A method for detecting the volume stability of steel slag based on image recognition technology

Abstract

The present application discloses a method for detecting the volume stability of steel slag based on image recognition technology, including: obtaining a target image to be recognized and extracting the color features of the target image; screening target image regions that meet the first preset condition according to the color features, where the target image regions contain at least one piece of steel slag; counting the surface area, the number of openings, and the pore diameter of the steel slag in the target image region according to the pixel points of the target image region; calculating the surface porosity of the steel slag according to the surface area, the number of openings, and the pore diameter of the steel slag; and determining that the volume stability of the steel slag is poor when the surface porosity is not less than the critical porosity. The method for detecting the volume stability of steel slag based on image recognition technology in the present application can batch and quickly and accurately judge the volume stability of steel slag, effectively improving the detection efficiency of the volume stability of steel slag.

Description

Technical Field

[0001] This application belongs to the technical field of image recognition. Specifically, it relates to a method for detecting the volume stability of steel slag based on image recognition technology. Background Art

[0002] Steel slag is the main solid waste in the iron and steel industry. Due to its characteristics such as wear resistance, anti-skid, high strength, and good adhesion, if steel slag is used as highway aggregate, it can not only effectively reduce the dependence on stone materials in engineering construction, solve the problems of shortage and uneven distribution of stone material resources, but also protect the ecological environment and reduce pollution.

[0003] In recent years, many studies have been carried out on the application of steel slag in road engineering, but it has not been widely promoted. The main reason is that steel slag with poor volume stability is prone to react with water during stacking and aging, resulting in steel slag expansion and even cracking, affecting the engineering quality. Therefore, before using steel slag for engineering construction, it is necessary to conduct refined screening of steel slag and screen out steel slag with poor volume stability. The existing technologies for judging the volume stability of steel slag usually adopt the following methods, such as measuring the content of free calcium oxide in steel slag, measuring the autoclave expansion rate of steel slag, etc. However, the above methods have certain deficiencies and cannot quickly and accurately judge the volume stability of steel slag in batches, and the detection efficiency is relatively low. Summary of the Invention

[0004] The purpose to be achieved by this application is to provide a method for detecting the volume stability of steel slag based on image recognition technology, which can quickly and accurately judge the volume stability of steel slag in batches, thereby effectively improving the detection efficiency of the volume stability of steel slag.

[0005] To achieve the above technical effects, this application provides a method for detecting the volume stability of steel slag based on image recognition technology, including: obtaining a target image to be recognized and extracting the color features of the target image; according to the color features, screening a target image area that meets the first preset condition, and the target image area contains at least one piece of steel slag; according to the pixel points of the target image area, counting the surface area, the number of openings, and the pore diameter of the steel slag in the target image area; calculating the surface porosity of the steel slag according to the surface area, the number of openings, and the pore diameter of the steel slag; and when the surface porosity is not less than the critical porosity, determining that the volume stability of the steel slag is poor.

[0006] The solution provided by this application is based on image recognition technology. By extracting the color features of the target image and screening the target image area based on preset conditions, it can more accurately identify the steel slag samples. Further, by statistically analyzing the surface area, number of pores, and pore diameter of the steel slag, and calculating the surface porosity accordingly, the volume stability of the steel slag is quantitatively evaluated through the surface porosity. Compared with the existing detection methods, this solution can significantly improve the detection efficiency. By automatically processing the image data, a large number of steel slag samples can be quickly identified and analyzed without professional and cumbersome experimental operations and long waiting times, thus achieving a rapid judgment of the volume stability of the steel slag.

[0007] As an improvement of the technical solution of this application, calculating the surface porosity of the steel slag according to the surface area, number of openings, and pore diameter of the steel slag includes: calculating the surface porosity of the steel slag according to the following formula:

[0008]

[0009] Wherein, V represents the surface porosity, S h represents the opening area, S n represents the surface area of the steel slag, α is a correction coefficient, and the opening area S h is calculated from the pore diameter and the number of openings of a single opening.

[0010] The solution provided by this application provides a way to quantitatively evaluate the volume stability of steel slag by establishing the relationships between the surface porosity of steel slag, the surface area, number of openings, and pore diameter of steel slag, and the relationship between the surface porosity of steel slag and volume stability. This method accurately identifies the opening conditions of steel slag particles based on image recognition, and then discriminates the volume stability of steel slag particles, reducing the interference of human factors and improving the objectivity and accuracy of the detection results.

