TEXTILE FIBER CLASSIFICATION USING CONVOLUTIONAL  NEURAL NETWORKS

CHALACHEW, MEKONNEN KASSIE

dc.contributor.author	CHALACHEW, MEKONNEN KASSIE
dc.date.accessioned	2022-11-16T12:19:55Z
dc.date.available	2022-11-16T12:19:55Z
dc.date.issued	2022-08
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/14433
dc.description.abstract	Fibers are usually twisted together to form yarns, and yarns are then woven or knit to form fabrics. All textiles are made of fibers, but not all fibers can be used to make textiles. Besides, fibers are classified into two major categories according to their origin that is manmade and natural, each major category has also a group. The knowledge of identifying the textile fibers helps a producer of garments forensic and designers to identify the type of fiber and the care to be taken in maintaining the fabrics made of a particular type of fiber. This is an important factor for labeling the garments, which includes specifying the fiber content in the garment. The application of imaging techniques on fiber classification had been studied but the number of fiber types are limited and all applied a single approach. This single approach will lead to misclassification and decrease the accuracy. Even if the latest microscopic identification approaches are also dependent on human beings' skill and eyes. The necked eye observation is not perfect and also the detection by the experts will be time-consuming and tiresome. Another important concept is real time detection of textile fibers in the manufacturing process hence quality is one of the determinant factors in textile industry. In this study, we apply a GLCM assisted CNN for textile fiber classification. We collect 746 microscopic image datasets of cotton, wool and towel from Ethiopian Institute of Textile and Fashion Technology [EiTEX] and cameo online textile fiber image library. After collecting the dataset, preprocessing like interpolation, noise reduction and segmentation applied to get an enhanced image. A CNN trained model is developed for classification and extraction. Our model has a concatenating with residual module to get a multi-level feature and for skipping operation that has a gap in sequential layers. As a backend python with Keras and Tensorflows tools are implemented in our model. Our models are evaluated using a metrics accuracy, precision, F1-score and recall. We test our model with different optimizers to determine the worst and best scores. The planned model achieves 99% and 77% with Adam and Adadelta optimizers respectively with batch sizes of 32 and 100 epochs. Our model sounds good with kernel regularizer and using GLCM as an input to the CNN parameter. The study has also a limitation of classifying a poorly preprocessed image dataset and classification of only cotton, wool, and towel prominent textile fiber types. Keywords: Preprocessing, Optimizers, Cotton, Wool, Towel, CNN, GLCM	en_US
dc.language.iso	en_US	en_US
dc.subject	FACULTY OF COMPUTING	en_US
dc.title	TEXTILE FIBER CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORKS	en_US
dc.type	Thesis	en_US