CN116309589B - Sheet metal part surface defect detection method and device, electronic equipment and storage medium - Google Patents
Sheet metal part surface defect detection method and device, electronic equipment and storage medium Download PDFInfo
- Publication number
- CN116309589B CN116309589B CN202310573670.1A CN202310573670A CN116309589B CN 116309589 B CN116309589 B CN 116309589B CN 202310573670 A CN202310573670 A CN 202310573670A CN 116309589 B CN116309589 B CN 116309589B
- Authority
- CN
- China
- Prior art keywords
- sheet metal
- metal part
- detected
- image
- preset
- 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
Links
- 239000002184 metal Substances 0.000 title claims abstract description 216
- 230000007547 defect Effects 0.000 title claims abstract description 161
- 238000001514 detection method Methods 0.000 title claims abstract description 54
- 238000000034 method Methods 0.000 claims abstract description 40
- 238000012545 processing Methods 0.000 claims description 43
- 238000004422 calculation algorithm Methods 0.000 claims description 18
- 238000007781 pre-processing Methods 0.000 claims description 17
- 238000000605 extraction Methods 0.000 claims description 13
- 238000004364 calculation method Methods 0.000 claims description 10
- 238000001914 filtration Methods 0.000 claims description 8
- 238000004590 computer program Methods 0.000 claims description 7
- 239000000284 extract Substances 0.000 claims description 6
- 238000010586 diagram Methods 0.000 description 10
- 238000000691 measurement method Methods 0.000 description 6
- 230000002093 peripheral effect Effects 0.000 description 5
- 238000005516 engineering process Methods 0.000 description 3
- 238000004458 analytical method Methods 0.000 description 2
- 238000006243 chemical reaction Methods 0.000 description 2
- 230000007797 corrosion Effects 0.000 description 2
- 238000005260 corrosion Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000033764 rhythmic process Effects 0.000 description 2
- 238000013500 data storage Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000000802 evaporation-induced self-assembly Methods 0.000 description 1
- 238000013507 mapping Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000010363 phase shift Effects 0.000 description 1
- 239000007787 solid Substances 0.000 description 1
- 238000004381 surface treatment Methods 0.000 description 1
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
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/44—Local feature extraction by analysis of parts of the pattern, e.g. by detecting edges, contours, loops, corners, strokes or intersections; Connectivity analysis, e.g. of connected components
-
- 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
- G06V10/764—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using classification, e.g. of video objects
-
- 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
-
- 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
- G06T2207/20036—Morphological image processing
-
- 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)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Multimedia (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Quality & Reliability (AREA)
- Computing Systems (AREA)
- Databases & Information Systems (AREA)
- Evolutionary Computation (AREA)
- General Health & Medical Sciences (AREA)
- Medical Informatics (AREA)
- Software Systems (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The disclosure relates to a sheet metal part surface defect detection method, a sheet metal part surface defect detection device, electronic equipment and a storage medium. The sheet metal part surface defect detection method comprises the following steps: acquiring a surface image of the sheet metal part to be detected, wherein the surface image of the sheet metal part to be detected is an image acquired after square-wave-shaped stripes are projected to the surface of the sheet metal part to be detected; extracting boundary lines of square wave-shaped stripes in the surface image of the sheet metal part to be detected to obtain a plurality of stripe boundary lines; extracting at least one target pixel point in the plurality of stripe boundary lines, and obtaining a target area based on the at least one target pixel point; according to the method and the device for detecting the surface defects of the sheet metal part, the types of the defects of the surface of the sheet metal part to be detected are determined based on the shape characteristics of the target area, and according to the method and the device for detecting the surface defects of the sheet metal part to be detected, the detection speed of the surface defects of the sheet metal part to be detected can be improved, meanwhile, the types of the defects of the surface of the sheet metal part to be detected are not limited by the types of the defects, the types of the defects of the surface of the sheet metal part to be detected can be accurately detected, and the accuracy of the types of the defects of the surface of the sheet metal part to be detected is improved.
Description
Technical Field
The disclosure relates to the technical field of surface quality detection, in particular to a sheet metal part surface defect detection method, a sheet metal part surface defect detection device, electronic equipment and a storage medium.
Background
At present, in the manufacturing process or surface treatment process of objects such as sheet metal parts, metal products and the like, defects such as scratches, cracks, roughness and the like exist on the surface of the manufactured objects due to the influence of factors such as manufacturing process, external operation environment and the like, however, the defects directly affect the overall appearance and quality of the objects, and therefore, the detection of the defects on the surface of the objects is particularly important.
The method for detecting the defects on the surface of the object generally adopts a stripe projection measurement method, wherein the stripe projection measurement method mainly comprises the steps of projecting a group of phase shift stripe patterns with sinusoidal light intensity to the surface of the object to be detected through a projector, collecting stripe images modulated by the surface of the object through a camera, processing the stripe images collected by the camera to obtain a binary image, calculating the phase value of each pixel coordinate in the binary image through a phase unfolding algorithm, calculating the corresponding height value through the mapping relation between the phase value and the surface height, obtaining the three-dimensional characteristics of the surface of the object, reconstructing the three-dimensional surface, and comparing the three-dimensional surface with a standard template to obtain the defect area. However, in practical applications of object surface detection, such as surface defect detection of sheet metal parts, a fast detection rhythm is usually required during detection, but when the above stripe projection measurement method is used for reconstructing a three-dimensional surface, a plurality of images are usually required to be shot, and the calculated amount is large, so that the detection requirement of the fast rhythm during object surface defect detection cannot be met. Therefore, in some detection technologies, straight line fitting is performed on streak lines in a binarized image, then secondary curve fitting is performed on points which cannot be fitted into straight lines, a deformation area of the surface of an object is determined according to the curvature of a secondary curve obtained by fitting, and then detection of defects of the surface of the object is achieved according to the deformation area.
However, in the above-mentioned fringe projection measurement method, the detection of the surface defects of the object is realized by means of straight line fitting, and the method can only be used for detecting concave-convex defects, but is difficult to detect defects such as scratches, necking, waves and the like.
Disclosure of Invention
In order to solve the technical problems, the disclosure provides a sheet metal part surface defect detection method, a sheet metal part surface defect detection device, electronic equipment and a storage medium.
