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The quality assurance of fabrics is a fundamental issue in the textile manufacturing industry. Automatic and accurate detection of defects is one of the most important and challenging tasks in order to guarantee the quality of fabrics. In this paper, we propose an approach for the defect detection on textiles with patterned texture using a rule-based classification system and the local binary features. In our proposal, rules are automatically learned from the textile samples using a rough-set-based approach. The proposed system analyzes the texture of fabrics using a combination of local binary features, which have shown to be highly discriminatory. Our approach is performed in two stages: training and testing. During the training stage, binary features from both defective and defect-free images are extracted and used to formulate an ensemble of the rough-set-based rules. For the testing stage, we submit different samples of fabrics, and they are classified as defective or defect-free. The proposed method is quantitatively evaluated on an extensive dataset of images of the defective fabrics. These experiments show that the proposed approach results in higher accuracy, in comparison with those obtained by the state-of-the-art methods. INDEX TERMS Textile defect detection, local binary features, rule-based classification, visual inspection.

Patterned fabrics may be regarded as periodic textures, which are defined as the regular tessellation of a primitive unit. A patterned fabric is considered as defective when a primitive unit is different from the others. In this paper, we propose a one-class classifier that uses Reduced Coordinated Cluster Representation (RCCR) as features. In the training step, the size of the primitive unit of defect-free fabrics is automatically estimated using a texture periodicity algorithm. After that, the fabrics are split into samples of one unit and their local structure is learnt with the RCCR features in a one-class classifier. During the test step, defective and non-defective fabrics are also split into samples and are analyzed unit by unit. If the features of a given unit do not satisfy the classification criterion, it is considered to be a defect. Among the advantages of the RCCR is that it represents structural information of textures in a low-dimensional feature space with high discrimination performance. Results from experiments on an extensive database of real fabric images show that our method yields accurate detections, outperforming other state-of-the-art algorithms.

Power quality disturbances (PQD) in electric distribution systems can be produced by the utilization of non-linear loads or environmental circumstances, causing electrical equipment malfunction and reduction of its useful life. Detecting and classifying different PQDs implies great efforts in planning and structuring the monitoring system. The main disadvantage of most works in the literature is that they treat a limited number of electrical disturbances through personal computer (PC)-based computation techniques, which makes it difficult to perform an online PQD classification. In this work, the novel contribution is a methodology for PQD recognition and classification through discrete wavelet transform, mathematical morphology, decomposition of singular values, and statistical analysis. Furthermore, the timely and reliable classification of different disturbances is necessary; hence, a field programmable gate array (FPGA)-based integrated circuit is developed to offer a portable hardware processing unit to perform fast, online PQD classification. The obtained numerical and experimental results demonstrate that the proposed method guarantees high effectiveness during online PQD detection and classification of real voltage/current signals. Author Contributions: M.L.-R. conceived the methodology, performed the experimentation and compile the mathematical definition for the treated PQD; E.C.-Y. supervised the work, defined all the tasks and wrote the paper; L.M.L.-C. provided support during experimentation and simulations; H.M.-V. provided the equipment and guided the obtaining of the real signals used for validating the proposed approach; C.R.-D. designed and implemented the data acquisition system; R.A.L.-M. verified the PC implementation for signal models and processing algorithms. Conflicts of Interest:The authors declare no conflict of interest.