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