Evaluation of Yarn Quality in Fabric using Image Processing Techniques

The yarn quality estimate is a tough task in textile industries. In most of the cases, the task is accomplished by manual system. However, it is the classical problem in yarn based research where exact yarn quality production is ascertained based on mathematical yarn qualities like yarn diameter and length etc.. In the existing work, it is observed that the yarn quality has been deduced based on yarn length and diameter only. But merely these parameters do not provide much blending permutations and combinations in order to produce different quality variant yarn production. In the presented work, it is proposed to derive more yarn quality parameters like, uniformity of yarn along the axis, thickness profile of yarn along the horizontal axis along with length and diameter. Image processing tools are applied here in order to get the yarn image and extract the yarn features. Further, for yarn quality estimation, a microscopic analysis requires a lot of manual efforts and time and that on compromising on uniformity of quality judgment. The manual analysis does not provide the ease of on-line testing of yarn quality as it is normally off-line and not continuous. Therefore, in order to remove the difficulties in the existing system, a noval on-line testing of yarn quality using statistical analysis is proposed. In the presented work, it is proposed to derive yarn quality parameters like, uniformity of yarn along the axis, thickness profile of yarn along the horizontal axis along with length and diameter. A mathematical model is proposed to be developed that could predict the fabric quality based on yarn quality determination. A global feature vector set of yarn quality is proposed to be compiled that can be standardized for yarn quality determination. By the method, a quantitative evaluation index is proposed for the yarn quality. The proposed work will provide the basis of further studies on quality of yarn evenness and evaluation of yarn appearance.