Standing tree decay detection by using acoustic tomography images

Luis F. Espinosa; Andres F. Arciniegas; Flavio A. Prieto; Yolima Cortes; Loïc Brancheriau

doi:10.1117/12.2182915

30 April 2015 Standing tree decay detection by using acoustic tomography images

Luis F. Espinosa , Andres F. Arciniegas, Flavio A. Prieto, Yolima Cortes, Loïc Brancheriau

Proceedings Volume 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015; 953404 (2015) https://doi.org/10.1117/12.2182915
Event: The International Conference on Quality Control by Artificial Vision 2015, 2015, Le Creusot, France

Abstract

The acoustic tomographic technique is used in the diagnosis process of standing trees. This paper presents a segmentation methodology to separate defective regions in cross-section tomographic images obtained with Arbotom® device. A set of experiments was proposed using two trunk samples obtained from a eucalyptus tree, simulating defects by drilling holes with known geometry, size and position and using different number of sensors. Also, tomographic images from trees presenting real defects were studied, by testing two different species with significant internal decay. Tomographic images and photographs from the trunk cross-section were processed to align the propagation velocity data with a corresponding region, healthy or defective. The segmentation was performed by finding a velocity threshold value to separate the defective region; a logistic regression model was fitted to obtain the value that maximizes a performance criterion, being selected the geometric mean. Accuracy segmentation values increased as the number of sensors augmented; also the position influenced the result, obtaining improved results in the case of centric defects.

Citation Download Citation

Luis F. Espinosa , Andres F. Arciniegas, Flavio A. Prieto, Yolima Cortes, and Loïc Brancheriau "Standing tree decay detection by using acoustic tomography images", Proc. SPIE 9534, Twelfth International Conference on Quality Control by Artificial Vision 2015, 953404 (30 April 2015); https://doi.org/10.1117/12.2182915

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