CN113158928A - Image recognition-based anti-counterfeiting method for concrete test block - Google Patents
Image recognition-based anti-counterfeiting method for concrete test block Download PDFInfo
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Abstract
The invention discloses a concrete test block anti-counterfeiting method based on image recognition, which comprises the following steps: photographing and collecting a test block marked by the field characters through an APP installed on a mobile terminal in a detection field, uploading the photos meeting the requirements to a cloud for storage, and recording the photos as P1; the invention has the advantages that the operation is simple and convenient, the practicability is strong, the APP arranged on the mobile terminal is utilized by a sample collector again to photograph and collect delivered test blocks with characters, the image recognition technology is utilized to prevent the concrete test blocks delivered to a laboratory from being replaced and cause the phenomenon different from the test blocks made on site, the mobile phone APP is used for photographing and collecting the pictures of the test blocks uploaded on site, the pictures uploaded on the test blocks are photographed again after the test blocks are delivered to the laboratory, the image recognition algorithm is utilized for comparing the pictures, the compared similarity result is given, the operation is convenient, the test of a large number of test blocks is convenient, meanwhile, the average accuracy reaches ninety-five percent, and the behavior of counterfeiting of the concrete test blocks can be effectively prevented.
Description
Technical Field
The invention belongs to the field of image recognition, and particularly relates to a concrete test block anti-counterfeiting method based on image recognition.
Background
At present, the counterfeiting condition generally exists in the detection process of materials in a laboratory for detection and detection in the field of construction, particularly the detection of a concrete test block, the concrete test block delivered to a laboratory is not in the same batch with the actual use of a project site, and even is artificially forged and replaced, and a laboratory sample collector is difficult to judge whether a delivered concrete test block sample and a site sampling test block are the same, so that the detection data can not truly reflect the condition of the concrete used in the project site.
Currently, the inspection and detection industry also proposes different solutions to the problem, such as attaching a two-dimensional code to a manufactured test block or embedding a chip into the manufactured test block, but obviously, such methods do not solve the problem well, and a counterfeiter can perform test block counterfeiting through two-dimensional code or chip migration or counterfeiting.
The invention content is as follows:
the present invention is directed to solving the above problems by providing a method for preventing counterfeit of concrete test block based on image recognition, which solves the problems mentioned in the background art.
In order to solve the above problems, the present invention provides a technical solution:
a concrete test block anti-counterfeiting method based on image recognition comprises the following steps:
s1, photographing and collecting the test block marked with the field characters through the APP installed on the mobile terminal in the detection field, uploading the photos meeting the requirements to the cloud for storage, and recording the photos as P1;
s2, the sample collector uses the APP installed on the mobile terminal again to take pictures of the delivered test block with characters, and then the pictures meeting the requirements are uploaded to the cloud for storage and recorded as P2;
and S3, comparing and analyzing the character images in the P1 and the P2 by an image comparison module through an image comparison algorithm, and displaying the comparison result through the mobile terminal.
Preferably, the mobile terminal in step S1 is a mobile phone device with GPS time.
Preferably, the image comparison algorithm in step S3 specifically includes the following steps:
s31, reading in pictures P1 and P2, then respectively generating picture copies of 256 × 256 colors and gray scales, and recording the picture copies as P1_ ori, P1_ gray, and P2_ ori, P2_ gray;
s32, dividing the gray level maps of P1 and P2 to generate a template;
s33, rotating the grayscale images of P2 and P1 by-15 to 15 degrees to generate a picture to be matched, and taking the picture as a picture group to be matched;
s34, performing template matching operation, and obtaining similarity1, similarity2 and corresponding matching positions after matching is completed;
s35, obtaining and outputting the final similarity of the two pictures;
and S36, taking the template with higher similarity as an output picture out1, taking the other template as out2, identifying the position and the number of the template on out1, identifying the maximum matching position of each template on out2, and outputting two color pictures.
Preferably, the step S32 of extracting the template from P1 specifically includes the following steps:
s321, carrying out binarization on the P1_ gray to extract boundary points;
s322, calculating the number of boundary points in each block, recording the positions of the boundary points, and intercepting the Patch at the same position on the P1_ gray to be used as a matching template.
Preferably, an automatic clipping operation is required before the template matching operation in step S34.
Preferably, in step S35, the final similarity between the two pictures is an average of the two similarities.
Preferably, the number of 4 blocks that should be taken to calculate the number of boundary points in each block in step S322 is the largest.
