CN113971681A - Edge detection method for belt conveyor in complex environment - Google Patents

Edge detection method for belt conveyor in complex environment Download PDF

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CN113971681A
CN113971681A CN202111179616.6A CN202111179616A CN113971681A CN 113971681 A CN113971681 A CN 113971681A CN 202111179616 A CN202111179616 A CN 202111179616A CN 113971681 A CN113971681 A CN 113971681A
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edge
image
belt
points
conveyor
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徐成
曾祥进
张锐
王锴
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Wuhan Institute of Technology
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    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/13—Edge detection
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/136—Segmentation; Edge detection involving thresholding
    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
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    • G06T2207/00—Indexing scheme for image analysis or image enhancement
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    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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Abstract

The invention discloses a method for detecting the edge of a belt conveyor in a complex environment, which comprises the following steps: acquiring an original image of the belt conveyor in a natural environment; selecting a region of interest (ROI) of the conveyor belt by adopting a coordinate point frame; adjusting the image contrast by using a normalization processing method; enhancing the image in the vertical direction by using a Sobel edge detection operator; enhancing the extracted white lines in the vertical direction by using morphological expansion operation, expanding the white points, increasing the connected region of the edge lines, and performing binarization operation on the processed image; further executing an eight-connected domain algorithm on the processed image; finding out two head points and two tail points of the edge line in the extracted image, respectively connecting the four points in pairs, and finally marking the straight line in the original image. The invention can accurately and rapidly detect the edge position of the conveyor belt, judge whether the conveyor belt deviates or not, meet the industrial requirements, has strong anti-interference capability and good robustness, and can realize the real-time detection of the edge of the conveyor belt in a complex environment.