[0011] As an improvement of the technical solution of this application, obtaining the target image to be recognized includes: collecting images of the steel slag to be measured from multiple directions to obtain at least one steel slag image; extracting key feature points in each of the steel slag images; and aligning and merging each of the steel slag images according to the key feature points to obtain the target image.

[0012] As an improvement of the technical solution of this application, screening the target image area that meets the first preset condition according to the color feature includes: performing threshold segmentation on the target image in the HSV color space according to a preset color threshold range to obtain a binary image of the target image; identifying the connected regions in the binary image; and screening the connected regions that meet the first preset condition as the target image area according to the shape features of the connected regions, where the first preset condition includes but is not limited to the minimum area threshold, maximum area threshold, and shape factor of the connected regions.

[0013] As an improvement of the technical solution of the present application, the steps of calculating the surface area, the number of openings, and the pore diameter of the steel slag in the target image area based on the pixel points of the target image area include: calculating the surface area of the steel slag in the target image area according to the number of pixel points in the target image area; calculating the number of openings and the pore diameter of the steel slag in the target image area according to the color characteristics of the pixel points in the target image area.

[0014] On the basis of the above improvement, further, the step of calculating the surface area of the steel slag in the target image area according to the number of pixel points in the target image area includes: calculating the number of pixel points in the connected area in the target image area; determining the surface area of the steel slag in the target image area according to the number of pixels, the resolution of the target image, and the unit length.

[0015] On the basis of the above improvement, further, the step of calculating the number of openings and the pore diameter of the steel slag in the target image area according to the color characteristics of the pixel points in the target image area includes: based on a starting pixel point in the target image area, determining adjacent pixel points of the starting pixel point in each target direction; when the color characteristics of the adjacent pixel points meet a second preset condition, using the adjacent pixel points as the starting pixel point, and determining adjacent pixel points of the starting pixel point in each target direction; when the color characteristics of the adjacent pixel points do not meet the second preset condition, determining the starting pixel point and all adjacent pixel points that meet the second preset condition as an opening; determining the pore diameter of the opening according to the starting pixel point and all adjacent pixel points that meet the second preset condition.

[0016] Further, the second preset condition includes: the brightness of the pixel point is not greater than 9%.

[0017] The beneficial effects of the present application are as follows:

[0018] 1. The solution provided by the present application is based on image recognition technology. By extracting the color characteristics of the target image and screening the target image area based on preset conditions, the steel slag sample can be recognized more accurately. Further, by calculating the surface area, the number of pores, and the pore diameter of the steel slag, and calculating the surface porosity accordingly, the volume stability of the steel slag can be quantitatively evaluated through the surface porosity. Compared with the existing detection methods, this solution can significantly improve the detection efficiency. By automatically processing image data, a large number of steel slag samples can be quickly recognized and analyzed without professional and cumbersome experimental operations and long waiting times, thus realizing a rapid judgment of the volume stability of the steel slag.

[0019] 2. The solution provided by this application provides a method for quantitatively evaluating the volume stability of steel slag by establishing the relationships between the surface porosity of steel slag and its surface area, the number of open pores, and the pore diameter, as well as the relationship between the surface porosity of steel slag and its volume stability. Based on image recognition, this method accurately identifies the open-pore situation of steel slag particles, thereby determining the volume stability of steel slag particles, reducing the interference of human factors, and improving the objectivity and accuracy of the detection results. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the following briefly introduces the drawings required for description in the embodiments or the prior art. Obviously, the drawings in the following description are some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can be obtained based on these drawings.

[0021] Figure 1 is a schematic flowchart of a method for detecting the volume stability of steel slag based on image recognition technology in an embodiment of this application;

[0022] Figure 2 is a schematic diagram showing the relationship between the surface porosity of steel slag and the immersion expansion rate. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0023] The following will describe in detail the embodiments of the technical solutions of this application. The following embodiments are only used to more clearly illustrate the technical solutions of this application, so they are only examples and cannot be used to limit the protection scope of this application. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0024] The following combines the attached Figure 1 and 2 to detail the technical solutions provided by each embodiment of this application.

