Engineeering & IT Projects and Resources: Vehicle Detection on Highways

Vehicle Detection on Highways

1. Introduction

The Vehicle Detection on Highways project is designed to detect cars and other vehicles in real-time or recorded video streams. This application is useful in traffic management, surveillance, and automated toll systems. The project can leverage YOLO (You Only Look Once) or Haar cascades for vehicle detection.

2. Prerequisites

• Python: Install Python 3.x from the official Python website.
• Required Libraries:
- opencv-python: Install using pip install opencv-python
- numpy: Install using pip install numpy
- torch: Install using pip install torch (if using YOLO)
- torchvision: Install using pip install torchvision (if using YOLO)
- Pretrained YOLO weights or Haar cascade XML files.
• Basic understanding of computer vision techniques.

3. Project Setup

1. Create a Project Directory:

- Name your project folder, e.g., `VehicleDetectionHighway`.
- Inside this folder, create the Python script file (`vehicle_detection.py`).

2. Install Required Libraries:

Ensure OpenCV and other necessary libraries are installed using `pip`.

4. Writing the Code

Below is an example code snippet for Vehicle Detection using YOLO:

import cv2
import torch
from torchvision import transforms

# Load YOLO model
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # Using YOLOv5 small model

# Function to detect vehicles
def detect_vehicles(frame):
    results = model(frame)
    vehicle_count = sum(1 for detection in results.pred[0] if detection[-1] in [2, 3, 5, 7]) # Car, Bus, Truck, Motorbike classes
    return vehicle_count, results

# Main function for video stream processing
def main():
    video_capture = cv2.VideoCapture('highway_video.mp4') # Replace with 0 for live feed

    while True:
        ret, frame = video_capture.read()
        if not ret:
            break

        vehicle_count, results = detect_vehicles(frame)

        # Annotate frame with vehicle count
        annotated_frame = results.render()[0]
        cv2.putText(annotated_frame, f'Vehicles Detected: {vehicle_count}', (10, 50),
                    cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)

        cv2.imshow('Vehicle Detection', annotated_frame)

        if cv2.waitKey(1) & 0xFF == ord('q'):
            break

    video_capture.release()
    cv2.destroyAllWindows()

if __name__ == "__main__":
    main()

5. Key Components

• Vehicle Detection: Identifies vehicles using YOLO's object detection capabilities.
• Vehicle Counting: Counts the number of detected vehicles in each frame.
• Real-Time Processing: Processes video streams in real-time or from a recorded source.

6. Testing

1. Ensure the YOLO model weights are properly loaded in the script.

2. Run the script:

python vehicle_detection.py

3. Verify the real-time detection and counting of vehicles in the video feed.

7. Enhancements

• Speed Estimation: Integrate algorithms to estimate the speed of detected vehicles.
• Lane-Specific Detection: Detect and count vehicles in specific lanes.
• Advanced Models: Use higher versions of YOLO or other deep learning models for better accuracy.

8. Troubleshooting

• Detection Issues: Ensure proper lighting and clear video input.
• Performance Lag: Use a smaller YOLO model or hardware acceleration.
• Compatibility: Verify the compatibility of the video codec and OpenCV version.

9. Conclusion

The Vehicle Detection on Highways project provides a robust solution for detecting and counting vehicles. It can be further extended to include additional functionalities such as speed estimation and automated toll processing.

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