A first aspect of an embodiment of the present disclosure provides a method for detecting a surface defect of a sheet metal part, including:
acquiring a surface image of the sheet metal part to be detected, wherein the surface image of the sheet metal part to be detected is an image acquired after square-wave-shaped stripes are projected to the surface of the sheet metal part to be detected;
extracting boundary lines of square wave-shaped stripes in the surface image of the sheet metal part to be detected to obtain a plurality of stripe boundary lines;
extracting at least one target pixel point in the plurality of stripe boundary lines, and obtaining a target area based on the at least one target pixel point;
and determining the defect type of the surface of the sheet metal part to be detected based on the shape characteristics of the target area.
A second aspect of the embodiments of the present disclosure provides a sheet metal part surface defect detection device, including:
the image acquisition module is used for acquiring an image of the surface of the sheet metal part to be detected, wherein the image of the surface of the sheet metal part to be detected is an image acquired after square-wave-shaped stripes are projected to the surface of the sheet metal part to be detected;
The boundary line extraction module is used for extracting boundary lines of square wave-shaped stripes in the surface image of the sheet metal part to be detected to obtain a plurality of stripe boundary lines;
the first determining module is used for extracting at least one target pixel point in the plurality of stripe boundary lines and obtaining a target area based on the at least one target pixel point;
and the second determining module is used for determining the defect type of the surface of the sheet metal part to be detected based on the shape characteristics of the target area.
A third aspect of the disclosed embodiments provides an electronic device, comprising:
a processor;
a memory for storing executable instructions;
the processor is used for reading the executable instructions from the memory and executing the executable instructions to realize the sheet metal part surface defect detection method provided in the first aspect.
A fourth aspect of the embodiments of the present disclosure provides a computer-readable storage medium storing a computer program, which when executed by a processor, causes the processor to implement a sheet metal part surface defect detection method provided in the first aspect.
Compared with the prior art, the technical scheme provided by the embodiment of the disclosure has the following advantages:
according to the sheet metal part surface defect detection method, device, electronic equipment and storage medium, the sheet metal part surface image to be detected can be obtained, the sheet metal part surface image to be detected is the image acquired after square wave-shaped stripes are projected to the sheet metal part surface to be detected, the boundary lines of the square wave-shaped stripes in the sheet metal part surface image to be detected are extracted after the sheet metal part surface image to be detected is obtained, a plurality of stripe boundary lines are obtained, at least one target pixel point in the stripe boundary lines is extracted, the target area is obtained based on the at least one target pixel point, and the defect type of the sheet metal part surface to be detected is determined based on the shape characteristics of the target area, so that the sheet metal part surface image to be detected can be obtained only once, the image acquired after the square wave-shaped stripes are projected to the sheet metal part surface to be detected is obtained, noise in the sheet metal part surface image to be detected is reduced, the accuracy of the obtained boundary lines of the square wave-shaped stripes is improved, the detection speed of the sheet metal part surface defect to be detected is improved, the defect type of the sheet metal part to be detected is not limited by the defect type is accurately detected, and the obtained defect type of the sheet metal part surface to be detected is accurately detected.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the disclosure and together with the description, serve to explain the principles of the disclosure.
In order to more clearly illustrate the embodiments of the present disclosure or the solutions in the prior art, the drawings that are required for the description of the embodiments or the prior art will be briefly described below, and it will be obvious to those skilled in the art that other drawings can be obtained from these drawings without inventive effort.
Fig. 1 is a flowchart of a sheet metal part surface defect detection method provided in an embodiment of the present disclosure;
FIG. 2-1 is a schematic view of a surface image of a sheet metal part to be detected according to an embodiment of the present disclosure;
2-2 are schematic diagrams of a sheet metal part surface defect to be detected provided by embodiments of the present disclosure;
FIG. 3 is a flowchart of another sheet metal part surface defect detection method provided by an embodiment of the present disclosure;
fig. 4 is a schematic structural diagram of a sheet metal part surface defect detecting device according to an embodiment of the present disclosure;
fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
Detailed Description
In order that the above objects, features and advantages of the present disclosure may be more clearly understood, a further description of aspects of the present disclosure will be provided below. It should be noted that, without conflict, the embodiments of the present disclosure and features in the embodiments may be combined with each other.
In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure, but the present disclosure may be practiced otherwise than as described herein; it will be apparent that the embodiments in the specification are only some, but not all, embodiments of the disclosure.
It should be understood that the various steps recited in the method embodiments of the present disclosure may be performed in a different order and/or performed in parallel. Furthermore, method embodiments may include additional steps and/or omit performing the illustrated steps. The scope of the present disclosure is not limited in this respect.
It should be noted that in this document, relational terms such as "first" and "second" and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, 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 one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
It should be noted that references to "one", "a plurality" and "a plurality" in this disclosure are intended to be illustrative rather than limiting, and those of ordinary skill in the art will appreciate that "one or more" is intended to be understood as "one or more" unless the context clearly indicates otherwise.
In general, a stripe projection measurement method is used for detecting defects on the surface of an object, however, the existing stripe projection measurement method can only be used for detecting concave-convex defects, and is difficult to detect defects such as scratches, necking, waves and the like. In view of this problem, embodiments of the present disclosure provide a method for detecting a surface defect of a sheet metal part, which is described below with reference to specific embodiments.
Fig. 1 is a flowchart of a sheet metal part surface defect detection method provided by an embodiment of the present disclosure, where the method may be performed by a sheet metal part surface defect detection device, and the sheet metal part surface defect detection device may be implemented in software and/or hardware, and the sheet metal part surface defect detection device may be configured in an electronic device, for example, a server or a terminal, where the terminal specifically includes a mobile phone, a computer, a tablet computer, or the like.
As shown in fig. 1, the method for detecting surface defects of a sheet metal part provided in this embodiment includes the following steps.
S110, acquiring a sheet metal part surface image to be detected, wherein the sheet metal part surface image to be detected is an image acquired after square wave-shaped stripes are projected to the sheet metal part surface to be detected.
In the embodiment of the disclosure, when the electronic device needs to detect the defect of the sheet metal part to be detected, or receives an instruction for detecting the defect of the sheet metal part to be detected, the electronic device obtains the surface image of the sheet metal part to be detected, and the surface image of the sheet metal part to be detected is an image acquired after square-wave-shaped stripes are projected to the surface of the sheet metal part to be detected.
In some embodiments of the present disclosure, when the defect detection needs to be performed on the sheet metal part to be detected, the electronic device may perform image acquisition on the surface of the sheet metal part to be detected based on the image sensor, obtain the surface image of the sheet metal part to be detected, and further obtain the surface image of the sheet metal part to be detected.
