Image based shade matching in woven fabric dyeing using PYTHON image similarity metrics
The woven sector plays a crucial role in Bangladesh's textile industry, with a significant portion of exports consisting of woven goods. However, this sector faces challenges due to frequent shade variations in woven fabrics, resulting in material losses and increased expenses. Currently, the textile industry lacks automatic shade variation methods, and existing systems are not suitable for integration into the production process due to speed restrictions or a focus solely on the texture and structure of the fabric, neglecting color matching.
To address these issues, this study proposes an automatic shade matching system designed to compare various wet cloth hues within a functioning dyeing machine. The system provides numerical values to facilitate informed shade matching decisions. By implementing this system, the industry can reduce personnel expenses, minimize timing requirements, and decrease material waste. To demonstrate the practical application of our proposed strategy, we evaluate its effectiveness using a range of image similarity metrics.
By developing an automatic shade matching system, we aim to enhance efficiency and accuracy in the woven sector of Bangladesh's textile industry. This innovation has the potential to revolutionize the process, enabling better color consistency, reduced costs, and improved overall productivity.
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