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

Philip Valentin, Tsampikos Kounalakis, Lazaros Nalpantidis

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2 Citationer (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.

OriginalsprogEngelsk
TitelIST 2018 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
Antal sider6
ForlagIEEE
Publikationsdato14 dec. 2018
Artikelnummer8577092
ISBN (Elektronisk)978-1-5386-6628-9
DOI
StatusUdgivet - 14 dec. 2018
Begivenhed2018 IEEE International Conference on Imaging Systems and Techniques, IST 2018 - Krakow, Polen
Varighed: 16 okt. 201818 okt. 2018

Konference

Konference2018 IEEE International Conference on Imaging Systems and Techniques, IST 2018
Land/OmrådePolen
ByKrakow
Periode16/10/201818/10/2018
NavnIEEE International Conference on Imaging Systems and Techniques (IST)
ISSN1558-2809

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