JPS63252205A - Imaging type boundary detection device - Google Patents

Abstract

PURPOSE:To reduce operational processing quantity and to increase the processing speed of an image, by calculating the inclination of the max. brightness change direction at every quantized pixel of the data of the image picked up over a two-dimensional direction prior to the processing for calculating the straight line passing through each pixel. CONSTITUTION:When running is started, the detection processing of a boundary is performed on the basis of the detection data of a number-of-rotation sensor S2 at every running over a set distance and, on the basis of this detection data, it is discriminated whether the boundary L2 on a terminal side is detected. When the boundary L2 is detected, the terminal position of a car body V to the present position is calculated on the basis of the position data on the image of said boundary and front and rear wheels 1, 2 are held to a steering neutral state up to the calculated position to allow the car body V to straightly advance and, thereafter, the car body V is turned toward the next working process on the basis of a preset stored turn pattern. When the boundary L2 on the terminal side is not detected, the shift to the boundary L1 on the detected distance side is calculated so as to allow the car body to automatically run along said boundary L1 to perform steering control.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to an imaging type boundary detection device that detects a straight line corresponding to the boundary between an untreated work site and a treated work site.

[Prior Art] This type of imaging-type boundary detection device described above utilizes the fact that the untreated work area and the treated work area appear to have different brightness, and performs image processing on captured image information. , a straight line passing through a portion with a large brightness change is detected as information corresponding to the boundary between an untreated work area and a treated work area.

Conventionally, the work area is imaged in two-dimensional directions, and from the imaged information, the differential value of brightness in one direction that intersects the boundary is calculated for each quantized pixel arranged in the two-dimensional direction. , Based on the differential value, pixels at locations where brightness changes are large are extracted by binarization processing, and then, using Hough transform processing, for each extracted pixel,
A large number of straight lines that pass through the pixel and differ in direction by a set angle are determined, and of all the determined straight lines, the one with the highest frequency is detected as information indicating the straight line corresponding to the boundary.

[Problem that the invention seeks to solve]

In the above conventional configuration, in the Hough transform processing, for each pixel to be processed, a large number of straight lines passing through that pixel must be found, and among the large number of straight lines found, Since the straight line with the maximum frequency must be found, the amount of calculation in image processing becomes extremely large, making it difficult to speed up image processing.

By the way, the information on the detected straight line is mainly used as control information for automatically driving the work vehicle along the boundary between the untreated work area and the treated work area. It is necessary to perform image processing at a high speed corresponding to the traveling speed of the work vehicle, and there is a desire for faster image processing.

The present invention has been made in view of the above-mentioned circumstances, and its purpose is to speed up image processing by reducing the amount of arithmetic processing.

[Means for solving problems]

The characteristic configuration of the imaging-type boundary detection device according to the present invention includes an imaging means for imaging a work area in two-dimensional directions, and imaging information of the imaging means to detect two orthogonal directions of each quantized pixel arranged in the two-dimensional direction. Differentiating means for determining the differential value of the brightness of each of the four,
A slope calculation means for calculating a slope corresponding to the maximum brightness change direction from the obtained differential values in two orthogonal directions, and a binarization means for extracting pixels at locations where the brightness change is large based on the differential values. , a straight line calculation means for calculating a straight line perpendicular to the slope of each pixel extracted by the binarization means;
The present invention is provided with straight line extracting means for extracting the line with the highest frequency among the straight lines found by the straight line calculation means, and the functions and effects thereof are as follows.

[For production]

In other words, the slope of the direction of maximum brightness change is first determined for each quantized pixel of image information captured in two-dimensional directions, and then binarized based on the differential value of each pixel. For each pixel, a straight line perpendicular to the previously determined slope is found, and among the found straight lines, the one with the highest frequency is extracted as the straight line corresponding to the boundary.

〔Effect of the invention〕

Therefore, since the slope of the maximum brightness change direction for each quantized pixel of image information captured in two-dimensional directions is determined before the process of determining a straight line passing through each pixel, the quantized pixel The process of finding a straight line that passes through each binarized pixel based on the differential value of The amount of computation required to determine , can be significantly reduced compared to the conventional method of determining a large number of straight lines passing through each pixel.

