Real time vehicle classification using deep learning-smart traffic management

Abstract

As global urbanization continues to expand, the challenges associated with traffic congestion and road safety have become more pronounced. Traffic accidents remain a major global concern, with road crashes resulting in approximately 1.19 million deaths annually, as reported by the WHO. In response to this critical issue, this research presents a novel deep learning-based approach to vehicle classification aimed at enhancing traffic management systems and road safety. The study introduces a real-time vehicle classification model that categorizes vehicles into seven distinct classes: Bus, Car, Truck, Van or Mini-Truck, Two-Wheeler, Three-Wheeler, and Special Vehicles. A custom dataset was created with images taken in varying traffic conditions, including different times of day and locations, ensuring accurate representation of real-world traffic scenarios. To optimize performance, the model leverages the YOLOv8 deep learning framework, known for its speed and precision in object detection. By using transfer learning with pre-trained YOLOv8 weights, the model improves accuracy and efficiency, particularly in low-resource environments. Themodel’s performance wasrigorously evaluated using key metrics such as precision, recall, and mean average precision (mAP). Themodelachieved aprecision of 84.6%, recall of 82.2%, mAP50 of 89.7%, and mAP50–95 of 61.3%, highlighting its effectiveness in detecting and classifying multiple vehicle types in real-time. Furthermore, the research discusses the deployment of this model in low-and middle-income countries where access to high-end traffic management infrastructure is limited, making this approach highly valuable in improving traffic flow and safety. The potential integration of this system into intelligent traffic management solutionscould significantlyreduceaccidents,improveroadusage,andprovidereal-timetrafficcontrol.Futurework includes enhancing the model’s robustness in challenging weather conditions such as rain, fog, and snow, integrating additional sensor data (e.g., LiDAR and radar), and applying the system in autonomous vehicles to improve decision-making in complex traffic environments.