Revolutionizing Traffic Management with AI-Powered Machine Vision: A Step Toward Smart Cities
This addresses traffic management challenges for urban planners and authorities, though it appears incremental as it builds on existing deep learning methods.
This study tackled urban traffic congestion and safety by developing an AI-powered machine vision system for real-time vehicle detection and anomaly recognition, achieving high accuracy with YOLOv8 and YOLOv11 models to optimize traffic flow.
The rapid urbanization of cities and increasing vehicular congestion have posed significant challenges to traffic management and safety. This study explores the transformative potential of artificial intelligence (AI) and machine vision technologies in revolutionizing traffic systems. By leveraging advanced surveillance cameras and deep learning algorithms, this research proposes a system for real-time detection of vehicles, traffic anomalies, and driver behaviors. The system integrates geospatial and weather data to adapt dynamically to environmental conditions, ensuring robust performance in diverse scenarios. Using YOLOv8 and YOLOv11 models, the study achieves high accuracy in vehicle detection and anomaly recognition, optimizing traffic flow and enhancing road safety. These findings contribute to the development of intelligent traffic management solutions and align with the vision of creating smart cities with sustainable and efficient urban infrastructure.