Timber Tracing with Multimodal Encoder-Decoder Networks

Zolotarev, Fedor; Eerola, Tuomas; Lensu, Lasse; Kälviäinen, Heikki; Haario, Heikki; Heikkinen, Jere; Kauppi, Tomi

doi:10.1007/978-3-030-29891-3_30

Cited by 5 publications

(6 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Usually, in sawmills, a conversion pipeline is not straightforward and differs from log to log. Therefore, a timber tracing system from the raw material to the end product can be employed for efficient process control, the optimization of sawing, and the prediction of final product quality [16]. The non-invasive board-to-log tracking technique was designed for this purpose in the DigiSaw project.…”

Section: Timber Tracingmentioning

confidence: 99%

Virtual sawing using generative adversarial networks

Daniel

Zolotarev

Eerola

et al. 2021

2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)

Self Cite

View full text Add to dashboard Cite

The global trend towards digitalization along with the 4th Industrial Revolution allows building new innovative solutions to optimize manufacturing. In particular, the highly competitive sawmill industry is not an exception. The industry always depends on efficient raw material utilization, and thus, exploration of the internal log structure is an important feature in the timber conversion process. One common approach for a comprehensive internal wood structure examination is virtual sawing that is predicting the outcome of sawing process based on log measurements. The main objective of this thesis was to study the suitability of state-of-the-art generative adversarial networks for virtual sawing. The specific aims were to choose an appropriate generative adversarial network architecture to build a trainable model as an extension to an existing virtual sawing system. The proposed method for image-to-image translation is capable to synthesize the photorealistic images of the boards based on log measurements. The defects (knots) on virtual boards were detectable and their locations correspond to those in real boards. PREFACEI would like to thank Heikki Kälviäinen, Lasse Lensu, Tuomas Eerola, and Fedor Zolotarev for sharing their immense knowledge and precious time with me. I am also grateful to my family, relatives, and friends for tremendous support during the studies.

show abstract

Section: Timber Tracingmentioning

confidence: 99%

Virtual sawing using generative adversarial networks

Daniel

Zolotarev

Eerola

et al. 2021

2021 36th International Conference on Image and Vision Computing New Zealand (IVCNZ)

Self Cite

View full text Add to dashboard Cite

show abstract

“…X-ray tomography has applications in various industrial fields such as sawmill industry, where it can be used for detecting knots, rotten parts and foreign objects in sawmills (Shustrov et al, 2019;Zolotarev et al, 2019). In oil and gas industry, X-ray tomography can be used to analyze drill-core rock samples to identifying pore structures (Mendoza et al, 2019) and other structural properties of rock.…”

Section: Introductionmentioning

confidence: 99%

“…In the experiments we concentrate on two industrial problems. The first one is sawmill industry application, doing log tomography for detecting knots, rotten parts and foreign objects in sawmills (Shustrov et al, 2019;Zolotarev et al, 2019). The second application is drill-core rock sample tomography, in oil and gas industry, for identifying pore structures (Mendoza et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Industrial X-ray Tomography by Data-Centric Statistical Methods

