Endre søk
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Crack detection in oak flooring lamellae using ultrasound-excited thermography
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Träteknik.
Luleå tekniska universitet, Institutionen för system- och rymdteknik, Signaler och system.ORCID-id: 0000-0001-6186-7116
Jönköping University.
Luleå tekniska universitet, Institutionen för teknikvetenskap och matematik, Träteknik.ORCID-id: 0000-0001-8404-7356
2018 (engelsk)Inngår i: Infrared physics & technology, ISSN 1350-4495, E-ISSN 1879-0275, Vol. 88, s. 57-69Artikkel i tidsskrift (Fagfellevurdert) Published
Abstract [en]

Today, a large number of people are manually grading and detecting defects in wooden lamellae in the parquet flooring industry. This paper investigates the possibility of using the ensemble methods random forests and boosting to automatically detect cracks using ultrasound-excited thermography and a variety of predictor variables. When friction occurs in thin cracks, they become warm and thus visible to a thermographic camera. Several image processing techniques have been used to suppress the noise and enhance probable cracks in the images. The most successful predictor variables captured the upper part of the heat distribution, such as the maximum temperature, kurtosis and percentile values 92–100 of the edge pixels. The texture in the images was captured by Completed Local Binary Pattern histograms and cracks were also segmented by background suppression and thresholding.

The classification accuracy was significantly improved from previous research through added image processing, introduction of more predictors, and by using automated machine learning. The best ensemble methods reach an average classification accuracy of 0.8, which is very close to the authors’ own manual attempt at separating the images (0.83).

sted, utgiver, år, opplag, sider
Elsevier, 2018. Vol. 88, s. 57-69
HSV kategori
Forskningsprogram
Träteknik; Signalbehandling
Identifikatorer
URN: urn:nbn:se:ltu:diva-65698DOI: 10.1016/j.infrared.2017.11.007ISI: 000423650700007Scopus ID: 2-s2.0-85034628056OAI: oai:DiVA.org:ltu-65698DiVA, id: diva2:1142016
Merknad

Validerad;2017;Nivå 2;2017-12-05 (andbra)

Tilgjengelig fra: 2017-09-18 Laget: 2017-09-18 Sist oppdatert: 2018-02-16bibliografisk kontrollert
Inngår i avhandling
1. Wood fingerprint recognition and detection of thin cracks
Åpne denne publikasjonen i ny fane eller vindu >>Wood fingerprint recognition and detection of thin cracks
2017 (engelsk)Doktoravhandling, med artikler (Annet vitenskapelig)
Abstract [en]

The first part of this thesis deals with recognition of wood fingerprints extracted from timber surfaces. It presents different methods to track sawn wood products through an industrial process using cameras. The possibility of identifying individual wood products comes from the biological variation of trees, where the genetic code, environment, and breakdown process means that every board has a unique appearance. Wood fingerprint recognition experiences many of the same challenges as found in human biometrics applications. 

The vision for the future is to be able to utilize existing imaging sensors in the production line to track individual products through a disordered and diverging product flow. The flow speed in wood industries is usually very high, 2-15 meters per second, with a high degree of automation. Wood fingerprints combined with automated inspection makes it possible to tailor subsequent processing steps for each product and can be used to deliver customized products. Wood tracking can also give the machine operators vital feedback on the process parameters. 

The motivation for recognition comes from the need for the wood industry to keep track of products without using invasive methods, such as bar code stickers or painted labels. In the project Hol-i-Wood Patching Robot, an automatic scanner- and robot system was developed. In this project, there was a wish to keep track of the shuttering panels that were going to be repaired by the automatic robots. 

In this thesis, three different strategies to recognize previously scanned sawn wood products are presented. The first approach uses feature detectors to find matching features between two images. This approach proved to be robust, even when subjected to moderate geometric- and radiometric image distortions. The recognition accuracy reached 100% when using high quality scans of Scots pine boards that had more than 20 knots. 

The second approach uses local knot neighborhood geometry to find point matches between images. The recognition accuracy reached above 99% when matching simulated Scots pine panels with realistically added noise to the knot positions, given the assumption that 85% of the knots could be detected.

The third approach uses template matching to match a small part of a board against a large set of full-length boards. Cropping and heavy downsampling was implemented in this study. The intensity normalized algorithms using cross-correlation (CC-N) and correlation coefficient (CCF-N) obtained the highest recognition accuracy and had very similar overall performance. For instance, the matching accuracy for the CCF-N method reached above 99% for query images of length 1 m when the pixel density was above 0.08 pixels/mm.

The last part of this thesis deals with the detection of thin cracks on oak flooring lamellae using ultrasound-excited thermography and machine learning. Today, many people manually grade and detect defects on wooden lamellae in the parquet flooring industry. The last appended paper investigates the possibility to use ensemble methods random forests and boosting to automate the process. When friction occurs in thin cracks they become warm and thus visible for a thermographic camera. Several image processing techniques were used to suppress noise and enhance likely cracks in the images. The best ensemble methods reached an average classification accuracy of 0.8, which was very close to the authors own manual attempt at separating the images (0.83).

sted, utgiver, år, opplag, sider
Luleå: Luleå University of Technology, 2017. s. 168
Serie
Doctoral thesis / Luleå University of Technology 1 jan 1997 → …, ISSN 1402-1544
HSV kategori
Forskningsprogram
Träteknik
Identifikatorer
urn:nbn:se:ltu:diva-65701 (URN)978-91-7583-967-7 (ISBN)978-91-7583-968-4 (ISBN)
Disputas
2017-10-20, Hörsal A, Skellefteå, 09:00 (engelsk)
Opponent
Veileder
Forskningsfinansiär
EU, FP7, Seventh Framework Programme, 284573
Tilgjengelig fra: 2017-09-19 Laget: 2017-09-18 Sist oppdatert: 2021-04-20bibliografisk kontrollert

Open Access i DiVA

fulltext(1370 kB)807 nedlastinger
Filinformasjon
Fil FULLTEXT01.pdfFilstørrelse 1370 kBChecksum SHA-512
a2c807eed7cfd208f9073ab05dd409fccd2de345b9936c1cea64d497654a856b657b19edf88718cef6f9c8cba3ff1db414859f9f5b7cd828c2bf5de593d55e45
Type fulltextMimetype application/pdf

Andre lenker

Forlagets fulltekstScopus

Person

Pahlberg, TobiasThurley, MatthewHagman, Olle

Søk i DiVA

Av forfatter/redaktør
Pahlberg, TobiasThurley, MatthewHagman, Olle
Av organisasjonen
I samme tidsskrift
Infrared physics & technology

Søk utenfor DiVA

GoogleGoogle Scholar
Totalt: 807 nedlastinger
Antall nedlastinger er summen av alle nedlastinger av alle fulltekster. Det kan for eksempel være tidligere versjoner som er ikke lenger tilgjengelige

doi
urn-nbn

Altmetric

doi
urn-nbn
Totalt: 432 treff
RefereraExporteraLink to record
Permanent link

Direct link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf