Apppublic

nithishbasireddy/el-defect-detection

sourceHugging Faceupdated 5mo agoView on Hugging Face

0likes

App README

🔬 EL Defect Detection System

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

—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)

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)

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

—Model: U-Net with ResNet34 encoder (segmentationmodelspytorch)
—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)

bash

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

bash

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