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Detection of ‘Orange Skin’ Type Surface Defects in Furniture Elements with the Use of Textural Features

Abstract : The accuracy of detecting the orange skin surface defect in lacquered furniture elements was tested. Textural features and an SVM classifier were used. Features were selected from a set of 50 features with the bottom-up feature selection strategy driven by the Fisher measure. The features selected were the Kolmogorow-Smirnow-based features, some of the Hilbert curve-based features, some of the maximum subregions features and also some of the thresholding-based features. The Otsu thresholding and percolation-based features were all rejected. The images of size $$300\,\times \,300$$300×300 pixels cut from the original, larger images were treated as objects. There were three quality classes: very good, good and bad. In the cross-validation process where the testing sets consisted of 90 and the training sets of 910 objects the accuracies ranged from 90% to 98% and the average accuracy was 94%. The tests revealed that more research should be done on the choice of features for this problem.
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Submitted on : Tuesday, December 5, 2017 - 2:56:38 PM
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Michał Kruk, Bartosz Świderski, Katarzyna Śmietańska, Jarosław Kurek, Leszek Chmielewski, et al.. Detection of ‘Orange Skin’ Type Surface Defects in Furniture Elements with the Use of Textural Features. 16th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Jun 2017, Bialystok, Poland. pp.402-411, ⟨10.1007/978-3-319-59105-6_34⟩. ⟨hal-01656207⟩

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