---
title: Crack Detection System
emoji: 🔍
colorFrom: red
colorTo: yellow
sdk: streamlit
sdk_version: 1.32.0
app_file: crack-detection.py
pinned: false
license: apache-2.0
---

# 🔍 Crack Detection System

An AI-powered crack detection system using ResNet50 deep learning model. This application can analyze images and detect structural cracks with high accuracy.

## Features

- **High Accuracy**: ~98% accuracy on test dataset
- **ResNet50 Model**: Pre-trained on ImageNet and fine-tuned for crack detection
- **Real-time Detection**: Upload images and get instant predictions
- **Visual Feedback**: Clear visualization of results with confidence scores

## Model Details

- **Architecture**: ResNet50 with custom classification head
- **Training Dataset**: 40,000 images of cracked and non-cracked surfaces
- **Performance Metrics**:
  - Accuracy: ~98%
  - AUC: ~99.9%

## Classes

- **negative**: No crack detected (Class 0)
- **positive**: Crack detected (Class 1)

## How to Use

1. Upload an image (JPG, JPEG, PNG, or BMP format)
2. The system will analyze the image
3. View the prediction result with confidence score
4. Check the debug info in the sidebar for detailed prediction values

## Technical Stack

- **Framework**: Streamlit
- **Deep Learning**: TensorFlow/Keras
- **Model**: ResNet50
- **Image Processing**: PIL/Pillow, NumPy

## Model Performance

The model includes performance visualizations:
- Confusion Matrix
- ROC Curve
- Sample Predictions

---

Built with ❤️ using Streamlit and TensorFlow