Description

Edge detection method for belt conveyor in complex environment
Technical Field
The invention relates to the technical field of image processing, in particular to a method for detecting the edge of a belt conveyor in a complex environment.
Background
The belt conveyer is a continuous conveying machine widely used in coal, building, metallurgy, electric power and other industries. Belt deflection is one of the most common failures of belt conveyors. The deviation not only can cause the accident occurrence frequency of the conveyor to be increased, and the production is influenced, but also can cause the materials to be scattered outwards, so that the operation economy of the transportation system is reduced. Whether the conveying belt deviates or not can be effectively detected by carrying out edge detection on the conveying belt, so that the position of the conveying belt can be adjusted through a terminal.
At present, the deviation correcting measures for the deviation of the conveying belt can be mainly divided into an artificial algorithm, a mechanical and photoelectric automatic deviation correcting algorithm and the like. The mechanical deviation correction algorithm has low equipment cost but poor precision; the photoelectric automatic deviation rectifying algorithm is high in precision and good in deviation rectifying effect, but the manufacturing cost of equipment is high. With the continuous development of science and technology, the deviation of the conveying belt is accurately and efficiently detected through computer vision and image processing technology, the labor intensity of manual detection can be reduced, the automation level of an enterprise is improved, potential faults of the conveying belt can be found as soon as possible, and the fault detection efficiency and precision are improved.
In addition, since the conveyor belt vision monitoring system needs to operate on line, the information amount of image data is very large, and a simple and efficient image processing algorithm needs to be adopted to realize the online operation of the system. If the processing of the conveyor belt image is realized by software, the complexity of a processing algorithm needs to be considered so as not to influence the real-time performance of the system.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method for detecting the edge of a belt conveyor in a complex environment, which is used for judging whether the conveyor belt deviates or not by detecting the edge position of the conveyor belt so as to achieve the purposes of accurately detecting and correcting the position of the conveyor belt in real time.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides a method for detecting the edge of a belt conveyor in a complex environment, which comprises the following steps:
the method comprises the following steps of firstly, acquiring an original image of the belt conveyor in a natural environment;
selecting a region of interest (ROI) of the conveyor belt by adopting a specified coordinate point frame in an original image of the belt conveyor;
step three, adjusting the image contrast of the ROI by using a normalization processing method, and designing two different normalization parameters according to the problem of the edge identification difference of the conveyor belt under the condition that the conveyor belt is different from the conveyor belt without materials;
step four, enhancing the image in the vertical direction by using a Sobel edge detection operator so as to enhance the edge line, and extracting a white line in the vertical direction, namely the edge line;
step five, enhancing the extracted white lines in the vertical direction by using morphological expansion operation, expanding the white points, increasing the connected area of the edge lines, and performing binarization operation on the processed image;
step six, further executing an eight-connected domain algorithm on the processed image, and deleting large-area white points in the middle area of the conveyor belt;
and seventhly, finding out two head points and two tail points of the edge line in the extracted image, connecting the four points in pairs respectively, and finally marking the straight line in the original image to obtain the edge detection result of the belt conveyor.
Further, the specific method of the first step of the invention is as follows:
and fixing a camera above the belt conveyor, and shooting the belt conveyor through the fixed camera position to obtain images of the belt conveyor in a natural environment.
Further, the specific method of the second step of the present invention is:
and using the designated coordinate value to select a partial area frame containing the edge of the conveyor belt and divide the partial area frame from the background image.
Further, the specific method of the third step of the present invention is:
the formula of the normalization process is:
Figure BDA0003296522970000021
wherein A iskRepresenting the array that needs to be normalized, AiRepresenting the original array, wherein P represents the array obtained after normalization operation; a. theKDo not belong to { max (A)i),min(Ai) When A is reachedKIs equal to max (A)i) When p is 1, when AKIs equal to min (A)i) When p is 0, A is normalizedkThe values of the array are translated or scaled to a specified range.
Further, the specific method of the fourth step of the present invention is:
carrying out sobel edge detection in the vertical direction on the normalized image so as to enhance the vertical line;
