Method for rapidly detecting steel slag mixed in aggregate for concrete
Through the method of image acquisition and grayscale analysis, it is possible to identify whether steel slag is mixed in the aggregate for concrete, which solves the problem of difficulty in distinguishing with existing technology, achieves rapid and accurate detection, and ensures the quality of concrete production.
Patent Information
- Application Number
- CN202411463191.5
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-21
- Publication Date
- 2025-01-24
AI Technical Summary
The prior art is difficult to effectively distinguish whether steel slag is mixed into the aggregate for concrete, resulting in the impact of the volume stability and durability of concrete.
Through image collection and grayscale analysis, color differences are used to identify whether steel slag is mixed in concrete aggregates. Specific steps include collecting images of aggregate samples for concrete, performing image preprocessing, analyzing grayscale distribution, setting standard interval thresholds, and determining whether steel slag is mixed in through comparison.
It achieves rapid and accurate detection of whether steel slag is mixed into concrete aggregates, improves the convenience, sensitivity and accuracy of detection, and ensures the quality of concrete production.
Smart Images
Figure CN119354973A_ABST
Abstract
Description
Technical Field
[0001] The invention relates to a rapid detection method for steel slag mixed in concrete aggregate, belonging to the field of material detection. Background Art
[0002] The chemical composition, surface morphology, particle grading, and firmness of steel slag are similar to those of ordinary sand and gravel, but steel slag contains a large amount of active ingredients such as free CaO and MgO, resulting in poor stability. If it is mixed into aggregates, it will seriously affect the volume stability and durability of mortar and concrete products. In recent years, in concrete construction projects, the mixing of steel slag into concrete aggregates has caused concrete to collapse, crack, and even more serious quality and safety accidents. Therefore, how to effectively distinguish steel slag mixed into aggregates has become an important issue that needs to be solved in the construction industry.
[0003] At present, concrete production companies mostly distinguish steel slag from concrete aggregates based on differences in color, morphology, etc. However, as the types of concrete aggregates increase, their colors and morphologies also become more diverse, making it difficult to distinguish steel slag from concrete aggregates based on experience, and the difficulty of identification has increased significantly. Summary of the invention
[0004] The purpose of the present invention is to overcome the shortcomings of the prior art, based on the obvious color difference between steel slag particles and concrete aggregate, provide a rapid detection method for steel slag mixed in concrete aggregate, effectively solve the problem that it is difficult to distinguish steel slag mixed in concrete aggregate, thereby effectively ensuring the production quality of concrete.
[0005] The present invention adopts the following technical solution: a rapid detection method for steel slag mixed in concrete aggregate, comprising the following steps:
[0006] Step 1: comprehensively collect a number of concrete aggregate samples, and use an image acquisition device to respectively acquire images of all samples to obtain images of a number of corresponding concrete aggregate samples;
[0007] Step 2: Use analysis software to perform image preprocessing on the images of several corresponding concrete aggregate samples to obtain the grayscale distribution of each image;
[0008] Step 3: Analyze and process the grayscale distribution of each sample image separately, as follows:
[0009] 3a. Count the number of pixels at each gray level;
[0010] 3b. Set the grayscale standard interval and calculate the pixel ratio of each standard interval;
[0011] Step 4: Select a specific standard interval based on experience, compare the pixel ratios of the specific standard interval of each image, and select the maximum pixel ratio of the specific standard interval as the standard interval threshold for identifying concrete aggregate and steel slag;
[0012] Step 5: Capture the image of the unknown aggregate sample, repeat the operations of steps 1 to 3, and obtain the pixel ratio of each standard interval of the image of the unknown aggregate sample;
[0013] Step 6: Determine whether steel slag is mixed into the unknown aggregate sample based on the selected standard interval threshold; wherein, if the pixel point ratio of the specific standard interval of the unknown aggregate sample is higher than the standard interval threshold, it is determined that steel slag is mixed into the unknown aggregate sample; otherwise, it is determined that steel slag is not mixed into the unknown aggregate sample.