In some embodiments of the present disclosure, when an instruction for performing defect detection on a sheet metal part to be detected is received, the electronic device may search a surface image of the sheet metal part to be detected from a preset storage library based on an identifier of the sheet metal part to be detected carried in the defect detection instruction, and further obtain the surface image of the sheet metal part to be detected.
S120, extracting boundary lines of square wave-shaped stripes in the surface image of the sheet metal part to be detected, and obtaining a plurality of stripe boundary lines.
In the embodiment of the disclosure, after the electronic device acquires the surface image of the sheet metal part to be detected, boundary lines of square wave-shaped stripes in the surface image of the sheet metal part to be detected are extracted, and a plurality of stripe boundary lines are obtained.
Specifically, after the surface image of the sheet metal part to be detected is obtained, the electronic device may perform image processing on the surface image of the sheet metal part to be detected, and extract boundary lines of square wave-shaped stripes in the surface image based on a preset algorithm to obtain a plurality of stripe boundary lines.
Alternatively, the image processing may include gray scale processing, binarization processing, denoising processing, and the like, without limitation.
Wherein each square wave-shaped stripe corresponds to an upper boundary line and a lower boundary line.
Fig. 2-1 is a schematic diagram of a surface image of a sheet metal part to be detected according to an embodiment of the present disclosure, as shown in fig. 2-1, a horizontal bright area is square wave-shaped stripes, such as stripe 1, stripe 2, and the like, in the surface image of the sheet metal part to be detected.
S130, extracting at least one target pixel point in the plurality of stripe boundary lines, and obtaining a target area based on the at least one target pixel point.
In the embodiment of the disclosure, after obtaining a plurality of stripe boundary lines corresponding to square wave stripes in a sheet metal part surface image to be detected, the electronic device extracts at least one target pixel point in the plurality of stripe boundary lines, and obtains a target area based on the at least one target pixel point.
In the embodiment of the disclosure, the target pixel point is a pixel point meeting a preset condition among pixel points of a stripe boundary line in a sheet metal part surface image to be detected.
Alternatively, the preset condition may be a pixel point whose distance from the surrounding pixel points along the stripe direction is greater than a preset threshold value, or a pixel point whose gradient value is greater than a preset threshold value.
The target area is an area where at least one target pixel point is located within a preset distance range.
Specifically, after obtaining a plurality of stripe boundary lines corresponding to square wave stripes in a sheet metal part surface image to be detected, the electronic device determines pixel points which do not meet preset conditions in each pixel point based on pixel point coordinates in each stripe boundary line, determines the pixel points as target pixel points, and further obtains a target area according to the pixel coordinates of the target pixel points.
S140, determining the defect type of the surface of the sheet metal part to be detected based on the shape characteristics of the target area.
In the embodiment of the disclosure, after obtaining the target area, the electronic device determines the defect type of the surface of the sheet metal part to be detected based on the shape characteristics of the target area.
In embodiments of the present disclosure, the defect types may include concave-convex type defects, scratches, necking, waviness, and the like.
Specifically, after the electronic device obtains the target area, determining the defect type of the surface of the sheet metal part to be detected according to the shape characteristic corresponding to the target area and the shape characteristic corresponding to each defect type.
Alternatively, the shape feature may be a shape formed by the target area, such as a circle, an elongated shape, or the like.
In the embodiment of the disclosure, the surface image of the sheet metal part to be detected can be acquired by acquiring the surface image of the sheet metal part to be detected, the square wave-shaped stripes are projected onto the surface of the sheet metal part to be detected, the boundary lines of the square wave-shaped stripes in the surface image of the sheet metal part to be detected are extracted after the surface image of the sheet metal part to be detected is acquired, a plurality of stripe boundary lines are obtained, at least one target pixel point in the plurality of stripe boundary lines is extracted, a target area is obtained based on the at least one target pixel point, the defect type of the surface of the sheet metal part to be detected is determined based on the shape characteristics of the target area, therefore, the surface image of the sheet metal part to be detected can be acquired only once, the image acquired after the square wave-shaped stripes are projected onto the surface of the sheet metal part to be detected is acquired, noise in the surface image of the sheet metal part to be detected is reduced, the accuracy of the obtained square wave-shaped stripes is improved, the detection speed of the surface defect of the sheet metal part to be detected is further improved, the defect type of the sheet metal part to be detected can be accurately detected, and the defect type of the surface of the sheet metal part to be detected is not limited by the defect type is accurately detected.
In the embodiment of the disclosure, when at least one target pixel point in the correspondence of the plurality of stripe boundary lines is not extracted, the surface of the sheet metal part to be detected is indicated to have no defect.
On the basis of the above embodiment of the present disclosure, before S110, that is, before the acquisition of the surface image of the sheet metal part to be detected, the sheet metal part surface defect detection method may further include: generating square wave-shaped stripes based on a preset frequency; projecting square wave-shaped stripes to the surface of the sheet metal part to be detected.
In the embodiment of the disclosure, the preset frequency is a preset frequency for generating square wave-shaped stripes, the frequency can be set according to the requirement of a user, and the number and the density of the generated square wave-shaped stripes are different according to different preset frequencies.
In the embodiment of the disclosure, the electronic device may project the square wave-shaped stripes to the surface of the sheet metal part to be detected through a projection device such as a projector.
In some embodiments of the present disclosure, the electronic device may project the generated square-wave-shaped fringes onto the surface of the sheet metal part to be detected by controlling a built-in projection device.
In other embodiments of the present disclosure, the electronic device may be connected to the peripheral projection device in a wired or wireless manner, so as to control the peripheral projection device to project the generated square-wave-shaped stripe onto the surface of the sheet metal part to be detected.
Specifically, the electronic equipment generates square wave-shaped stripes according to preset frequency, and then controls the projection equipment to project the square wave-shaped stripes onto the surface of the sheet metal part to be detected, head portrait acquisition is carried out on the surface of the sheet metal part to be detected, on which the square wave-shaped stripes are projected, based on the image acquisition equipment, and then the surface image of the sheet metal part to be detected is obtained.
Alternatively, the image capturing device may be an image capturing device built in the electronic device, such as a camera; or an image acquisition device such as a camera or the like, which is peripheral to the electronic device.
In the embodiment of the disclosure, the surface defect of the sheet metal part to be detected can be detected by projecting square-wave-shaped stripes onto the surface of the sheet metal part to be detected, so that the accuracy of detecting the surface defect of the sheet metal part to be detected is improved.