providing a vertical edge detection template according to the characteristics of the conveyor belt image; according to the characteristic that the straight line is horizontal and the pixel value between the straight lines is larger than that on the straight line, a difference expanding method is provided to detect the edge in the vertical direction; the vertical edge detection template is as follows:
Figure BDA0003296522970000031
wherein, Gy represents a result value after vertical sobel edge detection, and A represents an image pixel matrix needing edge detection; the template is 3x3 in size, and is convolved with the image to enhance the points in the vertical direction of the image and to weaken the points in the horizontal direction to some extent to reduce the interference.
Further, the specific method of the fifth step of the present invention is:
using a fixed threshold value to carry out binarization operation on the image, setting the gray value of a pixel point on the image as 0 or 255, setting a threshold value T, and dividing the data of the image into two parts by using T: pixel groups larger than T and pixel groups smaller than T; that is, the part greater than the threshold is set to 255 and the part smaller than the threshold is set to 0, and the expression is:
Figure BDA0003296522970000032
wherein dst (x, y) represents an output image threshold, maxval represents a maximum gray-scale value, src (x, y) represents an original image gray-scale value, and thresh represents a set threshold T; according to the influence of an image under illumination, different binarization threshold values are set for the conveyor belts in different scenes, the threshold value left value is reduced to enhance the conveyor belt edge in the scene with weak contrast, and the threshold value left value is increased to reduce the influence of other noise points in the scene with strong contrast.
Further, the specific method of the sixth step of the present invention is:
carrying out eight-union domain processing on the extracted binary image, and counting the number of gray values of surrounding pixel points of each pixel, wherein the gray values of the surrounding pixel points are 0 or 255; according to the binarization processing, if a pixel is a part of the interference factor, the gray value of the pixel in the binarization result is 255, namely white; if a pixel is background, its gray value should be 0 and black;
so for a noise, its surrounding pixels should be all black background pairs, i.e. a noise pixel is white and the surrounding 8 neighboring pixels are all black; if the picture resolution is high enough, a noise may actually consist of many pixels, i.e. one pixel is white and the adjacent 8 pixels are black with more than a fixed value, then this pixel is noise; this fixed value threshold is fixed for the conveyor belt under different circumstances.
Further, the specific method of the seventh step of the present invention is:
according to the processed image, finding out two head points and two tail points of the edge line in the extracted image, respectively connecting the four points in pairs, finally marking a straight line in an original image and calibrating the edge of the conveyor belt; and judging whether the position of the edge of the conveying belt is within a set range according to a standard, judging that the conveying belt is not off tracking if the detected position of the edge of the conveying belt is within the set range, and otherwise, judging that the conveying belt is off tracking.
The invention has the following beneficial effects: the method is suitable for detecting the edge of the belt conveyor under the complex background, and can accurately acquire the edge of the belt conveyor by methods of image enhancement, gradient edge extraction, eight-way communication domain and the like, so that whether the belt conveyor deviates or not is judged according to the position of the edge of the belt conveyor. The invention has strong anti-interference capability and good robustness, and can realize the real-time edge detection of the belt conveyor in a complex environment.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a flow chart of an edge detection algorithm for a belt conveyor of the present invention;
FIG. 2 is a conveyor belt normalization graph extracted by the present invention;
FIG. 3 is a diagram of a conveyor belt sobel enhanced vertical line extracted by the present invention;
FIG. 4 is a binary image of the extracted conveyor belt according to the present invention;
FIG. 5 is a plot of eight-way domain noise floor for extracted conveyor belts according to the present invention;
FIG. 6 is a diagram of the present invention marking extracted belt edge results.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
The flow chart of the method for detecting the edge of the belt conveyor in the complex environment is shown in fig. 1 and is carried out in the following way:
step 1: acquiring an image of the belt conveyor under the natural environment through a fixed camera;
step 2: in order to uniformly process the belt conveyor subsequently and avoid background interference, before detection, an ROI (region of interest) region of the belt conveyor needs to be extracted, coordinates of four points are selected in a fixed coordinate mode, the four points are connected in sequence to frame the ROI region, and then the part outside the frame selection is used as a mask and is completely covered by black.
