CN113850756A - Label defect detection method based on template comparison - Google Patents
Label defect detection method based on template comparison Download PDFInfo
- Publication number
- CN113850756A CN113850756A CN202110972099.1A CN202110972099A CN113850756A CN 113850756 A CN113850756 A CN 113850756A CN 202110972099 A CN202110972099 A CN 202110972099A CN 113850756 A CN113850756 A CN 113850756A
- Authority
- CN
- China
- Prior art keywords
- template
- detected
- sample
- pass filtering
- images
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Withdrawn
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
- G06T7/001—Industrial image inspection using an image reference approach
Landscapes
- Engineering & Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Analysis (AREA)
- Image Processing (AREA)
Abstract
The invention discloses a label defect detection method based on template comparison in the field of label detection, which comprises the following steps: the method comprises the following steps: selecting a template image; step two: making a template, generating a plurality of template characteristic images, and making a standard model of an object or a region to be detected; step three: carrying out picture calibration on a sample to be detected; step four: acquiring a template characteristic image, and comparing the template characteristic image with the sample characteristic image; step five: and (4) testing the characteristic images of the multiple samples to be detected, judging whether the accuracy rate reaches the standard, and directly jumping to the step one to manufacture the template again if the accuracy rate does not reach the standard. The method has strong adaptability when detecting standard plane products with complex contents, and different products can be used only by creating different templates, so the operation is simple and convenient; meanwhile, the algorithm complexity is greatly reduced compared with the conventional algorithm, the operation speed is guaranteed, and the method is suitable for scenes with high real-time requirements.
Description
Technical Field
The invention relates to the field of label detection, in particular to a label defect detection method based on template comparison.
Background
In industrial detection, the detection requirements for labels are increasing day by day, and the label detection generally encounters the problems of complex background, more textures, more characters and the like, so that great challenges are brought to defect identification; the invention uses a template comparison mode to detect products from different degrees, and has the advantages of greatly inhibiting false detection caused by complex textures and flexibly customizing the defect detection size.
The traditional defect detection mode is generally to filter the information belonging to the label by analyzing the characteristics of the defect and then filtering the information in a filtering mode so as to highlight the defect; however, the method has a good effect on products with simple contents and uncomplicated backgrounds, and is easy to cause false detection on labels with a large amount of contents.
Disclosure of Invention
The invention aims to provide a label defect detection method based on template comparison, which has strong adaptability and simple operation.
The purpose of the invention is realized as follows: a label defect detection method based on template comparison comprises the following steps:
the method comprises the following steps: selecting a template image;
step two: making a template, generating a plurality of template characteristic images, and making a standard model of an object or a region to be detected;
step three: carrying out picture calibration on a sample to be detected;
step four: acquiring a template characteristic image, and comparing the template characteristic image with the sample characteristic image;
step five: and (4) testing the characteristic images of the multiple samples to be detected, judging whether the accuracy rate reaches the standard, and directly jumping to the step one to manufacture the template again if the accuracy rate does not reach the standard.
Preferably, the method for manufacturing the template in the second step is as follows:
step A: selecting a to-be-detected object without defects to take a picture, and selecting an area to be detected in the picture or the whole object to perform template manufacturing according to needs;
and B: the two filtered images are obtained by filtering the regions through low-pass filtering and high-pass filtering, the low-pass filtering is used for removing the defects of the regions with higher change frequency in the background and larger detection area, and the high-pass filtering is used for detecting the regions with more obvious change and smaller area.
And C: and (3) performing binarization processing on the two obtained filtered images, and performing binarization on the same region for multiple times according to a lighting condition, wherein if 85, 150 and 200 are set as threshold values in a region brightness range of 0-255, 6 template characteristic images can be generated.
Preferably, the low-pass filtering and the high-pass filtering adopt a low-pass filter based on a Gaussian operator and a high-pass filtering based on a Krisch operator.
Preferably, the method of the third step is as follows:
selecting a plurality of angular points with obvious characteristics on the template characteristic image, finding out corresponding angular points on a sample to be detected, carrying out affine and perspective transformation on the sample to be detected, and aligning the sample to be detected with the template.
Preferably, the method of the fourth step is as follows:
and (3) generating 6 sample characteristic images of the sample to be detected by the method of the second step, comparing the 6 sample characteristic images with the 6 template characteristic images generated by the second step, and restricting the precision of the defect by setting a gray difference threshold, a defect width threshold, a defect area threshold and the like because the sample characteristic images are not completely the same as the template characteristic images.
