Defect detection on hardwood logs using high resolution three dimensional laser scan data

Thomas, Liya; Mili, Lamine; Shaffer, Clifford A.; Thomas, E.

doi:10.1109/icip.2004.1418735

Cited by 12 publications

(18 citation statements)

References 7 publications

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“…The minimal distance above the approximated cylinder of our characteristics identified so far is 1.2 cm. This is more or less the same as 0.5 inches (1.3 cm), comparable with the work by Thomas and Thomas (2011). Their approach is employed within sawmills post felling of the tree, whereas we offer a solution for standing in-situ trees.…”

Section: Discussionsupporting

confidence: 57%

“…Thomas and Thomas (2011) have reached a multi-purpose solution whereas this study only focuses on a specific characteristic. The minimal distance above the approximated cylinder of our characteristics identified so far is 1.2 cm.…”

Section: Discussionmentioning

confidence: 99%

“…In contrast to Thomas and Thomas (2011) we have not yet implemented a fully automated detection system, but the mathematic methodology may serve as a basis to do so. Thomas and Thomas (2011) have reached a multi-purpose solution whereas this study only focuses on a specific characteristic.…”

Section: Discussionmentioning

confidence: 99%

“…However, no further information about how to detect and classify exterior knots in TLS data was suggested. Thomas and Thomas (2011) used scan data collected in a sawmill to automatically identify bark defects. They also established a bark height map with the application of a statistical approach.…”

Section: Introductionmentioning

confidence: 99%

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A new approach to assessing tree stem quality characteristics using terrestrial laser scans

Kretschmer¹,

Kirchner²,

Morhart³

et al. 2013

Silva Fenn.

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A new approach to assessing tree stem quality characteristics using terrestrial laser scans Kretschmer U., Kirchner N., Morhart C., Spiecker H. (2013). A new approach to assessing tree stem quality characteristics using terrestrial laser scans. Silva Fennica vol. 47 no. 5 article id 1071. 14 p. Highlights• Minimal deviations of the bark surface can be detected and visualized based on terrestrial laser scan data.• Additionally the geometrical properties of bark scars and branched knots can be assessed.• Two methods using two different approaches are presented: (1) a method using intensity data and (2) a method using bark surface models. AbstractThis paper presents an approach to assess and measure bark characteristics as indicators of wood quality using terrestrial laser scan data. In addition to the detection and measurement by use of the intensity information of the scan data a new approach was established. Bark surface models are calculated for each tree. They offer the representation of the bark as a height model. The reference is the tree stem approximated by a chain of cylinders. Minimal deviations of the bark surface can be detected and visualized and the geometrical properties of bark scars and branched knots can be assessed. Results of the measurement of 18 scars are presented using the two approaches:(1) a method using intensity data or (2) using bark surface models. The selection of the adequate approach depends on the stem characteristics. In a next step, methods for automatic measurement of bark scars will be developed.

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Section: Discussionsupporting

confidence: 57%

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A new approach to assessing tree stem quality characteristics using terrestrial laser scans

Kretschmer¹,

Kirchner²,

Morhart³

et al. 2013

Silva Fenn.

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“…The goal of this ANN grading approach is to be able to accurately grade lumber within the log before sawing. Using laser scanning vision systems, the defects on the surface of a hardwood log can be detected (Thomas and Thomas, 2011) and the internal defect manifestations estimated (Thomas, 2016). Using this full defect information, log sawing can be optimized to return the highest NHLA grade and value of boards possible.…”

Section: Introductionmentioning

confidence: 99%

An artificial neural network for real-time hardwood lumber grading

Thomas

2017

Computers and Electronics in Agriculture

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a b s t r a c tComputerized grading of hardwood lumber according to NHLA rules would permit fast assessment of sawn lumber and the evaluation of potential edging and trimming operations to improve lumber value. More importantly, to enable optimization of the hardwood lumber sawing process, a fast means of evaluating the potential value of boards before they are sawn is necessary. As log and lumber scanning systems become prevalent and common, these needs become more pressing. From an automation perspective, the NHLA lumber grades are difficult to implement efficiently in a computer program. Exhaustive approaches that examine every potential cutting size and combination to determine the grade give accurate grading solutions, at the cost of computation time. Other approaches have examined heuristic methods that implement key parts of the grading rules, or used artificial neural network methods, both with the loss of accuracy. Here, a different approach to computerized grading is examined that takes a hybridized approach using projected yield from cut-up simulation and neural network methods. This new hybrid approach has the advantage of both accuracy and high-processing speed. Such an approach lends itself to log sawing optimization with respect to NHLA grades and market values when internal log defect information is known.Published by Elsevier B.V.

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An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data

Nguyen

Constant

Colin

2021

Annals of Forest Science

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Key messageWe designed a novel method allowing to automatically detect and measure defects on the surface of trunks including branches, branch scars, and epicormics from terrestrial LiDAR data by using only high-density 3D information. We could automatically detect and measure the defects with a diameter as small as 0.5 cm on either oak (Quercus petraea (Matt.) Liebl.) or beech (Fagus sylvatica L.) trees that display either rough or smooth bark.• Context Ground-based light detection and ranging (LiDAR) technology describes standing trees with a high level of detail. This provides an opportunity to assess standing tree quality and to use this information in forest inventory. Assuming the availability of a very high level of detail, we could extract information about the surface defects, mainly inherited from past ramification and having a strong impact on wood quality.• Aims Within the general framework of the development of a computing method able to detect, identify, and quantify the defects on the trunk surface described from 3D data produced by a terrestrial LiDAR, this study focuses on the relevance of the whole process for two tree species with contrasted bark roughness (Quercus petraea (Matt.) Liebl. and Fagus sylvatica L.) in terms of detection, identification of the defects, and comparison with measurements performed manually on the bark surface.• Methods First, a segmentation algorithm detected singularities on the trunk surface. Next, a Random Forests machine learning algorithm identified the most probable defect type and allowed the elimination of false detections. Finally, we estimated the position, horizontal, and vertical dimensions of each defect from 3D data, and we compared them to those observed directly on the trunk by an operator. • ResultsThe defects were detected and classified with a high accuracy with an average F 1 score (harmonic mean of precision and recall) of 0.74. There were differences in computed and observed defect areas, but a much closer agreement for the number of defects. • ConclusionThe information about the defects present on the trunk surface measured from terrestrial LiDAR data can be used in an automated procedure for grading standing trees or roundwoods.

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Defect detection on hardwood logs using high resolution three dimensional laser scan data

Cited by 12 publications

References 7 publications

A new approach to assessing tree stem quality characteristics using terrestrial laser scans

A new approach to assessing tree stem quality characteristics using terrestrial laser scans

An artificial neural network for real-time hardwood lumber grading

An innovative and automated method for characterizing wood defects on trunk surfaces using high-density 3D terrestrial LiDAR data

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