[0025] Please refer to Figure 1 , which is a schematic flowchart of a method for detecting the volume stability of steel slag based on image recognition technology provided by an embodiment of this application. The method may include the following steps:

[0026] S1: Obtain a target image to be recognized and extract the color features of the target image.

[0027] Specifically, the target image is an image obtained by photographing the steel slag whose volume stability needs to be detected. To ensure that the steel slag features in the target image are clear, in one implementation, the obtaining of the target image to be recognized includes the following sub-steps:

[0028] S11: Collect images of the steel slag to be measured from multiple directions to obtain at least one steel slag image;

[0029] S12: Extract the key feature points in each of the steel slag images;

[0030] S13: Align and merge each of the steel slag images according to the key feature points to obtain the target image.

[0031] The above-mentioned multiple directions include i directions, where i = 1, 2, 3, 4, 5, 6, namely front, back, left, right, up, and down. Since the morphology of the steel slag may vary in each direction, when aligning and merging each of the steel slag images, it is easy for steel slag images at different angles to overlap, resulting in the surface area of the steel slag in the steel slag image being larger than the actual surface area. Exemplarily, in this embodiment, the ORB feature point detection and matching algorithm can be used to perform ORB feature point detection on at least two steel slag images respectively, extract the key feature points in each of the steel slag images, and obtain the feature point sets of each steel slag image. The above-mentioned key feature points include corner points. Furthermore, binary feature descriptors are generated by comparing randomly selected pixel pairs around the key feature points. Since the binary feature descriptors have rotational invariance, a matching algorithm can be used to match the feature descriptors of at least two images to obtain matching point pairs. The matching point pairs are screened according to the matching quality, and the incorrect matching point pairs are removed. Multiple steel slag images are aligned and merged to obtain a target image with all the features of the steel slag to be measured. In this embodiment, the key feature points extracted from the multi-directional steel slag images are used for image alignment, which can ensure that the steel slag images taken at different angles can be accurately aligned when merged, avoiding image overlap and incorrect increase in surface area caused by the multi-directional differences in the morphology of the steel slag. The merged target image can more comprehensively display the three-dimensional morphology of the steel slag, reduce the morphological missing caused by the shooting angle limitation, and improve the accuracy of subsequent recognition.

[0032] It should be noted that the change of illumination conditions will affect the perception of color. To avoid the loss of the features of steel slag particles caused by external factors such as illumination, the recommended illumination intensity during image collection is 500 lux to 900 lux.

[0033] The color features include at least one of hue, saturation, and brightness. The target image captured by the image acquisition device is generally in the RGB color space. To extract the color features of the target image, the target image needs to be converted to the HSV color space.

[0034] S2: According to the color features, screen the target image regions that meet the first preset condition, and the target image regions contain at least one steel slag.

[0035] In one implementation, in order to efficiently and accurately identify the target image region containing steel slag and reduce the interference of non-steel slag substances, this step includes the following sub-steps:

[0036] S21: Perform threshold segmentation on the target image in the HSV color space according to a preset color threshold range to obtain a binary image of the target image;

[0037] Specifically, the above color threshold range is that the pixel brightness is not higher than 40%. According to the statistical results of the brightness of steel slag under the illumination intensity of 500 lux to 900 lux, the brightness of the pores in the steel slag generally does not exceed 9%, and the surface brightness of the dense steel slag does not exceed 40%. Threshold segmentation of the target image in the HSV color space can distinguish pixel points belonging to the color of steel slag from pixel points of non-steel slag colors. The binary image obtained after threshold segmentation only includes the steel slag marked as the foreground and the background marked.

[0038] S22: Identify the connected regions in the binary image;

[0039] Each object in the image can be regarded as a connected region. This step further determines the steel slag to be measured in the image by identifying the connected regions. Exemplarily, this step can use common connected component analysis algorithms for identification, such as depth-first search DFS or breadth-first search BFS.

[0040] S23: According to the shape characteristics of the connected regions, screen the connected regions that meet the first preset conditions as the target image region, and the first preset conditions include but are not limited to the minimum area threshold, maximum area threshold, and shape factor of the connected regions.

