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--- |
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language: |
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- en |
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tags: |
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- defect-detection |
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- image-classification |
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- machine-learning |
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- quality-control |
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- ensemble-learning |
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- neural-networks |
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datasets: |
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- custom_paper_surface_defect |
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pipeline_tag: image-classification |
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model-index: |
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- name: Paper Defect Detection |
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results: |
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- task: |
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type: image-classification |
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name: Surface Defect Detection |
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metrics: |
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- type: accuracy |
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value: 0.81 |
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name: Ensemble Test Accuracy |
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- type: f1 |
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value: 0.8 |
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name: F1 Score |
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library_name: sklearn |
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metrics: |
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- accuracy |
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--- |
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# Paper Defect Detection |
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## Model Description |
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This model is designed for automated surface defect detection in manufacturing using a hybrid approach that combines classical machine learning and deep learning techniques. |
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### Model Architecture |
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The model uses a hybrid architecture combining: |
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- Logistic Regression |
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- SVM |
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- Naive Bayes |
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- CNN |
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- Ensemble Voting Classifier |
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### Feature Extraction Methods |
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- Histogram of Oriented Gradients (HOG) |
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- Gabor Filters |
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- Canny Edge Detection |
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- Wavelet Transforms |
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## Performance |
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| Model | Train Accuracy | Test Accuracy | |
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|--------------------|----------------|---------------| |
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| Logistic Regression| 0.99 | 0.79 | |
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| SVM | 0.86 | 0.80 | |
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| Ensemble Model | 0.90 | 0.81 | |
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## Limitations |
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- Performance may degrade for defect types not represented in the training data |
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- Variations in lighting or textures can affect classification accuracy |
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- This was a university project with room for improvement |
