---
title: EL Defect Detection System
emoji: 🔬
colorFrom: blue
colorTo: red
sdk: docker
pinned: false
---

# 🔬 EL Defect Detection System

Production-grade electroluminescence (EL) defect detection for solar PV modules.

## Features

- **Upload** any EL image (full module, single cell, any brightness/size)
- **Automatic grid detection** segments modules into individual cells
- **Deep learning** (U-Net with ResNet encoder) detects defects per cell
- **Quantitative analysis**: crack length (mm), dark area (%), severity classification
- **PASS/FAIL decision** with configurable quality thresholds
- **Visual overlays**: color-coded defect masks on original images
- **Downloadable reports** (JSON + overlay images)

## Defect Types Detected

| Color | Defect | Description |
|-------|--------|-------------|
| 🔴 Red | Dark/Inactive | Area disconnected from cell circuit |
| 🔵 Blue | Crack | Micro-crack in silicon |
| 🔷 Cyan | Cross Crack | Crack at ribbon edge (high importance) |
| 🟢 Green | Busbar | Metal busbar connection (feature) |

## Architecture

```
input image → preprocessing (CLAHE) → grid detection → cell extraction
→ U-Net inference → mask cleaning → crack/dark analysis → PASS/FAIL
```

## Technical Details

- **Model**: U-Net with ResNet34 encoder (segmentation_models_pytorch)
- **Loss**: 0.5 × Dice + 0.5 × Weighted CrossEntropy
- **Dataset**: E-SCDD (snt-ubix/e-scdd) — 30 classes remapped to 5
- **Preprocessing**: CLAHE, percentile normalization, adaptive denoising
- **Grid Detection**: Projection profiles + FFT periodicity + peak detection
- **Crack Analysis**: Skeletonization + distance transform + false positive filtering
- **Dark Detection**: Adaptive threshold (0.6 × mean intensity)

## Running Locally

```bash
pip install -r requirements.txt
streamlit run src/app/app.py
```

## Training

```bash
python src/train.py --encoder resnet34 --epochs 100 --batch_size 8
```