CN112541894A - Product deformation detection method - Google Patents
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- 238000004364 calculation method Methods 0.000 abstract description 4
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- G06T7/0002—Inspection of images, e.g. flaw detection
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Abstract
The invention relates to a product deformation detection method, which comprises the following steps: s1: registering a reference image; s2: processing the reference image and setting detection parameters and a judgment threshold; s3: processing an image to be detected; s4: and outputting the detection result according to the processing result and the judgment threshold value. The S1: the reference image is registered as registering one reference image: (1) the reference image is a normal product image shot by a user and is used for setting a detection area and a detection condition; (2) the user selects a detection area for detecting the deformation in the reference image, wherein the detection area comprises at least two repeated shapes. According to the product deformation detection method, the calculation complexity is low, the detection result can avoid the influence of illumination change and image noise through methods such as gradient integration and sliding average, the position of the central line of the repeated shape of the product can be stably detected, and meanwhile, the upper limit unit of the judgment condition set by the method is one pixel, so that the detection precision is high, and the micro deformation can be recognized by matching with a high-definition camera and a lens.
Description
Technical Field
The invention relates to the technical field of image processing, in particular to a product deformation detection method.
Background
The Image processing technology is a technology for processing Image information by a computer, and mainly comprises Image digitization, Image enhancement and restoration, Image data coding, Image segmentation, Image recognition and the like, wherein geometric figures (Graphics) comprise points, lines, surfaces, colors and the like, are generated by a drawing program and are a set of a series of drawing instructions, are generally manufactured by various drawing software, and dot matrix images (images) are formed by combining various pixel points and colors and are obtained by using equipment such as a video camera, a scanner, a digital camera and the like or can be generated by using the drawing software.
The existing deformation detection method is a method adopting template matching, namely, comparing a current product image with a normal product image, calculating the similarity of the current product image and the normal product image, if the similarity is lower than a certain threshold value, the product is considered to be deformed, otherwise, the product is considered to be normal, because a similarity matching algorithm is needed, the algorithm is generally time-consuming under the condition of larger image area, in addition, the detection precision of the method is not high, if the product is only slightly deformed, the method is likely to miss detection, and the method is also a method of differentiating the current product image and the normal product image, calculating the pixel area of a differential image, if the area is higher than a certain threshold value, the current product is considered to be deformed, otherwise, the method is normal, although the calculation is simple and efficient, the method is often influenced by image noise, and is likely to miss detection or miss detection.
Disclosure of Invention
The invention aims to provide a product deformation detection method, which aims to solve the problems that the existing deformation detection method proposed in the background technology adopts a template matching method, namely, a current product image is compared with a normal product image, the similarity of the current product image and the normal product image is calculated, if the similarity is lower than a certain threshold value, the product is considered to be deformed, otherwise, the product is considered to be normal, because a similarity matching algorithm is needed, the algorithm usually consumes time under the condition of larger image area, in addition, the detection precision of the method is not high, if the product only slightly deforms, the method is likely to miss detection, the method is that the current product image and the normal product image are differentiated, the pixel area of a differential image is calculated, if the area is higher than a certain threshold value, the current product deforms, otherwise, the method is normal, although the calculation is simple and efficient, however, the image noise often affects the image, and the false detection or the missing detection is likely to occur.
A product deformation detection method comprises the following steps:
s1: registering a reference image;
s2: processing the reference image and setting detection parameters and a judgment threshold;
s3: processing an image to be detected;
s4: and outputting the detection result according to the processing result and the judgment threshold value.
Preferably, the step of S1: the reference image is registered as registering one reference image:
(1) the reference image is a normal product image shot by a user and is used for setting a detection area and a detection condition;
(2) selecting a detection area for detecting deformation in a reference image by a user, wherein the detection area comprises at least two repeated shapes;
(3) the reference image detection area image is set to P0.
Preferably, the step of S2: processing a reference image, setting detection parameters and a judgment threshold value as a set detection direction, wherein the detection direction is divided into a horizontal direction and a vertical direction, setting an edge threshold value percentage as P, and setting an edge smoothing coefficient W, and the method specifically comprises the following steps:
(1) copy detection area image P0
(2) Carrying out binarization processing on the P0 to obtain an image P1, and setting a binarization threshold value as automatic;
(3) processing the P1 along the set detection direction by using a Sobel operator to obtain a gray map P2;
(4) integrating the P2 along a direction vertical to the set detection direction to obtain a one-dimensional array Q1;
(5) smoothing the Q1 by using a moving average algorithm to obtain Q2, wherein the size of a smoothing window is a smoothing coefficient W set by a user;
(6) calculating the maximum value in Q2 as Max;
(7) and calculating an edge threshold T-Max P according to the set edge threshold percentage P.
