American Journal of Applied Sciences

Comparison of Edge Detection Techniques Applied in the Identification of Centerline Segregation on Steel Slabs

Luciene Coelho Lopez Queiroz and André Riyuiti Hirakawa

DOI : 10.3844/ajassp.2015.567.571

American Journal of Applied Sciences

Volume 12, Issue 8

Pages 567-571


The method Baumann, or Sulphur print as it is also known, is one of the tools used to evaluate the operating conditions of continuous casting machine and quality control of the production of steel slabs. The correct evaluation of centerline segregation severity, analyzed in Sulphur print, is essential to control this process. However, given the classification complexity between the different severity levels, the classification process becomes dependent on experts experience and knowledge. In the light of human interference in this analysis, differences on classification results are possible, since the activity is manual and occasionally an expert can be stricter than others during the classification process. The evaluation of this scenario has motivated the search for the development of computational resources able to identify and classify the defect of centerline segregation. The edge detection techniques are applied to identify the centerline segregation, to reduce the amount of data to be processed and discard information considered irrelevant image while preserving the structural features of the regions of interest that will be used later to classify the severity of the defect. This paper presents a comparison between Sobel, Laplacian of Gaussian and Canny edge detection methods applied to the digital images of the samples generated during Baumann method for identifying the defect of centerline segregation.


© 2015 Luciene Coelho Lopez Queiroz and André Riyuiti Hirakawa. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.