Optimised filters for texture defect detection

This paper presents a new approach to texture defect detection based on a set of optimised filters. Each filter is applied to one wavelet sub-band and its size and shape are tuned for a defect type. The wavelet transform provides a very efficient way to decompose a complex texture into a set of base components (wavelet sub-bands), which are then analysed by each filter to detect a kind of defect. The proposed methodology has been successfully applied to leather inspection, achieving the detection rate of highly trained human operators. The process is also fast enough to be used for in-line inspection.Agência de Inovação (ADI) - sistema de Geo--referenciação Móvel assistido por Imagem (SiGMA

Computational Analysis of Neutron Scattering Data

This work explores potential methods for use in the detection and classification of defects within crystal structures via analysis of diffuse scattering data generated by single crystal neutron scattering experiments. The proposed defect detection methodology uses machine learning and image processing techniques to perform image texture analysis on neutron diffraction patterns generated by neutron scattering simulations. Once the methodology is presented, it is tested via a series of defect detection problems of increasing difficulty which utilize neutron scattering data simulated by a number of simulation techniques. As the problem difficulty is increased, the defect detection methodology is refined in order to adapt to challenges presented by the more difficult detection problems. The refinement process includes the development of a data-driven scaling method that aids in the texture analysis process by enhancing diffuse scattering textures in the diffraction patterns. The evaluation process for the defect detection methodology includes analysis and comparison of the computational complexities of the machine learning and image processing techniques. As part of this complexity analysis, a detailed study of the ORB keypoint extraction algorithm is also conducted and the computational complexity of the ORB algorithm is derived

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%

Optimal morphological filter design for fabric defect detection

This paper investigates the problem of automated defect detection for textile fabrics and proposes a new optimal morphological filter design method for solving this problem. Gabor Wavelet Network (GWN) is adopted as a major technique to extract the texture features of textile fabrics. An optimal morphological filter can be constructed based on the texture features extracted. In view of this optimal filter, a new semi-supervised segmentation algorithm is then proposed. The performance of the scheme is evaluated by using a variety of homogeneous textile images with different types of common defects. The test results exhibit accurate defect detection with low false alarm, thus confirming the robustness and effectiveness of the proposed scheme. In addition, it can be shown that the algorithm proposed in this paper is suitable for on-line applications. Indeed, the proposed algorithm is a low cost PC based solution to the problem of defect detection for textile fabrics. © 2005 IEEE.published_or_final_versio

Genetic learning based texture surface inspection

This paper presents a novel approach of visual inspection for texture surface defects. It is based on the measure of texture energy acquired by a kind if high performance 2D detection mask, which is learned by genetic algorithms. Experimental results of texture defect inspection on textile images are presented to illustrate the merit and feasibility of the proposed method.<br /