Product-adapted grading of Scots pine sawn timber by an industrial CT-scanner using a visually-trained machine-learning method

Olofsson, Linus; Broman, Olof; Oja, Johan; Sandberg, Dick

doi:10.1080/17480272.2021.1955298

Cited by 6 publications

(3 citation statements)

References 9 publications

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“…The location and size of the defects were determined based on image processing technology. We determined the sawing method based on sawn timber grading rules [22].…”

Section: Log Sawing Optimizationmentioning

confidence: 99%

Intelligent Automation Manufacturing for Betula Solid Timber Based on Machine Vision Detection and Optimization Grading System Applied to Building Materials

Zhang

Diao

et al. 2023

Forests

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Wood material is the foundation of wood structure architecture, and its production technology and equipment technology decide the development and upgrading of modern wood structure architecture. Aiming at the problems of low automation degree, low material utilization rate, low production efficiency and high labor costs in the process of traditional wood processing, we explore the integration and innovation of the traditional wood processing industry and modern industrial Internet information technology on the basis of studying the properties of Betula (Betula costata) solid wood materials, wood comprehensive utilization rate, wood structure component development and processing technology requirements, and form an intelligent, automatic and industrial production mode for building materials. Through technology and methods such as mechanical design, automation technology, machine vision, deep learning, optimization algorithm, electronic design automation, computer aided manufacturing, etc., the key technologies of intelligent automatic optimization of wood materials were studied, and intelligent automatic production lines of Betula species identification, log optimization sawing, solid timber longitudinal multiblade sawing, sawn timber quality detection and solid timber optimizing cross-cuts are built. Based on the machine vision method, features are extracted; a tree species, defect classification and recognition model database is established; an image processing algorithm with high recognition accuracy, as well as fast processing speed and high robustness are studied; non-destructive testing and classification methods of machine vision are optimized; key problems of online rapid classification, detection and optimization of sawing are solved and production quality and processing efficiency are improved. Finally, the timber defect detection accuracy and Betula timber yield are analyzed, and the comprehensive utilization value of optimized sawing timber is compared with the comprehensive utilization value of manually marking sawing timber. The processing cost and efficiency of Betula sawing timber with an intelligent automatic production line are calculated. The test results show that the average detection accuracy of timber defect type, size and location is 89.69%, 89.69%, 92.25% and 82.29%, respectively, and the detection stability is high. By adopting intelligent automatic detection, classification and optimization sawing production line of wood, the comprehensive utilization value of optimized sawing timber is 14.13% higher than that of manual marking sawing timber, and 16,089.29 m3 more building materials can be processed annually. In the process of intelligent automatic wood processing, the intelligent detection system is used to detect defects, improve production performance and production efficiency and reduce labor costs. Compared with the traditional wood processing process, the method studied in this paper is improved to optimize the production line processing performance and processing technology. The research and development of an intelligent automatic production system for solid wood can promote the application and development of an automatic industrial production mode for sawn timber for the wood structure construction industry, deepen the integration of artificial intelligence technology, Internet technology and the whole wood processing industry and lead the upgrading of building materials for wood structures to an intelligent manufacturing production mode.

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“…The location and size of the defects were determined based on image processing technology. We determined the sawing method based on sawn timber grading rules [22].…”

Section: Log Sawing Optimizationmentioning

confidence: 99%

Intelligent Automation Manufacturing for Betula Solid Timber Based on Machine Vision Detection and Optimization Grading System Applied to Building Materials

Zhang

Diao

et al. 2023

Forests

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“…Olofsson and colleagues developed a holistic-subjective appearance grading of sawn timber by using CT scanner measurements and a board scanner to generate a set of variables only regarding knots. The variable sets and the grades of the sawn timber were modeled by using a projection to latent structure (PLS) models. − …”

Section: Managing Wood Complexity With Machine Learningmentioning

confidence: 99%

Sustainability in Wood Products: A New Perspective for Handling Natural Diversity

2022

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Wood is a renewable resource with excellent qualities and the potential to become a key element of a future bioeconomy. The increasing environmental awareness and drive to achieve sustainability is leading to a resurgence of research on wood materials. Nevertheless, the global climate changes and associated consequences will soon challenge the wood-value chains in several regions (e.g., central Europe). To cope with these challenges, it is necessary to rethink the current practice of wood sourcing and transformation. The goal of this review is to address the intrinsic natural diversity of wood, from its origin to its technological consequences for the present and future manufacturing of wood products. So far, industrial processes have been optimized to repress the variability of wood properties, enabling more efficient processing and production of reliable products. However, the need to preserve biodiversity and the impact of climate change on forests call for new wood processing techniques and green chemistry protocols for wood modification as enabling factors necessary for managing a more diverse wood provision in the future. This article discusses the past developments that have resulted in the current wood value chains and provides a perspective about how natural variability could be turned into an asset for making truly sustainable wood products. After briefly introducing the chemical and structural complexity of wood, the methods conventionally adopted for industrial homogenization and modification of wood are discussed in relation to their evolution toward increased sustainability. Finally, a perspective is given on technological potentials of machine learning techniques and of novel functional wood materials. Here the main message is that through a combination of sustainable forestry, adherence to green chemistry principles and adapted processes based on machine learning, the wood industry could not only overcome current challenges but also thrive in the near future despite the awaiting challenges.

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“…Grading is one of the most crucial procedures in timber manufacturing process (Gazo et al 2018;Olofsson et al 2021). The assigned grades provide an efficient way to define the timber quality and help buyers estimate the cost and possible waste in the subsequent manufacturing processes.…”

Section: Introductionmentioning

confidence: 99%

Timber knot detector with low false-positive results by integrating an overlapping bounding box filter with faster R-CNN algorithm

Chen

Liu

Fang

et al. 2023

BioRes

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Knot detection is an important aspect of timber grading. Reducing the false-positive frequency of knot detection will improve the accuracy of the predicted grade, as well as the utilization of the graded timber. In this study, a framework for timber knot detection was proposed. Faster R-CNN, a state-of-the-art defect identification algorithm, was first employed to detect timber knots because of its high true-positive frequency. Then, an overlapping bounding box filter was proposed to lower the false positive frequency achieved by Faster R-CNN, where a single knot is sometimes marked several times. The filter merges the overlapping bounding boxes for one actual knot into one box and ensures that each knot is marked only once. The main advantage of this framework is that it reduces the false positive frequency with a small computational cost and a small impact on the true positive frequency. The experimental results showed that the detection precision improved from 90.9% to 97.5% by filtering the overlapping bounding box. The framework proposed in this study is competitive and has potential applications for detecting timber knots for timber grading.

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Product-adapted grading of Scots pine sawn timber by an industrial CT-scanner using a visually-trained machine-learning method

Cited by 6 publications

References 9 publications

Intelligent Automation Manufacturing for Betula Solid Timber Based on Machine Vision Detection and Optimization Grading System Applied to Building Materials

Intelligent Automation Manufacturing for Betula Solid Timber Based on Machine Vision Detection and Optimization Grading System Applied to Building Materials

Sustainability in Wood Products: A New Perspective for Handling Natural Diversity

Timber knot detector with low false-positive results by integrating an overlapping bounding box filter with faster R-CNN algorithm

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