Loom malfunctions are the main cause of faulty fabric production. A fabric inspection system is a specialized computer vision system used to detect fabric defects for quality assurance. In this paper, a deep-learning algorithm was developed for an on-loom fabric defect inspection system by combining the techniques of image pre-processing, fabric motif determination, candidate defect map generation, and convolutional neural networks (CNNs). A novel pairwise-potential activation layer was introduced to a CNN, leading to high accuracy of defect segmentation on fabrics with intricate features and imbalanced dataset. The average precision and recall of detecting defects in the existing images reached, respectively, over 90% and 80% at the pixel level and the accuracy on counting the number of defects from a publicly available dataset exceeded 98%.
Many previous studies on cotton maturity used a sole parameter to rank the maturity of a cotton sample containing a large number of fibers. In light of the complexity of maturity distributions, the sole-parameter approach does not appear to be reliable and rational for cotton maturity evaluation. More distributional parameters should be examined and included in the new classification methods. This paper (1) introduces important changes in the image-analysis algorithms for cotton cross-section measurements to enhance the consistency of fiber detections in order to reduce the bias on immature fibers, (2) investigates the characteristics and patterns of cotton maturity distributions, and (3) presents the experimental results on the cross-section images selected from seven cotton varieties that have a wide range of maturities. It is found that the skewness of a maturity distribution is an essential parameter for classifying the distribution pattern and that the dead fiber content and the mature fiber content are the important distributional parameters for assessing cotton maturity.
Fabric prints may contain intricate and nesting color patterns. To evaluate colors on such a fabric, regions of different colors must be measured individually. Therefore, precise separation of colored patterns is paramount in analyzing fabric colors for digital printing, and in assessing the colorfastness of a printed fabric after a laundering or abrasion process. This paper presents a self-organizing-map (SOM) based clustering algorithm used to automatically classify colors on printed fabrics and to accurately partition the regions of different colors for color measurement. The main color categories of an image are firstly identified and flagged using the SOM’s density map and U-matrix. Then, the region of each color category is located by divining the U-matrix map with an adaptive threshold, which is determined by recursively decreasing it from a high threshold until all the flagged neurons are assigned to different regions in the divided map. Finally, the regions with high color similarity are merged to avoid possible over-segmentation. Unlike many other clustering algorithms, this algorithm does not need to pre-define the number of clusters (e.g. main colors) and can automatically select a distance threshold to partition the U-matrix map. The experimental results show that the intricate color patterns can be precisely separated into individual regions representing different colors.
This article presents a clustering algorithm based on node‐growing self‐organizing map (NGSOM) to classify colors on color images automatically and accurately partition the regions of different colors for color measurement. Based on the CIEDE2000 criterion, pixels in a multicolor image are grouped into a number of visually distinguishable color regions in which pixel distribution information is provided as the input of the NGSOM network for further segmentation. As an unsupervised clustering algorithm, the NGSOM randomly selects two initial nodes from the input source without a predefined network structure and grows nodes according to color differences between the first best matching unit (FBMU) and the second best matching unit (SBMU) of the current input data. Unlike a traditional SOM, the NGSOM utilizes learning rates varying with the pixel distributions of major color regions. The node‐growing procedure is terminated when all the input data are examined. Compared with some commonly used color clustering algorithms, the proposed algorithm possesses a better peak signal‐to‐noise ratio (PSNR) and higher time efficiency. The NGSOM can be used for a wide range of applications, including fabric colorfastness assessment, painting conservation, and scenic identification in aerial images.
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