README.md

---
license: mit
pipeline_tag: object-detection
tags:
- tensorflow
- keras
- opencv
- wall-crack-detection
- real-time
- mobile
datasets: []
---

# 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**.

---

## 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)

---

## Project Structure

```bash
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



---

## 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.