Preferably, the automatic cutting operation specifically includes: firstly, reading a picture, then extracting straight lines parallel to the picture boundary by adopting a Hough transform function provided by opencv, then determining two top and bottom horizontal straight lines and a left-most longitudinal straight line in the obtained straight lines, framing the position of a cement block, cutting, obtaining a square picture, and adjusting the resolution for subsequent similarity matching.
The invention has the beneficial effects that: the method is simple and convenient to operate and high in practicability, the image recognition technology is utilized to prevent the concrete test block delivered to a laboratory from being replaced to cause a phenomenon different from a test block made on site, the picture of the test block on site is shot and acquired through the mobile phone APP, the picture uploaded after the test block is delivered to the laboratory is shot again in a matching mode, the comparison of the pictures is carried out through the image recognition algorithm, the compared similarity result is given, the operation is convenient, the test of a large number of test blocks is facilitated, meanwhile, the average accuracy rate reaches ninety-five percent, and the behavior of the concrete test block counterfeiting can be effectively prevented.
Description of the drawings:
for ease of illustration, the invention is described in detail by the following detailed description and the accompanying drawings.
FIG. 1 is a schematic flow chart of the operation of the present invention;
FIG. 2 is a schematic diagram of the operation of the present invention.
The specific implementation mode is as follows:
as shown in fig. 1-2, the following technical solutions are adopted in the present embodiment:
example (b):
a concrete test block anti-counterfeiting method based on image recognition comprises the following steps:
s1, photographing and collecting the test block marked with the field characters through the APP installed on the mobile terminal in the detection field, uploading the photos meeting the requirements to the cloud for storage, and recording the photos as P1;
s2, the sample collector uses the APP installed on the mobile terminal again to take pictures of the delivered test block with characters, and then the pictures meeting the requirements are uploaded to the cloud for storage and recorded as P2;
and S3, comparing and analyzing the character images in the P1 and the P2 by an image comparison module through an image comparison algorithm, and displaying the comparison result through the mobile terminal.
Wherein, the mobile terminal in the step S1 is a mobile phone device with GPS time.
Wherein, the image comparison algorithm in step S3 specifically includes the following steps:
s31, reading in pictures P1 and P2, then respectively generating picture copies of 256 × 256 colors and gray scales, and recording the picture copies as P1_ ori, P1_ gray, and P2_ ori, P2_ gray;
s32, dividing the gray level maps of P1 and P2 to generate a template;
s33, rotating the grayscale images of P2 and P1 by-15 to 15 degrees to generate a picture to be matched, and taking the picture as a picture group to be matched;
s34, performing template matching operation, and obtaining similarity1, similarity2 and corresponding matching positions after matching is completed;
s35, obtaining and outputting the final similarity of the two pictures;
and S36, taking the template with higher similarity as an output picture out1, taking the other template as out2, identifying the position and the number of the template on out1, identifying the maximum matching position of each template on out2, and outputting two color pictures.
Wherein the step S32 of extracting the template from the P1 specifically includes the steps of:
s321, carrying out binarization on the P1_ gray to extract boundary points;
s322, calculating the number of boundary points in each block, recording the positions of the boundary points, and intercepting the Patch at the same position on the P1_ gray to be used as a matching template.
In step S34, an automatic clipping operation is required before the template matching operation is performed.
In step S35, the final similarity between the two pictures is an average of the two similarities.
In step S322, the maximum number of 4 blocks should be taken when calculating the number of boundary points in each block.
The automatic cutting operation specifically comprises the following steps: firstly, reading a picture, then extracting straight lines parallel to the picture boundary by adopting a Hough transform function provided by opencv, then determining two top and bottom horizontal straight lines and a left-most longitudinal straight line in the obtained straight lines, framing the position of a cement block, cutting, obtaining a square picture, and adjusting the resolution for subsequent similarity matching.
In the embodiment, the text-marked concrete test block is photographed and uploaded on site through an APP installed on a mobile terminal and recorded as P1, then the text-marked concrete test block is sent to a laboratory, laboratory workers photograph and upload the text-marked test block again through the APP installed on the mobile terminal and recorded as P2, then a text picture collected on the concrete test block is read, meanwhile, a binary extraction boundary is carried out on the picture, straight lines parallel to the picture boundary are extracted by adopting a Hough transform function provided by opencv, the two horizontal straight lines at the top and the bottom and a left longitudinal straight line are determined in the obtained straight lines, the position of the cement block is framed and cut, a square picture is obtained, the resolution is adjusted to be 256 x 256, the subsequent similarity calculation is used, then the gray level maps of P1 and P2 are divided, a template is generated, the gray level maps of P2 and P1 are rotated by-15 to 15 degrees, generating a picture to be matched, using the picture as a picture group to be matched, then performing template matching operation, obtaining similarity1 and similarity2 and corresponding matching positions after matching is completed, using a template with higher similarity as an output picture out1, using the other template as out2, identifying the position and the number of the template on out1, identifying the maximum matching position of each template on out2, outputting two color pictures, if the square of the distance between the similar positions of the two pictures does not exceed 2000, the similarity is +0.1, and if the square of the distance between the similar positions exceeds 10000, the similarity is-0.1.
