CN113160154B - Method and system for detecting paint spraying defects of five-star feet based on machine vision - Google Patents

Method and system for detecting paint spraying defects of five-star feet based on machine vision Download PDF

Info

Publication number
CN113160154B
CN113160154B CN202110378214.2A CN202110378214A CN113160154B CN 113160154 B CN113160154 B CN 113160154B CN 202110378214 A CN202110378214 A CN 202110378214A CN 113160154 B CN113160154 B CN 113160154B
Authority
CN
China
Prior art keywords
image
area
detected
median filtering
camera
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.)
Active
Application number
CN202110378214.2A
Other languages
Chinese (zh)
Other versions
CN113160154A (en
Inventor
陈国金
徐超达
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Hangzhou Dianzi University
Original Assignee
Hangzhou Dianzi University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Hangzhou Dianzi University filed Critical Hangzhou Dianzi University
Priority to CN202110378214.2A priority Critical patent/CN113160154B/en
Publication of CN113160154A publication Critical patent/CN113160154A/en
Application granted granted Critical
Publication of CN113160154B publication Critical patent/CN113160154B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

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
    • G06T7/0008—Industrial image inspection checking presence/absence
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/20—Image enhancement or restoration using local operators
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T5/00—Image enhancement or restoration
    • G06T5/90—Dynamic range modification of images or parts thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/60—Analysis of geometric attributes
    • G06T7/62—Analysis of geometric attributes of area, perimeter, diameter or volume
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/74—Image or video pattern matching; Proximity measures in feature spaces
    • G06V10/75—Organisation of the matching processes, e.g. simultaneous or sequential comparisons of image or video features; Coarse-fine approaches, e.g. multi-scale approaches; using context analysis; Selection of dictionaries
    • G06V10/751—Comparing pixel values or logical combinations thereof, or feature values having positional relevance, e.g. template matching
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/01—Arrangements or apparatus for facilitating the optical investigation
    • G01N2021/0106—General arrangement of respective parts
    • G01N2021/0112—Apparatus in one mechanical, optical or electronic block
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10004—Still image; Photographic image
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20024—Filtering details
    • G06T2207/20032—Median filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30164—Workpiece; Machine component
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
    • Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
    • Y02P90/30—Computing systems specially adapted for manufacturing

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Health & Medical Sciences (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Pathology (AREA)
  • Immunology (AREA)
  • Biochemistry (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Multimedia (AREA)
  • Software Systems (AREA)
  • Medical Informatics (AREA)
  • Evolutionary Computation (AREA)
  • Databases & Information Systems (AREA)
  • Computing Systems (AREA)
  • Signal Processing (AREA)
  • Geometry (AREA)
  • Quality & Reliability (AREA)
  • Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
  • Image Processing (AREA)

Abstract

The invention discloses a method and a system for detecting paint spraying defects of five-star feet based on machine vision, wherein the method for detecting the paint spraying defects of the five-star feet based on the machine vision comprises the following steps: s1, adjusting parameters of a detection device, and opening a camera; s2, acquiring an image of the part to be detected acquired by the camera; s3, median filtering processing is carried out on the part image to be detected, and an image after the median filtering processing is obtained; s4, converting the processed image into a binarized image, and performing fixed threshold binarization processing on the binarized image to obtain a region with gray values; s5, calculating the area of the region to obtain the area of the part to be detected; s6, performing template matching on the image subjected to the median filtering processing obtained in the step S3 to obtain similarity; s7, calculating the difference between the area of the part to be detected and the area of the standard part obtained in the step S5, and judging whether the part to be detected meets the condition according to the obtained similarity and the calculated difference, so as to obtain a final detection result.

