COMPARATIVE ANALYSIS OF CONVOLUTIONAL NEURAL NETWORKS (CNN), SUPPORT VECTOR MACHINE (SVM) AND “RANDOM FOREST” ALGORITHMS IN DETECTING KNITTED FABRIC DEFECTS
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This study is devoted to a comparative analysis of the effectiveness of Convolutional Neural Networks (CNN), Support Vector Machine (SVM) and Random Forest algorithms for detecting defects in knitted fabrics. As a result of experiments carried out on a dataset consisting of 5000 images, the CNN model showed an accuracy of 96.8%, SVM 89.3% and Random Forest 91.2%. The study shows that CNN is preferable in situations requiring high accuracy, whereas Random Forest is preferable when computational resources are limited. These results are of practical importance for designing automated quality control systems for the knitwear industry.
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