2020
DOI: 10.1088/1742-6596/1478/1/012020
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Using Machine Vision to Improve the Efficiency of Lumber Mills

Abstract: This work provides rationale for the implementation of a machine vision-based approach for promoting timber processing efficiency. With efforts to combat the climate change, criteria for the success of wood industries shifted. Now, they need to ensure economic efficiency while taking the reduction in carbon intensity into account. This may be achieved in either of two ways, through the improvement of energy efficiency in production and by minimizing waste. So far, the traditional methods for the improvement of… Show more

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Cited by 6 publications
(3 citation statements)
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“…There is no doubt that intensive thinning leads to a better use of nutrient resources [23][24][25][26].…”
Section: Figure 2 Age-related Changes In Total Yield At Low-thinned A...mentioning
confidence: 99%
“…There is no doubt that intensive thinning leads to a better use of nutrient resources [23][24][25][26].…”
Section: Figure 2 Age-related Changes In Total Yield At Low-thinned A...mentioning
confidence: 99%
“…In the ILSVRC-2010 (ImageNet Large-Scale Visual Recognition Challenge-2010), Yuan, Chiang, Tang, and Haro [3] trained a DL model to perform classification of images, accomplishing best-in-class results. The impact of deep configuration depth on machine vision efficiency was discussed by Kunickaya et al, [4]. This framework has already sparked a lot of interest in applying this novel technique to clinical computer vision problems, thanks to these productive research findings.…”
Section: Introductionmentioning
confidence: 99%
“…The purpose of ISO 17225-4:2021 is to establish an unambiguous and transparent classification standard for graded wood chips. Several attributes fall under the wood chip quality standards, including MC [2,3,[5][6][7], ash content [5,18,19,28,33,41], particle size distribution [18,26,[46][47][48][49][50][51][52][53], and the amount of some inorganic elements such as chlorine.…”
Section: Introductionmentioning
confidence: 99%