Description

Method and system for detecting paint spraying defects of five-star feet based on machine vision
Technical Field
The invention relates to the technical field of image processing, in particular to a method and a system for detecting paint spraying defects of five stars feet based on machine vision.
Background
The traditional appearance defect detection method comprises manual visual inspection and stroboscopic light detection. The automatic production line speed is very fast, and the human eye can not catch accurate defect information at all, and especially some less defects, the human naked eyes can not distinguish whether qualified or not at all, and this causes the problem that defect detection precision is low, the false detection rate is high. Stroboscopic detection is based primarily on the static response of the human retina to a pulsed flash. The method combines a specific camera and a stroboscopic light source, and determines the condition of the surface of the workpiece by fixedly observing the detector. The method has the defects of low reliability of detection results and low degree of automatic detection.
In recent years, a rapidly advancing machine vision technology based on an image processing technology can solve this problem exactly. Visual technology is an emerging technology that has evolved over the last decades. Machine vision can replace human vision to perform inspection, target tracking, robot guiding and other tasks. Particularly where repeated, rapid acquisition of accurate information from images is required. The theory of vision technology relates to aspects of image processing, pattern recognition, artificial intelligence and the like, and the vision technology can be applied to various aspects of production process, life, scientific research and the like. Particularly, the automatic production line replaces manual work to perform quick and single product inspection work, and the quick and accurate effect can be achieved.
The application of machine vision in quality detection accounts for 80% of the whole industrial application, wherein the largest application industries are: automobiles, pharmaceuticals, electronics and electricity, manufacturing, packaging, food, beverages, and the like. The machine vision detection is non-contact nondestructive detection, and has irreplaceable superiority compared with the traditional detection means, so that the machine vision detection has wide application, and the surface defect detection adopting the machine vision is urgent.
Disclosure of Invention
The invention aims at overcoming the defects of the prior art and provides a method and a system for detecting paint spraying defects of five stars feet based on machine vision.
In order to achieve the above purpose, the present invention adopts the following technical scheme:
A method for detecting paint spraying defects of five stars feet based on machine vision comprises the following steps:
S1, adjusting parameters of a detection device, and opening a camera;
S2, acquiring an image of the part to be detected acquired by the camera;
S3, carrying out median filtering treatment on the acquired part image to be detected to obtain an image after median filtering treatment;
S4, converting the image subjected to median filtering processing into a binarized image, and carrying out fixed threshold binarization processing on the binarized image to obtain an area with gray values;
s5, calculating the area with the gray value area to obtain the area of the part to be detected;
S6, performing template matching on the image subjected to the median filtering processing obtained in the step S3 to obtain similarity;
s7, calculating the difference between the area of the part to be detected and the area of the standard part obtained in the step S5, and judging whether the part to be detected meets the condition according to the obtained similarity and the calculated difference, so as to obtain a final detection result.
Further, in the step S1, the camera is turned on by using an open_ framegrabber operator.
Further, in the step S2, the image of the part to be inspected is acquired by using grab _image_async operator.
Further, in the step S3, median filtering processing is performed on the acquired part image to be detected by using a media_image operator.
Further, in the step S4, the image after median filtering is converted into a binarized image by using an rgb1_to_gray operator; the fixed threshold binarization of the binarized image is performed using a threshold operator.
Further, the area having the gray value region calculated in step S5 is calculated using the area_center operator.
Further, the template matching in the step S6 is performed by using a find_shape_model operator.
Further, in the step S5, an area having a gray value region is calculated, expressed as:
A＝∑(r,c)∈Rg(r，c)
wherein g (r, c) represents a gray function; r represents a region; a represents an area.
Correspondingly, the detection system for the paint spraying defects of the five-star feet based on the machine vision comprises a camera, an illumination module, an image acquisition module, a main control module, a transmission device and a part to be detected; the camera is connected with the image acquisition module, and the image acquisition module is connected with the main control module; the part to be detected is arranged on the conveying device.
Further, the main control module comprises an image real-time acquisition module, an image preprocessing module, a threshold selection module, an image template creation module and an image template matching module.
Compared with the prior art, the invention has the beneficial effects that:
1. the defect part of the part can be distinguished obviously, and the defect area can be identified accurately.
2. The invention can replace the existing manual detection mode, has high measurement precision, high automation degree, reduced production cost and stable detection, and solves the problems of unstable product, high detection cost, low working efficiency and the like caused by manual detection.
3. The invention adopts an integrated and modularized design, has simple structure, reasonable design, convenient realization and low cost.
4. The automatic paint spraying device is high in automation degree, convenient to use and operate, capable of eliminating subjective errors of people and improving the precision of identifying paint spraying defects of parts.
5. Different detection standards can be formulated by clients to meet the requirements of different clients.
Drawings
Fig. 1 is a schematic structural diagram of a detection system for paint spraying defects of five stars feet based on machine vision according to a first embodiment;