And step 3: in order to obtain a better boundary extraction effect, before edge detection, normalization processing is carried out on the image, and the contrast of the boundary is improved. The contrast is reduced by setting the parameters in the normalization function to smaller values in the presence of materials; the contrast is improved by setting the parameters in the normalization function to larger values in the absence of material. The normalization process is to generalize the statistical distribution of the unified sample, and is to limit the processed data to a certain range, and the formula is as follows:
Figure BDA0003296522970000051
wherein A isKNot in { max (ai), min (ai) }, when AKEqual to max (ai), p is 1, equal to min (ai), p is 0. The values of the array are translated or scaled to a specified range using a normalization algorithm. When the normalization process is used, the right value is increased for a graph with small contrast, and the left value is increased for a graph with large contrast, so that edge information can be better displayed, as shown in fig. 2.
And 4, step 4: after normalization processing is used, the sobel edge detection in the vertical direction enhances vertical lines. Inspired by the traditional edge detection algorithm, the vertical edge detection template is provided according to the characteristics of a conveyor belt image. The noise generated by the interference lines on the middle part of the conveyor belt and the coal on the conveyor belt during coal conveying becomes fuzzy after normalization processing, so that the noise is not enhanced when the vertical enhancement is performed. The traditional edge detection algorithm is poor in effect, a difference expanding method is provided according to the characteristic that a straight line is horizontal and the pixel value between the straight lines is larger than that on the straight line, the edge in the vertical direction is detected, and a template of the method is shown as the following formula.
Figure BDA0003296522970000061
The template is 3x3 in size, and is convolved with the image to enhance the points in the vertical direction of the image and to weaken the points in the horizontal direction to some extent to reduce the interference, as shown in fig. 3.
And 5: and (3) carrying out binarization operation on the image by using a fixed threshold, and setting the gray value of a pixel point on the image to be 0 or 255, namely, enabling the whole image to present an obvious visual effect only including black and white. To directly extract a target object from a multi-valued digital image, a common method is to set a threshold T, and divide the image data into two parts by T: pixel groups larger than T and pixel groups smaller than T. The binarization function is limited by using a CV _ THRESH _ BINARY type, wherein the purpose of the CV _ THRESH _ BINARY type is to set the part which is larger than the threshold value as 255 and the part which is smaller than the threshold value as 0, and the expression is as follows:
Figure BDA0003296522970000062
wherein maxval represents the maximum gray value of 255, and if the image is greater than the set maximum threshold thresh, the image is replaced by maxval; otherwise, it is replaced with 0.
According to the influence of an image under illumination, different binarization threshold values are set for the conveyor belts in different scenes, the threshold value left value is reduced to enhance the conveyor belt edge in the scene with weak contrast, and the threshold value left value is increased to reduce the influence of other noise points in the scene with strong contrast.
Step 6: and carrying out eight-union domain processing on the extracted binary image, wherein the method is similar to mean filtering, but for each pixel, the gray value of the surrounding pixels is not taken as the average value, but the number of the gray values of the surrounding pixels is counted to be 0 or 255. As can be known from the foregoing binarization processing, if a pixel is a part of the interference factor, the gray value of the pixel in the binarization result must be 255, that is, white; if a pixel is background, its gray value should be 0 and black. Thus for isolated noise, its surroundings should be all black, or most points black pixels.
So for a noise, its surrounding pixels should be all black background pairs, precisely a noise pixel is white and the surrounding 8 neighboring pixels are all black. Of course, if the picture resolution is high enough, a noise may actually be composed of many pixels, so the judgment condition should be relaxed at this time, that is, one pixel is white and the adjacent 8 pixels are black and larger than a fixed value, then this pixel is noise. This threshold is fixed for the conveyor belt under different circumstances, so the size can be set at this, and the best threshold is found out several times. Through tests, the 8-connected domain noise reduction method is very effective for removing small noise points, and the calculation amount is not large. Comparing fig. 4 with fig. 5, it is apparent that some of the white noise is reduced in fig. 5.
And 7: and (3) measuring the edge of the conveyor belt in the image, respectively searching four starting points of two straight lines on the basis of the straight line obtained in the step (6), then determining a straight line between two points, respectively connecting the four points in pairs, and calibrating the edge of the conveyor belt. And judging whether the position of the edge of the conveyor belt is within a set range according to the standard, and giving a detection result.
It will be understood that modifications and variations can be made by persons skilled in the art in light of the above teachings and all such modifications and variations are intended to be included within the scope of the invention as defined in the appended claims.

