TY - GEN
T1 - Weld classification using gray level co-occurrence matrix and local binary patterns
AU - Valentin, Philip
AU - Kounalakis, Tsampikos
AU - Nalpantidis, Lazaros
PY - 2018/12/14
Y1 - 2018/12/14
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85060711163&partnerID=8YFLogxK
U2 - 10.1109/IST.2018.8577092
DO - 10.1109/IST.2018.8577092
M3 - Article in proceeding
AN - SCOPUS:85060711163
T3 - IEEE International Conference on Imaging Systems and Techniques (IST)
BT - IST 2018 - IEEE International Conference on Imaging Systems and Techniques, Proceedings
PB - IEEE
T2 - 2018 IEEE International Conference on Imaging Systems and Techniques, IST 2018
Y2 - 16 October 2018 through 18 October 2018
ER -