CN114693954A - Method and system for extracting features of light spots in coal mine - Google Patents
Method and system for extracting features of light spots in coal mine Download PDFInfo
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- CN114693954A CN114693954A CN202210351039.2A CN202210351039A CN114693954A CN 114693954 A CN114693954 A CN 114693954A CN 202210351039 A CN202210351039 A CN 202210351039A CN 114693954 A CN114693954 A CN 114693954A
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Abstract
The invention relates to a method and a system for extracting light spot characteristics in an underground coal mine, and belongs to the technical field of light spot characteristic extraction. An original image of the laser direction indicator is obtained first. And then carrying out threshold processing on the original image to obtain N facula characteristic profiles. And finally, determining the division number and the screening condition according to the shape characteristics of the light spots, randomly dividing N light spot characteristic profiles according to the division number to obtain K combinations, screening the K combinations according to the screening condition to obtain the light spot characteristics of the laser direction instrument, and further quickly, stably and reliably extracting the light spot characteristics under the working conditions of high dust, low illumination and complex background in the coal mine.
Description
Technical Field
The invention relates to the technical field of light spot feature extraction, in particular to a method and a system for extracting light spot features under a coal mine.
Background
The intellectualization of the coal mine tunneling working face is the basis for realizing the intellectualization of the coal mine, and the pose detection and control technology of the tunneling equipment is the premise for realizing the intellectualization and the unmanned realization of the coal mine tunneling working face. The machine vision adopts image sensing as an information acquisition means, takes an image as an information carrier, and realizes directional tunneling and shaping cutting of the tunneling equipment by preprocessing and characteristic extraction of the acquired image and establishing a measurement model to solve the pose of the tunneling equipment.
The existing technology for detecting the machine vision pose of the underground coal mine mainly takes the light spot characteristics of three laser direction indicators as image characteristics so as to solve the pose of tunneling equipment. Therefore, aiming at the working conditions of high dust, low illumination and complex background under the coal mine, the rapid, stable and reliable extraction of the laser pointer spot characteristics is very important.
Disclosure of Invention
The invention aims to provide a method and a system for extracting the characteristics of light spots in a coal mine, which can quickly, stably and reliably extract the characteristics of the light spots under the working conditions of high dust, low illumination and complex background in the coal mine.
In order to achieve the purpose, the invention provides the following scheme:
a coal mine underground light spot feature extraction method comprises the following steps:
acquiring an original image of a laser direction indicator;
carrying out threshold processing on the original image to obtain N light spot characteristic profiles;
determining the number of divisions and screening conditions according to the shape characteristics of the light spots;
randomly dividing the N light spot characteristic profiles according to the dividing number to obtain K combinations; each combination comprises the divided light spot characteristic profiles;
and screening the K combinations according to the screening conditions to obtain the light spot characteristics.
A coal mine underground light spot feature extraction system comprises:
the original image acquisition module is used for acquiring an original image of the laser direction indicator;
the threshold processing module is used for carrying out threshold processing on the original image to obtain N light spot characteristic profiles;
the parameter determining module is used for determining the division number and the screening condition according to the shape characteristics of the light spots;
the dividing module is used for randomly dividing the N light spot characteristic profiles according to the dividing number to obtain K combinations; each combination comprises the divided light spot characteristic profiles;
and the extraction module is used for screening the K combinations according to the screening conditions to obtain the light spot characteristics.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a method and a system for extracting the characteristics of underground coal mine faculae. And then carrying out threshold processing on the original image to obtain N facula characteristic profiles. And finally, determining the division number and the screening condition according to the shape characteristics of the light spots, randomly dividing N light spot characteristic profiles according to the division number to obtain K combinations, screening the K combinations according to the screening condition to obtain the light spot characteristics of the laser direction instrument, and further quickly, stably and reliably extracting the light spot characteristics under the working conditions of high dust, low illumination and complex background in the coal mine.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.
FIG. 1 is a flow chart of the extraction method provided in example 1 of the present invention;
fig. 2 is an extraction schematic diagram of the extraction method provided in embodiment 1 of the present invention;
FIG. 3 is a schematic view of a triangular feature provided in example 1 of the present invention;
fig. 4 is a system block diagram of the extraction system provided in embodiment 2 of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a method and a system for extracting the characteristics of light spots in a coal mine, which can quickly, stably and reliably extract the characteristics of the light spots under the working conditions of high dust, low illumination and complex background in the coal mine.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
Example 1:
the embodiment is used for providing a method for extracting the characteristics of the light spots in the coal mine, and as shown in fig. 1 and fig. 2, the method comprises the following steps:
s1: acquiring an original image of a laser direction indicator;
specifically, an original image f (x, y) of the laser pointer can be obtained through the camera, and the original image is shown in fig. 2.