In the embodiment of the present disclosure, extracting boundary lines of square wave-shaped fringes in a surface image of a sheet metal part to be detected in S120 to obtain a plurality of fringe boundary lines may specifically include: acquiring a gray level image of a preset channel in a surface image of a sheet metal part to be detected; performing binarization processing on the gray level image to obtain a binarized image; and extracting boundary lines of each square-wave-shaped stripe in the binarized image based on a preset algorithm to obtain a plurality of stripe boundary lines.
In the embodiment of the disclosure, the preset channel may be a certain channel of the surface image of the sheet metal part to be detected in a preset color space, for example, the preset color space may be a Red Green Blue (RGB) color space or a hue-saturation-brightness (Hue Saturation Value, HSV) color space. Specifically, the preset channel may be one of an R channel, a G channel, and a B channel, or one of an H channel, an S channel, and a V channel, which are not limited herein.
Alternatively, the binarization process may be a binarization process of the gray-scale map by a fixed threshold binarization method.
Alternatively, the preset algorithm may be a contour extraction algorithm, such as an open source computer vision library contour acquisition method (Open Source Computer Vision Library find Contours, openCV find Contours).
Specifically, the electronic device can further perform image processing on the surface image of the sheet metal part to be detected after the surface image of the sheet metal part to be detected is obtained, extract a gray level image of a preset channel in the surface image of the sheet metal part to be detected, perform binarization processing on the extracted gray level image through a fixed threshold binarization method to obtain a binarized image, and further extract boundary lines of each square wave-shaped stripe in the binarized image based on a preset algorithm such as a contour extraction algorithm to obtain a plurality of stripe boundary lines, so that accuracy of the obtained plurality of stripe boundary lines is improved.
In some embodiments of the present disclosure, obtaining a gray scale map of a preset channel in a surface image of a sheet metal part to be detected may specifically include: converting the surface image of the sheet metal part to be detected into a target color space to obtain a target image corresponding to the surface image of the sheet metal part to be detected; and extracting a gray level map of a preset channel in the target image based on the image information of the target image.
In the disclosed embodiments, the target color space may be an HSV color space. The preset channel may be an H-channel in HSV color space.
In the embodiment of the present disclosure, the specific implementation of converting the surface image of the sheet metal part to be detected into the HSV color space is similar to the existing specific implementation of converting the image from the RGB color space into the HSV color space, and will not be described herein.
Specifically, the electronic equipment converts the surface image of the sheet metal part to be detected into the HSV color space through a specific method of converting the RGB color space into the HSV color space, so as to obtain a target image corresponding to the surface image of the sheet metal part to be detected in the HSV color space, and further extracts an image of an H channel from the target image to serve as a gray scale image according to image information of the target image.
In the embodiment of the disclosure, the boundary line of the square wave-shaped stripe is extracted by extracting the gray level diagram of the surface image of the sheet metal part to be detected in the H channel of the HSV color space, and the noise in the gray level diagram of the H channel is minimum, so that the accuracy of the obtained boundary line of the square wave-shaped stripe is further improved.
In the embodiment of the present disclosure, extracting at least one target pixel point in the plurality of stripe boundary lines in S130 may specifically include: performing first-order differential calculation along the stripe direction on a plurality of pixel points in each stripe boundary line to obtain first-order differential values respectively corresponding to the plurality of pixel points; and comparing the first-order differential value with a preset threshold value, and determining the pixel point corresponding to the target differential value which is larger than the preset threshold value in the first-order differential value as a target pixel point.
In the embodiment of the present disclosure, the preset threshold is a preset threshold for determining whether the pixel point is a target pixel point.
Specifically, the electronic device performs first-order differential calculation on a plurality of pixel points in the stripe boundary line along the stripe direction according to a preset step distance aiming at each stripe boundary line, namely, calculates the difference value of adjacent pixel points of the preset step distance to obtain first-order differential values respectively corresponding to the plurality of pixel points, compares the first-order differential values with a preset threshold value, and determines the pixel point corresponding to a target differential value which is larger than or equal to the preset threshold value as a target pixel point when the first-order differential value is larger than or equal to the preset threshold value; and when the first-order differential value is smaller than a preset threshold value, determining the pixel point corresponding to the target differential value smaller than the preset threshold value as a non-target pixel point.
The preset step distance may be a preset step distance, for example, the step distance is 5.
Each target pixel point corresponds to a pixel coordinate.
Further, obtaining the target region based on the at least one target pixel point may specifically include: establishing an image mask based on at least one target pixel point and pixel coordinates corresponding to the at least one target pixel point; and preprocessing at least one target pixel point on the image mask to obtain a target region, wherein the preprocessing comprises expansion processing, closed operation processing and average filtering processing.
Specifically, the electronic device places at least one pixel point on a pure black background image with a preset size according to pixel coordinates corresponding to the at least one target pixel point, establishes an image mask, further performs preprocessing on the at least one target pixel point on the image mask, and obtains a target area according to a processing result.
The preprocessing comprises expansion processing, closed operation processing, mean value filtering processing, pixel point clustering processing and the like.
The expansion processing is to select a pixel point of a rectangular neighborhood corresponding to each pixel point, and take the maximum value of each pixel point and the pixel points in the rectangular neighborhood as the value of the pixel point, so that the area of a brighter area of the obtained image is enlarged, and the area of a darker area is reduced, wherein the rectangular neighborhood can be of an elliptic structure, a cross-shaped structure and the like.
The closed arithmetic processing is expansion-first and then corrosion processing, wherein the corrosion processing is reverse operation of the expansion processing, namely, the minimum value of each pixel point and the pixel points in the rectangular neighborhood of each pixel point is used as the value of the pixel point, so that the tiny black hole can be eliminated.
The mean value filtering process adopts a neighborhood average method, namely, the average value of each pixel point and the pixels in the neighborhood is used as the value of the pixel point.
In the embodiment of the disclosure, the electronic device performs expansion processing, closing operation processing, mean filtering processing and the like on at least one target pixel point, so that the obtained target region is more accurate.
In an embodiment of the present disclosure, before S140, the sheet metal part surface defect detection method may further include: and obtaining the corresponding relation between the preset defect type and the preset shape characteristic.
The corresponding relation between the preset defect type and the preset shape characteristic is a relation between the defect type and the shape characteristic corresponding to the defect type, which is pre-established according to working experience and the like.
For example, if the shape feature is circular, the corresponding defect type is concave-convex defect; the shape is characterized by a long strip shape, and the corresponding defect type is scratches and the like.
