WO2023038214A1 - Dispositif et procédé de détection de défaut de surface - Google Patents

Dispositif et procédé de détection de défaut de surface Download PDF

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Publication number
WO2023038214A1
WO2023038214A1 PCT/KR2022/004988 KR2022004988W WO2023038214A1 WO 2023038214 A1 WO2023038214 A1 WO 2023038214A1 KR 2022004988 W KR2022004988 W KR 2022004988W WO 2023038214 A1 WO2023038214 A1 WO 2023038214A1
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WO
WIPO (PCT)
Prior art keywords
defect detection
inspection target
defect
pattern
photographing
Prior art date
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PCT/KR2022/004988
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English (en)
Korean (ko)
Inventor
윤기욱
김태웅
송정민
김의석
Original Assignee
라온피플 주식회사
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Publication of WO2023038214A1 publication Critical patent/WO2023038214A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/24—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures
    • G01B11/25—Measuring arrangements characterised by the use of optical techniques for measuring contours or curvatures by projecting a pattern, e.g. one or more lines, moiré fringes on the object
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
    • G01B11/00—Measuring arrangements characterised by the use of optical techniques
    • G01B11/30—Measuring arrangements characterised by the use of optical techniques for measuring roughness or irregularity of surfaces
    • 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
    • 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
    • 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
    • G01N21/94—Investigating contamination, e.g. dust
    • 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
    • G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
    • G01N21/956—Inspecting patterns on the surface of objects

Definitions

  • Embodiments disclosed herein relate to a surface defect detection apparatus and method capable of detecting surface defects of products that are difficult to detect defects by photography due to surface gloss.
  • the quality inspections of mass-produced products there is a visual inspection.
  • the product is inspected by optically analyzing the image taken of the exterior of the product, finding the part estimated to be defective, and classifying the defective product.
  • the use of pre-learned artificial intelligence models in the exterior inspection process has improved inspection performance, and the inspection results have reached a reliable level even without human visual inspection.
  • the inspection of mass-produced products is becoming more efficient in terms of time and cost.
  • Korean Patent Registration No. 10-1611823 “Exterior Inspection Method” discloses an automatic exterior inspection device and method for detecting defects such as dents or stains on the surface of home appliances by photographing the exterior while transporting the appliance.
  • Embodiments disclosed herein are aimed at presenting an apparatus and method for detecting surface defects of a product having a glossy surface.
  • Embodiments disclosed in this specification are aimed at presenting a device and method for detecting surface defects capable of improving defect detection performance of a product having a glossy surface.
  • Embodiments disclosed in this specification are intended to provide a surface defect detection device and method capable of detecting detailed locations of defects by selectively photographing a plurality of coating layers forming a glossy surface.
  • Embodiments disclosed in this specification are intended to provide a surface defect detection device and method capable of detecting all defects formed on a glossy surface, even if the locations or extension directions thereof vary.
  • a surface defect detection method performed by a surface defect detection apparatus includes obtaining a photographed image of an inspection target; The method includes: detecting a defect using an acquired captured image, wherein the acquiring of the captured image includes: irradiating pattern light having a stripe pattern at regular intervals on a surface of an object to be inspected; and obtaining a photographed image by photographing the reflected light reflected from the surface of the object to be inspected.
  • a surface defect detection apparatus includes an illumination unit for irradiating pattern light having a stripe pattern at regular intervals on a surface of an inspection target; a photographing unit for acquiring a photographed image by photographing reflected light reflected from the surface of the inspection target by the pattern light irradiated by the lighting unit; a storage unit for storing the photographed image acquired by the photographing unit;
  • the control unit may include a controller configured to adjust characteristics of pattern light emitted from the lighting unit, control the photographing unit, and detect a defect of the inspection target using a photographed image stored in the storage unit.
  • the method for detecting surface defects includes acquiring a photographed image of an object to be inspected; The method includes: detecting a defect using an acquired captured image, wherein the acquiring of the captured image includes: irradiating pattern light having a stripe pattern at regular intervals on a surface of an object to be inspected; A step of acquiring a photographed image by photographing reflected light reflected from the surface of the inspection target may be included.
  • the surface defect detection method in a computer program stored in a medium to perform the surface defect detection method includes acquiring a photographed image of an inspection target; The method includes: detecting a defect using an acquired captured image, wherein the acquiring of the captured image includes: irradiating pattern light having a stripe pattern at regular intervals on a surface of an object to be inspected; A step of acquiring a photographed image by photographing reflected light reflected from the surface of the inspection target may be included.
