A4 Conference proceedings

Timber Tracing with Multimodal Encoder-Decoder Networks


Open Access publication

Publication Details
Authors: Zolotarev Fedor, Eerola Tuomas, Lensu Lasse, Kälviäinen Heikki, Haario Heikki, Heikkinen Jere, Kauppi Tomi
Publisher: Springer Verlag (Germany): Series
Publication year: 2019
Language: English
Related Journal or Series Information: Lecture Notes in Computer Science
Title of parent publication: CAIP 2019: Computer Analysis of Images and Patterns
Journal acronym: LNCS
Volume number: 11679
Start page: 342
End page: 353
Number of pages: 12
ISBN: 978-3-030-29890-6
eISBN: 978-3-030-29891-3
ISSN: 0302-9743
eISSN: 1611-3349
JUFO-Level of this publication: 1
Open Access: Open Access publication
Location of the parallel saved publication: http://urn.fi/URN:NBN:fi-fe2019110536719

Abstract

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 encoder-decoder architectures were evaluated and the effectiveness of the proposed method was demonstrated using challenging data collected from a real sawmill.


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