JPS62192623A - Automatic detecting method for knot of lumber - Google Patents

Abstract

PURPOSE:To detect automatically a knot of the lumber surface and to select automatically a grade of a lumber by calculating a difference of respective color sensor outputs of average color space and detecting the knot based on the peak value. CONSTITUTION:First, the objective lumber W is installed at the prescribed position and a measuring range of the knot of the objective lumber W is determined and set in a microcomputer C and next, line data Rn, Gn and Bn (respective outputs of red, green and blue of the nth color sensors S1-Sk) are taken in by the color sensors S1-Sk and a color difference En of respective lines is calculated and a peak of the color difference En in the lines is detected and further, the position of a peak part is recorded by the microcomputer C and it is decided whether or not it exceeds the measuring range expected in advance. Then, in case of not exceeding the measuring range, the color sensor group S1-Sk is moved to the next line and the knot is again detected starting to take in the line data and in case of exceeding the measuring range, the two-dimensional picture processing of the knot is performed and the knot detection is finished.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a method for automatically detecting knots in wood.

[Conventional technology J] With the recent progress of the Japanese economy, the demand for pulp materials has increased significantly, and the characteristics of Japanese forests have been replaced by large-diameter trees such as cedar and m. The use of hardwoods, small-diameter trees, and scrap wood is inevitably increasing.

Under these conditions, the method of cutting small-diameter trees is directly related to production cast, and has been highlighted as a very important issue. Currently, the work of removing wood is based on the intuition and experience of craftsmen, but it is thought that the quality of wood will continue to decline in the future, so how to efficiently select materials will be an important point from an economic efficiency perspective. .

[Problems to be solved by the invention] The present invention automatically detects defects such as dead knots that appear on the surface of wood, automatically performs wood removal work, and can also select the grade of wood. This paper attempts to provide a method for automatically detecting clauses that are to be used.

[Means for Solving the Problems] In order to achieve the above object, the method of the present invention is based on the output obtained by arranging a large number of color sensors arranged in a row to face the target wood. Calculate the color plane equation obtained from the average red, green, and blue output values of each color sensor, calculate the difference between the outputs of each color sensor in the average color space as a color difference, and then calculate the color difference based on the peak value of those color differences. This method is characterized by detecting clauses.

[Example] Hereinafter, an example of an automatic detection device for implementing the method of the present invention and an example of the present invention will be described in detail with reference to the drawings.

FIG. 1 shows a block diagram of an automatic detection apparatus for implementing the present invention.

In this automatic detection device, each color sensor Sl-Sk arranged in a line has three outputs of red, green, and blue, and each color sensor S1-Sk has a photocurrent/voltage conversion Circuit (1/V), filter circuit F to cut the fluctuation component caused by the commercial frequency of the indoor lighting (fluorescent lamp) L used to illuminate the target wood.
, a buffer amplifier As are connected in sequence, and are connected to a multiplexer X, a level adjustment amplifier [, and a microcomputer C via an A/+1 converter.

The above color sensor group is installed parallel to the surface of the target wood W having knots N, and if it can output the red, green, and blue components of the wood surface, a-
5i type color sensor, Si photodiode color sensor, line type color CCD, line type color MOS
Any color sensor such as an image device may be used. However, there are slight changes in the block diagram depending on each sensor. Here, an example will be explained in which an A-9I type color sensor whose visibility is close to that of a human being is used.

Regarding lighting, it is necessary to arrange the lighting so that the wood surface is illuminated as uniformly as possible, but considering conditions such as the gloss of the wood surface, 45° lighting vertical reception method or vertical lighting 4
5゛ light reception method is recommended.

Since the data captured by the color sensor group is line information, the color sensor group is slid on an optical bench in a direction perpendicular to its arrangement direction at regular intervals, and line data is taken at each moving position. By doing this, the entire measurement range of the wood surface is covered, data is thereby imported into the microcomputer as a two-dimensional image, and based on that data, the chromaticity of each part of the wood surface can be determined by calculations described below.

FIG. 2 illustrates details of the color sensor group. Each of the color sensors S1 to Sk is arranged in a straight line on the back surface of the rod lens L2. Further, regarding the rod lens, from the point of view of cost, it is shown that a rod lens divided into a plurality of pieces is used, but a single integrated Hayashi lens is functionally optimal.

The automatic knot detection device exemplified by the block diagram 2ram in FIG. Calculate the color plane equation obtained from the average red, green, and blue output values of each sensor,
Differences between the outputs of each color sensor in the average color space are calculated as color differences, and nodes are detected based on the peak values of these color differences.Therefore, the structure in the block diagram above can be modified within the scope of this purpose. J&
, part of the anoline can be omitted.

For example, if the indoor lighting is a direct current light source and is used in an environment where no external light enters, the filter circuit F can be omitted. Further, although the number of manual channels of the A/El converter is 6 channels of C1, O-CH, and 5 in FIG. 1, the larger the number of channels, the better the processing speed will be. Therefore, the design depends on the number of inputs actually used.

Next, a surface detection operation using the detection apparatus shown in FIGS. 1 and 2 will be explained.

