Development of Automated Physical Defect Inspection Systems in Greige Tarpaulin Fabric Based on Machine Learning Algorithms

Abstract

The textile industries in Indonesia have challenges, one of which is improving the efficiency of processes. PT NTX is a weaving industry company that produces gray tarpaulin textile products. In this industry, the inspection process for gray cloth resulting from the weaving process is generally carried out conventionally, using a cloth inspection machine and visually with the human eye. Traditional inspection methods cannot be applied to greige tarpaulin textile products since the characteristics of the greige tarpaulin textile are very prone to shifting or slipping of the woven thread construction if it is pulled, touched, or rewound. The process of detecting defects and quality control of greige tarpaulin textile products is carried out by the operator on the loom during the weaving process. This process, of course, will result in the need for inspection operators with high skills, and the consistency of inspection results is very dependent on the condition of the inspection operator. This research has used image processing techniques based on machine learning algorithms to overcome this problem by examining the product directly on the machine. This research has used the Mean Pixel Value method combined with the Logistic Regression Model and the Local Binary Pattern method combined with the Support Vector Machine as image process techniques. Based on the research results, the Mean Pixel Value method combined with the Logistic Regression Model had an accuracy rate of 61% on greige tarpaulin images, and the Local Binary Pattern method with the Support Vector Machine had an accuracy rate of 68%.