As an optional implementation manner, before S2, the extraction method of this embodiment further includes: distortion correction is performed on the original image f (x, y) to obtain a corrected image g (x, y), gaussian filtering is performed on the corrected image g (x, y) to obtain a filtered output image k (x, y), and S2 is performed with the filtered output image as a new original image. The rectified image and the filtered output image are shown in fig. 2.
S2: carrying out threshold processing on the original image to obtain N light spot characteristic profiles;
the threshold processing referred to in this embodiment includes: feature separation (negation operation) and feature detection (HSV operation). Specifically, S2 may include:
(1) characteristic separation: negating the original image to obtain a negated image;
the characteristic of the underground coal mine light spot is white, the characteristic of the background is black, and the black proportion is larger than white, so that the pixel value of each pixel point in the original image is inverted to obtain the inverted image. Inverting the pixel value of the pixel point means: the pixel value 0 becomes 255, 1 becomes 254, …, 254 becomes 1, 255 becomes 0, that is, the sum of the pixel value of the pixel and the inverted pixel value of the pixel is 255. After inversion, the spot characteristic appears black and the background characteristic appears white.
(2) And (3) feature detection: and detecting black outlines in the inverted images by using black HSV values in the hexagonal cone model HSV to obtain N facula characteristic outlines, wherein N is more than or equal to 3.
More specifically, when the filtered output image is used as a new original image, then the step S2 is to perform threshold processing on the filtered image k (x, y) to obtain N spot feature profiles, which may include:
(1) characteristic separation:
the spot characteristic of the underground coal mine is white, the background characteristic is black, and the black ratio is greater than white, so that the filtering image k (x, y) is subjected to negation operation, namely the pixel value of each pixel point of the filtering image k (x, y) is changed from 0 to 255, from 1 to 254, …, from 254 to 1, and from 255 to 0. After inversion, the spot characteristic appears black and the background characteristic appears white. The inverted image is shown in fig. 2.
(2) Feature detection
In the image after the negation, black outlines are detected by utilizing black HSV values in the hexagonal cone model HSV, so that N (N is more than or equal to 3) light spot characteristic outlines are obtained, specifically, the image after the negation is converted into a HSV color space of the hexagonal cone model, black pixel points are determined according to the black HSV values, so that the black outlines are obtained through detection, and the black outlines are the light spot characteristic outlines. The black HSV value is determined according to Opencv, for example, in the hexagonal pyramid model HSV, the black HSV value is represented as: hmin＝0；Smin＝0；Vmin＝0；Hmax＝180；Smax＝255；Vmax466. The image after feature detection is shown in fig. 2.
S3: determining the number of divisions and screening conditions according to the shape characteristics of the light spots;
s4: randomly dividing the N light spot characteristic profiles according to the dividing number to obtain K combinations; each combination comprises the divided light spot characteristic profiles;
s5: and screening the K combinations according to the screening conditions to obtain the light spot characteristics.
In the embodiment, after the N spot feature profiles are obtained by using S2, the spot features are extracted by using a spot feature extraction method based on shape features. The shape features of the light spot can be triangular or other shapes, the processing method of each shape is the same, and the shape features of the light spot are used for extracting the light spot features. Here, the present embodiment assumes that the feature formed by the three laser pointers is triangular, and therefore, it is necessary to perform recognition by using the characteristics of the triangle at this time. In the following, the present embodiment further describes the light spot feature extraction process by taking the shape feature of the light spot as a triangle as an example:
when the shape of the light spot is characterized as a triangle, S3 may include:
(1) the number of vertexes included in the shape feature of the light spot is used as the division number, and the triangle includes three vertexes, so that the division number is 3.
(2) And determining screening conditions according to the shape characteristics of the light spots, wherein the screening conditions corresponding to the triangle comprise side length characteristics, coordinate characteristics and inclination angle characteristics.
