CN117115102A - Machine vision-based steel coil head identification method - Google Patents
Machine vision-based steel coil head identification method Download PDFInfo
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- CN117115102A CN117115102A CN202311047451.6A CN202311047451A CN117115102A CN 117115102 A CN117115102 A CN 117115102A CN 202311047451 A CN202311047451 A CN 202311047451A CN 117115102 A CN117115102 A CN 117115102A
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- 229910000831 Steel Inorganic materials 0.000 title claims abstract description 95
- 239000010959 steel Substances 0.000 title claims abstract description 95
- 238000000034 method Methods 0.000 title claims abstract description 35
- 238000001514 detection method Methods 0.000 claims description 25
- 230000011218 segmentation Effects 0.000 claims description 16
- 238000001914 filtration Methods 0.000 claims description 6
- 238000005286 illumination Methods 0.000 claims description 5
- 238000013507 mapping Methods 0.000 claims description 5
- 230000002146 bilateral effect Effects 0.000 claims description 4
- 230000002708 enhancing effect Effects 0.000 claims description 3
- 238000004519 manufacturing process Methods 0.000 abstract description 8
- 238000010008 shearing Methods 0.000 description 3
- 230000008859 change Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 230000008569 process Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008901 benefit Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
- 238000005520 cutting process Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000003709 image segmentation Methods 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000005096 rolling process Methods 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
- 229910000859 α-Fe Inorganic materials 0.000 description 1
Classifications
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- 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
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/60—Analysis of geometric attributes
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/70—Determining position or orientation of objects or cameras
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
Abstract
The application provides a machine vision-based steel coil head identification method, and relates to the technical field of steel wire production processes. Comprising the following steps: the image acquisition unit is suitable for acquiring image information of the steel coil and transmitting the image information to the processing unit, and the processing unit acquires head information of the steel coil through a FAST algorithm after acquiring the image information. Through this scheme, can automatic identification department steel coil head's image information, the location is accurate.
Description
Technical Field
The application relates to the technical field of steel wire production processes, in particular to a steel wire coil head identification method based on machine vision.
Background
In the rolling production process of the steel coil, as the initial few turns of each bundle of steel coil are not subjected to water penetration treatment, ferrite grains in the head of the steel coil are large in the inner turns, the tissue components are uneven, the mechanical properties such as tensile strength, hardness and rigidity are unstable, and the use requirements cannot be met. Therefore, in the finishing step before the shipment of the coil, the number of coils of the coil head needs to be cut out according to the coil diameter specification. However, at present, cutting out the head of the steel coil for several circles in China is still manually completed by a master worker, and the manual completion of the operation mainly has the following problems: 1. the production site has severe environment, high heat and high risk, is not suitable for long-time work of workers, and has high labor cost and lower efficiency; 2. other process steps in the production flow of the steel coil are all automatic, but the head identification and shearing step of the steel coil is a short plate for realizing the whole process production automation of the production line, and in the process of the head identification of the steel coil, the problem of inaccurate identification often occurs, and the head identification commonly used at present generally adopts a FAST algorithm.
However, since the steel coil head belongs to a corner with a larger size in the image, the corner with the larger size is unsuitable to adopt the traditional FAST algorithm, the position of the steel coil head in the image cannot be accurately detected, and the threshold is manually set according to the condition of the image, so that the automatic detection of the algorithm is not facilitated, the threshold is required to be manually set for each image to be detected, the workload is huge, and the automatic production is not facilitated.
Disclosure of Invention
The application discloses a machine vision-based steel coil head identification method, which is simple in structure and convenient to operate, and aims to solve the problem of inaccurate detection of the existing steel coil head.
The application adopts the following scheme:
the application provides a machine vision-based steel coil head identification method, which comprises the following steps: the image acquisition unit is suitable for acquiring steel coil image information and transmitting the steel coil image information to the processing unit, and the processing unit performs the following steps after acquiring the image:
s1, converting the size, and converting the size of the picture into a specified size after obtaining the picture;
s2, carrying out bilateral filtering on the picture with the transformed size to remove picture noise;
s3, self-adaptive histogram equalization, namely, calculating a local histogram in the steel coil image, redistributing the brightness value of the image according to the histogram, changing the contrast of the image, correcting uneven illumination in the image, and enhancing local details;
s4, graying the image, and carrying out graying treatment on the image by using a component method to obtain a gray image;
s5, detecting and obtaining an angular point through a FAST algorithm, wherein the angular point is the head of the steel coil; and determining the optimal segmentation threshold value of the foreground and the background of the steel coil in the gray image by using an Ojin method, and taking the difference value between the gray value of the steel coil and the optimal segmentation threshold value as the threshold value of a FAST algorithm.
