Automated Data Collection System for Advanced Vehicle Detection and Traffic Analysis

This paper presents a real-time traffic analysis and vehicle detection solution aimed at boosting emergency response times, particularly for ambulance detection. The system employs the YOLOv11 model to identify ambulances across various conditions and uses four side cameras at key intersections for comprehensive traffic monitoring. By fine-tuning the YOLOv11 model with a custom dataset, the system achieves high accuracy in detecting ambulances in diverse scenarios. The integrated framework dynamically adjusts traffic signals, suggests alternative routes to reduce delays, and sends automated notifications to authorities and emergency services. This approach enhances traffic management, prioritizes ambulances, and supports public safety and efficient emergency response. The system's real-time capabilities and scalability make it a valuable solution for modern cities aiming to improve traffic flow and emergency service efficiency. The research demonstrates that utilizing advanced technologies can effectively address critical challenges in urban traffic control and emergency management, ultimately enhancing public safety and streamlining emergency response processes.