AbdullahImran/Paper-Defect-Detection

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

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