AI-based YOLO V4 Intelligent Traffic Light Control System


Keywords: Traffic light system, Artificial intelligence, YOLOV4, intelligent monitoring, Signal switching algorithm


With the growing number of city vehicles, traffic management is becoming one of the most persistent challenges. Traffic bottlenecks cause more 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, the effective result of wait times for the Commuters at the traffic signal point is 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 lower vehicle waiting times.



How to Cite

Boppuru, P. R., Kukatlapalli, P. K. ., Chowdary, C. R. ., & Hussain , J. . (2023). AI-based YOLO V4 Intelligent Traffic Light Control System. Journal of Automation, Mobile Robotics and Intelligent Systems, 16(4), 53-61.

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