Detection of fabric defects with intertwined frame vector feature extraction

Abstract

In textile industry, defect detection plays a vital role in diagnosing and improving the process. However, fabric defect detection is a process performed by the human eye, as methods that provide fast and sufficient performance are not yet widely used. In this paper, a new feature extraction method for defect detection is presented that is faster and provides higher accuracy. The developed method is basically a feature extraction process from a window taken from the image. Intertwined frames are defined in the matrix which form the window. A center of gravity is defined for each frame. For each frame, a frame vector is drawn from the center pixel to the center of gravity. The results of various vector functions among the frame vectors are used as features. These features are used in classification algorithms for defect detection. The AITEX dataset was used, and the proposed method was compared with the traditional texture feature extraction methods in terms of performance and time. It has been shown that the proposed method the proposed method works 55% faster than the fastest algorithm and provides at least 1.8% more accurate results. In this way, the method can be used on low-level devices.