Specifically, the electronic device may obtain, in a preset memory, a correspondence between a preset defect type and a preset shape feature.
Further, determining the defect type of the surface of the sheet metal part to be detected based on the shape characteristics of the target area may specifically include: determining shape characteristics of a target area; based on the shape characteristics and the corresponding relation between the preset defect types and the preset shape characteristics, determining the target defect types corresponding to the shape characteristics, and determining the target defect types as the defect types of the surface of the sheet metal part to be detected.
Specifically, after obtaining a target area, the electronic device performs feature analysis on the target area, determines shape features of the target area, performs similarity calculation on the shape features and preset shape features, determines a determined defect type corresponding to the preset shape features with similarity values larger than or equal to a preset threshold value as a target defect type corresponding to the shape features, and further determines the target defect type as a defect type of the surface of the sheet metal part to be detected.
In some embodiments of the present disclosure, after obtaining at least one target pixel, performing preprocessing operations such as expansion processing, closing operation processing, mean value filtering processing, and the like on the at least one target pixel, performing clustering processing on the at least one preprocessed target pixel, obtaining a plurality of target areas according to a clustering result, determining a shape feature of each target area according to each target area in the plurality of target areas, and obtaining a target defect type corresponding to the shape feature according to the shape feature and a correspondence between a preset defect type and a preset shape feature, thereby obtaining a target defect type of each target area, further obtaining a plurality of target defect types, and determining the plurality of target defect types as defect types of the surface of the sheet metal part to be detected.
The target defect types corresponding to the plurality of target areas may be the same or different.
In the embodiment of the disclosure, a plurality of target areas can be obtained by preprocessing at least one target pixel point, and the defect type of the surface of the sheet metal part to be detected is determined based on the target defect types corresponding to the plurality of target areas, so that the defect type of the surface of the sheet metal part to be detected is comprehensive and accurate.
Fig. 2-1 is a schematic diagram of a surface image of a sheet metal part to be detected, as shown in fig. 2-1, in which, in order to obtain the surface image of the sheet metal part to be detected, fig. 2-2 is a schematic diagram of a surface defect of the sheet metal part to be detected, as shown in fig. 2-2, after gradient analysis, that is, one section of differential calculation, is performed on the surface image of the sheet metal part to be detected, in fig. 2-2, a plurality of bright areas, such as an area 004, an area 005 and an area 006, can be seen, wherein the area 004 corresponds to the area 001, the area 005 corresponds to the area 002, the area 006 corresponds to the area 003, and further, bright spot pixels in the area 004, the area 005 and the area 006, that is, at least one target pixel are preprocessed, so as to obtain a plurality of target areas, and obtain a target defect type corresponding to each target area according to shape characteristics corresponding to the plurality of target areas, so as to obtain a defect type of the surface defect of the sheet metal part to be detected.
Fig. 3 is a flowchart of another method for detecting a surface defect of a sheet metal part according to an embodiment of the present disclosure, and as shown in fig. 3, the method for detecting a surface defect of a sheet metal part may include the following steps:
s310, generating square wave stripes based on a preset frequency.
S320, projecting square-wave-shaped stripes to the surface of the sheet metal part to be detected.
S330, acquiring a surface image of the sheet metal part to be detected.
S340, acquiring a gray level image of a preset channel in the surface image of the sheet metal part to be detected.
S350, binarizing the gray level image to obtain a binarized image.
S360, based on a preset algorithm, extracting boundary lines of each square-wave-shaped stripe in the binarized image to obtain a plurality of stripe boundary lines.
S370, performing first-order differential calculation on the plurality of pixel points in the stripe boundary line along the stripe direction to obtain first-order differential values corresponding to the plurality of pixel points respectively.
S380, comparing the first-order differential value with a preset threshold value, and determining the pixel point corresponding to the target differential value larger than the preset threshold value in the first-order differential value as a target pixel point.
S390, based on at least one target pixel point and the pixel coordinates corresponding to the at least one target pixel point, an image mask is established.
S3010, preprocessing at least one target pixel point on the image mask to obtain a target region.
S3020, obtaining a corresponding relation between the preset defect type and the preset shape characteristic.
S3030, determining the shape characteristics of the target area.
S3040, determining a target defect type corresponding to the shape feature based on the shape feature and the corresponding relation between the preset defect type and the preset shape feature, and determining the target defect type as the defect type of the surface of the sheet metal part to be detected.
In the embodiment of the present disclosure, the specific implementation manners of steps S310 to S3040 are similar to those in the above embodiment of the present disclosure, and are not described herein.
In the embodiment of the disclosure, square wave-shaped stripes can be generated based on preset frequency, the square wave-shaped stripes are projected to the surface of a sheet metal part to be detected, a gray level map of a preset channel in the surface image of the sheet metal part to be detected is obtained, the gray level map is subjected to binarization processing to obtain a binarized image, and then boundary lines of each square wave-shaped stripe in the binarized image are extracted based on a preset algorithm to obtain a plurality of stripe boundary lines, first-order differential calculation along the stripe direction is performed on a plurality of pixel points in the stripe boundary lines to obtain first-order differential values corresponding to the pixel points respectively, the first-order differential values are compared with a preset threshold, pixel points corresponding to the target differential values which are larger than the preset threshold in the first-order differential values are determined to be target pixel points, an image mask is established based on pixel coordinates corresponding to at least one target pixel point and at least one target pixel point, the at least one target pixel point is preprocessed on the established image mask, a target area is obtained, a preset shape is acquired, a corresponding defect type is determined to the preset shape is not to be detected, and the surface of the sheet metal part can be more accurately detected by the surface type of the sheet metal part is detected, and the type of the defect type is not detected, and the type of the surface of the sheet metal part is more accurately is detected by the surface type of the defect type is detected, and the accuracy of the obtained surface defects of the sheet metal part to be detected is improved.
Fig. 4 is a schematic structural diagram of a sheet metal part surface defect detection device according to an embodiment of the present disclosure.
In the embodiment of the disclosure, the sheet metal part surface defect detection device may be disposed in an electronic device, which is understood to be a part of functional modules in the electronic device. Specifically, the electronic device may be a server or a terminal, where the terminal specifically includes a mobile phone, a computer, a tablet computer, or the like, which is not limited herein.
As shown in fig. 4, the sheet metal part surface defect detection apparatus 400 may include an image acquisition module 410, a boundary line extraction module 420, a first determination module 430, and a second determination module 440.
