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.
With the rise of labor costs and the advancement of automation in the textile industry, fabric defect detection has become a hot research field in recent years. We proposed a learning-based framework for automatic detection of fabric defects. Firstly, we use a fixed-size square slider to crop the original image to a certain step and regularity. Then an improved histogram equalization is used to enhance each cropped image. Furthermore, the Inception-V1 model is employed to predict the existence of defects in the local area. Finally, we apply the LeNet-5 model, which plays the role of a voting model, to recognize the type of the defect in the fabric. In brief, the proposed framework mainly consists of two steps, namely local defect prediction and global defect recognition. Experiments on the dataset have demonstrated the superior performance in fabric defect detection.
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