A new feature extraction process based on SFTA and DWT to enhance classification of ceramic tiles quality

We propose a combination of image processing methods to detect ceramic tiles defects automatically. The primary goal is to identify faults in ceramic tiles, with or without texture. The process consists of four steps: preprocessing, feature extraction, optimization, and classification. In the second step, gray-level co-occurrence matrix, segmentation-based fractal texture analysis, discrete wavelet transform, local binary pattern, and a novel method composed of segmentation-based fractal texture analysis and discrete wavelet transform are applied. The genetic algorithm was used to optimize the parameters. In the classification step, k-nearest neighbor, support vector machine, multilayer perceptron, probabilistic neural network, and radial basis function network were assessed. Two datasets were used to validate the proposed process, totaling 782 ceramic tiles. In comparison with the other feature extraction methods commonly used, we demonstrate that the use of SFTA with DWT had a remarkable increase in the overall accuracy, without compromising computational time. The proposed method can be executed in real time on actual production lines and reaches a defect detection accuracy of 99.01% for smooth tiles and 97.89% for textured ones.