Claims (8)

1. The method for detecting the edge of the belt conveyor in the complex environment is characterized by comprising the following steps:
the method comprises the following steps of firstly, acquiring an original image of the belt conveyor in a natural environment;
selecting a region of interest (ROI) of the conveyor belt by adopting a specified coordinate point frame in an original image of the belt conveyor;
step three, adjusting the image contrast of the ROI by using a normalization processing method, and setting two different normalization parameters according to the problem of the edge identification difference of the conveyor belt under the condition that the conveyor belt is different from the conveyor belt without materials;
step four, enhancing the image in the vertical direction by using a Sobel edge detection operator so as to enhance the edge line, and extracting a white line in the vertical direction, namely the edge line;
step five, enhancing the extracted white lines in the vertical direction by using morphological expansion operation, expanding the white points, increasing the connected area of the edge lines, and performing binarization operation on the processed image;
step six, further executing an eight-connected domain algorithm on the processed image, and deleting large-area white points in the middle area of the conveyor belt;
and seventhly, finding out two head points and two tail points of the edge line in the extracted image, connecting the four points in pairs respectively, and finally marking the straight line in the original image to obtain the edge detection result of the belt conveyor.
2. The method for detecting the edge of the belt conveyor under the complex environment according to claim 1, wherein the specific method of the first step is as follows:
and fixing a camera above the belt conveyor, and shooting the belt conveyor through the fixed camera position to obtain images of the belt conveyor in a natural environment.
3. The method for detecting the edge of the belt conveyor under the complex environment according to claim 1, wherein the specific method of the second step is as follows:
and using the designated coordinate value to select a partial area frame containing the edge of the conveyor belt and divide the partial area frame from the background image.
4. The method for detecting the edge of the belt conveyor under the complex environment according to claim 1, wherein the specific method of the third step is as follows:
the formula of the normalization process is:
Figure FDA0003296522960000021
wherein A iskRepresenting the array that needs to be normalized, AiRepresenting the original array, wherein P represents the array obtained after normalization operation; a. theKDo not belong to { max (A)i),min(Ai) When A is reachedKIs equal to max (A)i) When p is 1, when AKIs equal to min (A)i) When p is 0, A is normalizedkThe values of the array are translated or scaled to a specified range.
5. The method for detecting the edge of the belt conveyor under the complex environment according to claim 1, wherein the specific method of the fourth step is as follows:
carrying out sobel edge detection in the vertical direction on the normalized image so as to enhance the vertical line;
providing a vertical edge detection template according to the characteristics of the conveyor belt image; according to the characteristic that the straight line is horizontal and the pixel value between the straight lines is larger than that on the straight line, a difference expanding method is provided to detect the edge in the vertical direction; the vertical edge detection template is as follows:
Figure FDA0003296522960000022
wherein, Gy represents a result value after vertical sobel edge detection, and A represents an image pixel matrix needing edge detection; the template is 3x3 in size, and is convolved with the image to enhance the points in the vertical direction of the image and to weaken the points in the horizontal direction to some extent to reduce the interference.
6. The method for detecting the edge of the belt conveyor under the complex environment according to claim 1, wherein the concrete method of the step five is as follows:
using a fixed threshold value to carry out binarization operation on the image, setting the gray value of a pixel point on the image as 0 or 255, setting a threshold value T, and dividing the data of the image into two parts by using T: pixel groups larger than T and pixel groups smaller than T; that is, the part greater than the threshold is set to 255 and the part smaller than the threshold is set to 0, and the expression is:
Figure FDA0003296522960000023
wherein dst (x, y) represents an output image threshold, maxval represents a maximum gray-scale value, src (x, y) represents an original image gray-scale value, and thresh represents a set threshold T; according to the influence of an image under illumination, different binarization threshold values are set for the conveyor belts in different scenes, the threshold value left value is reduced to enhance the conveyor belt edge in the scene with weak contrast, and the threshold value left value is increased to reduce the influence of other noise points in the scene with strong contrast.
7. The method for detecting the edge of the belt conveyor under the complex environment according to claim 1, wherein the specific method of the sixth step is as follows:
carrying out eight-union domain processing on the extracted binary image, and counting the number of gray values of surrounding pixel points of each pixel, wherein the gray values of the surrounding pixel points are 0 or 255; according to the binarization processing, if a pixel is a part of the interference factor, the gray value of the pixel in the binarization result is 255, namely white; if a pixel is background, its gray value should be 0 and black;
so for a noise, its surrounding pixels should be all black background pairs, i.e. a noise pixel is white and the surrounding 8 neighboring pixels are all black; if the picture resolution is high enough, a noise may actually consist of many pixels, i.e. one pixel is white and the adjacent 8 pixels are black with more than a fixed value, then this pixel is noise; this fixed value threshold is fixed for the conveyor belt under different circumstances.
8. The method for detecting the edge of the belt conveyor under the complex environment according to claim 1, wherein the specific method of the seventh step is as follows:
according to the processed image, finding out two head points and two tail points of the edge line in the extracted image, respectively connecting the four points in pairs, finally marking a straight line in an original image and calibrating the edge of the conveyor belt; and judging whether the position of the edge of the conveying belt is within a set range according to a standard, judging that the conveying belt is not off tracking if the detected position of the edge of the conveying belt is within the set range, and otherwise, judging that the conveying belt is off tracking.
CN202111179616.6A 2021-10-11 2021-10-11 Edge detection method for belt conveyor in complex environment Pending CN113971681A (en)

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Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116309565A (en) * 2023-05-17 2023-06-23 山东晨光胶带有限公司 High-strength conveyor belt deviation detection method based on computer vision
CN116573366A (en) * 2023-07-07 2023-08-11 江西小马机器人有限公司 Belt deviation detection method, system, equipment and storage medium based on vision

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN116309565A (en) * 2023-05-17 2023-06-23 山东晨光胶带有限公司 High-strength conveyor belt deviation detection method based on computer vision
CN116573366A (en) * 2023-07-07 2023-08-11 江西小马机器人有限公司 Belt deviation detection method, system, equipment and storage medium based on vision
CN116573366B (en) * 2023-07-07 2023-11-21 江西小马机器人有限公司 Belt deviation detection method, system, equipment and storage medium based on vision

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