In the description of the present invention, it is to be understood that the terms "coaxial", "bottom", "one end", "top", "middle", "other end", "upper", "one side", "top", "inner", "front", "center", "both ends", and the like, indicate orientations or positional relationships based on those shown in the drawings, and are only for convenience of description and simplicity of description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus, are not to be construed as limiting the present invention.
Furthermore, the terms "first", "second", "third", "fourth" are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated, whereby the features defined as "first", "second", "third", "fourth" may explicitly or implicitly include at least one such feature.
In the present invention, unless otherwise expressly specified or limited, the terms "mounted," "disposed," "connected," "secured," "screwed" and the like are to be construed broadly, e.g., as meaning fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; the terms may be directly connected or indirectly connected through an intermediate, and may be communication between two elements or interaction relationship between two elements, unless otherwise specifically limited, and the specific meaning of the terms in the present invention will be understood by those skilled in the art according to specific situations.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (8)
1. A concrete test block anti-counterfeiting method based on image recognition is characterized by comprising the following steps:
s1, photographing and collecting the test block marked with the field characters through the APP installed on the mobile terminal in the detection field, uploading the photos meeting the requirements to the cloud for storage, and recording the photos as P1;
s2, the sample collector uses the APP installed on the mobile terminal again to take pictures of the delivered test block with characters, and then the pictures meeting the requirements are uploaded to the cloud for storage and recorded as P2;
and S3, comparing and analyzing the character images in the P1 and the P2 by an image comparison module through an image comparison algorithm, and displaying the comparison result through the mobile terminal.
2. The image recognition-based anti-counterfeiting method for the concrete test block according to claim 1, wherein the mobile terminal in the step S1 is a mobile phone device with GPS time.
3. The image recognition-based anti-counterfeiting method for the concrete test block according to claim 2, wherein the image comparison algorithm in the step S3 specifically comprises the following steps:
s31, reading in pictures P1 and P2, then respectively generating picture copies of 256 × 256 colors and gray scales, and recording the picture copies as P1_ ori, P1_ gray, and P2_ ori, P2_ gray;
s32, dividing the gray level maps of P1 and P2 to generate a template;
s33, rotating the grayscale images of P2 and P1 by-15 to 15 degrees to generate a picture to be matched, and taking the picture as a picture group to be matched;
s34, performing template matching operation, and obtaining similarity1, similarity2 and corresponding matching positions after matching is completed;
s35, obtaining and outputting the final similarity of the two pictures;
and S36, taking the template with higher similarity as an output picture out1, taking the other template as out2, identifying the position and the number of the template on out1, identifying the maximum matching position of each template on out2, and outputting two color pictures.
4. The image recognition-based anti-counterfeiting method for the concrete test block as claimed in claim 3, wherein the step S32 of extracting the template from the P1 specifically comprises the following steps:
s321, carrying out binarization on the P1_ gray to extract boundary points;
s322, calculating the number of boundary points in each block, recording the positions of the boundary points, and intercepting the Patch at the same position on the P1_ gray to be used as a matching template.
5. The image recognition-based anti-counterfeiting method for the concrete test block according to claim 4, wherein an automatic cutting operation is required before the template matching operation in the step S34.
6. The image recognition-based anti-counterfeiting method for the concrete test block according to claim 3, wherein the final similarity degree of the two pictures in the step S35 is an average value of the two similarity degrees.
7. The image recognition-based anti-counterfeiting method for the concrete test block as claimed in claim 4, wherein the maximum number of 4 blocks is selected when the number of the boundary points in each block is calculated in step S322.
8. The image recognition-based anti-counterfeiting method for the concrete test block according to claim 5, wherein the automatic cutting operation specifically comprises the following steps: firstly, reading a picture, then extracting straight lines parallel to the picture boundary by adopting a Hough transform function provided by opencv, then determining two top and bottom horizontal straight lines and a left-most longitudinal straight line in the obtained straight lines, framing the position of a cement block, cutting, obtaining a square picture, and adjusting the resolution for subsequent similarity matching.
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