Real-Time Smart Traffic Monitoring System Using YOLO-Based Object Detection
Dr. Mungara Kiran Kumar,
Hoshitha Ganeshuni,
Syed Ikram,
Harini Narahari,
Sreenandan Kavuri
Urbanization has rapidly increased vehicle numbers, causing severe traffic congestion and road safety
issues. Traditional systems relying on manual surveillance and static sensors are inefficient and prone to
errors, making them unsuitable for dynamic traffic conditions. This project, Real-Time Traffic Detection
using YOLO-based Object Detection, aims to develop an automated, accurate, and efficient system for
detecting and classifying traffic elements such as vehicles, pedestrians, and traffic congestion using
the YOLO (You Only Look Once) object detection algorithm. YOLO is a powerful algorithm that can
detect vehicles, pedestrians, and traffic signals from live camera feeds instantly. The proposed system
integrates advanced feature extraction and attention mechanisms to boost small object detection,
enhance robustness in diverse conditions, and ensure real-time performance. Experimental results
indicate that this intelligent system not only improves road safety but also provides data-driven insights
for optimizing traffic and planning smart cities, maintaining efficiency and adaptability for seamless
deployment in embedded platforms.
