Smart Adaptive Traffic Management System Setup Guide Welcome to the setup guide for the Smart Adaptive Traffic Management System project. This guide will walk you through the process of setting up and running the project.
Prerequisites Before you begin, ensure you have the following prerequisites:
Python 3.6 or higher installed on your system. OpenCV library installed (pip install opencv-python). YOLOv4 pre-trained model files (yolov4.cfg, yolov4.weights) available in the same directory as the project files. Installation Clone or download the project repository to your local machine. Place the YOLOv4 pre-trained model files (yolov4.cfg, yolov4.weights) in the project directory. Usage Open a terminal or command prompt and navigate to the project directory.
Run the vehicle_detection.py script to detect vehicles in an image:
python vehicle_detection.py The script will prompt you to enter the path to the image you want to analyze. It will then display the image with detected vehicles outlined in green rectangles.
Run the traffic_signal.py script to adjust green signal time based on vehicle count:
python traffic_signal.py The script will read the vehicle count from the vehicle_count.txt file (make sure the file is in the same directory). It will calculate the adjusted green signal time and display the result.
Further Customization You can further customize the project by adjusting the base green time, vehicle multiplier, and other parameters in the traffic_signal.py script. Additionally, you can explore advanced YOLO model versions and incorporate real-time video streams for vehicle detection.
Conclusion Congratulations! You've successfully set up and explored the Smart Adaptive Traffic Management System project. Feel free to experiment, customize, and integrate additional features as you delve deeper into traffic management and computer vision.
For more information and resources, refer to the project documentation and relevant sources on YOLO models and traffic management.