Recognition of boards using wood fingerprints based on a fusion of feature detection methods

Pahlberg, Tobias; Hagman, Olle; Thurley, Matthew

doi:10.1016/j.compag.2014.12.014

Cited by 13 publications

(4 citation statements)

References 25 publications

(21 reference statements)

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“…where A l (L i ) stands for the conductivity matrix of the L i , A Hessian matrix is then used to detect the interest points. The image of different sizes needs to be normalized with respect to scale [38] with formula (15). After finding the Hessian matrix, the points with higher gray values than neighbors are picked up as the key-points.…”

Section: ) Key Pointmentioning

confidence: 99%

“…Taking the wood image using a microscope or magnifying glass, and classifying the wood species with computing models such as neural networks or deep learning models are the two most discussed topics in this field. In recent years, the methods based on the image have succeeded in wood species recognition [11]- [15]. R.Schraml and H.Hofbauer attempted to use the fingerprint and iris recognition methods to distinguish the individual wood entities [16].…”

Section: Introductionmentioning

confidence: 99%

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Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

Sun

Cao

et al. 2021

IEEE Access

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The tracking of the wood product is an important technology in the trade activity of rare plants. Normally, factories use Quick Response (QR) and Radio-Frequency Identification (RFID) to identify the individual wood product, but these technologies are not safe enough because they can be easily falsified. The traditional methods are hard to catch the detail of the slim wood texture from the wood product. In this study, a novel method is employed to resolve these problems using a biometric feature on the surface of the real wood product to distinguish the individual wood product. AKAZE is used to extract the keypoint of wood texture. A sub-area detection technique along with a serialization method is then developed to improve the rate of identification. The sub-area detection technique deals with picking out a sub-region in which there are enough AKAZE points as small as possible. The serialization method is also utilized to reduce the redundant process of feature extraction. The experimental results demonstrate that the values of accuracy, recall, and F 1 reach 0.98, 0.96, and 0.96, respectively. The match time that uses serialized function is reduced to 1/3 of which has no application in the original image. Validated results also reveal that our proposed methodology improve the robustness of the wood product identification, and it can be used in Wood Traceability System (WTS) with the blockchain to resolve the digital trust problem and the fast distinction issues of the real wood product.

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Section: ) Key Pointmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

Sun

Cao

et al. 2021

IEEE Access

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“…Most of the solutions focus either on the re-identification of logs before sawing (log-tolog matching) or on matching boards before and after drying (board-to-board matching). The methods include cross-section images of the log ends [11], laser scans and RFID-tags for logs [2], and image-based matching for boards [8,9]. While the existing solutions provide a high tracing accuracy for the beginning and the end of the sawmill process, extending the tracing to cover the actual sawing (board-to-log matching) remains a challenge due to the following obvious reasons: 1) the material goes through a remarkable transformation and 2) the imaging modalities are different.…”

Section: Timber Tracingmentioning

confidence: 99%

Timber Tracing with Multimodal Encoder-Decoder Networks

Zolotarev

Eerola

Lensu

et al. 2019

Computer Analysis of Images and Patterns

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Tracking timber in the sawmill environment from the raw material (logs) to the end product (boards) provides various benefits including efficient process control, the optimization of sawing, and the prediction of end-product quality. In practice, the tracking of timber through the sawmilling process requires a methodology for tracing the source of materials after each production step. The tracing is especially difficult through the actual sawing step where a method is needed for identifying from which log each board comes from. In this paper, we propose an automatic method for board identification (board-to-log matching) using the existing sensors in sawmills and multimodal encoder-decoder networks. The method utilizes point clouds from laser scans of log surfaces and grayscale images of boards. First, log surface heightmaps are generated from the point clouds. Then both the heightmaps and board images are converted into "barcode" images using convolutional encoder-decoder networks. Finally, the "barcode" images are utilized to find matching logs for the boards. In the experimental part of the work, different encoderdecoder architectures were evaluated and the effectiveness of the proposed method was demonstrated using challenging data collected from a real sawmill.

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“…The system trains some defect classifiers, such as a matching algorithm, support vector machine (SVM), neural network and deep learning, to determine the defect region [15,16]. Luleå University of Technology, Sweden, T. Pahlberg [17] fused a Block Match algorithm and Speeded-Up Robust Features (SURF) algorithm to achieve the detection of the defect features of Scottish pine, obtaining good matching accuracy and improving the recognition rate.…”

Section: Introductionmentioning

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.

show abstract

Recognition of boards using wood fingerprints based on a fusion of feature detection methods

Cited by 13 publications

References 25 publications

Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

Wood Product Tracking Using an Improved AKAZE Method in Wood Traceability System

Timber Tracing with Multimodal Encoder-Decoder Networks

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

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