Suuronen¹,

Emzir²,

Lasanen³

et al. 2020

Preprint

View full text Add to dashboard Cite

X-ray tomography has applications in various industrial fields such as sawmill industry, oil and gas industry, chemical engineering, and geotechnical engineering. In this article, we study Bayesian methods for the X-ray tomography reconstruction. In Bayesian methods, the inverse problem of tomographic reconstruction is solved with help of a statistical prior distribution which encodes the possible internal structures by assigning probabilities for smoothness and edge distribution of the object. We compare Gaussian random field priors, that favour smoothness, to non-Gaussian total variation, Besov, and Cauchy priors which promote sharp edges and high-contrast and lowcontrast areas in the object. We also present computational schemes for solving the resulting high-dimensional Bayesian inverse problem with 100,000-1,000,000 unknowns. In particular, we study the applicability of a no-U-turn variant of Hamiltonian Monte Carlo methods and of a more classical adaptive Metropolis-within-Gibbs algorithm for this purpose. These methods also enable full uncertainty quantification of the reconstructions. For faster computations, we use maximum a posteriori estimates with limited-memory BFGS optimisation algorithm. As the first industrial application, we consider sawmill industry X-ray log tomography. The logs have knots, rotten parts, and even possibly metallic pieces, making them good examples for non-Gaussian priors. Secondly, we study drill-core rock sample tomography, an example from oil and gas industry. We show that Cauchy priors produce smaller number of artefacts than other choices, especially with sparse high-noise measurements, and choosing Hamiltonian Monte Carlo enables systematic uncertainty quantification. Impact StatementIndustrial X-ray tomography reconstruction accuracy depends on various factors, like the equipment, measurement geometry and constraints of the target. For example dynamical systems are harder targets than static ones. The harder and noisier the setting becomes, the more emphasis goes on mathematical modelling of the targets. Bayesian statistical inversion is a common choice for difficult measurement settings, and its limitations mainly come from the choice of the a priori models. Gaussian models are widely studied, but they provide smooth reconstructions. Total variation priors are not invariant under mesh changes, so doing systematic uncertainty quantification, like data-centric sensor optimisation, cannot be done with them. Besov and Cauchy priors however provide systematic non-Gaussian random field models, which can be used for contrast-boosting tomography. The drawback is higher computational cost. Hence, the techniques developed here are useful for non-time-critical applications with difficult measurement settings. In these cases, the methods developed may provide significantly better reconstructions than the traditional methods, like filtered back-projection.

show abstract

“…1. Introduction X-ray tomography has applications in various industrial fields such as sawmill industry, where it can be used for detecting knots, rotten parts, and foreign objects in logs (Shustrov et al, 2019;Zolotarev et al, 2019). In oil and gas industry, X-ray tomography can be used to analyze drill-core rock samples for identifying pore structures (Mendoza et al, 2019) and other structural properties of rock.…”

mentioning

confidence: 99%

“…We use Markov chain Monte Carlo (MCMC) and optimization methods for computing conditional mean (CM) and maximum a posteriori (MAP) estimates, respectively. We consider a sawmill industry log tomography application for detecting knots, rotten parts, and foreign objects in sawmills (Shustrov et al, 2019;Zolotarev et al, 2019). As an oil and gas industry application, we have drill-core rock sample tomography for identifying pore structures (Mendoza et al, 2019).…”

mentioning

confidence: 99%

Enhancing industrial X-ray tomography by data-centric statistical methods

Suuronen

Emzir

Lasanen

et al. 2020

DCE

View full text Add to dashboard Cite

X-ray tomography has applications in various industrial fields such as sawmill industry, oil and gas industry, as well as chemical, biomedical, and geotechnical engineering. In this article, we study Bayesian methods for the X-ray tomography reconstruction. In Bayesian methods, the inverse problem of tomographic reconstruction is solved with the help of a statistical prior distribution which encodes the possible internal structures by assigning probabilities for smoothness and edge distribution of the object. We compare Gaussian random field priors, that favor smoothness, to non-Gaussian total variation (TV), Besov, and Cauchy priors which promote sharp edges and high- and low-contrast areas in the object. We also present computational schemes for solving the resulting high-dimensional Bayesian inverse problem with 100,000–1,000,000 unknowns. We study the applicability of a no-U-turn variant of Hamiltonian Monte Carlo (HMC) methods and of a more classical adaptive Metropolis-within-Gibbs (MwG) algorithm to enable full uncertainty quantification of the reconstructions. We use maximum a posteriori (MAP) estimates with limited-memory BFGS (Broyden–Fletcher–Goldfarb–Shanno) optimization algorithm. As the first industrial application, we consider sawmill industry X-ray log tomography. The logs have knots, rotten parts, and even possibly metallic pieces, making them good examples for non-Gaussian priors. Secondly, we study drill-core rock sample tomography, an example from oil and gas industry. In that case, we compare the priors without uncertainty quantification. We show that Cauchy priors produce smaller number of artefacts than other choices, especially with sparse high-noise measurements, and choosing HMC enables systematic uncertainty quantification, provided that the posterior is not pathologically multimodal or heavy-tailed.

show abstract

Timber Tracing with Multimodal Encoder-Decoder Networks

Cited by 5 publications

References 9 publications

Virtual sawing using generative adversarial networks

Virtual sawing using generative adversarial networks

Enhancing Industrial X-ray Tomography by Data-Centric Statistical Methods

Enhancing industrial X-ray tomography by data-centric statistical methods

Contact Info

Product

Resources

About