FIG. 2 is a schematic illustration of a master part provided in accordance with one embodiment;
fig. 3 is a schematic diagram of a part to be inspected according to a first embodiment.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the following embodiments and features in the embodiments may be combined with each other without conflict.
The invention aims at overcoming the defects of the prior art and provides a method and a system for detecting paint spraying defects of five stars feet based on machine vision.
Example 1
The embodiment provides a detection system for paint spraying defects of five-star feet based on machine vision, which is shown in fig. 1, and comprises a camera 1, an illumination module 2, an image acquisition module 3, a main control module 4, a transmission device 5, a part to be detected 6 and the like; the camera is connected with the image acquisition module, and the image acquisition module is connected with the main control module; the part to be detected is arranged on the conveying device.
The CCD camera is adopted, and mainly has the advantages of no strong light interference, small distortion and high shooting speed, and is used for collecting images of the part 6 to be detected;
the lighting module 2 is used for accurately compensating the illumination of the surface of the part 6 to be detected;
the image acquisition module 3 is an image acquisition card and is used for acquiring images by utilizing a CCD camera, inputting the obtained image analog voltage signals into the image acquisition card and converting the image analog voltage signals into digital signals which are convenient for computer processing;
The main control module 4 is a main control computer and is used for detecting defects of the image acquired by the camera 1 according to the digital signal input by the image acquisition module 3;
the transmission device 5: and the device is used for conveying the part 6 to be detected into the field of view of the CCD camera.
The implementation mode of the detection system of the embodiment specifically comprises the following steps: the method comprises the steps that a transmission device is adopted to send standard parts into a view field of a camera, then an imaging system and an image acquisition module are used for converting acquired images into digital signals, the digital signals are sent into a main control module, a main control module control core is used for preprocessing the acquired images of original standard parts by using an image processing technology to improve image quality, interested feature quantities are extracted from the images, and template creation of the two standard parts is carried out; and then the standard part is replaced by a part to be detected, the part to be detected is sent into the view field of the camera, then the imaging system and the image acquisition module convert the acquired image into digital signals and send the digital signals into the main control module, the main control module controls the core, the image processing technology is used for preprocessing the acquired image of the original part to be detected to improve the image quality, the interesting characteristic quantity is extracted from the image, and finally the template matching and the comparison of the characteristic quantity of the two are carried out to judge whether the part to be detected is qualified or not.
In this embodiment, the main control module includes: the system comprises an image real-time acquisition module, an image preprocessing module, a threshold selection module, an image template creation module and an image template matching module.
The image preprocessing module adopts median filtering to eliminate image noise and protect the edges of the signals from being blurred;
the image template creation module uses the create_shape_model operator to create a template from the median filtered standard shape, which is passed into the image template as a shape model for matching.
The image template matching module uses an operator find_shape_model to find the best instance matched with the shape model in the input image, returns the position and rotation angle of the found model instance, and returns each found model instance and the original shape model in the form of a fraction.
Fig. 2 is a schematic diagram of a standard part, fig. 3 is a schematic diagram of a part to be detected, and if there is an obvious defect in a place which is not sprayed by the spray gun, the area is obtained according to the threshold selection range, and then the area is compared with the area obtained by the standard part according to the threshold selection range, so that a result is obtained.
Compared with the prior art, the beneficial effects of the embodiment are as follows:
1. the defect part of the part can be distinguished obviously, and the defect area can be identified accurately.
2. The invention can replace the existing manual detection mode, has high measurement precision, high automation degree, reduced production cost and stable detection, and solves the problems of unstable product, high detection cost, low working efficiency and the like caused by manual detection.
3. The invention adopts an integrated and modularized design, has simple structure, reasonable design, convenient realization and low cost.
4. The automatic paint spraying device is high in automation degree, convenient to use and operate, capable of eliminating subjective errors of people and improving the precision of identifying paint spraying defects of parts.
5. Different detection standards can be formulated by clients to meet the requirements of different clients.
Example two
The embodiment provides a method for detecting paint spraying defects of five-star feet based on machine vision, which comprises the following steps:
S1, adjusting parameters of a detection device, and opening a camera;
S2, acquiring an image of the part to be detected acquired by the camera;
S3, carrying out median filtering treatment on the acquired part image to be detected to obtain an image after median filtering treatment;
S4, converting the image subjected to median filtering processing into a binarized image, and carrying out fixed threshold binarization processing on the binarized image to obtain an area with gray values;
s5, calculating the area with the gray value area to obtain the area of the part to be detected;
S6, performing template matching on the image subjected to the median filtering processing obtained in the step S3 to obtain similarity;
s7, calculating the difference between the area of the part to be detected and the area of the standard part obtained in the step S5, and judging whether the part to be detected meets the condition according to the obtained similarity and the calculated difference, so as to obtain a final detection result.
