Fabrics defects detecting using image processing and neural networks

In textile industry, the quality of fabrics is a very important factor of competitiveness given that defects have a negative effect on the market value of the product. For this raison, it is necessary to master good quality fabric rolls from the looms. Typically, the fabric inspection is performed by a human controller that uses a display system and relies on personal knowledge. The objective of our work is to develop a system for the detection and classification of defects in a simple and efficient way using techniques of image processing. Therefore, we propose to provide an inspection process that aims to detect and classify defects in warp and weft using a computer program developed in Matlab that analyzes images of fabrics samples acquired using a flat scanner. All information about the weaving defects may be stored in a database dedicated to the quality management of fabrics.