As widely known, ‘pilling’ may be defined as a surface defect of textile fabrics consisting of a number of pills (i.e., roughly spherical masses) made of entangled fibres.
Since pilling represents, for the final user, a non-desired feature, its control, and measurement, is one of the main issues for textile industries. Standing on the surface of the fabric and unpleasantly perceived by the final user, pills are generally caused by the combination of washing and wearing of fabrics. Due to the abrasion of the fabric surface, loose fibres entangle into short fine hairs (fuzz) and, subsequently, develop into spherical bundles anchored to the surface of the fabric. The tendency of a fabric to be subjected to pills formation, as explained in the D4970/D4970M-10e1 Standard (ASTM, 2010) and in the European Standard EN ISO 12945–2:2000, is a very complex process closely linked to a number of parameters such as fibre content, title, number of twists, coverage factor of the mesh, type of fibre or blends, fibre dimensions, yarn construction and fabric finishing treatments.
Moreover, the pilling resistance of a specific fabric in actual wear varies more with the general conditions of use and of the individual wearers. Consequently, due to the complexity of the phenomenon, resistance to pilling (also referred as ‘pilling assessment') is recognized to be one of the most important foci of significant industrial research activity. In fact, the presence of pilling seriously compromises the textile's acceptability to consumers. One of the most diffused methods, to date, for pilling assessment consists of subjectively evaluating the fabric quality using appropriate equipment and a set of standard references, as described below.
Obviously, fabrics take a long time to be pilled in normal use; therefore, resistance to pilling is usually tested by simulated accelerated wear, followed by a manual assessment of the degree of pilling based on a visual comparison of the sample to a set of test images.
Fabrics are abraded by tumbling, brushing, or rubbing specimens with abrasive materials in machines (such as Martindale or Pilling box), and then compared by skilled workers with visual standards, which may be actual fabrics or photographs. On the basis of such a comparison, the experts define resistance to pilling using the so called ‘degree of pilling’ i.e., an index varying on a (arbitrary) scale ranging from a degree of 5, which means no pilling, to 1, which means very severe pilling. Awkwardly, even if this approach is carried out by highly skilled workers, the reliability of pilling evaluation is quite limited and, as stated in, the accuracy is less than 80%.
In recent decades, automated visual inspection (AVI) of fabrics for quality control showed an increasing trend in the textile industry, and several approaches have been proposed in the scientific literature. Pilling assessment using machine vision systems makes no exception: a number of approaches have been proposed in order to explore image processing-based techniques for pills detection and for automated pilling assessment.
Early work was carried out in 1990; images of fabric samples obtained using Martindale are captured under near-tangential illumination, thus acquiring images with high pill-to-background contrast. Such images are binarized using two different thresholds, and then compared with a set of standard images. In pill regions on fabric samples are localized by combining template matching techniques and image thresholding. In operations in both the spatial and frequency domains are introduced to segment pills from the textured background of the fabric web. Such a method calculates the total area occupied by pills in the sample image and assigns a degree of pilling. In statistical features such as mean, variance and median are employed to detect defects. In several other works (for instance in [12], just to cite one) digital image processing was used to determine pills size, number, total area and the mean area of pills on a fabric surface, especially based on thresholding. In an edge-flow based fabric pilling segmentation algorithm that utilizes image colour, texture and phase of the edge flow vector was adopted in order to implement the pilling segmentation of various complex fabrics. This approach determines the total number of pills and the area and volume occupied by such pills.
A remarkable approach to extract pill features from fabric images was proposed in [14]; using a two-dimensional Gaussian fit theory authors train a ‘pill template’ using actual pill images, and determine a reasonable threshold for image segmentation using a histogram-fitting technique. Using such an approach five parameters to describe pill properties (pill number, mean area of pills, total area of pills, contrast and density) are defined; from such data a definition of pilling grade is also provided.
Two-dimensional Fourier analysis and wavelet were used in with the purpose of objectively evaluating textile surface changes, including pilling. A more recent approach used frequency domain image processing to separate periodic structures in the image (the fabric weave/knit pattern) from non-periodic structures in the image (the pills). However, frequency domain analysis cannot provide location information. In a CCD camera was used to capture the image of a laser line projected onto the surface of a series of fabric specimens; by means of trigonometric calculations the three-dimensional shapes of the inspected fabrics are then evaluated. Such 3D reconstruction is then used to determine the number, area, and density of pills.
Even if the above-mentioned methods implement different strategies for assessing pilling of fabrics, almost all of them are focused on pill detection i.e., on image segmentation. This segmentation is, in turn, aimed at determining parameters such as the number and density of pills and/or the area occupied by the pills on the fabric surface. Once this task is performed, pilling is obtained as a parameter inferred from the number of pills, or by a comparison between the image of non-defected fabric with the one with pills. Moreover, almost all methods use, at some point, an image binarization using one or more thresholds and morphological operations on images.
The present work is meant to propose a different strategy for pilling evaluation based on the combination of image processing techniques and an AI-based approach. Instead of analysing images of pilled fabrics in order to segment the pills from the fabric web, the main idea of the paper is to devise a computer-based method able to extract a number of objective parameters from images of pilled fabric samples so that a feedforward backpropagation artificial neural network (FFBP ANN) can be trained to determine the degree of pilling (i.e., to classify the fabric into a number of quality classes). Images of pilled fabric samples are acquired using an appositely devised machine vision system. Then, the acquired images are pre-processed in order to 1) discard colour information, 2) to correct non-uniformity in illumination and 3) to extract the 11 parameters described in the next section. As mentioned above, such parameters are used to train the ANN. Finally, the trained ANN is used as a tool to assess the degree of pilling of new fabrics. The work is partially based on a previous one by the same authors whose aim was to detect and classify a number of defects possibly occurring on raw fabrics such as stains, thin and thick bars, fillings, double fillings, weft threads, double warp threads and broken ends.
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