Further, the algorithm execution steps of the discipline method are as follows:
(1) Calculating a gray image histogram, counting the number of pixel points on 256 pixel values of 0-255, and normalizing;
(2) Setting a dividing threshold value i, and counting the proportion w of the pixel points with gray values in the range of 0-i in the image to the whole image according to the dividing threshold value 0 And an average gray value u 0 And the proportion w of the pixel points in the whole image with gray values in the range of i-255 1 And an average gray value u 1 At the same time, the total average gray level u of the image is calculated according to the following formula 2 And inter-class variance g:
u 2 ＝w 0 *u 0 +w 1 *u 1 ，
g＝w 0 (u 0 -u 2 )2+w 1 (u 1 -u 2 )2；
(3) The segmentation threshold i is increased by one gray value, the previous step is continuously executed until the last gray value, and i corresponding to the maximum inter-class variance g is used as the optimal threshold output for segmenting the foreground and the background of the image.
Further, the gray pixel value of the steel coil in the gray image is in the range of 150 to 210.
Further, the method also comprises a step S6, after the steel coil head is obtained through the step S5, the information is converted into three-dimensional coordinates.
Further, in S5, the coil diameter information is converted into a radius of the detection circle in the FAST corner detection algorithm by mapping.
The beneficial effects are that:
the method starts from the actual engineering requirement, analyzes the characteristics of the head of the steel coil, converts the steel coil diameter information into the radius of a detection circle in a FAST corner detection algorithm through mapping, takes the difference between the gray pixel value of the steel coil in a picture and the optimal segmentation threshold value of the foreground background of the picture as the comparison threshold value of the FAST corner detection algorithm, realizes the detection of the head of the steel coil in the picture, and obtains the position of the head of the steel coil in the picture, thereby positioning the three-dimensional coordinate of the head of the steel coil, then transmitting the three-dimensional coordinate to a computer, and guiding a manipulator to finish the grabbing and shearing actions of the head of the steel coil. In the scheme, the defect that the algorithm needs to manually set a threshold value is overcome, so that the algorithm is adapted to the detection of the head of the steel coil, and the automatic detection is realized.
Drawings
Fig. 1 is a flow chart of a machine vision based method for identifying a head of a steel coil according to an embodiment of the present application.
Detailed Description
With reference to fig. 1, this embodiment provides a machine vision-based method for identifying a head of a steel coil, including: the image acquisition unit is suitable for acquiring steel coil image information and transmitting the steel coil image information to the processing unit, and the processing unit performs the following steps after acquiring the image:
s1, converting the size, and converting the size of the picture into a specified size after obtaining the picture;
s2, carrying out bilateral filtering on the picture with the transformed size to remove picture noise;
s3, self-adaptive histogram equalization, namely, calculating a local histogram in the steel coil image, redistributing the brightness value of the image according to the histogram, changing the contrast of the image, correcting uneven illumination in the image, and enhancing local details;
s4, graying the image, and carrying out graying treatment on the image by using a component method to obtain a gray image;
s5, detecting and obtaining an angular point through a FAST algorithm, wherein the angular point is the head of the steel coil; and determining the optimal segmentation threshold value of the foreground and the background of the steel coil in the gray image by using an Ojin method, and taking the difference value between the gray value of the steel coil and the optimal segmentation threshold value as the threshold value of a FAST algorithm.
In this embodiment, the image acquisition unit described herein may be an industrial camera for taking pictures of the steel coil from multiple directions, and the processing unit is an existing industrial software or industrial recognition system, which is capable of processing the image data and executing corresponding algorithms. The basic principle of the FAST algorithm is as follows: when the number of the pixels in the adjacent area of a certain pixel point and the pixels in different areas of the point is enough, the point is considered as a corner point. Specifically, the definition in this embodiment is as follows: and comparing the pixel gray values of the P point serving as the center with the pixel gray values of 16 pixel points on the neighborhood with the radius of 3 pixels, and if the difference value between the pixel gray values of more than 75% of the pixel points and the pixel gray values of the P point is larger than a set threshold value, considering the point as a corner point.