Preferably, the line scan camera is used to acquire the template feature image.
Compared with the prior art, the invention has the advantages that:
the invention mainly aims at detecting standard plane products with complex contents, has strong adaptability to the contents of the products, can be used by only creating different templates for different products, and is simple and convenient to operate.
A4096-point line scan camera is selected in the aspect of hardware, so that the illumination uniformity of images is guaranteed, the algorithm complexity is reduced greatly compared with that of a conventional algorithm, the operation speed is guaranteed, and the method is very suitable for scenes with high real-time requirements.
Drawings
FIG. 1 is a schematic flow chart of the present invention.
Detailed Description
As shown in fig. 1, a method for detecting a defect of a tag based on template comparison includes the following steps:
the method comprises the following steps: selecting a template image;
step two: making a template, generating a plurality of template characteristic images, and making a standard model of an object or a region to be detected;
step three: carrying out picture calibration on a sample to be detected;
step four: acquiring a template characteristic image, and comparing the template characteristic image with the sample characteristic image;
step five: and (4) testing the characteristic images of the multiple samples to be detected, judging whether the accuracy rate reaches the standard, and directly jumping to the step one to manufacture the template again if the accuracy rate does not reach the standard.
The method for manufacturing the template in the second step comprises the following steps:
step A: selecting a to-be-detected object without defects to take a picture, and selecting an area to be detected in the picture or the whole object to perform template manufacturing according to needs;
and B: the two filtered images are obtained by filtering the regions through low-pass filtering and high-pass filtering, the low-pass filtering is used for removing the defects of the regions with higher change frequency in the background and larger detection area, and the high-pass filtering is used for detecting the regions with more obvious change and smaller area.
And C: and (3) performing binarization processing on the two obtained filtered images, and performing binarization on the same region for multiple times according to a lighting condition, wherein if 85, 150 and 200 are set as threshold values in a region brightness range of 0-255, 6 template characteristic images can be generated.
The low-pass filtering and the high-pass filtering adopt a low-pass filter based on a Gaussian operator and a high-pass filtering based on a Krisch operator.
The method of the third step is as follows:
selecting a plurality of angular points with obvious characteristics on the template characteristic image, finding out corresponding angular points on a sample to be detected, carrying out affine and perspective transformation on the sample to be detected, and aligning the sample to be detected with the template.
The method of the fourth step is as follows:
and (3) generating 6 sample characteristic images of the sample to be detected by the method of the second step, comparing the 6 sample characteristic images with the 6 template characteristic images generated by the second step, and restricting the precision of the defect by setting a gray difference threshold, a defect width threshold, a defect area threshold and the like because the sample characteristic images are not completely the same as the template characteristic images.
The present invention is not limited to the above-mentioned embodiments, and based on the technical solutions disclosed in the present invention, those skilled in the art can make some substitutions and modifications to some technical features without creative efforts according to the disclosed technical contents, and these substitutions and modifications are all within the protection scope of the present invention.
Claims (5)
1. A label defect detection method based on template comparison is characterized by comprising the following steps:
the method comprises the following steps: selecting a template image;
step two: making a template, generating a plurality of template characteristic images, and making a standard model of an object or a region to be detected;
step three: carrying out picture calibration on a sample to be detected;
step four: acquiring a template characteristic image, and comparing the template characteristic image with the sample characteristic image;
step five: and (4) testing the characteristic images of the multiple samples to be detected, judging whether the accuracy rate reaches the standard, and directly jumping to the step one to manufacture the template again if the accuracy rate does not reach the standard.
2. The method for detecting label defects based on template comparison as claimed in claim 1, wherein the method for making the template in the second step is as follows:
step S1: selecting a to-be-detected object without defects to take a picture, and selecting an area to be detected in the picture or the whole object to perform template manufacturing according to needs;
step S2: the two filtered images are obtained by filtering the regions through low-pass filtering and high-pass filtering, the low-pass filtering is used for removing the defects of the regions with higher change frequency in the background and larger detection area, and the high-pass filtering is used for detecting the regions with more obvious change and smaller area.
Step S3: and (3) performing binarization processing on the two obtained filtered images, and performing binarization on the same region for multiple times according to a lighting condition, wherein if 85, 150 and 200 are set as threshold values in a region brightness range of 0-255, 6 template characteristic images can be generated.
3. The method of claim 2, wherein the method comprises: the low-pass filtering and the high-pass filtering adopt a low-pass filter based on a Gaussian operator and a high-pass filtering based on a Krisch operator.
