Classifying web defects with a back-propagation neural network by color image processing

The aim of this research is to construct an appropriate back-propagation neural network topology to automatically recognize neps and trash in a web by color image processing. After studying the ideal background color under moderate conditions of brightness and contrast to overcome the translucent problem of fibers in a web, specimens are reproduced in a color BMP image file format. Assuming that neps and trash can be distinguished without difficulty from the color image, the image-taking device in the system can be easily altered as long as the optical conditions for other color image resources (i.e., CCD) are considered to ensure image quality. With a back-propagation neural network, the RGB (red, green, and blue) values corresponding with the image pixels are used to perform the recognition, and three categories (i.e., normal web, nep, and trash) can be recognized. The numbers and areas of both neps and trash can also be determined. According to experimental analysis, the recognition rate can reach 99.63% under circumstances in which the neural network topology is 3-3-3. Both contrast and brightness are set at 60% with an azure background color. The results show that both neps and trash can be recognized well, and the method is suitable not only for cotton and man-made fibers of different lengths, but also for different web thicknesses as to a limit of 32.9 g/m(2). Since neps and trash in a web can be recognized, yam quality not only can be assessed but also improved using a reference for adjusting manufacturing parameters.