Development of Machine Vision Solution for Grading Of Tasar Silk Yarn

Quality of Tasar fabric demands uniform coloured silk yarn during weaving. But, the variation of yarn colour depends on various natural factors like eco-race and feeding of silk worms, weather conditions etc and other production factors. So, silk yarns need to be sorted after production. At present, yarns are sorted manually by a group of experts which is subjective in nature. Again, due to lustrous nature of silk yarn, it reflects light and therefore it is difficult to ascertain the exact colour manually. Slight variation in colour is difficult to detect manually but the market demands lots with perfectly uniformly coloured yarns within the lot though the inter-lot variation in colour is encouraged. So, there is need to develop a solution which can grade the silk yarn objectively, reliably and mimic the human perception. This paper proposes a new machine vision solution for automatic grading of silk yarn based on its colour. The system consists of an enclosed cabinet which encompasses of a low cost digital camera, uniform illumination arrangement, weighing module, mechanical arrangement for sample holding and a grading software which applies image analysis technique using CIELab colour model with rotational invariant statistical feature based hierarchical grading algorithm for colour characterization. Performance of the system has been validated with the human experts and accuracy has been calculated as 91%.