Fabric defect detection using adaptive dictionaries

Abstract

In this paper, we present a new fabric defect detection algorithm based on learning an adaptive dictionary. Such a dictionary can efficiently represent columns of normal fabric images using a linear combination of its elements. Benefiting from the fact that defects on a fabric appear to be small in size, a dictionary can be learned directly from a testing image itself instead of a reference, allowing more flexibility to adapt to varying fabric textures. When modeling a test image using the learned dictionary, columns involving anomalies of the test image are likely to have larger reconstruction errors than normal ones. The anomalous regions (defects) can be easily enhanced in the residual image. Then, a simple threshold operation is able to segment the defective pixels from the residual image. To adapt more defects, especially some linear defects, we rotate the test image by a slight degree and re-analyze the rotated image. Compared to the Fourier method, experimental results on 47 real-world test images with defects reveal that our algorithm is able to adapt to varying fabric textures and exhibits more accurate defect detection.

Fabric defect detection via learned dictionary-based visual saliency

“…[37]; Second row: saliency maps by Li et al [38]; Third row: saliency maps by Zhu et al [39]; Fourth row: saliency maps by our method. In order to further demonstate the effectiveness of our proposed method, the comparing results of our method and Zhu et al [40] is demonstrated on Figure 4. The first row is the detection reuslts genearted by Zhu et al [40], and the second row shows the results obtained by our method.…”

“…In order to further demonstate the effectiveness of our proposed method, the comparing results of our method and Zhu et al [40] is demonstrated on Figure 4. The first row is the detection reuslts genearted by Zhu et al [40], and the second row shows the results obtained by our method. From the figure, we can see that the Zhu et al [40] can effectively detect the fabirc images when the defect and background have obvios difference, such as the fourth and fifth images.…”

A High-Precision Fusion Algorithm for Fabric Quality Inspection

“…Alper Selver M. et al [6] proposed a texture detection based on texture statistics and gradient search, combined with differential histogram and cooccurrence matrix for fabric texture analysis, so as to speed up the processing speed of detection. In the Model-based inspection methods, Zhou J et al [7] proposed a fabric defect detection algorithm based on an adaptive dictionary learning, using a linear combination of the dictionary to effectively represent the structure character of the normal fabric images. Then, Qu T et al [8] improved the algorithm, considered the _________________________________________ problem of different defect size and proposed a double-scale complete dictionary, which improved the self-adaptability of the detector.…”

Defect Detection in Tire X-Ray Images Using Weighted Texture Dissimilarity

“…By analyzing the reconstruction error between the image under inspection and the reconstructed image from representation coefficients, defects can be detected. Similarly, some detection methods based on sparse representation have been introduced in different application fields [11,12]. These methods use a sparse constraint to learn an adaptive representation dictionary from test images.…”