As shown in fig. 3, the left vertex of the triangle base is taken as a starting point, the three vertices of the triangle are defined counterclockwise as A, B, C, the lengths of the opposite sides of the three vertices are a, B and C, the coordinates of vertex a are (x1, y1), the coordinates of vertex B are (x2, y2), and the coordinates of vertex C are (x3, y 3). In fig. 3, A, B, C indicates light spots corresponding to the feature profiles of 3 light spots, a triangle drawn by a solid line is a triangle composed of 3 light spots, a triangle drawn by a dotted line on the left side is a left yaw limit position when a left inclination angle of the triangle composed of 3 light spots is θ, and a triangle drawn by a dotted line on the right side is a right yaw limit position when a right inclination angle of the triangle composed of 3 light spots is θ. The screening conditions include: the side length characteristic is as follows: a/b is more than or equal to 0.8 and less than or equal to 1.2, and the coordinate characteristics are as follows: x1< x3< x2, and the tilt angle characteristic is: - θ < arctan ((y2-y1)/(x2-x 1)).
Based on the determined division number and the screening condition, the light spot feature extraction process of the embodiment may include:
(1) characteristic grouping:
s4 is the feature grouping process, and S4 may include: the N light spot characteristic profiles are randomly divided, every 3 light spot characteristic profiles are in one group to obtain K combinations, namely the random 3 light spot characteristic profiles in the N light spot characteristic profiles are in one group and are divided into K combinations, and specifically, the random division can be carried out in a combination mode in the permutation and combination mode, namely
(2) Feature identification
S5 is a feature recognition process, and S5 may include:
and (2.1) screening the K groups according to the screening conditions, and determining the groups meeting the screening conditions, namely screening M groups of light spot characteristic profiles which simultaneously meet the side length characteristic, the coordinate characteristic and the inclination angle characteristic from the K groups of light spot characteristic profiles.
(2.2) judging whether the number of the combinations meeting the screening condition is greater than 1, namely judging whether M is greater than 1;
(2.3) if not, namely M is equal to 1, determining that the light spots in the combination meeting the screening condition are the required light spot characteristics of the three laser direction instruments;
and (2.4) if the result is yes, namely M is greater than 1, determining the spot characteristics according to the C point coordinates of all combinations meeting the screening condition.
For M groups of combinations meeting the screening condition, screening again through the positions of C point coordinates (x3, y3) in the combinations in the background, if the shape features are positioned at the upper right corner of the image, continuing to screen the combination with the maximum x3 in the M combinations, wherein the included light spots are the light spot features of the three required laser direction instruments; if the shape features are positioned at the upper left corner of the image, continuously screening the combination with the minimum x3 in the M combinations, wherein the included light spots are the light spot features of the three required laser direction instruments; if the shape features are located at the lower right corner of the image, continuously screening the combinations with the largest x3 and the largest y3 in the M combinations, wherein the included light spots are the required light spot features of the three laser direction instruments; if the shape feature is located at the lower left corner of the image, the combinations with the smallest x3 and the largest y3 in the M combinations are continuously screened, and the included light spots are the required three laser pointer spot features.
After the light spot characteristics of the three laser direction instruments are obtained, the light spot characteristics are used as pose resolving input data, and then the pose resolving process of the tunneling equipment can be completed.
In the embodiment, negation and HSV are combined for use, so that the image binarization is realized without threshold segmentation, and the feature recognition is carried out based on the shape and the shape feature of the target to be detected, so that the rapid, stable and reliable extraction of the spot feature is realized aiming at the working conditions of high dust, low illumination and complex background under the coal mine.
Example 2:
this embodiment is used for providing a colliery facula characteristic extraction system in pit, as shown in fig. 4, the extraction system includes:
an original image acquisition module M1, configured to acquire an original image of the laser pointer;
a threshold processing module M2, configured to perform threshold processing on the original image to obtain N spot feature profiles;
the parameter determining module M3 is used for determining the dividing number and the screening condition according to the shape characteristics of the light spots;
the dividing module M4 is used for randomly dividing the N light spot characteristic profiles according to the dividing number to obtain K combinations; each combination comprises the divided light spot characteristic profiles;
and the extraction module M5 is used for screening the K combinations according to the screening conditions to obtain the light spot characteristics.
The emphasis of each embodiment in the present specification is on the difference from the other embodiments, and the same and similar parts among the various embodiments may be referred to each other. For the system disclosed by the embodiment, the description is relatively simple because the system corresponds to the method disclosed by the embodiment, and the relevant points can be referred to the method part for description.
