Vehicle Detection and Counting using YOLO11

YOLOv11 (You Only Look Once) is a state-of-the-art object detection model known for its speed and accuracy. It uses deep learning techniques to efficiently detect and track objects in images and videos, making it ideal for real-time applications like vehicle counting and traffic monitoring.

This project implements vehicle detection and counting using YOLOv11 and OpenCV. It processes a video file to track and count vehicles that cross a predefined red line, providing real-time visualizations of the detections and counts.

🚀 Features

• Real-time vehicle detection and tracking using YOLOv11.
• Counts vehicles that cross a red line in the video.
• Bounding boxes and track IDs displayed for each detected vehicle.
• Video output with overlayed tracking results is saved.

🛠️ Tech Stack

• Python
• YOLOv11 (Ultralytics)
• OpenCV (Computer Vision Library)
• PyTorch (for YOLO model)
• Numpy (Array manipulations)

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📂 Project Setup

1️⃣ Install Dependencies

Ensure you have Python 3.8+ installed. Then, install the required libraries:

pip install ultralytics opencv-python numpy torch torchvision torchaudio

2️⃣ Download YOLO11 Model

Download the YOLO11 weights file (yolo11l.pt) from this link , here in this link scroll down till you reach the "🔥Performance" section and click on YOLO11l model to download the weights. Once downloaded, place it in the project directory.

3️⃣ Run the Project

python main.py

🎥 Input and Output

• Input: Video file (./test videos/test video_1.mp4)
• Output: Processed video saved as output_video.mp4
• Visualization: Displays the tracking results with bounding boxes and counts

📜 Code Explanation

1. Loads YOLO model using Ultralytics.
2. Reads input video and extracts properties like width, height, and FPS.
3. Processes each frame to detect and track vehicles (cars, bikes, etc.).
4. Draws a red line and counts vehicles crossing it.
5. Saves processed video with detected objects and counts.
6. Displays real-time output while processing.

🎯 Customization

• Change the input video path in cap = cv2.VideoCapture('./test videos/test video_1.mp4').
• Modify line_y_red = 430 to change the red line position.
• Adjust classes=[1,2,3,5,6,7] to track specific object categories:
  • 1 - Bicycle 🚲
  • 2 - Car 🚗
  • 3 - Motorcycle 🏍️
  • 5 - Bus 🚌
  • 6 - Train 🚆
  • 7 - Truck 🚛

📝 Future Improvements

• Add support for real-time webcam input.
• Implement speed estimation of detected vehicles.
• Export vehicle count data to a CSV file.

🤝 Contributing

Feel free to fork the repository, improve the project, and create a pull request!

📜 License

This project is licensed under the MIT License.

📷 Preview

📧 Contact

For any queries, reach out to me at [email protected].

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⚡ Happy Coding! 🚗🚦