https://github.com/csoren66/industrial-defect-detection
This project implements a deep learning model to automatically detect defects in industrial equipment through image classification. The model classifies equipment images into 'defective' and 'non-defective' categories.
https://github.com/csoren66/industrial-defect-detection
cnn-model keras-tensorflow object-detection python tensorflow
Last synced: 3 months ago
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This project implements a deep learning model to automatically detect defects in industrial equipment through image classification. The model classifies equipment images into 'defective' and 'non-defective' categories.
- Host: GitHub
- URL: https://github.com/csoren66/industrial-defect-detection
- Owner: csoren66
- Created: 2024-11-09T12:42:59.000Z (7 months ago)
- Default Branch: main
- Last Pushed: 2024-11-12T09:05:42.000Z (7 months ago)
- Last Synced: 2025-01-13T19:51:58.321Z (5 months ago)
- Topics: cnn-model, keras-tensorflow, object-detection, python, tensorflow
- Language: Jupyter Notebook
- Homepage:
- Size: 907 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Industrial Defect Detection
## Overview
This project implements a deep learning model to automatically detect defects in industrial equipment through image classification. The model classifies equipment images into 'defective' and 'non-defective' categories.
## Project Structure
```
industrial-defect-detection/
├── dataset/
│ ├── casting_data/casting_data/train/ # Training image dataset
│ └── /casting_data/casting_data/test/ # Testing image dataset
├── models/ # Saved model checkpoints
├── notebooks/
│ ├── Industrial_Image_Detection.ipynb
├── requirements.txt
└── README.md
```
## Requirements
- opencv-python
- pandas
- numpy
- seaborn
- matplotlib
- holoviews
- tensorflow
- scikit-learn
- shap
- json
Install dependencies:
```bash
pip install -r requirements.txt
```
## Dataset
The dataset consists of industrial equipment images divided into:
- Defective
- Non-defective
## Model Architecture
The project uses a CNN-based architecture with:
```
Model: "sequential"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d (Conv2D) (None, 150, 150, 16) 800
max_pooling2d (MaxPooling2 (None, 75, 75, 16) 0
D)
conv2d_1 (Conv2D) (None, 75, 75, 32) 4640
max_pooling2d_1 (MaxPoolin (None, 37, 37, 32) 0
g2D)
conv2d_2 (Conv2D) (None, 37, 37, 64) 18496
max_pooling2d_2 (MaxPoolin (None, 18, 18, 64) 0
g2D)
flatten (Flatten) (None, 20736) 0
dense (Dense) (None, 224) 4645088
dropout (Dropout) (None, 224) 0
dense_1 (Dense) (None, 1) 225
=================================================================
Total params: 4669249 (17.81 MB)
Trainable params: 4669249 (17.81 MB)
Non-trainable params: 0 (0.00 Byte)
_________________________________________________________________
```
## Contributing
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/AmazingFeature`)
3. Commit your changes (`git commit -m 'Add some AmazingFeature'`)
4. Push to the branch (`git push origin feature/AmazingFeature`)
5. Open a Pull Request
## License
This project is licensed under the MIT License - see the LICENSE file for details.