Identification of wool and mohair fibres with texture feature extraction and deep learning - YILDIZ, KAZIM (INST ENGINEERING TECHNOLOGY-IET, 2020)
Wool and mohair fibres are both animal-based fibres and having circular scales on their microscopic images from the longitudinal view. Although they look very similar in their microscopic view, they show different physical/chemical properties which determine their usage area. Thus, in textile industry, they need to be separated carefully from each other. The separation of wool/mohair fibres is an important issue and can be performed with human eye by using the microscopic images, that is not time/cost effective and not objective. The novelty of the presented study is to design an objective, easy, rapid, time and cost-effective method in order to separate wool fibre from mohair fibre by using a texture analysis based identification method. For this purpose, microscopic images of both wool and mohair fibres were preprocessed as the texture images. Local binary pattern-based feature extraction process and deep learning were separately used to get determinative information from the fibres. In order to identify the samples, the classification based method was completed. Experimental results indicated that an accurate texture analysis for this kind of animal fibres is possible to identify wool and mohair fibres by using deep learning and machine learning with 99.8% and 90.25% accuracy rates, respectively.
Keywords: production engineering computing; wool; textile industry; image texture; feature extraction; image classification; natural fibres; learning (artificial intelligence); microscopic images; separate wool fibre; mohair fibre; texture analysis based identification method; deep learning; animal fibres; animal-based fibres; LOCAL BINARY PATTERNS; IMAGE-BASED METHOD; ANIMAL FIBERS; NEURAL-NETWORKS; CASHMERE; CLASSIFICATION; RECOGNITION; PROJECTION; WAVELET; FILTER