README.md

---
language: en
license: mit
tags:
  - yolov5
  - computer-vision
  - object-detection
  - microscopy
  - microplastics
  - academic
  - ViBOT
model-index:
  - name: Microparticle-Detection-YOLOv5
    results:
      - task:
          type: object-detection
          name: Object Detection
        dataset:
          name: Custom PMMA Microplastic Microscopy Dataset
          type: microscopy
        metrics:
          - type: precision
            value: 0.795
          - type: recall
            value: 0.895
          - type: [email protected]
            value: 0.97
---

# 🧫 High-Speed Microparticle Detection and Tracking using YOLOv5

**Author:** Martin Badrous  
**Affiliation:** ViBOT Master’s Program, Bourgogne University  
**Year:** 2021  
**License:** MIT  

---

## 🧠 Abstract

This repository hosts the deep learning model developed for the master’s thesis  
**“High-Speed Microparticle Detection and Tracking” (2021)**,  
conducted under the **ViBOT Master’s Program, Bourgogne University**.

The model implements a **YOLOv5-based object detection pipeline** for identifying and counting **PMMA microplastic particles** in high-speed microscopy imagery.  
It represents the second and deep learning phase of the research, following an earlier classical image processing approach.

---

## 📚 Research Context

Microparticle detection is a key task in studying particulate contamination and flow behavior under a microscope.  
Traditional methods based on thresholding and contour detection struggle with illumination, blur, and focus variations.  
To overcome these limitations, this work explores a **deep learning detector (YOLOv5)** trained to recognize two microplastic classes under 20× magnification.

### Target Classes
- `PMMA10`
- `PMMA20`

### Model Architecture
- **Model:** YOLOv5s (PyTorch)
- **Input Resolution:** 640 × 640
- **IoU Threshold:** 0.5  
- **Confidence Threshold:** 0.5  
- **Min Detection Area:** 500 px  
- **Scale:** 0.30 µm²/pixel  
- **Epochs:** 50  
- **Batch Size:** 16  

---

## 🧪 Dataset and Training

- **Dataset Split:** 70% train / 20% validation / 10% test  
- **Data Source:** Custom microscope videos at 20× magnification  
- **Augmentations:** Gaussian noise, blur, brightness, contrast, and saturation (rotation disabled)  
- **Optimization:** SGD with cosine learning rate decay  
- **Framework:** PyTorch (YOLOv5 Ultralytics)

### Evaluation Results

| Metric | PMMA10 | PMMA20 | Mean |
|:--------|:--------|:--------|:------|
| Precision | 0.80 | 0.79 | 0.795 |
| Recall | 0.88 | 0.91 | 0.895 |
| [email protected] | 0.97 | 0.97 | **0.97** |

---

## 🧩 Post-Processing and Analysis

After detection, each particle undergoes morphological analysis via three computed features:

| Feature | Description |
|----------|--------------|
| **Otsu Binarized Area (px)** | Measures the segmented particle area |
| **Laplacian Variance** | Quantifies image focus/sharpness |
| **Chi-Square Histogram Distance** | Measures similarity to a reference “ideal” particle histogram |

These features are visualized in a 3D scatter space to assess detection focus, size distribution, and classification consistency.

---

## 🧭 Model Structure

```
Microparticle-Detection-YOLOv5-HF/
├── README.md          # Academic model card (this file)
├── config.yaml        # Metadata (architecture and params)
├── model_card.json    # Structured metadata for Hugging Face Hub
└── best.pt            # YOLOv5 trained weights
```

---

## 🧩 How to Use

```python
import torch

# Load custom YOLOv5 model
model = torch.hub.load('ultralytics/yolov5', 'custom', path='best.pt')
results = model('microscope_sample.jpg')
results.show()
```

---

## 📘 Citation

> **Badrous, M.** (2021). *High-Speed Microparticle Detection and Tracking.*  
> ViBOT Master’s Program, Bourgogne University.

---

## 🧑‍💻 Author

**Martin Badrous**  
ViBOT Master’s Program, Bourgogne University (2021)

---

## 🪶 License

This model is released under the **MIT License**.

---

## 🧭 Future Work

- Integrate motion tracking and particle trajectory reconstruction  
- Extend dataset to diverse microplastic materials and shapes  
- Deploy as an interactive Hugging Face Space (Streamlit demo)  
- Explore YOLOv8 or RT-DETR for real-time microscopy inference

---