🔍 Anomaly Detection for Infrastructure Monitoring

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🚀 Project Highlights

🔥 96.1% F1-score in detecting concrete cracks without labeled training data
🧠 Variational Autoencoder learns healthy infrastructure patterns
⚡ Training Pipeline - Complete notebook for model training
📊 Complete Analysis - From data preprocessing to deployment

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💡 The Challenge

Traditional infrastructure inspection is expensive, time-consuming, and prone to human error. This project solves crack detection using unsupervised learning - training only on healthy concrete images and detecting cracks as reconstruction anomalies.

🎯 The Solution

Training Phase

graph LR
    A[Healthy Images Only] --> B[VAE Learning] --> C[Normal Patterns] --> D[💾 Model]

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Inference Phase

graph LR
    E[Test Image] --> F[Reconstruct] --> G[Compare Error] --> H{High Error?}
    H -->|Yes| I[🚨 ANOMALY]
    H -->|No| J[✅ NORMAL]

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📈 Results That Matter

📊 Metric	🎯 Score	📝 Impact
AUC	99.3%	Industry-grade performance
Precision	97.8%	Minimal false alarms
Recall	94.5%	Catches nearly all cracks
F1-Score	96.1%	Balanced detection

📊 Reconstruction Error Analysis

Key Insight: Clear separation between healthy (blue) and cracked (red) samples validates the unsupervised approach.

🎨 Test Reconstruction Visualizations

Visualization of test sample reconstructions.

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🚀 Quick Start

1️⃣ Train Your Model ⭐

git clone https://github.com/PratikNichite/Anomaly-Detection-for-Infrastructure-Monitoring.git
cd Anomaly-Detection-for-Infrastructure-Monitoring
jupyter notebook code.ipynb  # 👈 Train model from scratch

2️⃣ Use Trained Model

import torch
model = VAE()
model.load_state_dict(torch.load('best_vae.pth'))  # After training

# Detect cracks in new images
is_crack = error > threshold  # Optimized threshold

3️⃣ Dependencies

pip install torch torchvision opencv-python matplotlib seaborn jupyter

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🏗️ Technical Architecture

Encoder → [256×256×3] → [128×128×32] → [64×64×64] → [32×32×128] → [Latent: 128]

Decoder → [Latent: 128] → [32×32×128] → [64×64×64] → [128×128×32] → [256×256×3]

🎯 Why VAE?

• Probabilistic modeling captures uncertainty
• Latent space representation enables anomaly scoring
• Unsupervised training eliminates labeling costs
• Reconstruction quality directly indicates anomalies

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📁 Project Structure

📦 Anomaly-Detection-for-Infrastructure-Monitoring
├── 📔 code.ipynb           # 🌟 MAIN NOTEBOOK - Complete analysis
├── 🤖 best_vae.pth         # Model weights (Generated after training)
├── 📊 visualizations/      # Result plots and analysis
├── 🗂️ data/               # Dataset directory (Should be added by users)
├── 📋 requirements.txt     # Python dependencies
└── 📄 documentation.pdf   # Technical documentation

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🎯 Key Achievements

✅ Unsupervised Learning Success - No manual crack labeling required
✅ High Performance - 96%+ F1-score on real infrastructure data
✅ Production Ready - Optimized model with deployment pipeline
✅ Comprehensive Analysis - End-to-end ML workflow demonstrated
✅ Scalable Solution - Can process thousands of images efficiently

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License

This project is licensed under the MIT License - see the LICENSE file for details.

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