# The Algorithms of Image Processing and Analysis in the Textile Fabrics Abrasion Assessment
## 1 Introduction  
The abrasion resistance and susceptibility of textiles to surface damage is an important issue for their utility properties and is widely discussed in the subject literature. The abrasion resistance parameters are crucial in durability evaluation, especially when the new kind of treatment of textile product surface is created. For example, the incorporation of iron ores as component in polymers coating had helped to improve some important properties of textile surfaces, such as antibacterial activity, protection against UV or flame retardant [1]. Similarly, [PERSON] et al. investigated [2] the new type of enzymatic treatment dedicated for textiles made of cotton fibres. One of the aspects considered in publications was the assessment of abrasion resistance of finished textiles, measured using loss of the sample mass method. Moreover, [PERSON] et al. [3] thought, that the abrasion resistance was an important factor in the design process of a new kind of prints dedicated to water resistant sportswear. The proper abrasion resistance was also highly important for textile products when electronics elements have been incorporated (smart textiles), as was investigated in research work [4]. These elements could have different functions, for example, transmitting an ECG signal in medical applications or collecting energy for smart textile accessories which use fibres with piezoelectric properties, as was described in research works [5; 6; 7; 8] The fibres-based construction of photovoltaic units, dedicated to electric power harvesting, not only for smart textile garments purposes, but as a renewable power source, was investigated by [PERSON] et al. [9]. The authors evaluated the different weaves' influence on the ability of photovoltaic fabric to harvest electric energy. Recently, deep learning techniques forfabric evaluation were used. The main advantage of these image analysis tools is to carry out analysis based on numerical indications found by neural networks themselves. This technique was highly efficient in fabric defect detection, with accuracy above 94 percent as presented in research work [10]. In other publications, defects, especially regarding colour disturbances were investigated by [PERSON] et al. [11]. The convolutional neural network (CNN) was involved in this evaluation. In all the above mentioned publications, researchers evaluated newly developed surface modifications or commercial products, among others, using an abrasion resistance parameter, which influences the utility properties of textiles, mainly their mechanical durability, but aesthetic properties as well. The commonly used test methods for abrasion resistance assessment have been based on a few criteria, which define the breakage point of fabrics. The breakage criteria, described in below mentioned standards have been as follows: loss of fabric mass [12], achieving a specified breakage point (certain number of broken threads in fabric, loss of fabric pile either fully or partially, occurrence of holes) [13], or colour change of sample[14]. The above mentioned methods [12; 13] are based on the subjective, visual identification of breakage existence assessment. In the case of the subjective type of abrasion resistance evaluation, in many cases the unwanted wide range of obtained results (low precision) has been observed. Taking into consideration the character of the abrasion assessment process, efforts were made to elaborate the new alternative test technique which supports this evaluation. [PERSON] et al investigated [15] the numerical model for the relationship between the loss of woollen fabrics mass and Martindale abrasion test duration. The exponential function proposed by the authors should be convex, concave or similar to a straight line. It was found that the most characteristic point in the woollen woven fabric abrasion process was the loss of fabric mass obtained after 500 rubs of the Martindale device.