[0014] Preferably, in step 1, the concrete aggregate samples are river sand and / or machine-made sand supplied to the concrete mixing station, and 3 to 5 types are selected in order of color from dark to light.
[0015] Preferably, in step 2, the image preprocessing includes converting the color image into a gray image, unifying the brightness, and selecting the grayscale distribution area to be analyzed.
[0016] Preferably, in step 3a, the grayscale level range S is 1 to x, where x is an integer from 1 to 255; the number of pixels corresponding to each grayscale level in the image of the concrete aggregate sample is counted, wherein the number of pixels with a grayscale level of 1 is recorded as C1, the number of pixels with a grayscale level of 2 is recorded as C2, and so on, the number of pixels with a grayscale level of x is recorded as C x .
[0017] Preferably, in step 3b, the grayscale range S is divided into n standard intervals at a fixed and reasonable interval interval m, wherein the first standard interval is recorded as S1, the second standard interval is recorded as S2, and so on, the nth standard interval is recorded as S n ; Wherein, m and n are both positive integers.
[0018] Preferably, in step 3b, if the grayscale level range S cannot be divided by the interval m, the integer part obtained by dividing the grayscale level range S by m is recorded as n, and the remainder range at the end of the grayscale level range S is discarded.
[0019] Preferably, in step 3b, the accumulated number of pixels of each gray level in each gray level standard interval in the image of the concrete aggregate sample is calculated, wherein the accumulated number of pixels of each gray level in the first standard interval S1 is P1=C1+C2+C3+…+C m The cumulative number of pixels of each gray level in the second standard interval S2 is P2 = Cm+1 +C m+2 +C m+3 +......+C 2m , and so on, the nth standard interval S n The accumulated number of pixels of each gray level in is P n =C (n-1)m+1 +C (n-1)m+2 +C (n-1)m+3 +......+C nm .
[0020] Preferably, in step 3b, the ratio of the accumulated number of pixels of each gray level in each gray level standard interval in the image of the concrete aggregate sample to the total number of pixels in the image is calculated, referred to as the pixel ratio, wherein the pixel ratio of the first standard interval S1 is R1=P1 / (P1+P2+…+P n ), the pixel ratio of the second standard interval S2 is R2=P2 / (P1+P2+……+P n ), and so on, the nth standard interval S n The pixel ratio is R n =P n / (P1+P2+……+P n ).
[0021] Preferably, in step 4, a specific vth standard interval is selected based on experience, and the pixel ratio R of the vth standard interval of each image is v Compare and select the R with the largest value v The standard interval threshold M is used as the identification of concrete aggregate and steel slag.
[0022] Preferably, in step 4, the specific vth standard interval is selected based on: the vth standard interval should not exceed C max The standard interval of the corresponding gray level; among them, C max It is the maximum number of pixels in all gray levels.
[0023] Preferably, in step 5, an image of the unknown aggregate sample is collected, and the operations of steps 1 to 3 are repeated to obtain the pixel ratio R of the unknown sample in the specific vth standard interval. v '.
[0024] Preferably, in step 6, if the unknown sample has a pixel ratio R in a specific vth standard interval v ' is greater than the standard interval threshold M, it is judged that steel slag is mixed into the unknown aggregate sample; otherwise, it is judged that steel slag is not mixed into the unknown aggregate sample.
[0025] Beneficial effects of the present invention: Based on the color difference between steel slag and concrete aggregate, the present invention performs image acquisition, grayscale analysis and calculation on a series of concrete aggregate samples with colors ranging from dark to light, and then selects the ratio R of the accumulated pixel number of each grayscale level in the specific vth standard interval to the total pixel number of the image by comparison. v The R of the sample with the largest value v The value is used as the standard interval threshold M for identifying concrete aggregate and steel slag. By comparing the relevant data of the sample to be tested with the threshold M, it is possible to quickly and accurately detect whether steel slag is mixed into the concrete aggregate, thereby improving the convenience, sensitivity and accuracy of detection. BRIEF DESCRIPTION OF THE DRAWINGS
[0026] Figure 1 : Super depth of field photos of river sand as aggregate for concrete and its pixel ratio.
