Weld classification using gray level co-occurrence matrix and local binary patterns

Philip Valentin, Tsampikos Kounalakis, Lazaros Nalpantidis

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

1 Citation (Scopus)

Abstract

This paper presents an algorithm that can classify weld seams from images, exploiting machine learning techniques. Manual visual inspection is the primary way of evaluating weld seams, in cases where the primary goal is to keep inspection costs low. Such, visual inspections entail manual interpretation and evaluation, which are both time consuming and the result often depends on the person assigned to the task. These drawbacks render automatic visual inspection appealing. Thus, this paper seeks to find a possible solution for the visual inspection of welds, where two feature extraction methods are examined and tested in conjunction with two different classifiers. We investigate whether visual inspection based on texture-describing features, processed with a machine learning algorithm, can detect flaws and defects in a weld merely by inspecting the surface of the object, in a way similar to how human eyes detect them and we achieve 96% classification accuracy on a new dataset.

Original languageEnglish
Title of host publicationIST 2018 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
Number of pages6
PublisherIEEE
Publication date14 Dec 2018
Article number8577092
ISBN (Electronic)978-1-5386-6628-9
DOIs
Publication statusPublished - 14 Dec 2018
Event2018 IEEE International Conference on Imaging Systems and Techniques, IST 2018 - Krakow, Poland
Duration: 16 Oct 201818 Oct 2018

Conference

Conference2018 IEEE International Conference on Imaging Systems and Techniques, IST 2018
CountryPoland
CityKrakow
Period16/10/201818/10/2018
SeriesIEEE International Conference on Imaging Systems and Techniques (IST)
ISSN1558-2809

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