Computed tomography log scanning – high technology for forestry and forest based industry

Gergeľ, Tomáš; Bucha, Tomáš; Gejdoš, Miloš; Vyhnáliková, Zuzana

doi:10.2478/forj-2019-0003

Cited by 19 publications

(14 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Research in the first stage processing of logs is important because in recent years, intelligent scanning technologies were developed in the wood industry [4]. To successfully implement this technology, it is necessary to guarantee the software updates and at the same time to guarantee a linkage to the second stage of wood processing.…”

Section: Discussionmentioning

confidence: 99%

“…When processing logs, the use of innovative technologies is essential for the wood processing industry. In order to make the wood processing industry more competitive in the future, an increase in the technological level of industry leaders is a priority, but on the other hand, this industry is very conservative, and therefore innovations must be introduced gradually and be presented through demonstration examples and objects [4]. It is clear from the literature collected that many authors have been involved in the creation of cutting patterns of logs.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction Model of Wooden Logs Cutting Patterns and Its Efficiency in Practice

et al. 2020

Self Cite

View full text Add to dashboard Cite

This article deals with the testing of a methodology for creating log cutting patterns. Under this methodology, programs were developed to optimize the log yield. Testing was conducted by comparing the values of the proportions of the individual products resulting from an implementation of the proposed cutting pattern of a specific log with the calculated values of these proportions of products using the tested methodology. For this test, nine pieces of logs (three pieces of oak, three pieces of beech and three pieces of spruce) were chosen, and then the proposed cutting pattern was applied on each log and the proportions of the resulting products were determined gravimetrically. The result of the statistical comparison is as follows: The prediction model that has been tested meets the basic requirement of insensitivity to the tree species. This means that the model tested does not create differences in the results based on the type of wood. In the case of timber, the model statistically significantly underestimates its proportion by 3.7%. The model underestimates the proportion of residues by 0.14%, but is not statistically significant. This model statistically significantly underestimates the proportion of sawdust by 2.25%. By evaluating the results obtained, we can conclude that the prediction model is a good basis for optimizing log yields. In its further development, it has to be supplemented with a log curvature parameter and for the most accurate yield optimization, in terms of the product quality, it must be connected with new scanning technologies as well. These will supplement the prediction model with information about internal and external wood defects and these defects will be taken into account then.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction Model of Wooden Logs Cutting Patterns and Its Efficiency in Practice

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…However, with the development of sensor technology and artificial intelligence technology, today's marketed wood defect recognition equipment is different from the past. These advanced scanners use a multi-sensor system, with targeted algorithms for different types of wood defects, and they work with front-end production demand solutions to achieve increased productivity, automation, and intelligent production (Gergeľ et al 2019). However, for most small and medium-sized wood processing companies, the high price of these devices prevents them from purchasing them (Pan et al 2021).…”

Section: Research On the Current Status Of Wood Defect Recognition Eq...mentioning

confidence: 99%

Review of the current state of application of wood defect recognition technology

Chen

Sun

Ren

et al. 2022

BioRes

View full text Add to dashboard Cite

Wood utilisation is an important factor affecting production costs, but the combined utilisation rate of wood is generally only 50 to 70%. During the production process, the rejection scheme of wood defects is one of the most important factors affecting the wood yield. This paper provides an overview of the main wood defects affecting wood quality, introduces techniques for detecting and identifying wood defects using different technologies, highlights the more widely used image recognition-based wood surface defect identification methods, and presents three advanced wood defect detection and identification equipment. In view of the relatively fixed wood defect recognition requirements in wood processing production, it is proposed that wood defect recognition technology should be further developed toward deep learning to improve the accuracy and efficiency of wood defect recognition.

show abstract

“…In the literature one can find many articles [24], [25], [26], [27], [28], [29] and reviews [4], [30] describing various kinds of approaches that can be used to locate specific structures like knots, cracks, resin pockets and alien objects inside logs from high quality reconstructions obtained using low speed CT scanning. Most of these approaches have a knot detection rate exceeding 90% with about 1% falsely detected knots.…”

Section: Segmentationmentioning

confidence: 99%

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

Springer¹,

Glielmo²,

Senchukova³

et al. 2022

Preprint

View full text Add to dashboard Cite

In industrial applications it is common to scan objects on a moving conveyor belt. If slice-wise 2D computed tomography (CT) measurements of the moving object are obtained we call it a sequential scanning geometry. In this case, each slice on its own does not carry sufficient information to reconstruct a useful tomographic image. Thus, here we propose the use of a Dimension reduced Kalman Filter to accumulate information between slices and allow for sufficiently accurate reconstructions for further assessment of the object. Additionally, we propose to use an unsupervised clustering approach known as Density Peak Advanced, to perform a segmentation and spot density anomalies in the internal structure of the reconstructed objects. We evaluate the method in a proof of concept study for the application of wood log scanning for the industrial sawing process, where the goal is to spot anomalies within the wood log to allow for optimal sawing patterns. Reconstruction and segmentation quality is evaluated from experimental measurement data for various scenarios of severely undersampled X-measurements. Results show clearly that an improvement of reconstruction quality can be obtained by employing the Dimension reduced Kalman Filter allowing to robustly obtain the segmented logs.

show abstract

Computed tomography log scanning – high technology for forestry and forest based industry

Cited by 19 publications

References 11 publications

Prediction Model of Wooden Logs Cutting Patterns and Its Efficiency in Practice

Prediction Model of Wooden Logs Cutting Patterns and Its Efficiency in Practice

Review of the current state of application of wood defect recognition technology

Reconstruction and segmentation from sparse sequential X-ray measurements of wood logs

Contact Info

Product

Resources

About