In this embodiment, since the steel coil head belongs to a corner point with a larger size in the image, a suitable radius of circumference of the pixel point compared with the P point and a threshold value need to be defined, otherwise, a problem of inaccurate identification occurs. Specifically, for the radius of the circumference of the pixel point, according to the principle of small-hole imaging, the distance between the steel coil and the image acquisition unit is unchanged, so that the number of pixels occupied by the diameter of the steel coil in the picture does not greatly change. Therefore, the steel coil diameter information can be converted into the radius of the detection circle in the FAST corner detection algorithm through mapping. The head of the steel coil with the measured diameter of 8mm takes up 18 pixels in the gray level image, and the diameter of the steel coil produced by factories has 8 specifications in total of 6-20 mm, and the corresponding pixels in the gray level image can be obtained by conversion according to the proportional relation, specifically: according to the principle of pinhole imaging, after the camera is calibrated, the size of the diameter of the steel coil head in the gray image and the size of the actual steel coil head are approximately in direct proportion, that is, the size of the steel coil diameter in the gray image can be estimated according to the actual steel coil size and calibrated parameters, namely: Φ1/Φ2=δ×d1/d2, where d1, d2 represent the diameter or radius of the circumference of the steel coil in the gray image, Φ1, Φ2 are the diameter of the actual steel coil head, d1, d2 represent the radius of the circumference of the coil in the gray image, and the parameter δ is a correction parameter, determined according to the installation position of the camera and the camera parameters; if d2 corresponding to phi 2 is required, phi 2 is substituted into the formula. The diameter of the steel coil is known before algorithm detection, and the circumference radius of the pixel point compared with the P point in the FAST algorithm can be obtained according to the diameter of the steel coil. For example, a steel coil having a diameter of 8mm should have a radius of 10 pixels for the pixel point to be compared with the P point, and the number of points on the circumference to be compared with the P point should be increased to 56.
In this embodiment, since the FAST algorithm threshold is set according to the picture condition, it is proposed to determine the optimal segmentation threshold of the foreground and the background of the picture steel coil by using the oxford method (OTSU), and use the difference between the gray value of the steel coil and the optimal segmentation threshold as the FAST algorithm threshold, so that the threshold can be automatically input according to the image information, without manual input, and the input efficiency is improved. The algorithm execution steps of the discipline method are as follows:
(1) Calculating a gray image histogram, counting the number of pixel points on 256 pixel values of 0-255, and normalizing;
(2) Setting a dividing threshold value i, and counting the proportion w of the pixel points with gray values in the range of 0-i in the image to the whole image according to the dividing threshold value 0 And an average gray value u 0 And the proportion w of the pixel points in the whole image with gray values in the range of i-255 1 And an average gray value u 1 At the same time, the total average gray level u of the image is calculated according to the following formula 2 And inter-class variance g:
u 2 ＝w 0 *u 0 +w 1 *u 1 ，
g＝w 0 (u 0 -u 2 )2+w 1 (u 1 -u 2 )2；
(3) And if the segmentation threshold i is increased by one gray value, continuing to execute the previous step until the last gray value, outputting the optimal segmentation threshold of the image segmentation foreground and background by taking i corresponding to the maximum inter-class variance g as the optimal segmentation threshold, and inputting the difference between the gray value of the steel coil and the optimal segmentation threshold as the threshold of the FAST algorithm.
In this embodiment, the gray pixel value of the steel coil in the gray image is in the range of 150 to 210. It is to be noted here that since an image employing this algorithm is susceptible to noise, it is necessary to perform noise reduction processing, that is, image gradation processing, on the image.
In the method, the size of the shot picture is larger, so that the detection speed can be influenced, and in S1, the size of the picture can be changed into a smaller size, the detection algorithm is quickened while the detection effect is not influenced, and meanwhile, the difference between the pixel numbers occupied by the steel coils with the same diameter can be ensured to be as small as possible. In addition, since the picture shot on site contains various noises, if the noise in the picture is removed by using the conventional gaussian filtering, the edge of the steel coil in the picture becomes blurred, so in the embodiment, the processing is performed by adopting a bilateral filtering mode in the S3, and the sharpness and smoothness of the edge information of the steel coil can be maintained. Meanwhile, as the change of illumination in the picture affects the result of the FAST algorithm, in S4, local histograms in the steel coil image are calculated, the brightness values of the image are redistributed according to the histograms, and the contrast of the image is changed, so that uneven illumination in the image is corrected, and local details are enhanced. In S5, the gray level processing is carried out on the image by using a component method to obtain a gray level image, and further, the highlighting has better stability when threshold calculation is carried out by a discipline method in the FAST algorithm. After being detected by the FAST algorithm, the double-sided filtering is finally carried out again, so that the boundary is clearer.