It should be noted that the method for detecting paint spraying defects of five stars feet based on machine vision provided by the embodiment is realized based on the detection system in the first embodiment.
In step S1, parameters of the detection device are adjusted and the camera is turned on.
And (3) parameter debugging is carried out on the detection device, and the camera is turned on by using open_ framegrabber.
The processing mode of the standard part is also included before the step S2, specifically:
A. Image acquisition is carried out by utilizing grab _image_async, and an image of the standard part is obtained;
B. Carrying out median filtering processing on the acquired image of the standard part by using media_image;
The median filtering is to select a window of a certain form to move on each point of the image, and replace the pixel gray value at the center point of the window with the median value of the pixel gray value in the window. By replacing the gray value in the center of the window with the median value, the step function and the ramp function can be effectively kept unchanged, and the pulse with the period value smaller than half of the window can be suppressed. According to the characteristics of the median filtering, the method is applied to denoising of digital images, image edge information can be well reserved, and certain uniformly distributed noise and impulse noise can be removed.
C. B, converting the image subjected to median filtering in the step B into a binarized image by utilizing rgb1_to_gray, and then carrying out fixed threshold binarization processing by utilizing threshold to obtain a region;
D. c, carrying out area calculation on the area obtained in the step C by using an area_center;
the area a of the region R having the gradation value g (R, c) in the image in this embodiment is expressed as:
A＝∑(r,c)∈Rg(r，c)
wherein g (r, c) represents a gray function; r represents a region; a represents an area.
This means that the area is defined by the volume of the gray function g (r, c). The center of gravity is defined by the first two normalized moments of the gray values g (r, c), e.g. (m 1,0, m0, 1), i.e. by where:
E. And B, creating a template by using the create_shape_model according to the image obtained in the step B.
In step S2, an image of the part to be inspected is acquired by the camera.
And carrying out image acquisition on the part to be detected by utilizing grab _image_async.
In step S3, median filtering processing is carried out on the acquired part image to be detected, and an image after median filtering processing is obtained.
And (3) performing median filtering processing on the image in the step S2 by using media_image.
In step S4, the image after the median filtering process is converted into a binarized image, and the binarized image is subjected to fixed-threshold binarization process to obtain a region having a gray value.
The image in step S3 is converted into a binarized image by rgb1_to_gray, and then fixed threshold binarization processing is performed by threshold to obtain a region having a gray value.
In step S5, the area having the gray value region is calculated, and the area of the part to be inspected is obtained.
The area calculation is performed on the area obtained in step S4 by using the area_center, and is expressed as:
A＝∑(r,c)∈Rg(r，c)
wherein g (r, c) represents a gray function; r represents a region; a represents an area.
In step S6, template matching is performed on the image after the median filtering processing obtained in step S3, so as to obtain a similarity.
And (3) performing template matching on the image in the step (S3) by utilizing find_shape_model to obtain parameters such as similarity and the like.
Template creation is performed using an image pyramid, an efficient but conceptually simple structure that interprets images in multiple resolutions. A pyramid of an image is a series of image sets of progressively lower resolution arranged in a pyramid shape and derived from the same original image.
In step S7, a difference between the area of the part to be detected and the area of the standard part obtained in step S5 is calculated, and whether the part to be detected meets the condition is judged according to the obtained similarity and the calculated difference, so as to obtain a final detection result.
And judging whether the to-be-tested piece meets the conditions according to the obtained similarity and the difference between the image area of the standard piece and the area of the to-be-tested piece.
In this embodiment, the template creation and template matching of step E and step S6 is mainly performed with the create_shape_model operator and the find_shape_model operator. The method comprises the steps of firstly creating a template according to a standard component, then matching the component to be tested with the template, returning the position and rotation of an example according to the values of rows, columns and angles, and returning a score as an important reference value of the method.
Compared with the prior art, the beneficial effects of the embodiment are as follows:
1. the defect part of the part can be distinguished obviously, and the defect area can be identified accurately.
2. The invention can replace the existing manual detection mode, has high measurement precision, high automation degree, reduced production cost and stable detection, and solves the problems of unstable product, high detection cost, low working efficiency and the like caused by manual detection.
3. The invention adopts an integrated and modularized design, has simple structure, reasonable design, convenient realization and low cost.
4. The automatic paint spraying device is high in automation degree, convenient to use and operate, capable of eliminating subjective errors of people and improving the precision of identifying paint spraying defects of parts.
5. Different detection standards can be formulated by clients to meet the requirements of different clients.
Note that the above is only a preferred embodiment of the present invention and the technical principle applied. It will be understood by those skilled in the art that the present invention is not limited to the particular embodiments described herein, but is capable of various obvious changes, rearrangements and substitutions as will now become apparent to those skilled in the art without departing from the scope of the invention. Therefore, while the invention has been described in connection with the above embodiments, the invention is not limited to the embodiments, but may be embodied in many other equivalent forms without departing from the spirit or scope of the invention, which is set forth in the following claims.