Moreover, in conjunction with this, the amount of calculation required to find the straight line with the highest frequency can be significantly reduced compared to the conventional method. With that,
It has now become possible to speed up image processing.

〔Example〕

Embodiments of the present invention will be described below based on the drawings.

As shown in Fig. 7, a pair of left and right front and rear wheels (1).

(2) are provided so that the steering can be operated freely, and a lawn mowing device (3) is suspended on the lower abdomen of the vehicle body (ν) so as to be movable up and down. The work vehicle used is configured.

An image sensor (Sl) serving as an imaging means for capturing an image of the work area in two-dimensional directions is provided so as to image the work area on the front side with respect to the vehicle body (V).

To explain the imaging field of view of the image sensor (Sl), the vehicle body (V) is aligned in an appropriate state with respect to the boundary (L) between the untreated work area (11) and the treated work area (C). In this state, the boundary (L) is positioned at the center of the field of view in the lateral direction.

To explain the structure of the vehicle body (V), as shown in FIG. 6, there is a hydraulic cylinder (4) for steering that operates the front and rear wheels (1) and (2) separately.

(5), and control valves (6), (7) for it
The engine (E)
The transmission motor (9) is operatively connected to the transmission arm (10) of the transmission (8).

Further, in order to detect the traveling distance of the vehicle body (V), a rotation speed sensor (S2) that outputs a set number of pulse signals per unit rotation speed is rotationally driven by the output of the transmission (8). It is set up like this.

A steering position detection potentiometer (R+) detects the steering position of the front and rear wheels (1) and (2).
), (Rz), and a shift position detection potentiometer (+?:l) that detects the operating state of the transmission (8), and the detection information thereof and the image sensor (S
Based on the information on the straight line corresponding to the boundary (L) obtained based on the imaging information of 1) and the detection information of the rotation speed sensor (S2), the vehicle body (V) is moved to the untreated work area (I).
A microcomputer-based control device (11) is provided to automatically travel along the boundary (L) between I) and the treated work area (C).

In the figure, (II) is a steering wheel for boarding operation, (Ro) is a potentiometer for detecting its operating position,
(12) is a speed change pedal for boarding operation.

However, various means for performing image processing on image information captured by the image sensor (Sl) and detecting a straight line corresponding to the boundary (L) are configured using the control device (11). .

To explain the automatic driving of the vehicle body (V), as shown in FIG. As one work process, the untreated work area (B) and the treated work area (C) on the side where the vehicle body (V) is along the length of the work process.
The steering will be controlled based on the information detected by the image processing so that the vehicle will automatically travel along the boundary (Ll) along the boundary (Ll) at the end of one work process.
, z) In other words, by repeating the process of automatically turning toward the starting end of the next work stroke that intersects the work stroke as the opposite side of the untreated work area (B) is reached, the so-called turning The mowing mode automatically mowing the lawn within a predetermined range.

To explain the operation of the control device (11), as shown in FIG. , a later-described boundary detection process is performed, and based on the detection information, it is determined whether or not the boundary (L) on the terminal side is detected.

When the boundary (L2) on the terminal side is detected, the terminal position relative to the current position of the vehicle body (V) is calculated based on the position information on the image, and the front and rear wheels (L2) are moved to the calculated position. By maintaining the steering in the neutral state in 1) and (2), the vehicle is driven straight ahead and then turned toward the next work process based on a turn pattern that has been set and stored in advance.

If the terminal side boundary (L2) is not detected,
The steering is controlled by calculating the deviation from the detected stroke side boundary (Ll) so that the vehicle automatically travels along the detected stroke side boundary (C1).

Then, for example, by determining whether a preset number of strokes has been traveled or whether a preset distance has been reached, it is determined whether the work is complete or not. In this case, the processes after the boundary detection process described above will be repeated. On the other hand, in the case of finishing the work, the vehicle body (V) will be stopped and the entire process will be completed.

To explain the calculation of the deviation with respect to the boundary (Ll) on the stroke side, the deviation in the position in the width direction of the vehicle body,
Although there is a deviation in the inclination with respect to the direction of the boundary (Ll), these may be calculated as quantitative values, or only the direction of deviation may be calculated.

Further, to explain the steering control processing, the parallel steering type corrects positional deviations in the width direction of the vehicle body, and the four-wheel steering type corrects directional deviations. However, the steering amount of the front and rear wheels (1) and (2) may be different to correct the positional and orientation deviations at the same time.