[0041] Specifically, after identifying all the connected regions, it is necessary to screen the target image regions that can be used for volume stability detection according to the preset shape feature conditions. Among them, the minimum area threshold in the first preset condition is used to exclude too small regions caused by noise or mis-segmentation, and these regions are usually not valid steel slag regions. The maximum area threshold is used to prevent misjudging too large non-steel slag regions as target regions, especially when there are large non-steel slag objects with similar colors in the image. The shape factor is a measure describing the shape of the connected region, and it can be obtained by calculating the ratio of the perimeter to the area of the region. By setting an appropriate shape factor threshold, steel slag regions with more regular shapes or meeting specific requirements can be further screened. The specific values of the above minimum area threshold, maximum area threshold, and shape factor can be set according to actual application requirements, and this embodiment does not make any restrictions on this.

[0042] S3: According to the pixel points of the target image region, count the surface area, number of pores, and pore diameter of the steel slag in the target image region.

[0043] Specifically, according to the statistical results of the brightness of steel slag under a light intensity of 500 lux to 900 lux, the lightness of the open pores of steel slag generally does not exceed 9%, and the surface lightness of dense steel slag is between 10% and 40%. Therefore, based on the difference in lightness between the open pores and the surface of steel slag, key parameters such as the surface area, number of open pores, and pore diameter of steel slag can be effectively counted according to the pixel points in the target image area.

[0044] S4: Calculate the surface porosity of the steel slag according to the surface area, number of open pores, and pore diameter of the steel slag.

[0045] In one implementation, the surface porosity of the above-mentioned steel slag can be calculated by the following formula (1):

[0046]

[0047] Among them, V represents the surface porosity, S h represents the open pore area, S n represents the surface area of the steel slag, α is a correction coefficient, and the open pore area S h is calculated from the pore diameter of a single open pore and the number of open pores.

[0048] Specifically, the surface porosity of steel slag is the ratio of the pore volume to the particle volume, which includes the voids on the surface and inside the steel slag. During the image recognition process, only the pore diameter and the number of open pores on the surface of the steel slag are considered, and a preliminary relationship of the surface porosity of the steel slag is established, that is, the open pore area of the steel slag is compared with the surface area of the steel slag. Since there is a deviation between the surface porosity of the steel slag calculated by this relationship and the actual surface porosity of the steel slag, according to the proportional relationship between the surface area and volume of multiple groups of steel slag, a correction coefficient α is introduced to reduce the deviation between the surface porosity of the steel slag calculated by the above formula (1) and the actual surface porosity of the steel slag. The value of the correction coefficient α is 0.6 to 0.8.

[0049] S5: When the surface porosity is not less than the critical porosity, determine that the volume stability of the steel slag is poor.

[0050] Specifically, the above-mentioned critical porosity is set to 6.0%.

[0051] The internal reason for the poor volume stability of steel slag is that free calcium oxide and free magnesium oxide come into contact with water and undergo a hydration reaction, resulting in the volume of the steel slag expanding by 0.98 to 1.28 times. The external reason for the poor volume stability of steel slag is the porous structure of the steel slag, and its abundant pore diameter and number of pores provide a necessary channel for the hydration of the steel slag. The decisive factor for the ease of the hydration reaction of the steel slag is the surface porosity of the steel slag. Therefore, the surface porosity of the steel slag is an important factor affecting the immersion expansion rate of the steel slag.

[0052] In order to determine the influence of the surface porosity of steel slag on the immersion expansion rate of steel slag, the inventors of the present application selected a certain amount of steel slag, calculated the surface porosity according to step S4, and calculated the immersion expansion rate after the steel slag was subjected to thermal soaking treatment.

[0053] Figure 2 The relationship diagram between the surface porosity of steel slag and the immersion expansion rate measured after the thermal soaking process is shown. From Figure 2 it can be seen that the immersion expansion rate of steel slag gradually increases with the increase of the surface porosity. When the surface porosity of steel slag is less than 6.0%, the steel slag is dense, with small pore diameters and fewer pores, and there are fewer hydration channels for free calcium oxide and free magnesium oxide, making it difficult to carry out hydration reactions. Therefore, the immersion expansion rate of steel slag is low, and the volume stability of steel slag is good; when the surface porosity of steel slag is greater than or equal to 6.0%, the steel slag surface is porous, with large pore diameters and more pores, and the hydration reactions of free calcium oxide and free magnesium oxide are easy to carry out, resulting in a higher immersion expansion rate of steel slag and poor volume stability. The existing specifications require that when steel slag is used as highway aggregate, its immersion expansion rate limit is 1.0% - 3.0%. Therefore, according to Figure 2 the results, in order to ensure that the immersion expansion rate of steel slag does not exceed 1.0%,

