PredictiveManish/wall-crack-detection

Wall Crack Detection Model

This project uses a deep learning model to detect cracks in walls using real-time video feed from a mobile phone camera.
It is built with TensorFlow, Keras, and OpenCV.

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Model Overview

• Model Type: Object Detection (Binary – Crack / No Crack)
• Framework: TensorFlow / Keras
• File: crack_detector.h5
• Input: Image frame (from video feed or camera)
• Output: Crack detection result (with bounding boxes or classification)

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Project Structure

project/
│-- crack_detector.h5       # Trained model file
│-- main.py                 # Real-time detection script
│-- concrete_data/          # Dataset folder (if training from scratch)
│   ├── train/
│   │   ├── Positive/
│   │   └── Negative/
│   └── val/
│       ├── Positive/
│       └── Negative/
│-- detection_log.csv        # Optional log for predictions
│-- README.md



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## Requirements

- Python 3.10+
- TensorFlow 2.x
- OpenCV
- NumPy

### Install Dependencies
```bash
pip install tensorflow opencv-python numpy

For Best Results, create a virtual Environment:

Using Conda:
conda --version
conda create --name wallcrack-env python=3.10
conda activate wallcrack-env
pip install tensorflow opencv-python numpy

Usage

Set up DroidCam IP or any camera stream URL in main.py

Run the detection script:

python main.py

The model will process live video feed and detect cracks in walls in real time.

Notes

If you want to retrain, use images in the concrete_data folder.

The detection_log.csv file can store timestamped predictions for later analysis.