Classification of Defects of Cotton Yarns Using Convolutional Neural Networks

The detection and classification of defects in cotton yarn are crucial in maintaining the quality of textile production. This is hardly getting attention in the literature due to the complexities and non-homogeneous features appearing in the cotton yarn. This study explores the application of transfer learning techniques in convolutional neural networks (CNNs) to classify yarn defects, including neps, thick and thin places, hairiness, and snarls, as well as identifying non-defective yarns. A dataset of 1,250 images was divided into five classes to evaluate three CNN models: ResNet-50, VGG-16, and Inception-v3. Inception-v3 achieved the highest validation accuracy at 98.8%, followed closely by VGG-16 with 98%, while ResNet-50 reached 77.2%. Inception-v3 and VGG-16 were successful in detecting complex yarn defects. The study further emphasizes the capability of CNNs to automate yarn defect identification by decreasing the processing time, by allowing CNN models to integrate with GPUs.