detection of Traffic Participants Using YOLO Algorithm.” 2018, 26th Telecommunications Forum (TELFOR). doi:10.1109/telfor.2018.8611986.
• [20] Wang Q, Zhang Q, Liang X, Wang Y, Zhou C, Mikulovich VI. “Traffic Lights Detection and Recognition Method Based on the Improved YOLOv4 Algorithm.” Sensors, vol. 22, no. 1, 2022, 200. https://doi.org/10.3390/s22010200.
• [21] Dave, P., Chandarana, A., Goel, P., & Ganatra, A. “An amalgamation of YOLOv4 and XGBoost for nextgen smart traffic management system.” PeerJ. Computer Science, vol. 7, 2021, e586. https://doi.org/10.7717/peerj-cs.586.
• [22] Ouallane, Asma Ait, et al. “Overview of Road Traffic Management Solutions based on IoT and AI.” Procedia Computer Science, vol. 198, 2022, 518-523. https://doi.org/10.1016/j.procs.2021.12.279
• [23] B. Ali Almansoori, S. Saif Almansoori, H. Almansoori, R. Ahmed Almansoori, I. Ahmed and K. Shahid, “AI-Based Adaptive Signaling for Traffic Control Around Roundabouts,” Advances in Science and Engineering Technology International Conferences (ASET), 2022, pp. 1-5, doi: 10.1109/ASET53988.2022.9735009.
• [24] Michael Osigbemeh, Michael Onuu, Olumuyiwa Asaolu. “Design and development of an improved traffic light control system using hybrid lighting system,” Journal of Traffic and Transportation Engineering (English Edition), vol. 4, no. 1, 2017, 88-95, https://doi.org/10.1016/j.jtte.2016.06.001.
• [25] Hussain, J., Prathap, B.R., Sharma, A. (2022). “An Improved and Efficient YOLOv4 Method for Object Detection in Video Streaming.” In: Shukla, S., Gao, XZ., Kureethara, J.V., Mishra, D. (eds), Data Science and Security. Lecture Notes in Networks and Systems, vol. 462. Springer, Singapore. https://doi.org/10.1007/978-981-19-2211-4_27
• [26] Muhammad Saleem, Sagheer Abbas, Taher M. Ghazal, Muhammad Adnan Khan, Nizar Sahawneh, Munir Ahmad. “Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques.” Egyptian Informatics Journal, 2022. https://doi.org/10.1016/j.eij.2022.03.003.
• [27] Yousef, Khalil M., Mamal N. Al-Karaki, and Ali M. Shatnawi. “Intelligent traffic light flow control system using wireless sensors networks.” J. Inf. Sci. Eng., vol. 26, no. 3, 2010, 753-768.
• [28] Alharbi, A., Halikias, G., Sen, A.A.A. et al. “A framework for dynamic smart traffic light management system.” Int. J. Inf. Technol. vol. 13, 2021, 1769–1776. https://doi.org/10.1007/s41870-021-00755-2.

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).