In another embodiment, the method further includes step S6, where step S6 is: after the steel coil head is obtained through step S5, the information is converted into three-dimensional coordinates. After the head is precisely identified and positioned in the image, the three-dimensional coordinates of the head position can be further identified and set according to the prior art. The three-dimensional coordinates are converted into the three-dimensional coordinates and then subjected to subsequent treatment, so that the positioning of the mechanical arm and other devices is facilitated.
The method starts from the actual engineering requirement, analyzes the characteristics of the head of the steel coil, converts the steel coil diameter information into the radius of a detection circle in a FAST corner detection algorithm through mapping, takes the difference value of the gray pixel value of the steel coil in a picture and the optimal segmentation threshold value of the foreground background of the picture as the comparison threshold value of the FAST corner detection algorithm, realizes the detection of the head of the steel coil in the picture, and obtains the position of the head of the steel coil in the picture, thereby positioning the three-dimensional coordinate of the head of the steel coil, transmitting the three-dimensional coordinate to a computer, and guiding a manipulator to finish the actions of grabbing, shearing and the like of the head of the steel coil. The method adopts the improved FAST corner detection algorithm for processing the traditional digital image to detect and identify the head of the steel coil in the picture, analyzes the steel coil in the picture, combines the characteristics of the head of the steel coil, improves the algorithm of the circumference radius in the FAST algorithm, and automatically sets the FAST threshold by combining the Ojin method, thereby solving the defect that the algorithm needs to manually set the threshold, adapting the algorithm to the detection of the head of the steel coil, and realizing automatic detection.
It should be understood that: the above is only a preferred embodiment of the present application, and the protection scope of the present application is not limited to the above examples, and all technical solutions belonging to the concept of the present application belong to the protection scope of the present application.
The description of the drawings in the embodiments above illustrates only certain embodiments of the application and should not be taken as limiting the scope, since other related drawings may be made by those of ordinary skill in the art without the benefit of the inventive faculty.
Claims (5)
1. A machine vision based method for identifying a head of a steel coil, comprising: the image acquisition unit is suitable for acquiring steel coil image information and transmitting the steel coil image information to the processing unit, and the processing unit performs the following steps after acquiring the image:
s1, converting the size, and converting the size of the picture into a specified size after obtaining the picture;
s2, carrying out bilateral filtering on the picture with the transformed size to remove picture noise;
s3, self-adaptive histogram equalization, namely, calculating a local histogram in the steel coil image, redistributing the brightness value of the image according to the histogram, changing the contrast of the image, correcting uneven illumination in the image, and enhancing local details;
s4, graying the image, and carrying out graying treatment on the image by using a component method to obtain a gray image;
s5, detecting and obtaining an angular point through a FAST algorithm, wherein the angular point is the head of the steel coil; and determining the optimal segmentation threshold value of the foreground and the background of the steel coil in the gray image by using an Ojin method, and taking the difference value between the gray value of the steel coil and the optimal segmentation threshold value as the threshold value of a FAST algorithm.
2. The machine vision based steel coil head recognition method according to claim 1, wherein the algorithm execution step of the discipline method is:
(1) Calculating a gray image histogram, counting the number of pixel points on 256 pixel values of 0-255, and normalizing;
(2) Setting a dividing threshold value i, and counting the proportion w of the pixel points with gray values in the range of 0-i in the image to the whole image according to the dividing threshold value 0 And an average gray value u 0 And the proportion w of the pixel points in the whole image with gray values in the range of i-255 1 And an average gray value u 1 At the same time, the total average gray level u of the image is calculated according to the following formula 2 And inter-class variance g:
u 2 ＝w 0 *u 0 +w 1 *u 1 ，
g＝w 0 (u 0 -u 2 )2+w 1 (u 1 -u 2 )2；
(3) The segmentation threshold i is increased by one gray value, the previous step is continuously executed until the last gray value, and i corresponding to the maximum inter-class variance g is used as the optimal threshold output for segmenting the foreground and the background of the image.
3. The machine vision based steel coil head recognition method of claim 2, wherein the gray pixel value of the steel coil in the gray image is in the range of 150 to 210.
4. The machine vision based steel coil head recognition method as set forth in claim 2, further comprising converting the information into three-dimensional coordinates after obtaining the steel coil head through step S6.
5. The machine vision based steel coil head recognition method of claim 1, wherein in S5, the steel coil diameter information is converted into a radius of a detection circle in a FAST corner detection algorithm by mapping.
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| CN202311047451.6A CN117115102A (en) | 2023-08-18 | 2023-08-18 | Machine vision-based steel coil head identification method |
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