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| 2. |
- Cool, Julie, et al.
(författare)
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Knot detection in coarse resolution CT images of logs
- 2017
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Ingår i: International Wood Machining Seminar (IWMS-23).
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Konferensbidrag (refereegranskat)abstract
- The use of X-ray computed tomography (CT) scanning of logs in sawmill is becoming a reality in the last few years, usually with rather costly and complex machines resembling medical scanners. However, a scanning solution has been developed that is less costly and more robust, and therefore more suited for sawmill needs. The rather coarse data from this machine has not been fully evaluated regarding possibilities to detect internal features such as knots. In this study, a knot detection algorithm developed for medical scanners was applied to images from a coarse resolution scanner, from four different logs of various species, and with different image resolution. The objective was to see if it was possible to detect knots automatically in the images. If so, the aim was to calculate the knot detection rate and the accuracy of detected knot size and position. These numbers were calculated compared to manually measured reference knots. This resulted in a knot detection rate of about 53 % overall, and a well detected knot position, but poorly detected knot size. It is possible to observe a certain difference between species and reconstruction resolution, however the material is too small to draw any definite conclusions. As a preliminary study, it provides input for further investigation on knot detection in coarse resolution X-ray CT images. Future work involves scanning more logs to get more data, and to pinpoint the resolution needed for accurate knot detection using the current algorithm.
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| 3. |
- Fredriksson, Magnus, 1984-, et al.
(författare)
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Automatic Knot Detection in Coarse-Resolution Cone-Beam Computed Tomography Images of Softwood Logs
- 2019
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Ingår i: Forest Products Journal. - : Forest Products Society. - 0015-7473. ; 69:3, s. 185-187
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Tidskriftsartikel (refereegranskat)abstract
- X-ray computed tomography (CT) scanning of sawmill logs is associated with costly and complex machines. An alternative scanning solution was developed, but its data have not been evaluated regarding detection of internal features. In this exploratory study, a knot detection algorithm was applied to images of four logs to evaluate its performance in terms of knot position and size. The results were a detection rate of 67 percent, accurate position, and inaccurate size. Although the sample size was small, it was concluded that automatic knot detection in coarse resolution CT images of softwoods is feasible, albeit for knots of sufficient size.
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| 4. |
- Fredriksson, Magnus, 1984-, et al.
(författare)
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Knot detection in computed tomography images of partially dried Jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs
- 2017
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Ingår i: International Wood Machining Seminar (IWMS-23).
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Konferensbidrag (refereegranskat)abstract
- X-ray computed tomography (CT) of logs means possibilities for optimizing breakdown in sawmills. This depends on accurate detection of knots to assess internal quality. However, as logs are stored in the log yard they dry to a certain extent, and this drying affects the density variation in the log, and therefore the X-ray images. For this reason, it is hypothetically difficult to detect log features in partially dried logs using X-ray CT. The objective of this research was to investigate the effect of drying on knot detection in Jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs from New Brunswick, Canada. An automatic knot detection algorithm was compared to manual measurements for this purpose, and the results show that knot detection was clearly affected by partial drying. Because dried heartwood and sapwood have similar densities, the algorithm had difficulties detecting the heartwood-sapwood border. Based on how well the heartwood-sapwood border was detected, it was statistically possible to sort logs into two groups: 1) Low knot detection rate, and 2) High knot detection rate. In that way, a decision can be made whether or not to trust the knot models obtained from CT scanning. Therefore, logs that are partially dried out and fall in the low knot detection rate should be handled cautiously because the optimization results based on CT knot detection cannot be fully trusted. Sawing of these logs could be optimized using only their outer shape, ignoring internal quality. Similarly, only logs having a regular heartwood shape should be used when scanning logs for research purposes or in databases of CT scanned logs. Finally, a larger knot detection rate was obtained for Jack pine. This could have been facilitated by the fact that pine trees usually have larger but less numerous knots than spruce trees.
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| 5. |
- Fredriksson, Magnus, 1984-, et al.
