Application of Neural Networks (NNs) for Fabric Defect Classification

The defect classification is as important as the defect detection in fabric inspection process. The detected defects are classified according to their types and recorded with their names during manual fabric inspection process. The material is selected as “undyed raw denim” fabric in this study. Four commonly occurring defect types, hole, warp lacking, weft lacking and soiled yarn, were classified by using artificial neural network (ANN) method. The defects were automatically classified according to their texture features. Texture feature extraction algorithm was developed to acquire the required values from the defective fabric samples. The texture features were assessed as the network input values and the defect classification is obtained as the output. The defective images were classified with an average accuracy rate of 96.3%. As the hole defect was recognized with 100% accuracy rate, the others were recognized with a rate of 95%

Review of Application of Artificial Neural Networks in Textiles and Clothing Industries over Last Decades

2010-2011 > Academic research: refereed > Chapter in an edited book (author

A Multi-Level Colour Thresholding Based Segmentation Approach for Improved Identification of the Defective Region in Leather Surfaces

Vision systems are recently adopted for defect detection in leather surface to overcome difficulties of labour intensive, time consuming manual inspection process. Suitable image processing techniques needs to be developed for accurate detection of leather defects. Existing research works have focused for gray scale based image processing techniques which requires conversion of colour images using an averaging method and it lacks sensitivity for detecting the leather defects due to the random and texture surface of the leather.  This work presents a colour processing approach for improved identification of leather defects using a multi-level thresholding function. In this work, the colour leather images are processed in ‘Lab’ colour domain for improving the human perception of discriminating the leather defects.  In the present work, the specific range of values for the colour attributes of different leather defect in colour leather samples are identified using the colour histogram.  MATLAB software routine is developed for identifying defects in specific ranges of colour attributes and the results are presented.  From the results, it is found that proposed provides a simpler approach for identifying the defective regions based on the colour attributes of the surface with improved human perception. The proposed methodology can be implemented in graphical processing units for efficiently detecting several types of defects using specific thresholds for the automated real-time inspection of leather defects

Defect Detection of Tiles Based On High Frequency Distortion

Quality control in Tiles Industry is of great importance. Therefore, it is effective to improve an automatic inspection system, instead of manpower, to increase accuracy and velocity and decrease costs. To this end, a new method to segment tile surfaces is offered in this study. This method aims at detecting defective areas in a tile, based on extracting features of edge defects. This method is based on the idea that human eye can better perceive the defects in a tile by looking at its edges. In the proposed method, first, in order to extract frequency characteristics resistant against transference, Undecimated Discrete Wavelet Packets transform is applied on images. Later, by computing local entropy values on high-frequency sub-bands images, those which appropriately include images defects are chosen to extract statistical features. Finally, Back propagation neural network method is used to determine segmented images containing defective areas. The obtained results, both visually and computationally indicates the higher efficacy of this method compared with the related state of the art methods.DOI:http://dx.doi.org/10.11591/ijece.v3i4.301

Siamese Basis Function Networks for Data-Efficient Defect Classification in Technical Domains

Training deep learning models in technical domains is often accompanied by the challenge that although the task is clear, insufficient data for training is available. In this work, we propose a novel approach based on the combination of Siamese networks and radial basis function networks to perform data-efficient classification without pretraining by measuring the distance between images in semantic space in a data-efficient manner. We develop the models using three technical datasets, the NEU dataset, the BSD dataset, and the TEX dataset. In addition to the technical domain, we show the general applicability to classical datasets (cifar10 and MNIST) as well. The approach is tested against state-of-the-art models (Resnet50 and Resnet101) by stepwise reduction of the number of samples available for training. The authors show that the proposed approach outperforms the state-of-the-art models in the low data regime