[0027] Figure 2 : Super depth of field photos of No. 1 machine-made sand for concrete aggregate and its pixel ratio.
[0028] Figure 3 : Super depth of field photos of No. 2 machine-made sand for concrete aggregate and its pixel ratio.
[0029] Figure 4 : Super depth of field photo of No. 1 steel slag and its pixel ratio.
[0030] Figure 5 : Super depth of field photos of No. 2 steel slag and their pixel ratios.
[0031] Figure 6 : Super depth of field photos of No. 3 steel slag and their pixel ratios.
[0032] Figure 7 : Super depth of field photos of No. 4 steel slag and its pixel ratio.
[0033] Figure 8 : Super depth of field photos of No. 5 steel slag and its pixel ratio.
[0034] Fig. 9 : Ultra-depth of field photograph of a mixed sample of 15% No. 1 steel slag mixed with No. 2 machine-made sand and its pixel ratio.
[0035] Fig.10 : Ultra-depth of field photograph of the mixed sample of 15% No. 2 steel slag mixed with No. 2 machine-made sand and its pixel ratio.
[0036] Fig.11 : Ultra-depth of field photograph of the mixed sample of 15% No. 3 steel slag mixed with No. 2 machine-made sand and its pixel ratio.
[0037] Fig.12: Ultra-depth of field photograph of a mixed sample of 15% No. 4 steel slag mixed with No. 2 machine-made sand and its pixel ratio.
[0038] Fig.13 : Ultra-depth of field photograph of a mixed sample of 15% No. 5 steel slag mixed with No. 2 machine-made sand and its pixel ratio.
[0039] Fig.14 : Super depth of field photo of unknown sample No. 1 and its pixel ratio.
[0040] Fig.15 : Super depth of field photo of unknown sample No. 2 and its pixel ratio.
[0041] Fig.16 : Super depth of field photo of unknown sample No. 3 and its pixel ratio.
[0042] Fig.17 : Super depth of field photo of unknown sample No. 4 and its pixel ratio.
[0043] Fig.18 : Super depth of field photo of unknown sample No. 5 and its pixel ratio.
[0044] Fig.19 : Super depth of field photo of unknown sample No. 6 and its pixel ratio. DETAILED DESCRIPTION
[0045] In order to better illustrate the technical solution of the present invention, the present invention will be further described below in conjunction with specific embodiments.
[0046] The rapid detection method of steel slag mixed in concrete aggregate of the present invention comprises the following steps:
[0047] Step 1: Collect concrete aggregates of different colors from the concrete mixing plant as samples, including No. 1 machine-made sand, No. 2 machine-made sand and river sand, and use an electron microscope to collect images of the samples to obtain images of the above three concrete aggregate samples respectively.
[0048] Step 2: Perform image preprocessing on the images of each concrete aggregate sample obtained in step 1, wherein the image preprocessing includes converting the color image into a gray image, unifying the brightness, and selecting the grayscale distribution area to be analyzed.