[0054] The solution provided by the present application provides a way to quantitatively evaluate the volume stability of steel slag by establishing the relationships between the surface porosity of steel slag and the surface area, the number of open pores, and the pore diameter of steel slag, and between the surface porosity of steel slag and the volume stability. This method accurately identifies the open pore situation of steel slag particles based on image recognition, and then discriminates the volume stability of steel slag particles, reducing the interference of human factors and improving the objectivity and accuracy of the detection results.

[0055] In one implementation, in order to ensure the accuracy of the surface porosity of steel slag, the steps of statistically calculating the surface area, the number of open pores, and the pore diameter of steel slag in the target image area according to the pixel points of the target image area include the following sub-steps:

[0056] S31: Statistically calculate the surface area of steel slag in the target image area according to the number of pixel points in the target image area;

[0057] In one implementation, this step includes: statistically calculating the number of pixel points in the connected region in the target image area; determining the surface area of steel slag in the target image area according to the number of pixels, the resolution of the target image, and the unit length.

[0058] Specifically, the resolution refers to the width and height of the image in pixels, and the unit length is the actual physical length represented by each pixel, and the unit is usually millimeters, centimeters, or meters. According to the number of pixels, the resolution of the target image, and the unit length, the actual physical surface area of steel slag in the target image area can be calculated.

[0059] S32: According to the color features of the pixel points in the target image region, count the number of openings and the pore diameters of the steel slag within the target image region.

[0060] In one implementation, this step includes the following sub-steps:

[0061] S321: Based on a starting pixel point within the target image region, determine the adjacent pixel points of the starting pixel point in each target direction;

[0062] S322: When the color features of the adjacent pixel points meet the second preset condition, use the adjacent pixel points as the starting pixel point, and determine the adjacent pixel points of the starting pixel point in each target direction;

[0063] S323: When the color features of the adjacent pixel points do not meet the second preset condition, determine the starting pixel point and all adjacent pixel points that meet the second preset condition as one opening;

[0064] S324: Determine the pore diameter of the opening according to the starting pixel point and all adjacent pixel points that meet the second preset condition.

[0065] Specifically, the second preset condition is that the pixel lightness is not greater than 9%. The starting pixel point is a random pixel point within the target image region. Recursive search is performed with this starting pixel point. When the color features of the adjacent pixel points fall within the range of the second preset condition, it is considered that the adjacent pixel point and the starting pixel point belong to the same opening region. When an adjacent pixel point that does not meet the second preset condition is encountered during the recursive search process, it indicates that the boundary of the current opening region has been searched. Thus, the number of openings of the steel slag within the target image region is determined. It should be noted that after step S324, it is necessary to convert the number of openings and the pore diameters of the steel slag into actual physical dimensions based on the resolution of the target image.

[0066] In order to verify the accuracy of the values of the surface area, the number of openings, and the pore diameters of the steel slag in the above embodiments, the inventors of this application set up a comparative experiment, using 50 steel slags in different situations as experimental samples, and the size ranges of the steel slags were all controlled within 10 - 15 mm. The surface area, the number of openings, and the pore diameters of each steel slag in the experimental samples were calculated respectively by the method of manually observing the microstructure of the steel slag and the method of image recognition. The recognition results are shown in Table 1.

