Intelligent Computer Vision System for Analysis and Characterization of Yarn Quality

The quality of yarn is essential in the control fabrics processes. There some commercial equipment that measures based on sensors, different types, used for collecting data about textile characteristic parameters. irregularity thread influences its physical properties/characteristics and there may be a possibility break during fabric manufacturing process. This can contribute to occurrence unwanted patterns deteriorate their quality. existing equipment, above-mentioned purpose, characterized by high size cost, allowing analysis only few main findings/results study are method as well developed algorithm, which allows defects more precise way. Thus, this paper presents development results obtained with design mechatronic prototype integrating computer vision system allows, among other parameters, classification, real time, hairs using artificial intelligence techniques. also determines characteristics inherent analysis, such as: linear mass, diameter, volume, twist orientation, step, average mass deviation, coefficient variation, hairiness coefficient, standard performing spectral analysis. A comparison designed was performed validating undertaken methodology.