[0049] Step 3: The grayscale distribution of the images of the three concrete aggregate samples is analyzed and processed respectively according to the following steps, as follows:
[0050] 3a. Count the number of pixels at each gray level of the sample image:
[0051] The grayscale level range S of the sample image is 1 to 255. The number of pixels corresponding to each grayscale level in the sample image is counted. The number of pixels with grayscale level 1 is recorded as C1, the number of pixels with grayscale level 2 is recorded as C2, and so on. The number of pixels with grayscale level 255 is recorded as C 255 ;
[0052] And thus obtain the C of No. 1 machine-made sand max The corresponding gray level is 107, C of No. 2 machine-made sand max The corresponding gray level is 95, the C of river sand max The corresponding gray level is 113; among them, C max is the maximum number of pixels in all gray levels of each sample image;
[0053] 3b. Set the grayscale standard interval and calculate the pixel ratio of each standard interval:
[0054] The grayscale range S (1 to 255) is divided into 10 standard intervals at intervals of 25, where the first standard interval is denoted as S1, the second standard interval is denoted as S2, and so on, and the tenth standard interval is denoted as S 10 , where S 10 The grayscale levels covered by the standard interval range from 226 to 250;
[0055] Calculate the cumulative number of pixels of each gray level in each gray level standard interval in the sample image, where the cumulative number of pixels of each gray level in the first standard interval S1 is P1 = C1 + C2 + C3 + ... + C 25 The cumulative number of pixels of each gray level in the second standard interval S2 is P2 = C 26 +C 27 +C 28 +......+C 50 , and so on, the 10th standard interval S 10 The accumulated number of pixels of each gray level in is P 10 =C 226 +C 227 +C 228 +......+C 250 ; After calculation, P1-P of No. 1 machine-made sand 10 They are 0, 6170, 70172, 136591, 81297, 19061, 2570, 493, 122, 110 respectively. P1-P of No. 2 machine-made sand 1o They are 0, 0, 59022, 87163, 139086, 28628, 10124, 2141, 22, 0 respectively. The P1-P 10They are 0, 680, 36403, 104313, 112580, 38226, 18343, 4128, 1641, 126 respectively;
[0056] Calculate the ratio of the accumulated number of pixels of each gray level in each gray level standard interval in the sample image to the total number of pixels in the image, referred to as the pixel ratio, where the pixel ratio of the first standard interval S1 is R1 = P1 / (P1+P2+…+P 10 ), the pixel ratio of the second standard interval S2 is R2=P2 / (P1+P2+……+P 10 ), and so on, the 10th standard interval S 10 The pixel ratio is R 10 =P 10 / (P1+P2+…+P 10 ); After calculation, R1-R 10 They are 0%, 1.96%, 22.30%, 43.41%, 25.84%, 6.06%, 0.82%, 0.16%, 0.04%, 0.03% respectively. R1-R 10 They are 0%, 0%, 18.09%, 26.72%, 42.64%, 8.78%, 3.10%, 0.66%, 0.01%, and 0%, respectively. The R1-R 10 They are 0%, 0.21%, 11.50%, 32.96%, 35.58%, 12.08%, 5.80%, 1.30%, 0.52% and 0.04% respectively.
[0057] Step 4: Based on experience and combined with the C values of No. 1 machine-made sand, No. 2 machine-made sand, and river sand obtained in Step 3, max The corresponding grayscale levels (respectively 95, 113, and 107), the third standard interval S3 (grayscale level range 51 to 75) is selected as the specific standard interval, the pixel point ratio R3 of the third standard interval S3 of No. 1 machine-made sand, No. 2 machine-made sand, and river sand (respectively 22.30%, 18.09%, and 11.50%) is compared, and 22.30% is selected as the standard interval threshold M for identifying concrete aggregate and steel slag.
[0058] Step 5: Capture an image of the unknown aggregate sample, and repeat the operations of steps 1 to 3 to obtain the pixel ratio R3' of the third standard interval of the image of the unknown aggregate sample.
[0059] Step 6: Determine whether steel slag is mixed into the unknown aggregate sample based on the selected standard interval threshold, wherein if the pixel point ratio R3' of the unknown sample in the third standard interval is greater than the standard interval threshold M, it is determined that steel slag is mixed into the unknown aggregate sample; otherwise, it is determined that steel slag is not mixed into the unknown aggregate sample.