[0067] Table 1 Comparison results of the accuracy of various data of the experimental samples between manual recognition and image recognition

[0068] Name Manual recognition Image recognition Number of steel slag (pcs) 50 50 Time used (s) 15780 6 Accuracy of steel slag surface area (%) 78 98 Accuracy of steel slag pore number (%) 87 100 Accuracy of steel slag pore diameter (%) 34 98

[0069] The results in Table 1 above can verify that the embodiment of the present application is based on an image recognition method, that is, according to the pixel points of the target image area, the surface area, the number of openings, and the pore diameter of the steel slag in the target image area are statistically analyzed. Compared with the prior art recognition method, it has higher accuracy and recognition speed. Therefore, the solution provided by the present application is based on image recognition technology. By extracting the color features of the target image and screening the target image area based on preset conditions, it can more accurately identify steel slag samples. Further, by statistically analyzing the surface area, the number of openings, and the pore diameter of the steel slag, and calculating the surface porosity accordingly, the volume stability of the steel slag is quantitatively evaluated through the surface porosity. Compared with the prior art detection methods, this solution can quickly identify and analyze a large number of steel slag samples through automatic processing of image data, without the need for professional and cumbersome experimental operations and long waiting times, thereby realizing a rapid judgment of the volume stability of the steel slag and significantly improving the detection efficiency.

[0070] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements for some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for detecting the volume stability of steel slag based on image recognition technology, characterized in that: include: Acquire a target image to be identified, and extract color features of the target image; According to the color feature, a target image region meeting a first preset condition is screened, wherein the target image region contains at least one steel slag; According to the pixel points of the target image area, the surface area, the number of openings and the aperture of the slag in the target image area are counted; Calculating the surface porosity of the steel slag according to the surface area, number of pores and pore diameter of the steel slag; When the surface porosity is not less than the critical porosity, it is determined that the volume stability of the steel slag is poor.

2. The method for detecting the volume stability of steel slag based on image recognition technology according to claim 1, characterized in that: Calculating the surface porosity of the steel slag according to the surface area, pore number and pore diameter of the steel slag includes: calculating the surface porosity of the steel slag according to the following formula: Where V represents the surface porosity, S h Indicates the opening area, S n represents the surface area of ​​slag, α is the correction factor, and the opening area S h It is calculated from the diameter of a single opening and the number of openings.

3. The method for detecting the volume stability of steel slag based on image recognition technology according to claim 1, characterized in that: The obtaining of the target image to be identified comprises: Collect images of the steel slag to be tested from multiple directions to obtain at least one steel slag image; Extracting key feature points from each of the slag images; The slag images are aligned and merged according to the key feature points to obtain the target image.

4. The method for detecting the volume stability of steel slag based on image recognition technology according to claim 1, characterized in that: The screening of the target image area meeting the first preset condition according to the color feature comprises: According to a preset color threshold range, threshold segmentation is performed on the target image in the HSV color space to obtain a binary image of the target image; identifying connected regions in the binary image; According to the shape features of the connected areas, connected areas that meet first preset conditions are screened as target image areas, where the first preset conditions include but are not limited to a minimum area threshold, a maximum area threshold and a shape factor of the connected areas.

5. The method for detecting the volume stability of steel slag based on image recognition technology according to claim 1, characterized in that: The counting of the surface area, the number of openings and the aperture of the slag in the target image region according to the pixel points in the target image region comprises: Counting the surface area of ​​the slag in the target image area according to the number of pixels in the target image area; According to the color features of the pixels in the target image area, the number and diameter of the pores of the slag in the target image area are counted.

6. The method for detecting the volume stability of steel slag based on image recognition technology according to claim 5, characterized in that: The counting of the surface area of ​​the slag in the target image area according to the number of pixels in the target image area comprises: Counting the number of pixels in the connected area within the target image area; The surface area of ​​the slag in the target image region is determined according to the number of pixels, the resolution of the target image and the unit length.

7. The method for detecting the volume stability of steel slag based on image recognition technology according to claim 5, characterized in that: The counting of the number and diameter of openings of the slag in the target image area according to the color features of the pixels in the target image area includes: Based on a starting pixel point in the target image area, determining adjacent pixel points of the starting pixel point in each target direction; When the color characteristics of the adjacent pixel points satisfy a second preset condition, the adjacent pixel points are used as the starting pixel points, and adjacent pixel points of the starting pixel points are determined in each target direction; When the color characteristics of the adjacent pixels do not satisfy the second preset condition, the starting pixel and all adjacent pixels satisfying the second preset condition are determined as one opening; The aperture of the opening is determined according to the starting pixel point and all adjacent pixel points that meet a second preset condition.

8. The method for detecting the volume stability of steel slag based on image recognition technology according to claim 7, characterized in that: The second preset condition includes: the brightness of the pixel is not greater than 9%.

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