公开(公告)号：US11442006B2

公开(公告)日：2022-09-13

申请号：US16687342

申请日：2019-11-18

Applicant: Boise Cascade Company

Inventor： David Bolton ,  Jude Richard Peek ,  Curtis Fennell

IPC: G01J5/02 ,  G01N21/3559 ,  G01N21/359 ,  G01N21/86 ,  G06T7/00 ,  G06T7/90 ,  F26B25/22 ,  G01N33/46 ,  H04N5/33 ,  H04N5/225 ,  H04N5/247 ,  G01N21/17

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based moisture level detection models, are used to accurately identify moisture content and the specific locations of the moisture on an entire surface of a veneer sheet or other wood product and provide moisture level prediction data for the veneer sheet or other wood product. Based on the moisture level prediction data for a given wood product, one or more actions are taken with respect to wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

公开(公告)号：US10933557B2

公开(公告)日：2021-03-02

申请号：US16697466

申请日：2019-11-27

Applicant: Boise Cascade Company

Inventor： David Bolton ,  Jude Richard Peek ,  Curtis Fennell

IPC: G01J5/00 ,  B27K5/00 ,  H04N5/33 ,  G01N21/359 ,  G06N7/02 ,  G06T1/20 ,  G01N21/3563

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, are used to detect irregularity in the surface of a wood product. Based on the detected irregularities at various locations in a given wood product, one or more actions are taken with respect to a production process used to produce the wood product to ensure the wood product is put to the most efficient, effective, and valuable use.

公开(公告)号：US10933556B2

公开(公告)日：2021-03-02

申请号：US16697461

申请日：2019-11-27

Applicant: Boise Cascade Company

Inventor： David Bolton ,  Jude Richard Peek ,  Curtis Fennell

IPC: G01J5/00 ,  B27K5/00 ,  H04N5/33 ,  G01N21/359 ,  G06N7/02 ,  G06T1/20 ,  G01N21/3563

Abstract: Near InfraRed NIR technology, including NIR cameras and detectors, and machine learning methods and systems, including one or more Machine Learning (ML) based surface irregularity prediction models, are used to accurately identify surface irregularities on a surface of a wood product, such as a veneer sheet or ribbon, and provide irregularity prediction data for the wood product. Based on the irregularity prediction data for a given wood product, one or more actions are taken with respect to wood product or the production process to ensure the wood product is put to the most efficient, effective, and valuable use.

公开(公告)号：US10825164B1

公开(公告)日：2020-11-03

申请号：US16205027

申请日：2018-11-29

Applicant: Boise Cascade Company

Inventor： David Bolton ,  Jude Richard Peek ,  Curtis Fennell

IPC: G06T7/00 ,  H04N5/247 ,  G06T7/90 ,  H04N9/04 ,  G06T7/70 ,  G06T3/60 ,  G06T3/20 ,  G06T7/60

Abstract: A black and white image of a wood product, such as a veneer sheet, is captured with a first camera and a color image of the wood product is captured with a second camera. Computer processing of the black and white image is performed to determine dimensions of the wood product, the existence of voids within the wood product, and the presence of debris on the wood product. Computer processing of the color image is performed to determine whether colored defects are present in the wood product. A grade is assigned to the wood product based on this computer processing. The wood product can then be sorted based on the grade.