4. The method for detecting the label defect based on the template comparison as claimed in claim 1, wherein the method of the third step is as follows:
selecting a plurality of angular points with obvious characteristics on the template characteristic image, finding out corresponding angular points on a sample to be detected, carrying out affine and perspective transformation on the sample to be detected, and aligning the sample to be detected with the template.
5. The method for detecting the label defect based on the template comparison as claimed in claim 1, wherein the method of the fourth step is as follows:
and (3) generating 6 sample characteristic images of the sample to be detected by the method of the second step, comparing the 6 sample characteristic images with the 6 template characteristic images generated by the second step, and restricting the precision of the defect by setting a gray difference threshold, a defect width threshold, a defect area threshold and the like because the sample characteristic images are not completely the same as the template characteristic images.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110972099.1A CN113850756A (en) | 2021-08-24 | 2021-08-24 | Label defect detection method based on template comparison |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202110972099.1A CN113850756A (en) | 2021-08-24 | 2021-08-24 | Label defect detection method based on template comparison |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN113850756A true CN113850756A (en) | 2021-12-28 |
Family
ID=78976067
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202110972099.1A Withdrawn CN113850756A (en) | 2021-08-24 | 2021-08-24 | Label defect detection method based on template comparison |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN113850756A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114354491A (en) * | 2021-12-30 | 2022-04-15 | 苏州精创光学仪器有限公司 | DCB ceramic substrate defect detection method based on machine vision |
-
2021
- 2021-08-24 CN CN202110972099.1A patent/CN113850756A/en not_active Withdrawn
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN114354491A (en) * | 2021-12-30 | 2022-04-15 | 苏州精创光学仪器有限公司 | DCB ceramic substrate defect detection method based on machine vision |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| WO2022062812A1 (en) | Screen defect detection method, apparatus, and electronic device | |
| CN107705288B (en) | Infrared video detection method for dangerous gas leakage under strong interference of pseudo-target motion | |
| US7480407B2 (en) | Surface region identification by image area segmentation | |
| CN109242853B (en) | PCB defect intelligent detection method based on image processing | |
| CN109472271B (en) | Printed circuit board image contour extraction method and device | |
| CN114240939B (en) | Method, system, equipment and medium for detecting appearance defects of mainboard components | |
| CN112308854B (en) | Automatic detection method and system for chip surface flaws and electronic equipment | |
| CN113034488B (en) | Visual inspection method for ink-jet printed matter | |
| JP2004077164A (en) | Defect inspection method | |
| CN112767369A (en) | Defect identification and detection method and device for small hardware and computer readable storage medium | |
| CN114820627A (en) | Textile quality detection method based on optical means | |
| CN113706490B (en) | Wafer defect detection method | |
| CN114463296B (en) | Light-weight part defect detection method based on single sample learning | |
| CN104118609A (en) | Labeling quality detecting method and device | |
| CN106228541A (en) | The method and device of screen location in vision-based detection | |
| JP2008175588A (en) | Visual examination device | |
| CN111739003B (en) | Machine vision method for appearance detection | |
| CN111426693A (en) | Quality defect detection system and detection method thereof | |
| CN113269769A (en) | Defect detection method, system and equipment based on image registration | |
| CN113850756A (en) | Label defect detection method based on template comparison | |
| JP2012108026A (en) | Detection method of foreign material-contaminated product | |
| CN111724375A (en) | Screen detection method and system | |
| Jahangir Alam et al. | Analysis of a printed complex image quality checking method of fabric cloth for development of an automated quality checking system | |
| CN113192061B (en) | Extraction method and device of LED package appearance detection image, electronic equipment and storage medium | |
| CN115239690A (en) | PCB line sequence detection method and system based on image recognition |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| TA01 | Transfer of patent application right |
Effective date of registration: 20230109 Address after: 271100 douxianmen village, Gaozhuang sub district office, Laiwu District, Jinan City, Shandong Province Applicant after: HUANENG LAIWU POWER GENERATION Co.,Ltd. Applicant after: Jingrui vision intelligent technology (Shanghai) Co.,Ltd. Address before: 200333 room 808, 8th floor, No.6 Lane 600, Yunling West Road, Putuo District, Shanghai Applicant before: Jingrui vision intelligent technology (Shanghai) Co.,Ltd. |
|
| TA01 | Transfer of patent application right | ||
| WW01 | Invention patent application withdrawn after publication |
Application publication date: 20211228 |
|
| WW01 | Invention patent application withdrawn after publication |