Plank.AI - Real-Time Plankton Detection and Tracking

🌊 Introduction

Plank.AI is an AI-powered application for real-time plankton detection, tracking, and tallying. It utilizes YOLOv5 for object detection and Norfair for object tracking, integrating seamlessly into a user-friendly PySide6 GUI for visualization and interaction.

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🚀 Features

• Real-time plankton detection using YOLOv5
• Object tracking with Norfair
• Customizable display options (Bounding Boxes, Labels, Object IDs, Plankton Size)
• Camera selection and recording capabilities
• Export detection results to Excel for analysis
• Plankton density calculation based on configurable parameters
• User-friendly GUI built with PySide6

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🛠️ Installation

Prerequisites

Ensure you have Python 3.8+ installed along with the required dependencies.

Install Dependencies

pip install -r requirements.txt

Run the Application

python trial.py

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📜 How It Works

1. The YOLOv5 model loads pre-trained weights for plankton detection.
2. The camera captures live frames, which are processed by the model.
3. Norfair tracking maintains object identities across frames.
4. The GUI displays real-time bounding boxes, object labels, track IDs, and plankton size.
5. The species tally is updated, and density calculations are available.
6. Users can export results to Excel for further analysis.

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🎛️ User Guide

Main Features

• Start, Pause, Stop Detection: Control the object detection process.
• Change Camera Source: Select from available webcam sources.
• Toggle Display Options: Show/hide bounding boxes, object labels, and track IDs.
• Adjust Confidence & Distance Thresholds: Customize detection sensitivity.
• Capture Screenshots & Record Videos: Save detection results.
• Switch Models for different Objectives: supports only 10x and 40x objectives.
• Export Data: Save species tally to an Excel file.
• Plankton Density Calculation: Automatically compute cell density in mL.

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📊 Exporting Data

Results can be exported as an Excel file (.xlsx) containing:

• Species Name
• Detection Count
• Cell Density per mL

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⚙️ Technologies Used

• Python 3.8+
• PySide6 (for GUI)
• OpenCV (for image processing)
• YOLOv5 (for object detection)
• Norfair (for tracking)
• Pandas (for data export)
• NumPy (for numerical processing)

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👥 Contributors

• Amor Lea T. Palatolon
• Daniel A. Papaya
• Peter Ville C. Carmen

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📌 Future Improvements

• ✅ Integration with cloud storage for dataset sharing.
• ✅ Support for custom YOLO models.
• ✅ Performance optimizations for real-time tracking.
• ✅ Enhanced post-processing features.
• ✅ Web Support Website App.

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📧 Contact

For inquiries and feedback, contact: 📩 peter.carmen0101@gmail.com