The image acquisition module 410 may be configured to acquire an image of a surface of the sheet metal part to be detected, where the image of the surface of the sheet metal part to be detected is an image acquired after square-wave-shaped stripes are projected onto the surface of the sheet metal part to be detected.
The boundary line extraction module 420 may be configured to extract boundary lines of square wave-shaped fringes in the surface image of the sheet metal part to be detected, so as to obtain a plurality of fringe boundary lines.
The first determining module 430 may be configured to extract at least one target pixel point in the plurality of stripe boundary lines, and obtain the target region based on the at least one target pixel point.
The second determining module 440 may be configured to determine a type of defect of the sheet metal part surface to be detected based on the shape characteristics of the target area.
In the embodiment of the disclosure, the surface image of the sheet metal part to be detected can be acquired by acquiring the surface image of the sheet metal part to be detected, the square wave-shaped stripes are projected onto the surface of the sheet metal part to be detected, the boundary lines of the square wave-shaped stripes in the surface image of the sheet metal part to be detected are extracted after the surface image of the sheet metal part to be detected is acquired, a plurality of stripe boundary lines are obtained, at least one target pixel point in the plurality of stripe boundary lines is extracted, a target area is obtained based on the at least one target pixel point, the defect type of the surface of the sheet metal part to be detected is determined based on the shape characteristics of the target area, therefore, the surface image of the sheet metal part to be detected can be acquired only once, the image acquired after the square wave-shaped stripes are projected onto the surface of the sheet metal part to be detected is acquired, noise in the surface image of the sheet metal part to be detected is reduced, the accuracy of the obtained square wave-shaped stripes is improved, the detection speed of the surface defect of the sheet metal part to be detected is further improved, the defect type of the sheet metal part to be detected can be accurately detected, and the defect type of the surface of the sheet metal part to be detected is not limited by the defect type is accurately detected.
In some embodiments of the present disclosure, the sheet metal part surface defect detection apparatus 400 may further include a streak generation module 450 and a streak projection module 460.
The stripe generation module 450 may be used to generate square wave stripes based on a preset frequency.
The stripe projection module 460 may be used to project square-wave-shaped stripes onto the surface of the sheet metal part to be inspected.
In some embodiments of the present disclosure, the boundary line extraction module 420 may include a gray map acquisition unit 4201, a binarization unit 4202, and a boundary line extraction unit 4203.
The gray-scale image obtaining unit 4201 may be configured to obtain a gray-scale image of a preset channel in an image of a surface of a sheet metal part to be detected.
The binarization unit 4202 may be used to binarize the gray scale image to obtain a binarized image.
The boundary line extraction unit 4203 may be configured to extract boundary lines of each square-wave-shaped stripe in the binarized image based on a preset algorithm, to obtain a plurality of stripe boundary lines.
In some embodiments of the present disclosure, the gray map acquisition unit 4201 may include a spatial conversion subunit and a gray map extraction subunit.
The space conversion subunit can be used for converting the surface image of the sheet metal part to be detected into a target color space to obtain a target image corresponding to the surface image of the sheet metal part to be detected.
The gray map extracting subunit may be configured to extract a gray map of a preset channel in the target image based on image information of the target image.
In some embodiments of the present disclosure, the first determination module 430 may include a differential calculation unit 4301 and a pixel point determination unit 4302.
The differential calculation unit 4301 may be configured to perform first-order differential calculation along the stripe direction on a plurality of pixel points in the stripe boundary line for each stripe boundary line, to obtain first-order differential values respectively corresponding to the plurality of pixel points.
The pixel point determining unit 4302 may be configured to compare the first-order differential values with a preset threshold value, and determine, as the target pixel point, a pixel point corresponding to a target differential value greater than the preset threshold value in the first-order differential values.
In some embodiments of the present disclosure, the first determination module 430 may further include a mask establishing unit 4303 and a preprocessing unit 4304.
The mask creation unit 4303 may be configured to create an image mask based on at least one target pixel point and pixel coordinates corresponding to the at least one target pixel point.
The preprocessing unit 4304 may be configured to perform preprocessing on the image mask on at least one target pixel to obtain a target area, where the preprocessing includes an expansion process, a closed-loop operation process, and an average filtering process.
In some embodiments of the present disclosure, the sheet metal part surface defect detection apparatus 400 may further include a relationship acquisition module 470.
The relationship obtaining module 470 may be configured to obtain a correspondence between the preset defect type and the preset shape feature.
In some embodiments of the present disclosure, the second determination module 440 may include a feature determination unit 4401 and a defect determination unit 4402.
The feature determination unit 4401 may be used to determine a shape feature of the target region.
The defect determining unit 4402 may be configured to determine a target defect type corresponding to the shape feature, and determine the target defect type as a defect type of the surface of the sheet metal part to be detected, based on the shape feature and a correspondence between the preset defect type and the preset shape feature.
It should be noted that, the sheet metal part surface defect detection apparatus 400 shown in fig. 4 may perform the steps in the above method embodiment, and implement the processes and effects in the above method embodiment, which are not described herein.
Fig. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present disclosure.
In the embodiment of the present disclosure, the electronic device shown in fig. 5 may be a server or a terminal, where the terminal specifically includes a mobile phone, a computer, a tablet computer, or the like, which is not limited herein.
As shown in fig. 5, the electronic device may include a processor 510 and a memory 520 storing computer program instructions.
In particular, the processor 510 described above may include a Central Processing Unit (CPU), or an application specific integrated circuit (Application Specific Integrated Circuit, ASIC), or may be configured as one or more integrated circuits that implement embodiments of the present disclosure.
Memory 520 may include mass storage for information or instructions. By way of example, and not limitation, memory 520 may comprise a Hard Disk Drive (HDD), floppy Disk Drive, flash memory, optical Disk, magneto-optical Disk, magnetic tape, or universal serial bus (Universal Serial Bus, USB) Drive, or a combination of two or more of these. Memory 520 may include removable or non-removable (or fixed) media, where appropriate. The memory 520 may be internal or external to the integrated gateway device, where appropriate. In a particular embodiment, the memory 520 is a non-volatile solid state memory. In a particular embodiment, the Memory 520 includes Read-Only Memory (ROM). The ROM may be mask-programmed ROM, programmable ROM (PROM), erasable PROM (Electrical Programmable ROM, EPROM), electrically erasable PROM (Electrically Erasable Programmable ROM, EEPROM), electrically rewritable ROM (Electrically Alterable ROM, EAROM), or flash memory, or a combination of two or more of these, where appropriate.