Preferably, the step of S3: processing the image to be detected to set the edge threshold percentage and the edge smoothing coefficient, detecting the position of the central line of the repeated shape in the detection area along the set detection direction, and calculating the number of the central lines and the maximum distance and the minimum distance between the adjacent central lines, wherein the unit is pixel.
Preferably, the step of S4: outputting a detection result according to the processing result and the judgment threshold value, wherein the detection result is that the number of center lines and the upper and lower limits of the center line distance are set according to the calculated number of the center lines of the normal products and the maximum and minimum distance, if the number of the center lines or the center line distance of the products to be detected exceeds the set upper and lower limits, deformation can be considered to occur, and the specific steps are as follows:
(1) traversing all elements Q2[ i ] in Q2, when | Q2[ i ] | > T and | Q2[ i ] | ≧ | Q2[ i-1] | and | Q2[ i ] | ≧ Q2[ i +1] |, considering that there is an obvious edge at the position i, and sequentially recording all i meeting the condition into an array Q3
(2) Traversing all elements Q3[ i ] in Q3, when i is an even number, forming a pair of an even subscript and an adjacent odd subscript, calculating the average value a ═ (Q3[ i ] + Q3[ i +1])/2.0, wherein a is the position of the center line of two edges, recording all a in an array Q4, and storing the position of all the center lines of the edges in Q4.
Preferably, the same detection regions are extracted, the positions of the center lines of the repetitive shapes in the detection regions are detected in the same detection direction, and the number of the center lines and the maximum and minimum distances between adjacent center lines are calculated in units of pixels.
Preferably, the deformation is considered to occur when the number of the central lines or the distance between the central lines of the product to be detected exceeds the set upper limit and the set lower limit, an NG signal is output, and otherwise an OK signal is output.
Compared with the prior art, the invention has the following beneficial effects: the method has low calculation complexity, enables the detection result to avoid illumination change and image noise influence through methods such as gradient integration, sliding average and the like, can stably detect the position of the central line of the repeated shape of the product, has high detection precision because the upper limit unit of the judgment condition set by the method is one pixel, and can identify tiny deformation by matching with a high-definition camera and a lens.
Drawings
FIG. 1 is a flow chart of a method for detecting deformation of a product according to the present invention;
FIG. 2 is a flow chart of the detection of the position of the center line of the repeated shape according to the method for detecting deformation of a product of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments.
The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Referring to fig. 1-2, the present invention provides a technical solution: a product deformation detection method comprises the following steps:
s1: registering a reference image;
s2: processing the reference image and setting detection parameters and a judgment threshold;
s3: processing an image to be detected;
s4: and outputting the detection result according to the processing result and the judgment threshold value.
Preferably, the step of S1: the reference image is registered as registering one reference image:
(1) the reference image is a normal product image shot by a user and is used for setting a detection area and a detection condition;
(2) selecting a detection area for detecting deformation in a reference image by a user, wherein the detection area comprises at least two repeated shapes;
(3) the reference image detection area image is set to P0.
Preferably, the step of S2: processing a reference image, setting detection parameters and a judgment threshold value as a set detection direction, wherein the detection direction is divided into a horizontal direction and a vertical direction, setting an edge threshold value percentage as P, and setting an edge smoothing coefficient W, and the method specifically comprises the following steps:
(1) copy detection area image P0
(2) Carrying out binarization processing on the P0 to obtain an image P1, and setting a binarization threshold value as automatic;
(3) processing the P1 along the set detection direction by using a Sobel operator to obtain a gray map P2;
(4) integrating the P2 along a direction vertical to the set detection direction to obtain a one-dimensional array Q1;
(5) smoothing the Q1 by using a moving average algorithm to obtain Q2, wherein the size of a smoothing window is a smoothing coefficient W set by a user;
(6) calculating the maximum value in Q2 as Max;
(7) and calculating an edge threshold T-Max P according to the set edge threshold percentage P.
Preferably, the step of S3: processing the image to be detected to set the edge threshold percentage and the edge smoothing coefficient, detecting the position of the central line of the repeated shape in the detection area along the set detection direction, and calculating the number of the central lines and the maximum distance and the minimum distance between the adjacent central lines, wherein the unit is pixel.