Claims (3)

1. A method for detecting paint spraying defects of five-star feet based on machine vision is characterized in that,
The method comprises the following steps:
S1, adjusting parameters of a detection device, and opening a camera;
S2, acquiring an image of the part to be detected acquired by the camera;
S3, carrying out median filtering treatment on the acquired part image to be detected to obtain an image after median filtering treatment;
S4, converting the image subjected to median filtering processing into a binarized image, and carrying out fixed threshold binarization processing on the binarized image to obtain an area with gray values;
s5, calculating the area with the gray value area to obtain the area of the part to be detected;
S6, performing template matching on the image subjected to the median filtering processing obtained in the step S3 to obtain similarity;
s7, calculating the difference between the area of the part to be detected and the area of the standard part, which is obtained in the step S5, and judging whether the part to be detected meets the condition according to the obtained similarity and the calculated difference, so as to obtain a final detection result;
in the step S1, the camera is opened by using an open_ framegrabber operator;
The step S2 is to collect the image of the part to be detected by using grab _image_async operator;
In the step S3, median filtering processing is performed on the acquired part image to be detected by using a media_image operator;
In the step S4, the image after median filtering is converted into a binarized image by using an rgb1_to_gray operator; the fixed threshold binarization processing is performed on the binarized image by using a threshold operator;
The area with gray value area calculated in the step S5 is calculated by using the area_center operator;
The template matching in the step S6 is performed by using a find_shape_model operator.
2. The method for detecting paint spraying defects of five stars feet based on machine vision according to claim 1, wherein,
The area having the gray value region is calculated in the step S5, expressed as:
A＝∑(r，c)∈Rg(r，c)
wherein g (r, c) represents a gray function; r represents a region; a represents an area.
3. A detection system based on the detection method of paint spraying defects of five stars feet based on machine vision according to any one of the claims 1-2, characterized in that,
The device comprises a camera, an illumination module, an image acquisition module, a main control module, a transmission device and a part to be detected; the camera is connected with the image acquisition module, and the image acquisition module is connected with the main control module; the part to be detected is arranged on the conveying device;
The main control module comprises an image real-time acquisition module, an image preprocessing module, a threshold selection module, an image template creation module and an image template matching module.
CN202110378214.2A 2021-04-08 2021-04-08 Method and system for detecting paint spraying defects of five-star feet based on machine vision Active CN113160154B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110378214.2A CN113160154B (en) 2021-04-08 2021-04-08 Method and system for detecting paint spraying defects of five-star feet based on machine vision

