Model Based Ceramic tile inspection using Discrete Wavelet Transform and Euclidean Distance

TL;DR

This paper presents a wavelet transform-based method for visual inspection of ceramic tiles, achieving high defect detection accuracy by comparing test images to reference images using Euclidean distance.

Contribution

It introduces a novel approach combining Haar Discrete Wavelet Transform and Euclidean distance for defect detection in ceramic tiles.

Findings

97% accuracy in defect detection

Effective feature extraction using third-level HDWT

Robust comparison method for defect identification

Abstract

Visual inspection of industrial products is used to determine the control quality for these products. This paper deals with the problem of visual inspection of ceramic tiles industry using Wavelet Transform. The third level the coefficients of two dimensions Haar Discrete Wavelet Transform (HDWT) is used in this paper to process the images and feature extraction. The proposed algorithm consists of two main phases. The first phase is to compute the wavelet transform for an image free of defects which known as reference image, and the image to be inspected which known as test image. The second phase is used to decide whether the tested image is defected or not using the Euclidean distance similarity measure. The experimentation results of the proposed algorithm give 97% for correct detection of ceramic defects.