Next, the boundary detection process will be described in detail based on the flowchart shown in FIG.

However, the boundary detection processing described below is performed by utilizing the phenomenon that the untreated work area (B) appears darker than the treated work area (C).

That is, as the boundary detection process is started, the image sensor (Sl) performs an imaging process, and the captured image information is quantized in accordance with a preset pixel density.

Next, as shown in FIG. 3, the pixel to be processed (e
) including 8 adjacent pixels (a=d, fi)
Using a 3x3 pixel mask that covers the pixels, perform the following H),
Based on equation (ii), the differential values (SX), (SY
) is performed for each pixel arranged in the two-dimensional direction.

5X(x,y) = (c+2f+i)-(a+2d+
g) −= (i) SY(x, y)= (g+2h+
i) - (a+2b+c) -... (ii) The process of calculating the differential values (SX) and (SY) of brightness changes in the X-axis direction and the y-axis direction, respectively, is carried out by the differentiating means (100). We will deal with it.

Then, as shown in equation (iii) below, by adding the absolute values of the above-mentioned differential values (SX) and (SV), a differential representing the magnitude of the brightness change of each pixel (x, y) is calculated. Find the value (JP).

JP (x, y) =lSXI+1SYl
...-(iii) Furthermore, using the following formula (iv),
From each of the differential values (SX) and (SY), the slope (BP) corresponding to the direction of maximum brightness change for each pixel (x+
) is calculated.

BP (x, y) = tan-' (SY/SX) ”...
(iv) However, the tilt (BP) is defined as an angular change in the clockwise direction with respect to the X-axis.

Note that the process of calculating this slope (BP) is carried out by the slope calculation means (
101).

Then, by performing binarization to extract only pixels whose differential value (JP) is greater than or equal to a preset darkness value, pixels at locations where brightness changes are large are extracted.

Note that this binarization process corresponds to the binarization means (102).

Next, as shown in FIG. 4, based on the value of the inclination (BP), pixels whose inclination is within ±30 degrees with respect to the The number of target pixel candidates is reduced in advance.

To explain, as mentioned above, the vehicle body (V) travels each work process along the boundary (L) between the untreated work area (B) and the treated work area (C). Therefore, even if the orientation of the vehicle body (V) or the position in the width direction with respect to the boundary (L) shifts, it will not shift significantly. Therefore, the slope of the direction of maximum brightness change of the pixel to be processed Even if only those for which the angle is within ±30 degrees are to be processed, the boundary (Ll) on the stroke side can be accurately detected.

Then, for each pixel to which the angle restriction has been applied, a straight line that passes through that pixel and is orthogonal to the slope (BP) is determined using Hough transform processing, as shown in equation (v) below, and Take that frequency as Hiss] Durham, then find a straight line with the maximum frequency from the histogram, and connect that straight line to the boundary (
It is detected as a straight line corresponding to Ll).

ρ""'X-C03D+y-sinθ......(v)
Subsequently, based on the value of the inclination (BIB), the direction 27 is perpendicular to the boundary (1, 1) on the stroke side.
Pixels with an inclination near 0 degrees (240 degrees or more to 300 degrees or less) are re-extracted. In other words, the terminal side boundary (L2)
is in the direction perpendicular to the boundary (Lυ) on the working stroke side, and as mentioned above, the vehicle body (V) is located at the boundary (1
, +), the range of inclination does not differ greatly. Therefore, as in the case of detecting the boundary (L1) in the direction along the work process, by adding angle restrictions and reducing the number of pixels to be processed, it is possible to speed up image processing without reducing accuracy. It's Nishide.

As shown in FIG. 5, the slope (B
A straight line that is perpendicular to P) and passes through the pixel is obtained by the Hough transform process described above, and from these straight lines, the one that has the highest frequency is extracted, thereby forming the terminal side boundary (L2 ) is detected.

Note that the process of finding a straight line for each pixel using Hough transform processing corresponds to the straight line calculating means (103), and the process of extracting the one with the highest frequency among the found straight lines corresponds to the straight line extracting means (104). ).