(författare)
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Knot detection in computed tomography images of partially dried Jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs from a Nelder type plantation
- 2017
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Ingår i: Canadian Journal of Forest Research. - : Canadian Science Publishing. - 0045-5067 .- 1208-6037. ; 47:7, s. 910-915
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Tidskriftsartikel (refereegranskat)abstract
- X-ray computed tomography (CT) of logs means possibilities for optimizing breakdown in sawmills. This depends on accurate detection of knots to assess internal quality. However, as logs are stored they dry to some extent, and this drying affects the density variation in the log, and therefore the X-ray images. For this reason it is hypothetically difficult to detect log features in partially dried logs using X-ray CT. This paper investigates the effect of improper heartwood-sapwood border detection, possibly due to partial drying, on knot detection in jack pine (Pinus banksiana Lamb.) and white spruce (Picea glauca (Moench) Voss) logs from New Brunswick, Canada. An automatic knot detection algorithm was compared to manual reference knot measurements, and the results showed that knot detection was affected by detected heartwood shape. It was also shown that logs can be sorted into two groups based on how well the heartwood-sapwood border is detected, to separate logs with a high knot detection rate from those with a low detection rate. In that way, a decision can be made whether or not to trust the knot models obtained from CT scanning. This can potentially aid both sawmills and researchers working with log models based on CT.
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| 6. |
- Habite, Tadios
(författare)
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Pith location and annual ring detection for modelling of knots and fibre orientation in structural timber : A Deep-Learning-Based Approach
- 2022
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Detection of pith, annual rings and knots in relation to timber board cross-sections is relevant for many purposes, such as for modelling of sawn timber and for real-time assessment of strength, stiffness and shape stability of wood materials. However, the methods that are available and implemented in optical scanners today do not always meet customer accuracy and/or speed requirements. The primary purpose of this doctoral dissertation was to gain an increased knowledge and a better understanding of how different characteristics and surface defects of timber boards can be identified automatically and robustly. The secondary purpose was to explore the possibilities of how such identified features/defects can be used to add value to the wood manufacturing industry. In the present study, three different methods were developed to non-destructively and automatically detect annual rings and pith location based on images obtained by optical scanning of the four longitudinal surfaces of the timber board. In the first method, a signal-processing-based approach and an optimisation algorithm were applied. In the second method, a deep-learning-based conditional generative adversarial network (cGAN) and a shallow artificial neural network (ANN) were used. In the third method, a single step deep-learning approach with a one-dimensional convolutional neural network (1D CNN) was applied. A novel stochastic model was also proposed to generate an unlimited number of virtual timber boards, with photo-realistic surfaces and known pith location, by which the proposed 1D CNN was trained before it was successfully applied to real timber boards. Concerning accuracy, all the three methods gave prediction errors of the same magnitude, between 4 mm and 6 mm. The 1D CNN method needed only 1.1 ms to locate the pith at a single section, which was 165 and 127 times faster than the signal-processing based and the cGAN based methods, respectively. Hence, the 1D CNN method proved to be the fastest, most operationally simple and robust method.In sawn timber, the presence of knots causes the fibres to deviate from the longitudinal direction of the board, leading to a significant reduction of strength and stiffness. In the current study, a computer algorithm was proposed to detect knots on board surfaces and to reconstruct the knots in three dimensions (3D) by using the detected pith location. Moreover, a fibre modelling method was also proposed and used to produce the 3D fibre orientation within the volume of timber boards. Furthermore, the detected pith location and annual rings visible on the board surfaces were also utilised to estimate the radial annual ring profiles along the longitudinal direction of timber boards.
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| 7. |
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| 8. |
- van Blokland, Joran, et al.
(författare)
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Machine learning-based prediction of internal checks in weathered thermally modified timber
- 2021
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Ingår i: Construction and Building Materials. - : Elsevier. - 0950-0618 .- 1879-0526. ; 281
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Tidskriftsartikel (refereegranskat)abstract
- This study investigated possibilities to predict the presence of internal checks in thermally modified Norway spruce timber after 2.5 years of weathering based on the initial properties of the boards. Machine-learning classification enabled sorting the input parameters based on their relative importance for accurate predictions. The parameters of thermally modified timber with the highest relative importance were annual ring width followed by initial moisture content, density and dynamic stiffness. Whereas after kiln drying these were, density, annual ring width, initial moisture content and acoustic velocity. The results showed that predictions are possible, and an accuracy of 67% was achieved by using annual ring width combined with density and initial moisture content, or acoustic velocity that can be determined after either kiln drying or thermal treatment. (C) 2020 Published by Elsevier Ltd.
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