Neural networks and mechatronics for detecting carpet pile direction. (17th April 2018)

• Record Type: Journal Article
• Authors: Marshall, Matthew
• Is Part Of: Journal of textiles and fibrous materials. Volume 1(2018)
• Publication Date: 2018-04-17
• Subjects: Automation -- fiber recognition -- image analysis -- yarn; Textile fabrics -- Periodicals; Fibrous composites -- Periodicals; 620.197
• DOI: 10.1177/2515221118769913
• Languages: English
• ISSNs: 2515-2211

Abstract

Determining carpet pile direction is necessary in the manufacture of sample boards and when installing carpet because it is desirable for any shading effects to be homogenous. Depending on the style of the carpet, determining pile direction can be time-consuming and difficult. Two approaches to automating it are developed and tested in order to improve sample board production rates. Significant research has already been performed in the automatic detection of pile lay orientation in carpet and fiber orientation in nonwovens. These methods are insufficient for the present application because they yield the angle of a line parallel to the carpet pile but not the direction along that line in which the pile points. In this work, labeled carpet samples of varying styles are used to train and validate a convolutional neural network (CNN). These samples are also used to test an electromechanical solution. The CNN is shown to provide 100% accuracy when determining pile lay orientation and 93% accuracy when determining pile direction. The electromechanical method for determining pile direction is 65% accurate when used alone and 90% accurate when combined with prior knowledge of the pile lay orientation. These values fall short of the 99% accuracy of an expert operator detecting pile direction but compare favorably to that of a beginner.