The minimum cover area in each color sensor S1 to Sk is
For example, if the wood is 7 mm square, if there are knots or grains larger than 7 mm square, there will naturally be a color difference between them and the Noh part.
By detecting this, the knot or wood grain can be distinguished from other parts.

The difference between knots and wood grain is that knots are generally circular in shape;
Tree 1 does not have such a shape. Also, death and life! ! The difference between dead knots is that the brightness of dead knots is significantly lower than that of other parts of the wood, or in the case of dead knots in through holes, the color is closer to that of the underlying material. The clause can be determined. The criteria for making these judgments is given by the calculation method detailed below f within the microcomputer C shown in Figure i1.

Based on the present invention, the microcomputer C calculates the average values of the red, green, and blue outputs of the 1 IfF color sensors in the color sensor group, calculates the color plane equation from them, and calculates the color plane equation. Differences in space are assumed to be color differences, and whether or not a node is a node is determined based on the peak values of these color differences, which will be explained in more detail below.

First, the average values i, c, and n of the red, green, and blue outputs of each color sensor are rt = y! ' Rn/k Q= ykGn/k * 11
# (1) n=BiBn/k [1+1]. (1) In the formula, RH, Gn, and an indicate the red, green, and R outputs of the nth color sensor, respectively.
Now, consider the average color plane P in the normalized RCII color space in the measured part of the wood.

P can be expressed by the following equation (average color plane equation).

1'E+C+B= 1 fist fist
・(2) Then, the mapping of the average color value in one line on the wood to the average color plane P comes to the point. Let this point be (rs, gs, bs).

Furthermore, if we calculate the point (Tn *gn +bn) on the P plane from the output values Rn, Gn, and Bn of each sensor, -...(
3) is calculated. The difference between these equations becomes the color difference En on the average color plane P. That is, the distance on the P plane is determined, and the color difference En is given by the following equation.

In this equation (4), ``If there are no knots in the wood,
En becomes close to O. Furthermore, when a dead mark of 7II11 angles or more exists, En of the color sensor located right next to the knot takes a peak value, so it is detected that there is a dead mark there.

Therefore, the existence of a knot and the position of the knot can be detected based on the peak value of En, that is, which color sensor corresponds to the peak value of the color difference En.

FIG. 3 illustrates the relationship between the average color plane P and the color difference En. As shown in the figure, the plane P is (1,0,OJ, (0,1,0
).

It is a plane containing each point (0, 0, 1).

Next, examples of the method of the present invention will be described.

Figure 5 shows the line a-a of the wood surface as shown in Figure 4.
, and b-31, , in which the color sensor fik shows 20 pieces of data, there is a dead node on both lines a-a and bb, but bb-ox's The dead node is a through hole.

According to this example, on line a-a, En takes a peak value at the 14th to 16th ta color sensors, while on line bb, the peak value occurs at the 9th to 10th color sensors. It can be seen that each of these peaks corresponds to exactly above the dead node in FIG.

FIG. 6 shows a flowchart of an example of surface detection according to the present invention.

When performing surface detection based on the same diagram, first, install the target wood at a predetermined position, determine the measurement range of the knots in the target wood, set it to the microcomputer, and then follow the steps described above. , line data R by color sensor
Incorporation of n, on, an.

The microcomputer calculates the EJI of the line, detects the peak of En on the line, records the position of the peak, and then determines whether it exceeds the preset measurement range. If the measurement range is not exceeded, move the color sensor group to the next line,
Detect the node starting from importing the line data again,
If the measurement range is exceeded, two-dimensional image processing of the knot is performed and one knot detection is completed.

[Effects of the Invention] As can be seen from the detailed description above, according to the present invention, the difference in color between knots and other parts of wood is used, so pattern recognition technology is used to Compared to cases such as detecting nodes.

Surface detection can be easily performed using a simple and inexpensive device, and the color plane equation obtained from the average value of the color sensor output is calculated, and the color plane equation is calculated based on the peak value of the color difference of each color sensor output in the average color space. Since the knots are detected by using the method, it can be easily applied to surface detection of various types of wood, which have different colors depending on the type.

[Brief explanation of drawings]

FIG. 1 is a block diagram of an automatic detection device for carrying out the method of the present invention, FIG. 2 is a perspective view showing an example of a linear color sensor used in the automatic detection device, and FIG. 3 is an average color model. An explanatory diagram showing the relationship between surface and color difference, Fig. 4 is a front view showing the state of knots on the wood surface used for actual measurements, Fig. 5 is a diagram showing 11 examples of actual measurement of En on the wood surface of Fig. 4, FIG. 6 is a flowchart showing an example of surface detection according to the present invention. S! ~Sk...Color sensor. W...Target wood. Designated Agent Figure 1 51 < Figure 2 Figure 4 Figure 5 Color Himb 11 k

Claims (1)

[Claims] 1. Based on the output obtained by placing a large number of color sensors arranged in a line facing the target wood, calculate the color plane equation obtained from the average red, green, and blue output values of each of those color sensors. A method for automatically detecting knots in wood, characterized in that the difference between the outputs of each color sensor in an average color space is calculated as a color difference, and knots are detected based on the peak value of the color difference.