https://github.com/aditya-ranjan1234/textile-damage-detection-app
A Streamlit-based web application for detecting damages and defects in textile materials using computer vision.
https://github.com/aditya-ranjan1234/textile-damage-detection-app
aiml cnn damage-detection textile
Last synced: 4 months ago
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A Streamlit-based web application for detecting damages and defects in textile materials using computer vision.
- Host: GitHub
- URL: https://github.com/aditya-ranjan1234/textile-damage-detection-app
- Owner: Aditya-Ranjan1234
- Created: 2025-06-13T06:03:14.000Z (4 months ago)
- Default Branch: main
- Last Pushed: 2025-06-13T06:58:20.000Z (4 months ago)
- Last Synced: 2025-06-13T07:30:53.287Z (4 months ago)
- Topics: aiml, cnn, damage-detection, textile
- Language: Python
- Homepage: https://textile-damage-detection-app.streamlit.app/
- Size: 345 KB
- Stars: 1
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Textile Damage Detection App
A Streamlit-based web application for detecting damages and defects in textile materials using computer vision and YOLOv8.
## Features
- Browse through 10 sample textile defect images
- Click on any image to view detailed detections
- Adjustable confidence threshold for detections
- Visual display of detected damages with bounding boxes
- Real-time results with detailed detection information
- Simple and intuitive user interface
## Installation
1. Clone this repository:
```bash
git clone
cd textile-damage-detection-app
```
2. Create and activate a virtual environment (recommended):
```bash
python -m venv venv
.\\venv\\Scripts\\activate # On Windows
source venv/bin/activate # On macOS/Linux
```
3. Install the required packages:
```bash
pip install -r requirements.txt
```
## Usage
1. Run the Streamlit app:
```bash
streamlit run app.py
```
2. Open your web browser and navigate to the provided local URL (usually http://localhost:8501)
3. Upload an image of textile material using the file uploader
4. Adjust the confidence threshold using the slider in the sidebar if needed
5. View the detection results and analysis
## Project Structure
```
textile-damage-detection-app/
├── app.py # Main application file
├── requirements.txt # Python dependencies
└── README.md # This file
```
## Future Improvements
- Integrate with a trained deep learning model for accurate defect detection
- Add support for batch processing of multiple images
- Implement export functionality for detection reports
- Add more detailed analysis and statistics
- Support for video input and real-time detection
## License
This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.