Open AccessA novel approach of ceramic tile crack detection using morphological operations
Author(s) -
Mahzaib Younas,
Qamar Nawaz,
Isma Hamid,
Syed Mushhad Mustuzhar Gilani,
Munwar Iqbal
Publication year - 2022
Publication title -
mehran university research journal of engineering and technology
Language(s) - English
Resource type - Journals
eISSN - 2413-7219
pISSN - 0254-7821
DOI - 10.22581/muet1982.2202.14
Subject(s) - tile , grayscale , computer science , artificial intelligence , computer vision , dilation (metric space) , image processing , ceramic tiles , rgb color model , process (computing) , image (mathematics) , enhanced data rates for gsm evolution , mathematics , materials science , combinatorics , composite material , operating system
In ceramic tiles manufacturing industry, tiles are manufactured at large scale which makes it more challenging to ensure the quality of each tile according to the set standards. Mostly, Statistical Process Control (SPC) is used by tile manufacturers at each step to monitor various processing parameters. SPC procedures are implemented manually that requires sufficient number of experienced human resource to identify defected tiles from a batch of tiles. The manual inspection also gives low accuracy of defect detection due to human errors and hard environment. Considering these drawbacks, in this paper an automated defect detection method is proposed which is based on image processing and morphological operation to ensure the quality and standard of tiles. The proposed method resizes and converts the input RGB image into grayscale image and removes any possible noisy artifacts. An edge detection algorithm is applied on grayscale image to enhance the edges representing the cracks. Afterwards, morphological erosion and dilation operations are applied, one at a time, to get two intermediate images. Finally, edges are detected by subtracting eroded intermediate image from dilated intermediate image. For detection, the proposed algorithm does not require any separate reference image. The algorithm is tested on an image set of sixty different defected tile images and attained 92% average detection accuracy.