CN113935981A - Flaw quantitative evaluation method based on AOI detection - Google Patents
Flaw quantitative evaluation method based on AOI detection Download PDFInfo
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Abstract
The invention relates to the technical field of AOI flaw detection, and discloses a flaw quantitative evaluation method based on AOI detection, which comprises the following steps: s1, separating flaws, separating the flaws from the background picture by using image difference steps before and after noise removal, gray stretching, filtering and filtering according to the areas where the flaws are located on the detected flaw picture, calculating the minimum circumscribed rectangle width according to the flaw outline, and respectively storing the separated flaw picture and the circumscribed rectangle frame diagram where the flaws are located. The defect quantitative evaluation method based on AOI detection can realize the functions of appearance defect quantitative evaluation and automatic detection, does not need to rely on manual work to judge through subjective experience, avoids subjective factor interference as much as possible, not only improves the accuracy of defect detection results, but also improves the defect detection efficiency and reduces the labor intensity of workers.
Description
Technical Field
The invention relates to the technical field of AOI flaw detection, in particular to a flaw quantitative evaluation method based on AOI detection.
Background
AOI is called automatic optical inspection in Chinese, and is equipment for inspecting common defects encountered in welding production based on an optical principle.
At present, in the field of AOI flaw detection, the detected flaws have sizes, whether each flaw affects the function and performance of a product, and whether the corresponding flaw is within an acceptable range, a quantitative evaluation method does not exist at present, and whether the flaws can be accepted or not is judged manually through subjective experience.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a flaw quantitative evaluation method based on AOI detection, which can realize the functions of appearance flaw quantitative evaluation and automatic detection, does not need to rely on manual judgment through subjective experience, avoids the interference of subjective factors as much as possible, not only improves the accuracy of flaw detection results, but also improves the efficiency of flaw detection, reduces the labor intensity of workers, and solves the problems that the existing flaw detection through manual work is influenced by more interference factors, the accuracy of the flaw detection results is easily influenced, and the flaw detection efficiency is lower.
(II) technical scheme
In order to realize the purposes that the method has the functions of quantitative evaluation and automatic detection of the appearance flaws, does not need to rely on manual judgment through subjective experience, avoids interference of subjective factors as much as possible, not only improves the accuracy of flaw detection results, but also improves the flaw detection efficiency and reduces the labor intensity of workers, the invention provides the following technical scheme: a flaw quantitative evaluation method based on AOI detection comprises the following steps:
s1, separating flaws, separating the flaws from a background picture by using image difference steps before and after noise removal, gray stretching, filtering and filtering according to the areas where the flaws are located on the detected flaw picture, calculating the minimum circumscribed rectangle width according to the flaw outline, and respectively storing the separated flaw picture and the circumscribed rectangle block diagram where the flaws are located;
s2, calculating the Area of the flaw, wherein the Area of the flaw is expressed by the number of the pixel points of the flaw because most of the detected flaws are irregular, and the Area of the flaw acquired by the pixel points is calculated according to the flaw image separated in the step S1;
s3 calculating the length and height of the minimum bounding rectangle, and calculating the width W and height H of the minimum bounding rectangle according to the bounding rectangle where the flaw separated in the step S1 is located;
s4, calculating contrast information, calculating the average gray level of the flaw and the average gray level of the minimum circumscribed rectangular frame, and then calculating the difference value of 2 values as the contrast information CR;
s5, calculating a comprehensive evaluation score, namely calculating the comprehensive evaluation score Sreore according to a formula after obtaining the width W and the height H of a flaw external rectangular frame, the Area of the flaw and contrast information CR;
and S6, finishing the evaluation, finishing the statistics of the related data, and finishing the related data into chart information.
Preferably, the specific defect separation step in step S1 is as follows:
a. acquiring a product image containing a flaw by a camera;
b. removing noise to obtain image information only with flaw information;
c. the gray level dynamic range during image processing is improved through gray level stretching;
d. obtaining an image only containing background information through a low-pass filter;
e. and performing difference on the images before and after filtering to obtain a flaw image.
Preferably, the specific calculation formula of the contrast information CR in step S4 is as follows:
Preferably, the calculation formula of the comprehensive evaluation score Sreore in step S5 is as follows:
score is α × W + β × H + δ × Area + ∈ × CR, where α, β, δ, and ∈respectivelydenote the weight of the width, height, Area, and contrast information in the quantitative evaluation model.
