Abstract
In this paper, we present a robust and reliable framework based on L0 gradient minimization (LGM) and the fuzzy c-means (FCM) method to detect various fabric defects with diverse textures. In our framework, the L0 gradient minimization is applied to process the fabric images to eliminate the influence of background texture and preserve sharpened significant edges on fabric defects. Then, the processed fabric images are clustered by using the fuzzy c-means. Through continuous iterative calculation, the clustering centers of fabric defects and non-defects are updated to realize the defect regions segmentation. We evaluate the proposed method on various samples, which include plain fabric, twill fabric, star-patterned fabric, dot-patterned fabric, box-patterned fabric, striped fabric and statistical-texture fabric with different defect types and shapes. Experimental results demonstrate that the proposed method has a good detection performance compared with other state-of-the-art methods in terms of both subjective and objective tests. In addition, the proposed method is applicable to industrial machine vision detection with limited computational resources.
Highlights
Fabric defect detection plays a crucial role in the automatic inspection in textile production processes
We present a novel method based on L0 gradient minimization address problems, present a novel method based on L0 for gradient minimization (LGM)
We present a novel method based on L0 gradient minimization and fuzzy c-means (FCM), which provides new perspective for thehas detection of fabric defects
Summary
Fabric defect detection plays a crucial role in the automatic inspection in textile production processes. Bollinger bands (BB) [7] and image decomposition (ID) methods [8] have been shown to perform robustly for dot-patterned, star-patterned and box-patterned fabrics It remains unknown whether these two methods can be used for plain, twill, and statistical-texture fabrics. 2019, 9, results on a certain texture, but it remains challenging to robustly and accurately handle the fabric methods are weak at differentiating defects with directional features. These methods achieve good defect image if it has a complicated patterns texture, low contrast between defect object and results on a certain texture, but it remains challenging to robustly and accurately handle the fabric background, various colors, and a low signal-to-noise ratio.
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