Preferably, the step of S4: outputting a detection result according to the processing result and the judgment threshold value, wherein the detection result is that the number of center lines and the upper and lower limits of the center line distance are set according to the calculated number of the center lines of the normal products and the maximum and minimum distance, if the number of the center lines or the center line distance of the products to be detected exceeds the set upper and lower limits, deformation can be considered to occur, and the specific steps are as follows:
(1) traversing all elements Q2[ i ] in Q2, when | Q2[ i ] | > T and | Q2[ i ] | ≧ | Q2[ i-1] | and | Q2[ i ] | ≧ Q2[ i +1] |, considering that there is an obvious edge at the position i, and sequentially recording all i meeting the condition into an array Q3
(2) Traversing all elements Q3[ i ] in Q3, when i is an even number, forming a pair of an even subscript and an adjacent odd subscript, calculating the average value a ═ (Q3[ i ] + Q3[ i +1])/2.0, wherein a is the position of the center line of two edges, recording all a in an array Q4, and storing the position of all the center lines of the edges in Q4.
Preferably, the same detection regions are extracted, the positions of the center lines of the repetitive shapes in the detection regions are detected in the same detection direction, and the number of the center lines and the maximum and minimum distances between adjacent center lines are calculated in units of pixels.
Preferably, the deformation is considered to occur when the number of the central lines or the distance between the central lines of the product to be detected exceeds the set upper limit and the set lower limit, an NG signal is output, and otherwise an OK signal is output.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art should be considered to be within the technical scope of the present invention, and the technical solutions and the inventive concepts thereof according to the present invention should be equivalent or changed within the scope of the present invention.
Claims (7)
1. A product deformation detection method comprises the following steps:
s1: registering a reference image;
s2: processing the reference image and setting detection parameters and a judgment threshold;
s3: processing an image to be detected;
s4: and outputting the detection result according to the processing result and the judgment threshold value.
2. A method for detecting deformation of a product according to claim 1, wherein: the S1: the reference image is registered as registering one reference image:
(1) the reference image is a normal product image shot by a user and is used for setting a detection area and a detection condition;
(2) selecting a detection area for detecting deformation in a reference image by a user, wherein the detection area comprises at least two repeated shapes;
(3) the reference image detection area image is set to P0.
3. The product deformation detection method according to claim 2, characterized in that: the S2: processing a reference image, setting detection parameters and a judgment threshold value as a set detection direction, wherein the detection direction is divided into a horizontal direction and a vertical direction, setting an edge threshold value percentage as P, and setting an edge smoothing coefficient W, and the method specifically comprises the following steps:
(1) copy detection area image P0
(2) Carrying out binarization processing on the P0 to obtain an image P1, and setting a binarization threshold value as automatic;
(3) processing the P1 along the set detection direction by using a Sobel operator to obtain a gray map P2;
(4) integrating the P2 along a direction vertical to the set detection direction to obtain a one-dimensional array Q1;
(5) smoothing the Q1 by using a moving average algorithm to obtain Q2, wherein the size of a smoothing window is a smoothing coefficient W set by a user;
(6) calculating the maximum value in Q2 as Max;
(7) and calculating an edge threshold T-Max P according to the set edge threshold percentage P.
4. A method for detecting deformation of a product according to claim 3, wherein: the S3: processing the image to be detected to set the edge threshold percentage and the edge smoothing coefficient, detecting the position of the central line of the repeated shape in the detection area along the set detection direction, and calculating the number of the central lines and the maximum distance and the minimum distance between the adjacent central lines, wherein the unit is pixel.
5. The product deformation detection method according to claim 4, characterized in that: the S4: outputting a detection result according to the processing result and the judgment threshold value, wherein the detection result is that the number of center lines and the upper and lower limits of the center line distance are set according to the calculated number of the center lines of the normal products and the maximum and minimum distance, if the number of the center lines or the center line distance of the products to be detected exceeds the set upper and lower limits, deformation can be considered to occur, and the specific steps are as follows:
(1) traversing all elements Q2[ i ] in Q2, when | Q2[ i ] | > T and | Q2[ i ] | ≧ | Q2[ i-1] | and | Q2[ i ] | ≧ Q2[ i +1] |, considering that there is an obvious edge at the position i, and sequentially recording all i meeting the condition into an array Q3
(2) Traversing all elements Q3[ i ] in Q3, when i is an even number, forming a pair of an even subscript and an adjacent odd subscript, calculating the average value a ═ (Q3[ i ] + Q3[ i +1])/2.0, wherein a is the position of the center line of two edges, recording all a in an array Q4, and storing the position of all the center lines of the edges in Q4.
6. The product deformation detection method according to claim 5, characterized in that: the same detection area is extracted, the position of the center line of the repeated shape in the detection area is detected along the same detection direction, and the number of the center lines and the maximum distance and the minimum distance between the adjacent center lines are calculated, wherein the unit is pixel.
7. The product deformation detection method according to claim 6, characterized in that: and if the number of the central lines or the distance between the central lines of the products to be detected exceeds the set upper limit and the set lower limit, deformation is considered to occur, an NG signal is output, and otherwise, an OK signal is output.
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