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Crack detection in oak flooring lamellae using ultrasound-excited thermography
Luleå University of Technology, Campus Skellefteå, Skellefteå, Sweden.
Luleå University of Technology, Luleå, Sweden.
Jönköping University, School of Engineering, JTH, Industrial Engineering and Management.
Luleå University of Technology, Campus Skellefteå, Skellefteå, Sweden.
2018 (English)In: Infrared physics & technology, ISSN 1350-4495, E-ISSN 1879-0275, Vol. 88, 57-69 p.Article in journal (Refereed) 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).

Place, publisher, year, edition, pages
Elsevier, 2018. Vol. 88, 57-69 p.
Keyword [en]
Crack detection, Ensemble classification, Machine learning, Parquet flooring, Ultrasound-excited thermography, Wood, Artificial intelligence, Building materials, Cracks, Decision trees, Floors, Grading, Image enhancement, Image processing, Learning algorithms, Learning systems, Statistical methods, Thermography (imaging), Ultrasonic applications, Background suppression, Classification accuracy, Image processing technique, Local binary patterns, Predictor variables, Thermographic cameras
National Category
Metallurgy and Metallic Materials
Identifiers
URN: urn:nbn:se:hj:diva-38305DOI: 10.1016/j.infrared.2017.11.007ISI: XYZScopus ID: 2-s2.0-85034628056OAI: oai:DiVA.org:hj-38305DiVA: diva2:1169841
Available from: 2017-12-29 Created: 2017-12-29 Last updated: 2017-12-29Bibliographically approved

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Popovic, Djordje

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