Segmentation of defects on log surface from terrestrial lidar data

Nguyen, Van-Tho; Kerautret, Bertrand; Debled-Rennesson, Isabelle; Colin, Francis; Piboule, Alexandre; Constant, Thiéry

doi:10.1109/icpr.2016.7900122

Cited by 2 publications

(14 citation statements)

References 9 publications

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“…After their acquisition, the TLS data were preprocessed to obtain a smooth mesh corresponding to a trunk portion. Next, the potential defects were detected by using a segmentation algorithm, which is an improved version of the previously published work (Nguyen et al, 2016b) and is summarized in section 2.5. Then, the potential defects were classified into defect types using trained random forests.…”

Section: Methodsmentioning

confidence: 99%

“…Our strategy to classify the defects on trunk surface was first to detect all potentially defective areas using a segmentation algorithm. The algorithm is an enhanced version of our previously published one (Nguyen et al, 2016b) that focuses on defects with little protuberance from tree bark. In this study, we proposed a preliminary step for segmenting tree branches.…”

Section: Segmentation Of Defectsmentioning

confidence: 99%

“…To detect these irregularities, we defined the centerline of the trunk as a reference. In the evaluation of the algorithm presented in Nguyen et al (2016b), the presence of branches was identified as an inconvenience for detecting smaller defects in a branch vicinity. Thus, in this work, the branches were first segmented by an algorithm that separates the points into two disjointed sets (illustrated in Figure 4): (1) set T contains closer points to the trunk surface, and 2set B contains the branches according to the following algorithm.…”

Section: Segmentation Of Defectsmentioning

confidence: 99%

“…In a previous work (Nguyen et al, 2016b), we successfully developed an algorithm to detect the defects on trunks surface. Using a suitable spatial resolution of the 3D data, the detection can segment potential defects with a dimension as small as 1 cm and small protrusion on trunks of different tree species.…”

Section: Introductionmentioning

confidence: 99%

“…The main objective of this work is to classify the potential defects detected on trunk surface by previously developed algorithms (Nguyen et al, 2016b). The other objective is to evaluate the performance of a robust and commonly used machine-learning algorithm, random forests, for the classification of bark singularities.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A machine-learning approach for classifying defects on tree trunks using terrestrial LiDAR

Nguyen

Constant

Kerautret

et al. 2020

Computers and Electronics in Agriculture

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Three-dimensional data are increasingly prevalent in forestry thanks to terrestrial LiDAR. This work assesses the feasibility for an automated recognition of the type of local defects present on the bark surface. These singularities are frequently external markers of inner defects affecting wood quality, and their type, size, and frequency are major components of grading rules. The proposed approach assigns previously detected abnormalities in the bark roughness to one of the defect types: branches, branch scars, epicormic shoots, burls, and smaller defects. Our machine learning approach is based on random forests using potential defects shape descriptors, including Hu invariant moments, dimensions, and species. The results of our experiments involving different French commercial species, oak, beech, fir, and pine showed that most defects were well classified with an average F 1 score of 0.86.

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Section: Methodsmentioning

confidence: 99%

Section: Segmentation Of Defectsmentioning

confidence: 99%

Section: Segmentation Of Defectsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A machine-learning approach for classifying defects on tree trunks using terrestrial LiDAR

Nguyen

Constant

Kerautret

et al. 2020

Computers and Electronics in Agriculture

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CNN-based Method for Segmenting Tree Bark Surface Singularites

Delconte¹,

Ngo²,

Kerautret³

et al. 2022

Image Processing on Line

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The analysis of trunk shape and, in particular, the geometric structures on the bark surface are of main interest for different applications linked to the wood industry or biological studies. Bark singularities are often external records of the history of the development of internal elements. The actors of the forest sector grade the trees by considering these singularities through standards. In this paper, we propose a method using terrestrial LiDAR data to automatically segment singularities on tree surfaces. It is based on the construction of a relief map combined with a convolutional neural network. The algorithms and the source code are available with an online demonstration allowing to test the defect detection without any software installation. Source CodeThe reviewed source code and documentation associated to the proposed algorithms are available from the web page of this article 1 . The correspondences between algorithms and source codes are pointed out, and compilation with usage instructions are included in the README.md file of the archive. Supplementary MaterialThe reference dataset, to be used for further comparisons, is provided with the article and peerreviewed. The (non peer-reviewed) instructions to generate the learned model are provided as supplementary material on the associated Github repository 2 .

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Segmentation of defects on log surface from terrestrial lidar data

Cited by 2 publications

References 9 publications

A machine-learning approach for classifying defects on tree trunks using terrestrial LiDAR

A machine-learning approach for classifying defects on tree trunks using terrestrial LiDAR

CNN-based Method for Segmenting Tree Bark Surface Singularites

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