The processor 510 reads and executes the computer program instructions stored in the memory 520 to perform the steps of a sheet metal part surface defect detection method provided by the embodiments of the present disclosure.
In one example, the electronic device may also include a transceiver 530 and a bus 540. Wherein, as shown in fig. 5, the processor 510, the memory 520 and the transceiver 530 are connected and communicate with each other through a bus 540.
Bus 540 includes hardware, software, or both. By way of example, and not limitation, the buses may include an accelerated graphics port (Accelerated Graphics Port, AGP) or other graphics BUS, an enhanced industry standard architecture (Extended Industry Standard Architecture, EISA) BUS, a Front Side BUS (FSB), a HyperTransport (HT) interconnect, an industry standard architecture (Industrial Standard Architecture, ISA) BUS, an InfiniBand interconnect, a Low Pin Count (LPC) BUS, a memory BUS, a micro channel architecture (Micro Channel Architecture, MCa) BUS, a peripheral control interconnect (Peripheral Component Interconnect, PCI) BUS, a PCI-Express (PCI-X) BUS, a serial advanced technology attachment (Serial Advanced Technology Attachment, SATA) BUS, a video electronics standards association local (Video Electronics Standards Association Local Bus, VLB) BUS, or other suitable BUS, or a combination of two or more of these. Bus 540 may include one or more buses, where appropriate.
The embodiment of the disclosure also provides a computer readable storage medium, which can store a computer program, and when the computer program is executed by a processor, the processor is caused to implement the sheet metal part surface defect detection method provided by the embodiment of the disclosure.
The storage medium may, for example, include a memory 520 of computer program instructions executable by the processor 510 of the electronic device to perform a sheet metal part surface defect detection method provided by embodiments of the present disclosure. Alternatively, the storage medium may be a non-transitory computer readable storage medium, for example, a ROM, a random access memory (Random Access Memory, RAM), a Compact Disc ROM (CD-ROM), a magnetic tape, a floppy disk, an optical data storage device, and the like.
The foregoing is merely a specific embodiment of the disclosure to enable one skilled in the art to understand or practice the disclosure. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present disclosure is not intended to be limited to the embodiments shown and described herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. The sheet metal part surface defect detection method is characterized by comprising the following steps of:
acquiring a surface image of a sheet metal part to be detected, wherein the surface image of the sheet metal part to be detected is an image acquired after square-wave-shaped stripes are projected to the surface of the sheet metal part to be detected;
extracting boundary lines of square wave-shaped stripes in the surface image of the sheet metal part to be detected to obtain a plurality of stripe boundary lines;
extracting at least one target pixel point in the plurality of stripe boundary lines, and preprocessing the at least one target pixel point to obtain a target area, wherein the preprocessing comprises expansion processing, closed operation processing and mean value filtering processing;
determining the defect type of the surface of the sheet metal part to be detected based on the shape characteristic of the target area and the corresponding relation between the preset defect type and the preset shape characteristic;
extracting boundary lines of square wave-shaped stripes in the surface image of the sheet metal part to be detected to obtain a plurality of stripe boundary lines, wherein the method comprises the following steps:
and carrying out image processing on the surface image of the sheet metal part to be detected, extracting boundary lines of square wave-shaped stripes in the surface image after image processing based on a preset algorithm, and obtaining a plurality of stripe boundary lines, wherein the preset algorithm comprises a contour extraction algorithm.
2. The sheet metal part surface defect detection method according to claim 1, wherein before the capturing of the sheet metal part surface image to be detected, the method further comprises:
generating square wave-shaped stripes based on a preset frequency;
and projecting the square-wave-shaped stripes to the surface of the sheet metal part to be detected.
3. The method for detecting surface defects of sheet metal parts according to claim 1, wherein the image processing of the sheet metal part surface image to be detected, extracting boundary lines of square wave-shaped stripes in the image processed surface image based on a preset algorithm, and obtaining a plurality of stripe boundary lines, comprises:
acquiring a gray level image of a preset channel in the surface image of the sheet metal part to be detected;
performing binarization processing on the gray level image to obtain a binarized image;
and extracting boundary lines of each square-wave-shaped stripe in the binarized image based on a preset algorithm to obtain a plurality of stripe boundary lines.
4. The method for detecting surface defects of sheet metal parts according to claim 3, wherein the step of obtaining the gray level map of the preset channel in the surface image of the sheet metal parts to be detected comprises the following steps:
converting the surface image of the sheet metal part to be detected into a target color space to obtain a target image corresponding to the surface image of the sheet metal part to be detected;
And extracting a gray level image of a preset channel in the target image based on the image information of the target image.
5. The method for detecting surface defects of sheet metal parts according to claim 1, wherein the extracting at least one target pixel point in the plurality of stripe boundary lines comprises:
performing first-order differential calculation along the stripe direction on a plurality of pixel points in each stripe boundary line to obtain first-order differential values respectively corresponding to the plurality of pixel points;
and comparing the first-order differential value with a preset threshold value, and determining a pixel point corresponding to a target differential value larger than the preset threshold value in the first-order differential value as a target pixel point.
6. The method for detecting surface defects of sheet metal parts according to claim 1, wherein the preprocessing the at least one target pixel point to obtain a target area comprises:
establishing an image mask based on the at least one target pixel point and the pixel coordinates corresponding to the at least one target pixel point;
and preprocessing the at least one target pixel point on the image mask to obtain a target region.
7. The sheet metal part surface defect detection method according to claim 1, wherein before the determination of the defect type of the sheet metal part surface to be detected based on the shape feature of the target area and the correspondence between the preset defect type and the preset shape feature, the method further comprises:
acquiring a corresponding relation between a preset defect type and a preset shape characteristic;
the determining the defect type of the surface of the sheet metal part to be detected based on the shape feature of the target area and the corresponding relation between the preset defect type and the preset shape feature comprises the following steps:
determining shape characteristics of the target area;
and determining a target defect type corresponding to the shape feature based on the shape feature and the corresponding relation between the preset defect type and the preset shape feature, and determining the target defect type as the defect type of the surface of the sheet metal part to be detected.
