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Intelligent detection method for wood optimizing cut-off saw based on deep learning

A detection method and deep learning technology, which are applied in optical testing flaws/defects, material analysis by optical means, measuring devices, etc., can solve the problems of accurate position recognition deviation of wood, unautomated production, visual fatigue, etc., and achieve automation The effect of high degree, improved automation level and strong fault tolerance

Inactive Publication Date: 2019-04-02
临沂众为智能科技有限公司 +2
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  • Abstract
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  • Claims
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AI Technical Summary

Benefits of technology

This technology allows for efficient removal and classification of wood flaws during cutting operations by automatically identifies them based on their characteristics or features. It also improves upon existing methods that require human input like measuring tools manually.

Problems solved by technology

This patented technical problem addressed in this patents relates to improving efficient use of wood for various applications such as construction or manufacturing purposes while reducing manual effort needed from operators who manually mark up each piece separately beforehand. Existing methods have limitations due to factors like poor visibility caused by lighting conditions, lack of accurate depth measurement tools, difficulty in identifying precise positions within large sections without human eye strain, and potential deviation between desired locations based upon their weightiness level.

Method used

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  • Intelligent detection method for wood optimizing cut-off saw based on deep learning
  • Intelligent detection method for wood optimizing cut-off saw based on deep learning
  • Intelligent detection method for wood optimizing cut-off saw based on deep learning

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Embodiment Construction

[0025] A preferred saw intelligent detection method based on deep learning, its specific implementation steps are as follows:

[0026] (1) The relationship between image coordinates and physical coordinates

[0027] Oc-XcYcZc is the camera coordinate system, o-uv is the image coordinate system ( figure 2 ). In the camera coordinate system, the camera origin and optical axis are the Z axis of the camera coordinate system. A point P in the space plane Π is projected into the image plane π, a world coordinate system is established in the space plane Π, and the homogeneous coordinates of point P are defined as P=(X W ,Y W ,Z W ,1) T , the image homogeneous coordinates of the corresponding image points are p=(u,v,1) T , in perspective projection geometry, we can get the following relation:

[0028]

[0029] Among them, f x , f y ,u 0 ,v 0 is the internal parameter of the camera, R, t is the external parameter of the camera, and s is the coefficient. Assume

[0030]...

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Abstract

The invention relates to wood industry automation technology, and provides an intelligent detection method for wood optimizing cut-off saw based on deep learning. The method comprises the following steps: automatically identifying defects and wood grades through the algorithms of image processing and deep learning to determine the actual location of defects and wood grades; and determining a finalcut-off list according to the calculation principle, and transmitting the list to an action actuator to cut the wood. The invention can complete the task of removal of wood defects and classificationof grades in the optimizing cut-off saw without manual participation, which completely realizes unattended operation, and has high degree of automation and strong fault tolerance, so that the efficiency of wood processing and the automation level of the industry can be improved.

Description

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Claims

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Application Information

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Owner 临沂众为智能科技有限公司
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