# A Machine Vision-Based Algorithm for Color Classification of Recycled Wool Fabrics
## 1 Introduction
Furthermore, a color-matching system based on the Friele theoretical model was proposed and tested for cotton blends [6], with a classification accuracy varying in the range of 70-90%. Unfortunately, the theoretical approaches described above provide excellent results for predicting the color of turbid media but can lead to improper results in predicting the reflectance factors of mixtures obtained by mixing pre-colored fibers, such as in the case of regenerated wool fabrics. Moreover, they require spectrophotometry as an elective technique for evaluating the absorption and scattering coefficients, leading to excessively long procedures to derive an appropriate classification and thus leaving manual classification still more convenient. To partially solve these issues, a number of ANN- and deep learning-based methods have been explored in the literature in recent years. Such methods have provided reliable and very practical approaches to help the colorist in color matching. For instance, in ref. [7; 8; 9; 10], a transfer function was derived by connecting the measured reflectance values of a first-attempt blend with the color spectrum obtained by a linear combination of the spectra of each component. Since the average color difference between the expected spectra and the actual carded fiber spectra was less than 0.55 for the CMC (2:1) tolerance method with an experimental collection of blends, the mentioned method proved to be successful for blends made up of more than 15 components. The main flaw of the suggested solution is that only one material was used and it was only accurate for 5% of recipe variations (e.g., wool). Furthermore, the aforementioned method also made use of spectrophotometry for the color classification. In ref. [11], a new deep learning-based approach for fabric color identification was proposed to overcome the drawbacks of the previously mentioned methods. In fact, it allowed for precise color identification varying in the range of 58-95%, depending on the color class, with an average precision of 80%, which was considered a good result for this kind of application in practice. In the same paper, the authors compared the performance of their method with the performance of those using several other convolutional neural networks, demonstrating that the proposed approach outperformed the previously proposed methods [12]. Machine vision (MV)-based color sorting systems have also been used in a number of industrial sorting systems, such as cotton seed sorting [13], oil ripening estimation [14], fruit sorting [15; 16] and leather shoe components [17]. However, reports of unique color sorting methods for recycled textiles are rare. One of the first attempts to create a reliable sorting system was provided in ref. [18], where an appositely devised tool used the combination of a statistical method (called the matrix approach), a self-organizing feature map (SOFM), and a feed-forward backpropagation artificial neural network (FFBP ANN)-based approach to correctly classify clothes by respecting the selection criteria provided by human know-how. Despite the promising outcomes of this work, the tool specifically called for the spectrophotometric preliminary classification of the fabric samples. This task is not trivial since calibrated equipment is required and dataset creation takes a long time. Not by chance, some researchers have used cameras to gather data about the color of the recycled samples and carry out the color sorting process. In particular, the authors in ref. [12] devised a computer vision-based color-sorting system for waste textile recycling. They tested the devised architecture to classify samples of colored fabrics according to the Panton TPX color series, obtaining excellent results. The average accuracy for color classification varied in the range of 62-100%. The classification carried out in this relevant paper was intended to categorize fabrics in accordance with the unique colors supplied by a textile company, which were sorted based on expert knowledge.