AI-Powered Industrial Quality Assurance System for Fancy Yarn Using Computer Vision and 3D Visualization

One of the problematic issues in textile production is quality control in fancy yarn because of the complicated structure and aesthetic needs of these goods. This latest paper introduces a novel integrated system that relates artificial intelligence and computer vision algorithms to provide quality assurance of fancy yarns. The developed system was given the ability to detect defects, check the thickness, pattern detection, and 3D view. The system operates on the plane yarn images in 8 states that comprise image acquisition, pre-processing, feature extraction, thickness checking, defect detection, pattern checking, quality checking and reporting. In accordance with the outcomes of the experiment, the suggested system demonstrates the accuracy of 94.7 percent in the defect detection, the precision of 96.2 percent in the thickness uniformity and the reliability of 92.5 percent in the pattern regularity. So we can decompose this to the following: letting manufacturers have a better spatial sense of the yarn properties and eliminating nonconformance with quality standards The combination of 3D visualization functions will give manufacturers a better sense of spatial awareness of the yarn properties and allow them to proactively manage the quality. The system significantly reduces inspection time by 78% as compared to the manual method while maintaining the quality within the batch for production.