Automated Rabbit Detection and Counting in Rabbit Farms Using Deep Learning

Overview

This project is a comprehensive AI-based system designed to detect and count rabbits in a farm environment using advanced object detection techniques. The primary goal is to provide an automated solution for real-time monitoring of rabbit populations to enhance farm management.

The system leverages TensorFlow Lite for efficient real-time detection and counting. Key features include:

• Detecting and counting rabbits in video footage.
• Tracking movements of rabbits across farm sections separated by barriers.
• Exporting results to a CSV file for reporting and analysis.

Read the full Documentation here: Automated Rabbit detection and counting in Rabbit farms

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Features

1. Real-Time Rabbit Detection:

   • Detect and count rabbits in each frame of video footage.
   • Visualize detection results with bounding boxes and confidence scores.

2. Batch Processing:

   • Process multiple video files simultaneously.
   • Export aggregated results to CSV for farm management.

3. Cross-Platform Compatibility:

   • Applications built for both macOS (.dmg) and Windows (.exe).

4. Scalability:

   • Handle large datasets using optimized cloud resources.

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Getting Started

Prerequisites

Before running the project, ensure the following are installed on your system:

• Python 3.9 or higher
• TensorFlow Lite
• LabelImg (optional, for annotating custom datasets)
• Supported Operating Systems: macOS, Windows, or Linux

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Installation

1. Clone the Repository:

   git clone https://github.com/yourusername/rabbit-detection-and-counting.git
   cd rabbit-detection-and-counting

2. Setup a virtual environment

   python -m venv venv
   source venv/bin/activate   # On Windows, use `venv\Scripts\activate`

3. Install Dependencies

   pip install -r requirements.txt

4. (Optional) Install LabelImg: If you want to annotate additional data, install the LabelImg tool::

pip install labelImg

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Running with Conda

Create a Conda environement

conda create --name rabbit_env python=3.9 -y
conda activate rabbit_env

Install Dependencies: Install the required libraries from the requirements.txt file

pip install -r requirements.txt

Run the Application from Command Line: Launch the app directly from the terminal:

python app.py

How to Run

Single Video Processing Process a single video file and generate a rabbit count:

python single_video_processing.py --input_video path/to/video.mp4 --output_folder path/to/output

Batch Video Processing Process multiple videos from a folder and generate a summary CSV:

python batch_processing.py --input_folder path/to/video/folder --output_csv path/to/output.csv

Run the app Launch the application and interact through the GUI:

python app.py

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Contributing

Contributions are welcome! Please follow these steps:

Fork this Repository: Click on the "Fork" button in the top right corner of this repository.

Clone Your Fork:

git clone https://github.com/yourusername/rabbit-detection-and-counting.git

Create a branch

git checkout -b feature-branch

Submit a pull request Once your changes are tested, submit a pull request.

Notes for Future Development

• Improve model accuracy using larger datasets and more advanced architectures.
• Explore real-time anomaly detection for farm management.
• Integrate the system with IoT devices for a fully automated farm monitoring solution.

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