This research presents a comparative analysis of Convolutional Neural Networks (CNN), Support Vector Machine (SVM), and Random Forest algorithms for defect detection in knitted fabrics. Experimental results on a dataset of 5000 images demonstrate that the CNN model achieved 96.8% accuracy, SVM 89.3%, and Random Forest 91.2%. The study indicates that CNN is preferable for scenarios requiring high precision, while Random Forest is more suitable with limited computational resources. These findings have practical implications for designing automated quality control systems in the knitting industry.

knitted fabric defects, convolutional neural networks, support vector machine

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