The principle and the embodiment of the present invention are explained by applying specific examples, and the above description of the embodiments is only used to help understanding the method and the core idea of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (10)
1. A coal mine underground light spot feature extraction method is characterized by comprising the following steps:
acquiring an original image of a laser direction indicator;
carrying out threshold processing on the original image to obtain N light spot characteristic profiles;
determining the number of divisions and screening conditions according to the shape characteristics of the light spots;
randomly dividing the N light spot characteristic profiles according to the dividing number to obtain K combinations; each combination comprises the divided light spot characteristic profiles;
and screening the K combinations according to the screening conditions to obtain the light spot characteristics.
2. The extraction method according to claim 1, wherein before thresholding the original image, the extraction method further comprises:
carrying out distortion correction on the original image to obtain a corrected image;
and performing Gaussian filtering on the corrected image to obtain a filtered output image, and taking the filtered output image as a new original image.
3. The extraction method according to claim 1, wherein the thresholding the original image to obtain N spot feature profiles specifically includes:
negating the original image to obtain a negated image;
and carrying out black contour detection on the inverted image by utilizing black HSV (hue, saturation and value) values in the hexagonal cone model to obtain N light spot characteristic contours.
4. The extraction method according to claim 3, wherein the inverting the original image to obtain an inverted image specifically comprises:
for each pixel point in the original image, negating the pixel value of the pixel point to obtain a negated image; the sum of the pixel value of the pixel point and the inverted pixel value of the pixel point is 255.
5. The extraction method according to claim 1, wherein the shape feature of the light spot is a triangle.
6. The extraction method according to claim 5, wherein the determining the number of partitions and the screening condition according to the shape feature of the light spot specifically includes:
taking the number of vertexes included in the shape characteristics of the light spots as the number of divisions, wherein the number of divisions is 3;
and determining screening conditions according to the shape characteristics of the light spots, wherein the screening conditions comprise side length characteristics, coordinate characteristics and inclination angle characteristics.
7. The extraction method according to claim 6, wherein the randomly dividing the N spot feature profiles according to the number of divisions to obtain K combinations specifically comprises:
and randomly dividing the N light spot characteristic profiles, wherein each 3 light spot characteristic profiles form a group, and obtaining K combinations.
8. The extraction method according to claim 6, wherein the left vertex of the base of the triangle is used as a starting point, the three vertices of the triangle are defined counterclockwise as A, B, C, the lengths of opposite sides of the three vertices are a, B and C, the coordinate of vertex A is (x1, y1), the coordinate of vertex B is (x2, y2), and the coordinate of vertex C is (x3, y 3);
the side length characteristic is then: a/b is more than or equal to 0.8 and less than or equal to 1.2;
the coordinate characteristics are: x1< x3< x 2;
the tilt angle characteristics are: - θ < arctan ((y2-y1)/(x2-x 1)).
9. The extraction method according to claim 8, wherein the screening K combinations according to the screening condition to obtain the spot feature specifically comprises:
screening the K combinations according to the screening conditions, and determining the combinations meeting the screening conditions;
judging whether the number of the combinations meeting the screening condition is more than 1;
if not, the combination meeting the screening condition is the light spot characteristic;
and if so, determining the light spot characteristics according to the C point coordinates of all combinations meeting the screening conditions.
10. The utility model provides a colliery is facula characteristic extraction system in pit which characterized in that, the extraction system includes:
the original image acquisition module is used for acquiring an original image of the laser direction indicator;
the threshold processing module is used for carrying out threshold processing on the original image to obtain N light spot characteristic profiles;
the parameter determining module is used for determining the division number and the screening condition according to the shape characteristics of the light spots;
the dividing module is used for randomly dividing the N light spot characteristic profiles according to the dividing number to obtain K combinations; each combination comprises the divided light spot characteristic profiles;
and the extraction module is used for screening the K combinations according to the screening conditions to obtain the light spot characteristics.
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| WO2019012887A1 (en) * | 2017-07-14 | 2019-01-17 | 大日本印刷株式会社 | Shape correction device and shape correction method for graphical pattern |
| CN108665459A (en) * | 2018-05-22 | 2018-10-16 | 释码融和（上海）信息科技有限公司 | A kind of image fuzzy detection method, computing device and readable storage medium storing program for executing |
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| 杨文娟，等: "基于三激光束标靶的煤矿井下长距离视觉定位方法", 《煤炭学报》, vol. 47, no. 2, 28 February 2022 (2022-02-28), pages 986 * |
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