In the production of industrial fabric, it needs automatic real-time system to detect defects on the fabric for assuring the defect-free products flow to the market. At present, many visual-based methods are designed for detecting the fabric defects, but they usually lead to high false alarm. Base on this reason, we propose a Sobel operator combined with patch statistics (SOPS) algorithm for defects detection. First, we describe the defect detection model. mean filter is applied to preprocess the acquired image. Then, Sobel operator (SO) is applied to deal with the defect image, and we can get a coarse binary image. Finally, the binary image can be divided into many patches. For a given patch, a threshold is used to decide whether the patch is defect-free or not. Finally, a new image will be reconstructed, and we did a loop for the reconstructed image to suppress defects noise. Experiments show that the proposed SOPS algorithm is effective.

Fabric defect detection has been an indispensable and important link in fabric production, many studies on the development of vision based automated inspection techniques have been reported. The main drawback of existing methods is that they can only inspect a particular type of fabric pattern in controlled environment. Recently, nonlocal self-similarity (NSS) based method is used for fabric defect detection. This method achieves good defect detection performance for small defects with uneven illumination, the disadvantage of NNS based method is poor for detecting linear defects. Based on this reason, we improve NSS based defect detection method by introducing a gray density function, namely an enhanced NSS (ENSS) based defect detection method. Meanwhile, mean filter is applied to smooth images and suppress noise. Experimental results prove the validity and feasibility of the proposed NLRA algorithm.