[0060] In order to determine the effectiveness of the grayscale standard interval threshold M setting of the present invention, verification is performed in the following ways:
[0061] 1) Select 5 steel slag samples from different steel mills, wherein No. 1 steel slag is taken from a steel mill in Dongli District, Tianjin, No. 2 steel slag is taken from a steel mill in Caofeidian District, Tangshan City, Hebei Province, No. 3 steel slag is taken from a steel mill in Tangshan City, Hebei Province, No. 4 steel slag is taken from a steel mill in Zhanjiang City, Guangdong Province, and No. 5 steel slag is taken from a steel mill in Chengde City, Hebei Province; repeat steps 1 to 3 of the above-mentioned detection method of the present invention to obtain the pixel point ratios of the third standard interval of the images of the steel slag samples from the 5 different steel mills, which are 30.1%, 55.44%, 28.13%, 45.25%, and 50.15%, respectively, which are all higher than the standard interval threshold M (22.30%), indicating that steel slag is mixed in the sample;
[0062] 2) According to weight percentage, 15% of the steel slag samples from the above five different steel mills were mixed with No. 2 machine-made sand to obtain five mixed samples, and then steps 1 to 3 of the above detection method of the present invention were repeated to obtain the pixel point ratios of the third standard interval of the images of the above five mixed samples, which were 28.52%, 28.05%, 25.12%, 24.14% and 28.25%, respectively, which were all higher than the standard interval threshold M (22.30%), indicating that steel slag was mixed in the samples.
[0063] Based on the above verification results, the present invention selects the maximum value of the pixel ratio in the third standard interval (grayscale level range 51 to 75) in the sample images of No. 1 machine-made sand, No. 2 machine-made sand, and river sand as the standard interval threshold M, which can effectively identify whether steel slag is mixed in the aggregate for concrete.
[0064] The method of the present invention is used to quickly detect unknown aggregate samples: 6 unknown aggregate samples are randomly selected, and steps 1 to 3 of the above-mentioned detection method of the present invention are repeated to obtain the pixel point ratios of the third standard interval of the images of the above-mentioned 6 unknown aggregate samples, which are 31.36%, 30.04%, 29.88%, 25.87%, 39.99% and 45.74%, respectively, which are all higher than the standard interval threshold M (22.30%), indicating that steel slag is mixed in the above-mentioned 6 unknown aggregate samples.
[0065] Finally, it should be noted that the above content is only used to illustrate the technical solution of the present invention, rather than to limit the scope of protection of the present invention. Simple modifications or equivalent substitutions of the technical solution of the present invention by ordinary technicians in this field do not deviate from the essence and scope of the technical solution of the present invention.
Claims
1. A rapid detection method for steel slag mixed in concrete aggregate, characterized in that: The following steps are involved: Step 1: comprehensively collect a number of concrete aggregate samples, and use an image acquisition device to respectively acquire images of all samples to obtain images of a number of corresponding concrete aggregate samples; Step 2: Use analysis software to perform image preprocessing on the images of several corresponding concrete aggregate samples to obtain the grayscale distribution of each image; Step 3: Analyze and process the grayscale distribution of each sample image separately, as follows: 3a. Count the number of pixels at each gray level; 3b. Set the grayscale standard interval and calculate the pixel ratio of each standard interval; Step 4: Select a specific standard interval based on experience, compare the pixel ratios of the specific standard interval of each image, and select the maximum pixel ratio of the specific standard interval as the standard interval threshold for identifying concrete aggregate and steel slag; Step 5: Capture the image of the unknown aggregate sample, repeat the operations of steps 1 to 3, and obtain the pixel ratio of each standard interval of the image of the unknown aggregate sample; Step 6: Determine whether steel slag is mixed into the unknown aggregate sample based on the selected standard interval threshold; wherein, if the pixel point ratio of the specific standard interval of the unknown aggregate sample is higher than the standard interval threshold, it is determined that steel slag is mixed into the unknown aggregate sample; otherwise, it is determined that steel slag is not mixed into the unknown aggregate sample.
