An Intelligent Traffic Control System Incorporating Deep Learning and Computer Vision with Prioritized and Dynamic Timing:
Traffic congestion poses a widespread challenge in global urban areas, leading to delays, productivity losses, and increased air pollution. Traditional traffic signal systems struggle to adapt to changing traffic dynamics, compromising road network efficiency. To address this issue, a smart traffic control system is proposed, utilizing advanced computer technology. This system employs real-time monitoring and analysis to dynamically adjust traffic signals, optimizing flow and reducing congestion-related issues. Deep learning and computer vision technologies are integrated to enhance the system's understanding of visual data and patterns. The YOLO tool is specifically used to quickly identify and prioritize emergency vehicles. The system efficiently manages traffic density, prioritizes emergency vehicles, and allocates waiting times to lanes based on real-time conditions. A functional model demo demonstrated effective decision-making capabilities.
Keywords:Traffic congestion, intelligent traffic control, deep learning, computer vision, YOLO, sustainable cities, emergency vehicle detection
Role of Deep Learning and Computer Vision : The work relies on the integration of deep learning and computer vision. Deep learning, exemplified by the YOLO (You Only Look Once) algorithm, is the cornerstone of the vehicle detection mechanism. YOLO's speed and accuracy in simultaneously identifying and classifying objects are crucial for realtime vehi-cle recognition.
The combination of computer vision and deep learning extracts contextually relevant information from video frames, serving as the foundation for traffic analysis. Through computer vision techniques, visual data is transformed into valuable insights, allowing the analysis of traffic patterns, vehicle behavior, and the presence of emergency vehicles. The synergy between deep learning and computer vision empowers the system to intelligently adapt to changing traffic dynamics. This facilitates efficient vehicle detection and dynamic optimization of traffic flow through real-time adjustments to traffic signals, guided by insights from computer vision.
Example:
Emergency Vehicles and Dynamic-lane prioritization The project employs cutting-edge technologies to identify and give priority to emergency vehicles with activated sirens, such as ambulances and firetrucks. In real-time traffic scenarios, these vehicles are recognized using both visual and auditory cues. The innovation lies in combining YOLO's object detection with sound signature recognition, specifically sirens. Training the model to identify emergency vehicles with activated sirens enables comprehensive detection in dynamic urban settings. Upon detecting such a vehicle, the system promptly prioritizes its lane by adjusting traffic signals, ensuring unimpeded passage. This prioritization minimizes delays and enhances emergency response efficiency. The integration of audio-visual detection and dynamic lane prioritization demonstrates the system's practicality and positive impact on urban safety. The outcome is a traffic flow optimization system that contributes to saving lives during emergencies, representing a significant step toward smarter and safer cities in the face of growing urban complexity.