8. Sheet metal part surface defect detection device, characterized by comprising:
the image acquisition module is used for acquiring a surface image of the sheet metal part to be detected, wherein the surface image of the sheet metal part to be detected is an image acquired after square-wave-shaped stripes are projected to the surface of the sheet metal part to be detected;
The boundary line extraction module is used for extracting boundary lines of square wave-shaped stripes in the surface image of the sheet metal part to be detected to obtain a plurality of stripe boundary lines;
the first determining module is used for extracting at least one target pixel point in the plurality of stripe boundary lines, preprocessing the at least one target pixel point to obtain a target area, wherein the preprocessing comprises expansion processing, closed operation processing and mean value filtering processing;
the second determining module is used for determining the defect type of the surface of the sheet metal part to be detected based on the shape characteristic of the target area and the corresponding relation between the preset defect type and the preset shape characteristic;
the boundary line extraction module is specifically configured to perform image processing on the surface image of the sheet metal part to be detected, extract boundary lines of square wave-shaped stripes in the surface image after image processing based on a preset algorithm, and obtain a plurality of stripe boundary lines, where the preset algorithm includes a contour extraction algorithm.
9. An electronic device, comprising:
a processor;
a memory for storing executable instructions;
wherein the processor is configured to read the executable instructions from the memory and execute the executable instructions to implement a sheet metal part surface defect detection method as claimed in any one of claims 1 to 7.
10. A computer readable storage medium, characterized in that the storage medium stores a computer program, which when executed by a processor causes the processor to implement a sheet metal part surface defect detection method as claimed in any one of the preceding claims 1-7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310573670.1A CN116309589B (en) | 2023-05-22 | 2023-05-22 | Sheet metal part surface defect detection method and device, electronic equipment and storage medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310573670.1A CN116309589B (en) | 2023-05-22 | 2023-05-22 | Sheet metal part surface defect detection method and device, electronic equipment and storage medium |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116309589A CN116309589A (en) | 2023-06-23 |
| CN116309589B true CN116309589B (en) | 2023-08-01 |
Family
ID=86801776
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310573670.1A Active CN116309589B (en) | 2023-05-22 | 2023-05-22 | Sheet metal part surface defect detection method and device, electronic equipment and storage medium |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116309589B (en) |
Family Cites Families (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104573635B (en) * | 2014-12-17 | 2018-04-13 | 华南理工大学 | A kind of little height recognition methods based on three-dimensional reconstruction |
| CN106053486A (en) * | 2016-07-14 | 2016-10-26 | 南京久盯自动化科技有限公司 | Steel ball surface defect detection device |
| CN107833220B (en) * | 2017-11-28 | 2021-06-11 | 河海大学常州校区 | Fabric defect detection method based on deep convolutional neural network and visual saliency |
| CN108257171A (en) * | 2018-01-09 | 2018-07-06 | 江苏科技大学 | Car radar assembling aperture detection method based on light vision |
| CN108535278A (en) * | 2018-04-18 | 2018-09-14 | 常州市安视智能科技有限公司 | Metal plate eyelet work product line punching defect detecting device based on machine vision and method |
| CN110538783A (en) * | 2019-08-30 | 2019-12-06 | 合肥通快钣金科技有限公司 | sheet metal part coating process |
| CN211567854U (en) * | 2019-12-27 | 2020-09-25 | 深圳市宇盛光电有限公司 | Wire harness plastic part for automobile double-layer sheet metal part |
| CN111896556B (en) * | 2020-08-04 | 2021-05-28 | 湖南大学 | Glass bottle bottom defect detection method and system based on machine vision |
| CN213903337U (en) * | 2020-11-16 | 2021-08-06 | 长沙金鸿顺汽车部件有限公司 | Automatic detection equipment for defects of sheet metal parts |
| CN114119466A (en) * | 2021-10-09 | 2022-03-01 | 武汉禾富信科技有限公司 | Method, device and equipment for detecting surface defects of aluminum foil and computer storage medium |
| CN114897847A (en) * | 2022-05-19 | 2022-08-12 | 京东方科技集团股份有限公司 | Image processing method and device, computer readable storage medium and electronic device |
| CN115049651B (en) * | 2022-08-15 | 2022-10-28 | 邹城市天晖软件科技有限公司 | Metal plate stamping abnormity detection method |
| CN115601368B (en) * | 2022-12-16 | 2023-05-16 | 山东天意高科技有限公司 | Sheet metal part defect detection method for building material equipment |
| CN115641336B (en) * | 2022-12-23 | 2023-03-21 | 无锡康贝电子设备有限公司 | Air conditioner sheet metal part defect identification method based on computer vision |
-
2023
- 2023-05-22 CN CN202310573670.1A patent/CN116309589B/en active Active
Also Published As
| Publication number | Publication date |
|---|---|
| CN116309589A (en) | 2023-06-23 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109141232B (en) | Online detection method for disc castings based on machine vision | |
| CN108629775B (en) | Thermal state high-speed wire rod surface image processing method | |
| CN111179243A (en) | Small-size chip crack detection method and system based on computer vision | |
| CN106934803B (en) | method and device for detecting surface defects of electronic device | |
| CN110059702B (en) | Object contour recognition method and device | |
| US10339396B2 (en) | Vehicle accessibility determination device | |
| WO2017020829A1 (en) | Resolution testing method and resolution testing device | |
| CN115908269B (en) | Visual defect detection method, visual defect detection device, storage medium and computer equipment | |
| JP6119663B2 (en) | Surface defect detection method and surface defect detection apparatus | |
| KR101272448B1 (en) | Apparatus and method for detecting region of interest, and the recording media storing the program performing the said method | |
| JP5852919B2 (en) | Crack detection method | |
| JP2014228357A (en) | Crack detecting method | |
| CN105718931B (en) | System and method for determining clutter in acquired images | |
| JP2016166853A (en) | Location estimation device and location estimation method | |
| CN117095004B (en) | Excavator walking frame main body welding deformation detection method based on computer vision | |
| JP2009097922A (en) | Visual inspection method and apparatus | |
| CN110660072A (en) | Method and device for identifying straight line edge, storage medium and electronic equipment | |
| CN111861979A (en) | Positioning method, positioning equipment and computer readable storage medium | |
| JP2014020926A (en) | Surface defect inspection device and surface defect inspection method | |
| CN112581452A (en) | Industrial accessory surface defect detection method and system, intelligent device and storage medium | |
| CN116309589B (en) | Sheet metal part surface defect detection method and device, electronic equipment and storage medium | |
| JP5036637B2 (en) | Image processing method and image processing apparatus for extracting uneven characters | |
| CN110008955B (en) | Method for testing character imprinting quality of surface of automobile brake pad | |
| JP3890844B2 (en) | Appearance inspection method | |
| CN116908185A (en) | Method and device for detecting appearance defects of article, electronic equipment and storage medium |
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 | ||
| GR01 | Patent grant |