(III) advantageous effects
Compared with the prior art, the invention provides a flaw quantitative evaluation method based on AOI detection, which has the following beneficial effects:
the flaw quantitative evaluation method based on AOI detection comprises the steps of firstly carrying out flaw separation based on an AOI detection technology when in appearance flaw detection, then carrying out flaw Area calculation, minimum circumscribed rectangle frame length and height calculation, contrast information calculation and comprehensive evaluation score calculation, wherein on a detected flaw picture, according to the Area of the flaw, the flaw is separated from a background picture by using image aberration steps before and after noise removal, gray stretching, filtering and filtering, the width of the minimum circumscribed rectangle is calculated according to the flaw outline, the separated flaw picture and the circumscribed rectangle frame diagram where the flaw is located are respectively stored, meanwhile, as most of the detected flaws are irregular shapes, the Area of the flaw is expressed by the number of pixel points of the flaw, the Area Area of the flaw is obtained by calculating the pixel points of the flaw picture according to the flaw picture separated in the step, and the width W and the height H of the circumscribed rectangle frame diagram are calculated, the method comprises the steps of calculating the average gray level of a flaw and the average gray level of a minimum external rectangular frame, then calculating the difference value of 2 values as contrast information CR, then calculating a comprehensive evaluation Score Sreore according to a formula after obtaining the width W and the height H of the flaw external rectangular frame, the Area of the flaw and the contrast information CR, wherein the larger the value of the integral evaluation Score Score is, the larger the influence of the flaw is, the specific quantification of the flaw detected by the AOI is realized, and the AOI automatic detection is time-saving and labor-saving.
Drawings
FIG. 1 is a schematic diagram of an overall structure of a flaw quantitative evaluation method based on AOI detection according to the present invention;
FIG. 2 is a flowchart illustrating an overall method for quantitatively evaluating defects based on AOI detection according to the present invention;
FIG. 3 is a schematic diagram illustrating the effect of defect separation in the method for quantitatively evaluating defects based on AOI detection according to the present invention;
FIG. 4 is a flowchart illustrating the separation of specific defects in a method for quantitatively evaluating defects based on AOI detection according to the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1-4, a method for quantitatively evaluating defects based on AOI detection includes the following steps:
s1, separating flaws, separating the flaws from a background picture by using image difference steps before and after noise removal, gray stretching, filtering and filtering according to the areas where the flaws are located on the detected flaw picture, calculating the minimum circumscribed rectangle width according to the flaw outline, and respectively storing the separated flaw picture and the circumscribed rectangle block diagram where the flaws are located;
s2, calculating the Area of the flaw, wherein the Area of the flaw is expressed by the number of the pixel points of the flaw because most of the detected flaws are irregular, and the Area of the flaw acquired by the pixel points is calculated according to the flaw image separated in the step S1;
s3 calculating the length and height of the minimum bounding rectangle, and calculating the width W and height H of the minimum bounding rectangle according to the bounding rectangle where the flaw separated in the step S1 is located;
s4, calculating contrast information, calculating the average gray level of the flaw and the average gray level of the minimum circumscribed rectangular frame, and then calculating the difference value of 2 values as the contrast information CR;
s5, calculating a comprehensive evaluation score, namely calculating the comprehensive evaluation score Sreore according to a formula after obtaining the width W and the height H of a flaw external rectangular frame, the Area of the flaw and contrast information CR;
and S6, finishing the evaluation, finishing the statistics of the related data, finishing the evaluation into chart information, and finishing the chart information to be convenient to watch.
The specific flaw separation step in step S1 is as follows:
a. acquiring a product image containing a flaw by a camera;
b. removing noise to obtain image information only with flaw information;
c. the gray level dynamic range during image processing is improved through gray level stretching;
d. obtaining an image only containing background information through a low-pass filter;
e. and performing subtraction on the images before and after filtering to obtain a flaw image, specifically quantifying the flaws detected by the AOI flaws through the steps, and automatically detecting the AOI flaws, wherein the detection process is time-saving and labor-saving.
The specific calculation formula of the contrast information CR in step S4 is as follows:
The calculation formula of the comprehensive evaluation score Sreore in step S5 is as follows:
the Score is α × W + β × H + δ × Area + ∈ × CR, where α, β, δ, and ∈ respectively denote weights of width, height, Area, and contrast information in the quantitative evaluation model, and a larger value of the overall evaluation Score indicates a larger influence of flaws, and the weight of each value can be adjusted according to the actual condition of the product.
