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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 |