Classifiers ensemble of transfer learning for improved drill wear classification using convolutional neural network

• Autorzy: Kurek Jarosław , Antoniuk Izabella , Górski Jarosław , Jegorowa Albina , Świderski Bartosz , Kruk Michał , Wieczorek Grzegorz , Pach Jakub , Orłowski Arkadiusz , Aleksiejuk-Gawron Joanna

Identyfikatory

DOI

10.22630/MGV.2019.28.1.2

• Języki publikacji: EN

Abstrakty

EN

In this paper we introduce the enhanced drill wear recognition method, based on classifiers ensemble, obtained using transfer learning and data augmentation methods. Red, green and yellow classes are used to describe the current drill state. The first one corresponds to the case when drill should be immediately replaced. The second one denotes a tool that is still in a good condition. The final class refers to the case when a drill is suspected of being worn out, and a human expert evaluation would be required. The proposed algorithm uses three different, pretrained network models and adjusts them to the drill wear classification problem. To ensure satisfactory results, each of the methods used was required to achieve accuracy above 90% for the given classification task. Final evaluation is achieved by voting of all three classifiers. Since the initial data set was small (242 instances), the data augmentation method was used to artificially increase the total number of drill hole images. The experiments performed confirmed that the presented approach can achieve high accuracy, even with such a limited set of training data.

Słowa kluczowe

EN

classifier ensemble; convolutional neural networks; data augmentation; deep learning; tool condition monitoring

• Wydawca: Institute of Information Technology of the Warsaw University of Life Sciences – SGGW
• Czasopismo: Machine Graphics and Vision
• Rocznik: 2019
• Tom: Vol. 28, No. 1/4
• Strony: 13--23
• Opis fizyczny: Bibliogr. 20 poz., il., tab., wykr.

Twórcy

autor

Kurek Jarosław

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0002-2789-4732

autor

Antoniuk Izabella

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0003-3157-3381+

autor

Górski Jarosław

• Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0001-5615-0735+

autor

Jegorowa Albina

• Institute of Wood Sciences and Furniture, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0002-8935-845X

autor

Świderski Bartosz

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0002-7490-8930+

autor

Kruk Michał

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0002-3451-6879

autor

Wieczorek Grzegorz

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0002-1654-1982

autor

Pach Jakub

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0002-3483-4340+

autor

Orłowski Arkadiusz

• Institute of Information Technology, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0002-6755-1830

autor

Aleksiejuk-Gawron Joanna

• Institute of Mechanical Engineering, Warsaw University of Life Sciences – SGGW, Warsaw, Poland

• https://orcid.org/0000-0001-9334-3413+

Bibliografia

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).