Segmentation-Based Woven Fabric Density Measurement

In the textile and clothing industry, woven fabric density not only has a significant impact on the quality of the woven fabrics but is also directly related to the comfort and durability of the clothing. Accurate measurement of woven fabric density by counting the number of yarns plays a vital role in the entire woven fabric manufacturing process. However, current yarn detection methods based on neural networks cannot accurately locate bent yarns because they usually use quadrilaterals or rectangles to mark each yarn and generate labels with yarn regions for the training model. In addition, when the yarn in the woven fabric is bent or deformed, the current projection-based postprocessing algorithm may produce multiple peak points on one yarn, thereby reducing the density measurement performance. To address these issues, we propose an arbitrary yarn detection network named AYDNet. AYDNet uses polygonal regions to represent yarn of arbitrary shapes and generate labels within the polygonal region for the training model, which enables it to accurately detect yarn of various shapes such as deformation and bending. To further accurately measure the density of the woven fabric, we intercept the local yarn region and use the approximate distance between the yarn center to remove the multiple peak points formed by the same yarn. To validate the effectiveness of our proposed methods, we build a dataset including woven fabrics of various types and colors. Experimental results on the self-built dataset show that the error rate of our proposed methods is less than 1.5% on different types, colors, and densities of woven fabrics, and our method outperforms the current state-of-the-art methods in terms of accuracy.