---
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