2. The rapid detection method for steel slag mixed in concrete aggregate according to claim 1, characterized in that: In step 1, the concrete aggregate samples are river sand and / or machine-made sand supplied to the concrete mixing station, and 3 to 5 kinds are selected in order of color from dark to light.
3. The rapid detection method for steel slag mixed in concrete aggregate according to claim 1, characterized in that: In step 2, the image preprocessing includes converting the color image into a gray image, unifying the brightness, and selecting the grayscale distribution area to be analyzed.
4. The rapid detection method for steel slag mixed in concrete aggregate according to claim 1, characterized in that: In step 3a, the grayscale level range S is 1 to x, where x is an integer from 1 to 255; the number of pixels corresponding to each grayscale level in the image of the concrete aggregate sample is counted, where the number of pixels with a grayscale level of 1 is recorded as C1, the number of pixels with a grayscale level of 2 is recorded as C2, and so on, the number of pixels with a grayscale level of x is recorded as C x .
5. The rapid detection method for steel slag mixed in concrete aggregate according to claim 4, characterized in that: In step 3b, the grayscale range S is divided into n standard intervals at a fixed and reasonable interval interval m, where the first standard interval is recorded as S1, the second standard interval is recorded as S2, and so on, the nth standard interval is recorded as S n ; Wherein, m and n are both positive integers; If the grayscale level range S cannot be divided by the interval m, the integer part obtained by dividing the grayscale level range S by m is recorded as n, and the remainder range at the end of the grayscale level range S is discarded.
6. The rapid detection method for steel slag mixed in concrete aggregate according to claim 5, characterized in that: In step 3b, the accumulated number of pixels of each gray level in each gray level standard interval in the image of the concrete aggregate sample is calculated, wherein the accumulated number of pixels of each gray level in the first standard interval S1 is P1=C1+C2+C3+......+C m The cumulative number of pixels of each gray level in the second standard interval S2 is P2 = C m+1 +C m+2 +C m+3 +......+C 2m , and so on, the nth standard interval S n The accumulated number of pixels of each gray level in is P n =C (n-1)m+1 +C (n-1)m+2 +C (n-1)m+3 +……+C nm .
7. The rapid detection method for steel slag mixed in concrete aggregate according to claim 6, characterized in that: In step 3b, the ratio of the accumulated number of pixels of each gray level in each gray level standard interval in the image of the concrete aggregate sample to the total number of pixels in the image is calculated, which is referred to as the pixel ratio. The pixel ratio of the first standard interval S1 is R1=P1 / (P1+P2+…+P n ), the pixel ratio of the second standard interval S2 is R2=P2 / (P1+P2+……+P n ), and so on, the nth standard interval S n The pixel ratio is R n =P n / (P1+P2+......+P n ).
8. The rapid detection method for steel slag mixed in concrete aggregate according to claim 1, characterized in that: In step 4, a specific vth standard interval is selected based on experience, and the pixel ratio R of the vth standard interval of each image is v Compare and select the R with the largest value v As the standard interval threshold M for identifying aggregate and steel slag for concrete; The selection basis of the specific vth standard interval is: the vth standard interval should not exceed C max The standard interval of the corresponding gray level; among them, C max It is the maximum number of pixels in all gray levels.
9. The rapid detection method for steel slag mixed in concrete aggregate according to claim 1, characterized in that: In step 5, the unknown aggregate sample is imaged and the operations of steps 1 to 3 are repeated to obtain the pixel ratio R of the unknown sample in the specific vth standard interval. v '.
10. The rapid detection method for steel slag mixed in concrete aggregate according to claim 1, characterized in that: In step 6, if the unknown sample has a pixel ratio R in a specific vth standard interval v ' is greater than the standard interval threshold M, it is judged that steel slag is mixed into the unknown aggregate sample; otherwise, it is judged that steel slag is not mixed into the unknown aggregate sample.