Conventional fabric inspection systems can detect defects of plain cloth, but not defects of some patterned cloth. Also their maintenance is rather difficult, since a laser scanning technique is commonly employed. Some conventional systems use an electronic scanning technique with line sensors (one-dimensional sensors). But because they use only a binary image processing technique, adapting themselves to changes in cloth brightness is difficult. In this paper, we describe the structure of an automated fabric inspection system and image processing algorithms that solve the above problems, and show some examples of defect detection.
The system features are as follows:
(1) Two-dimensional patterns are processed, thus not only soiled plain cloth, but also dyeing defects of cloth with polka dots, striped and checked patterns can be detected.
(2) Even if the cloth brightness changes, dirt on the cloth can be detected correctly, because the gray level image processing technique is employed.
(3) The detection algorithms are based on calculating an average or a standard deviation of the features such as brightness, shapes or sizes of the inspected objects.
(4) Since the image processors save the images of the defects data in their image memories the system can get the necessary data such as shapes and sizes of the defects.