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110378214.2A CN113160154B (en) 2021-04-08 2021-04-08 Method and system for detecting paint spraying defects of five-star feet based on machine vision

Publications (2)

Publication Number Publication Date
CN113160154A CN113160154A (en) 2021-07-23
CN113160154B true CN113160154B (en) 2024-07-02

Family

ID=76889083

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110378214.2A Active CN113160154B (en) 2021-04-08 2021-04-08 Method and system for detecting paint spraying defects of five-star feet based on machine vision

Country Status (1)

Country Link
CN (1) CN113160154B (en)

Families Citing this family (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN118334014A (en) * 2024-06-12 2024-07-12 泉州华数机器人有限公司 Visual detection method and device for glue spraying defect of sole

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111179243A (en) * 2019-12-25 2020-05-19 武汉昕竺科技服务有限公司 Small-size chip crack detection method and system based on computer vision
CN111474184A (en) * 2020-04-17 2020-07-31 河海大学常州校区 AOI character defect detection method and device based on industrial machine vision

Family Cites Families (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN108830832B (en) * 2018-05-15 2021-09-07 佛山市南海区广工大数控装备协同创新研究院 Plastic barrel surface defect detection method based on machine vision
CN109001212A (en) * 2018-07-17 2018-12-14 五邑大学 A kind of stainless steel soup ladle defect inspection method based on machine vision

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111179243A (en) * 2019-12-25 2020-05-19 武汉昕竺科技服务有限公司 Small-size chip crack detection method and system based on computer vision
CN111474184A (en) * 2020-04-17 2020-07-31 河海大学常州校区 AOI character defect detection method and device based on industrial machine vision

Also Published As

Publication number Publication date
CN113160154A (en) 2021-07-23

Similar Documents

Publication Publication Date Title
CN109490316B (en) Surface defect detection algorithm based on machine vision
CN103499585B (en) Based on noncontinuity lithium battery film defect inspection method and the device thereof of machine vision
Baygin et al. Machine vision based defect detection approach using image processing
CN110211101A (en) A kind of rail surface defect rapid detection system and method
US9007458B2 (en) Method for monitoring the quality of the primer layer applied to a motor-vehicle body prior to painting
CN110308151A (en) A kind of surface of workpiece defect identification method and device based on machine vision
Liu et al. Automatic detection technology of surface defects on plastic products based on machine vision
CN111189837A (en) Cigarette appearance online detection method and device
CN113177924A (en) Industrial production line product flaw detection method
CN210071686U (en) Fruit grading plant based on orthogonal binocular machine vision
CN104197836A (en) Vehicle lock assembly size detection method based on machine vision
CN111487192A (en) Machine vision surface defect detection device and method based on artificial intelligence
CN110096980A (en) Character machining identifying system
CN108596909A (en) Hollow cylinder Surface Flaw Detection system and method
CN110738644A (en) automobile coating surface defect detection method and system based on deep learning
CN111161237A (en) Fruit and vegetable surface quality detection method, storage medium and sorting device thereof
CN113019973A (en) Online visual inspection method for manufacturing defects of ring-pull cans
CN111539927A (en) Detection process and algorithm of automobile plastic assembly fastening buckle lack-assembly detection device
CN113160154B (en) Method and system for detecting paint spraying defects of five-star feet based on machine vision
CN108582075A (en) A kind of intelligent robot vision automation grasping system
CN111693535A (en) Touch screen defect detection equipment and method based on machine vision analysis
Sidehabi et al. The Development of Machine Vision System for Sorting Passion Fruit using Multi-Class Support Vector Machine.
Meng et al. Rubber hose surface defect detection system based on machine vision
CN110763688B (en) System and method for detecting redundant materials in blind holes
CN110969357A (en) Visual detection method for holes of aluminum alloy machined part

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
GR01 Patent grant