A COMPARITIVE STUDY OF FABRIC DETECTION AND CLASSIFICATION USING EFFICIENT HYBRID LEARNING ALGORITHM. - PAUL, ELDHO; R. S., SABEENIAN; M. E., PARAMASIVAM; VALLIAPPAN, MURUGAPPAN; KAVINKUMARK (Turkish Journal of Physiotherapy Rehabilitation, 2021)
Textile industries are one among important industries that contribute to the GDP of a nation. Century after century there has been advancement in this particular industry. The revenue generated by this sector is decided by the quality of the fabric items. Any fabric that is defect less has a good reception in the market and defective material produces only half of the production cost. Manual defect identification is a tiresome procedure where a individual is assigned to identify the defect in the running fabric. The success rate of such system is only 60%. An automated defect detection technique is the best solution for most of the textile industry as it produces around 96% efficiency in identifying the defect. The proposed algorithm is a new approach to embed with supervised and unsupervised learning classification techniques like support vector machine and K-Means clustering. The technique uses the GLCM texture features for the classification. The output depends on both of the classification results. The accuracy obtained in this embedded technique is 95%.