  • any one of the above-mentioned problem solving means it is possible to present a method for detecting surface defects of a product having a glossy surface and an apparatus for performing the same.
  • any one of the above-mentioned problem solving means it is possible to present a method for detecting surface defects capable of improving the detection performance of defects of a product having a glossy surface and an apparatus for performing the same.
  • a surface defect detection method capable of detecting a detailed location of a defect by selectively photographing a plurality of paint layers forming a glossy surface and a device for performing the same are presented.
  • any one of the above-described problem solving means it is possible to propose a surface defect detection method and a device for performing the same, which can detect all of the defects formed on the glossy surface even if the locations or extension directions thereof vary.
  • FIG. 1 is a block diagram schematically showing a functional configuration of a surface defect detection apparatus according to an embodiment.
  • FIG. 2 is a conceptual diagram showing some configurations of a surface defect detection apparatus according to an embodiment.
  • FIG. 3 is a diagram showing a method of detecting surface defects performed by a surface defect detection apparatus according to an embodiment step by step.
  • FIG. 4 is an exemplary diagram showing types of defects occurring in a coating layer of a product to be inspected having a glossy surface.
  • FIG 5 is an exemplary view showing an example of defects detected by a surface defect detection method according to an embodiment.
  • FIG. 1 is a block diagram schematically showing a functional configuration of a surface defect detection device according to an embodiment
  • FIG. 2 is a conceptual diagram showing some configurations of a surface defect detection device according to an embodiment.
  • the surface defect detection apparatus 100 is a device that acquires a photographed image of an inspection target and detects defects based on the obtained photographed image in order to detect defects on the surface of the inspection target having a glossy surface.
  • the surface defect detection apparatus 100 may be implemented as a general user terminal or a server-client system composed of a user terminal and a server.
  • the user terminal may be implemented as a computer or portable terminal capable of accessing a remote server through the network N or connecting to other terminals and servers.
  • the computer includes, for example, a laptop, desktop, or laptop equipped with a web browser
  • the portable terminal is, for example, a wireless communication device that ensures portability and mobility.
  • PCS Personal Communications System
  • PDC Personal Digital Cellular
  • PHS Personal Handyphone System
  • PDA Personal Digital Assistant
  • GSM Global System for Mobile communications
  • IMT International Mobile Telecommunication
  • CDMA Code Division Multiple Access
  • W-CDMA Wide-Code Division Multiple Access
  • Wibro Wireless Broadband Internet
  • Smart Phone Mobile WiMAX (Mobile Worldwide Interoperability for Microwave Access), etc. (Handheld)-based wireless communication device may be included.
  • a light irradiation unit or a camera corresponding to a lighting unit or a photographing unit may be integrally configured or connected to the user terminal in the form of a module.
  • the server implementing the server-client system may be implemented as a computer capable of communicating with a user terminal on which an application for interaction with a user or a web browser is installed through a network, or may be implemented as a cloud computing server.
  • the server may include a storage device capable of storing data or may store data through a third server.
  • the above-described user terminal and server may cooperate with each other to configure the surface defect detection device 100 implemented as one server-client system.
  • the surface defect detection device 100 interfaces with the user through the user terminal. While doing so, it is possible to operate in such a way that a photographed image of an inspection target is acquired from a user terminal, and a server processes it to detect a defect.
  • the surface defect detection apparatus 100 includes a controller 110.
  • the control unit 110 controls the overall operation of the surface defect detection apparatus 100 and may include a processor such as a CPU that controls various components included in the surface defect detection apparatus 100 to be described later.
  • the control unit 110 may execute a program stored in the storage unit 140 to be described later or calculate data using an algorithm or a machine learning model stored in the storage unit 140. Also, the control unit 110 may store the processed data in the storage unit 140 again.
  • control unit 110 A specific operation of the control unit 110 will be elaborated below.
  • the surface defect detection apparatus 100 may include a lighting unit 120 for irradiating pattern light to the inspection target in order to photograph the inspection target.
  • the pattern light is the light of the stripe pattern P having straightness as shown in FIG. 2, and at this time the stripe patterns are formed with a certain width and may be arranged in parallel at regular intervals.
  • the lighting unit 120 may include a lamp in which a plurality of LED elements are arranged, and such LED lamps may be arranged in a stripe pattern.
  • the lighting unit 120 may be configured to change characteristics of pattern light, such as color temperature, width of stripe patterns, interval between stripe patterns, direction in which stripe patterns extend, and the like.
