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YOLOv11n Object Detection on Video

This project performs real-time object detection on a video using the YOLOv11n model with GPU acceleration. The processed video with bounding boxes and confidence scores is saved as an output file.

Result

Features

  • Detect objects in a video using YOLOv11n.
  • GPU acceleration with PyTorch.
  • Draw bounding boxes with labels and confidence scores.
  • Save processed video.
  • Optional real-time display of processed frames.

Requirements

  • Python 3.12
  • GPU with CUDA support for faster inference.
  • Packages listed in requirements.txt.

Setup

  1. Clone the repository:
git clone https://github.com/ayhmdalila/traffic-detection
cd traffic-detection
  1. Install dependencies:
pip install -r requirements.txt

Usage

Run the main script:

python main.py

Run Docker container:

docker run --gpus all -v $(pwd):/app yolo-video-detector
  • Make sure to mount the project directory to /app in the container.
  • Ensure your GPU drivers and Docker NVIDIA runtime are properly set up.

Configuration

  • Frame size: Currently set to 640x360.
  • Frame skipping: Can skip frames by adjusting frame_skip.
  • YOLO confidence threshold: Set via conf parameter (default 0.4).
  • IOU threshold: Set via iou parameter (default 0.5).

Notes

  • GPU is required for optimal performance.
  • For CPU-only mode, remove .to("cuda") in main.py.
  • Supports .mp4, .mkv, and other common video formats.

License

Open source