IJMTES – FABRIC DEFECT DETECTION USING FUZZY WITH PSO AND CASCADED RANDOM FOREST

Journal Title : International Journal of Modern Trends in Engineering and Science

Paper Title : FABRIC DEFECT DETECTION USING FUZZY WITH PSO AND CASCADED RANDOM FOREST

Author’s Name : U Anitaa

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Volume 06 Issue 01 2019

ISSN no:  2348-3121

Page no: 11-15

Abstract – Defect detection on a fabric surface is one of the most important tasks of an automated visual inspection system. The most modern defect detection systems are required to operate in real-time and handle high-resolution images. One of main difficulties in system applications is that it cannot be used for general inspection of various types of surface without tuning the internal parameters. In this paper, we demonstrate how to solve the problem mentioned above by using efficient Clustering method and applying it to the Cascaded Random-Forest-based machine learning algorithm. Improvised Fuzzy with Particle Swarm optimisation is used to segment the defect Region in the Fabric. The Different types of Feature Extraction method can be applied generally to various types of surface and defect. For effective learning and reduction of false detection cascaded random forest and improvised Fuzzy with PSO methods are introduced. The experimental results demonstrate reliable fabric defect detection for various surface types without changing parameters.

Keywords –  Fabric Defect, Preprocessing, Fuzzy with PSO, Cascaded Random Forest years