However, in this boundary detection process, the screen used for explanation is a virtual one, and each of the boundaries (Ll),
The straight line corresponding to (Lx) is not actually drawn, and the value (ρ) obtained from the above <v> formula that has the maximum frequency
and the angle (
θ) is used as information corresponding to the detected straight line. Then, in the process of calculating the deviation with respect to the boundary and the calculation of the end position, these values (ρ), (θ
), the actual boundary (t r
).

The position of (l, z) will be calculated.

To explain how to determine whether or not the terminal side boundary (L2) has been detected, since the aforementioned boundary detection process is performed for each set distance, the terminal side boundary (L2) detected in the current boundary detection process is The position of L2) is the position of the terminal side boundary (L2) detected in the previous boundary detection process,
By determining whether the detected straight line is near the position where the set distance is added in the longitudinal direction of the vehicle body, it is determined whether the detected straight line is the boundary (L2) on the terminal side.

By the way, since the image sensor (Sl) is installed so as to lift and damage the work area on the front side of the vehicle body from diagonally above, each boundary (L) obtained from the captured image information is
1) and (L2) on the screen are inversely proportional to the distance to the actual boundaries (Ll) and (Lx) relative to the vehicle body (V), and become smaller at far distances.

Therefore, in each of the determination of the terminal side boundary (L2), the calculation of the distance to the terminal side boundary (tz), and the calculation of the deviation with respect to the detected stroke side boundary (Lυ), the detection on the screen Corresponding to the position, it is converted into an actual position with respect to the vehicle body (V).

[Another Embodiment] In the above embodiment, the case where the end detection is performed at the same time every time the boundary detection process is performed is illustrated, but for example,
The vehicle body (
V) may be performed only when the process approaches the end of the working process.

Furthermore, in the above embodiment, the binarization means (102) operates based on the magnitude of the differential value (JP) obtained by adding the differential value (SX) in the X-axis direction and the differential value (SY) in the y-axis direction. , we have given an example of a case in which binarization is performed, but for example, if the boundary on the process side (
When detecting the differential value (Ll), the differential value in the X-axis direction (
SX) may be used alone. Also, the boundary on the terminal side (L2)
When detecting the above-mentioned differential value in the y-axis direction (S
Only Y) may be used.

Further, in the above embodiment, a case is described in which Hough transform processing is used as the straight line calculation means (103) for calculating the straight line corresponding to the slope (BP) of each pixel extracted by the binarization means (102). Although an example has been given, the specific configuration of the linear calculation means (103) can be modified in various ways.

Further, in the above embodiment, the present invention is used as a means for automatically driving a lawn mowing work vehicle, but
The present invention can be applied as means for detecting the boundaries of various work areas, and the specific configuration of each part can be modified in various ways.

Incidentally, although reference numerals are written in the claims section for convenience of reference to the drawings, the present invention is not limited to the structure of the attached drawings.

[Brief explanation of the drawing]

The drawings show an embodiment of the static image type boundary detection device according to the present invention, FIG. 1 is a flowchart of boundary detection processing, FIG. 2 is a flowchart showing an outline of traveling control of a work vehicle, and FIG. An explanatory diagram of differential processing, Figs. 4 and 5 are explanatory diagrams of boundaries, Fig. 6 is a block diagram showing the control configuration, Fig. 7 is an overall side view of the work vehicle, and Fig. 8 is an explanatory diagram of the work area. (B)... Untreated work area, (C)...
Treated work area, pile...boundary, (Sl)...
...Imaging means, (SX), (SY)...
Differential value, (BP)... Slope, (100)...
... Differentiation means, (101) ... Slope calculation means, (102) ... Binarization means, (103) ...
... Linear calculation means, (104) ... Linear extraction means.

Claims (1)

[Claims] Boundary between untreated work area (B) and treated work area (C) (
An imaging-type boundary detection device that detects a straight line corresponding to L), an imaging means (S_1) that images the work area in two-dimensional directions, and from the imaging information of the imaging means (S_1),
Differentiating means (
100), the obtained differential values in each of the two orthogonal directions (S
A slope calculating means (101) for calculating the slope (BP) corresponding to the maximum brightness change direction from Binarization means (102) for extracting pixels, and the slope () of each pixel extracted by the binarization means (102).
Linear calculation means (103) for finding a straight line perpendicular to BP)
, straight line extraction means (
104).