To sum up, the method for quantitatively evaluating the flaws based on AOI detection comprises the steps of flaw separation based on an AOI detection technology when the flaws are detected in appearance, flaw area calculation, minimum circumscribed rectangle frame length and height calculation, contrast information calculation and comprehensive evaluation score calculation, wherein on a detected flaw image, the flaws are separated from a background image by the steps of removing noise, gray stretching, filtering and image difference before and after filtering according to the area where the flaws are located, in the separation step, a product image containing the flaws is collected by a camera, then the noise is removed to obtain image information only containing flaw information, then the gray level dynamic range during image processing is improved by gray stretching, then an image only containing the background information is obtained by a low-pass filter, and finally the images before and after filtering are used for difference to obtain the flaw image, calculating the minimum external rectangle width according to the flaw outline, respectively storing the separated flaw image and the external rectangle block diagram where the flaws are located, simultaneously, because most detected flaws are irregular in shape, expressing the Area of the flaws by using the number of pixel points of the flaws, calculating the pixel points of the flaw image separated according to the steps to obtain the Area of the flaws, calculating the width W and the height H of the flaw image in the external rectangle block diagram, then calculating the average gray scale of the flaws and the average gray scale of the minimum external rectangle frame, then calculating the difference value of 2 values as contrast information CR, then calculating a comprehensive evaluation Score Sreore according to a formula after obtaining the width W and the height H of the flaw external rectangle frame, the Area of the flaws and the contrast information CR, wherein the larger the value of the overall evaluation Score indicates the larger the influence of the flaws, and realizing the specific quantification of the flaws detected by AOI flaws, the method can realize the functions of quantitative evaluation and automatic detection of the appearance flaws without manual judgment through subjective experience, avoids interference of subjective factors as much as possible, improves the accuracy of flaw detection results, improves flaw detection efficiency and reduces labor intensity of workers.
It is to be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
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 (4)
1. A flaw quantitative evaluation method based on AOI detection is characterized by comprising the following steps:
s1, separating flaws, separating the flaws from a background picture by using image difference steps before and after noise removal, gray stretching, filtering and filtering according to the areas where the flaws are located on the detected flaw picture, calculating the minimum circumscribed rectangle width according to the flaw outline, and respectively storing the separated flaw picture and the circumscribed rectangle block diagram where the flaws are located;
s2, calculating the Area of the flaw, wherein the Area of the flaw is expressed by the number of the pixel points of the flaw because most of the detected flaws are irregular, and the Area of the flaw acquired by the pixel points is calculated according to the flaw image separated in the step S1;
s3 calculating the length and height of the minimum bounding rectangle, and calculating the width W and height H of the minimum bounding rectangle according to the bounding rectangle where the flaw separated in the step S1 is located;
s4, calculating contrast information, calculating the average gray level of the flaw and the average gray level of the minimum circumscribed rectangular frame, and then calculating the difference value of 2 values as the contrast information CR;
s5, calculating a comprehensive evaluation score, namely calculating the comprehensive evaluation score Sreore according to a formula after obtaining the width W and the height H of a flaw external rectangular frame, the Area of the flaw and contrast information CR;
and S6, finishing the evaluation, finishing the statistics of the related data, and finishing the related data into chart information.
2. The method for quantitatively evaluating the defects based on the AOI detection as claimed in claim 1, wherein the specific defect separation step in the step S1 is as follows:
a. acquiring a product image containing a flaw by a camera;
b. removing noise to obtain image information only with flaw information;
c. the gray level dynamic range during image processing is improved through gray level stretching;
d. obtaining an image only containing background information through a low-pass filter;
e. and performing difference on the images before and after filtering to obtain a flaw image.
3. The method according to claim 1, wherein the contrast information CR in step S4 is specifically calculated as follows:
4. The method according to claim 1, wherein the calculation formula of the comprehensive evaluation score Sreore in step S5 is as follows:
score is α × W + β × H + δ × Area + ∈ × CR, where α, β, δ, and ∈respectivelydenote the weight of the width, height, Area, and contrast information in the quantitative evaluation model.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114637270A (en) * | 2022-05-17 | 2022-06-17 | 成都秦川物联网科技股份有限公司 | Intelligent manufacturing industry Internet of things based on distributed control and control method |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN114637270A (en) * | 2022-05-17 | 2022-06-17 | 成都秦川物联网科技股份有限公司 | Intelligent manufacturing industry Internet of things based on distributed control and control method |
| CN114637270B (en) * | 2022-05-17 | 2022-08-23 | 成都秦川物联网科技股份有限公司 | Intelligent manufacturing industry Internet of things based on distributed control and control method |
| US11681283B1 (en) | 2022-05-17 | 2023-06-20 | Chengdu Qinchuan Iot Technology Co., Ltd. | Intelligent manufacturing industrial Internet of Things based on distributed control, control methods and media thereof |
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