  • the lighting unit 120 may adjust the color temperature of the pattern light to have two or more different values selected from, for example, 3000K (Kelvin) to 7000K.
  • the lighting unit 120 may include two or more types of LED elements emitting light of different color temperatures, and the color temperature may be adjusted while controlling only LED elements having a specific color temperature to emit light at a time. .
  • the lighting unit 120 may be configured to be rotatable so that the direction of the stripe pattern may be adjusted.
  • the lighting unit 120 includes a plurality of LED elements arranged in a matrix so that the interval or width of the stripe pattern can be adjusted, and while the LED elements selectively emit light, the light of the stripe pattern having a desired width and interval is emitted. It may also be configured to emit light.
  • LED elements arranged in a matrix form emit light at the rear
  • an LCD panel is configured at the front.
  • the LED elements at the rear emit light and emit pattern light. It may be configured to form.
  • the characteristics of the pattern light can be controlled by outputting a two-dimensional image of the pattern light to be irradiated by the LCD panel.
  • the color temperature of light emitted from the LED device can be controlled using an image output from the LCD panel.
  • the control unit 110 can drive the lighting unit 120 according to the characteristics of one or more pattern lights required according to the inspection target and obtain a photographed image. there is.
  • control unit 110 stores color temperature information of two or more pattern lights set so that the pattern light can reach each paint layer in the storage unit 140.
  • the lighting unit 120 may sequentially irradiate pattern light having different characteristics to the same inspection target according to the set color temperature.
  • the control unit 110 sets two directions of movement of the stripe pattern forming the pattern light.
  • the lighting unit 120 may obtain two or more captured images while sequentially changing and irradiating pattern light according to two or more set directions.
  • the surface defect detection device 100 may include a photographing unit 130 .
  • the photographing unit 130 is provided with an optical means such as a conventional camera module, and can photograph the surface of the inspection target while the lighting unit 120 radiates pattern light to the inspection target. Accordingly, the photographed image obtained by the photographing unit 130 may include an image of reflected light reflected from the glossy surface of the inspection target to which the pattern light is irradiated.
  • the photographed image I obtained by the photographing unit 130 may have a substantially similar shape corresponding to the stripe pattern P forming the pattern light. However, if there is a curve on the glossy surface of the inspection target, it may be formed into a shape that is bent according to the curve. Meanwhile, if there is a defect other than the intended curve, the stripe pattern may be distorted by the defect. Accordingly, the photographed image I obtained by the photographing unit 130 may include distortion due to defects.
  • the controller 110 may control the photographing unit 130 .
  • the controller 110 may change settings of the photographing unit 130 according to the characteristics of the pattern light emitted from the lighting unit 120 .
  • the controller 110 changes the settings of the photographing unit 130 according to the characteristics of the pattern light so that a captured image can be obtained clearly. can do.
  • a white balance set value of the photographing unit 130 may be adjusted to acquire a photographed image according to the changed color temperature.
  • control unit 110 changes the characteristics of the pattern light emitted from the lighting unit 120 for the same inspection target by a preset number of times, and at the same time, the photographing unit 130 photographs the inspection target whenever the characteristics of the pattern light are changed. By repeating this, it is possible to acquire a plurality of captured images of one inspection target.
  • a plurality of photographed images are obtained by photographing a specific region of the inspection target while changing only the characteristics of the irradiated pattern light without movement of the photographing unit 130 or the inspection target. Accordingly, a plurality of captured images of an inspection target described below are images captured while only changing illumination of the same part of the inspection target.
  • the surface defect detection apparatus 100 may include a storage unit 140 .
  • Various types of data such as files or programs may be installed and stored in the storage unit 140 .
  • the controller 110 may access and use data stored in the storage unit 140 or may store new data in the storage unit 140 . Also, the controller 110 may execute a program installed in the storage unit 140 .
  • the storage unit 140 may store information on characteristics of pattern light to be irradiated by the lighting unit 120 according to the type of inspection target. For example, when it is necessary to acquire three captured images while irradiating pattern light having three different color temperatures on one inspection target, the storage unit 140 sets the color temperature to be adjusted by the lighting unit 120. Values can be stored.
  • information on a plurality of pattern light characteristics including information on the characteristics of the pattern light, that is, color temperature, width or spacing of the stripe pattern, direction of the stripe pattern, and the like, may be previously stored in the storage unit 140 .
  • the storage unit 140 may store a plurality of captured images for each examination target. Of course, only one photographed image may be acquired and stored according to the type of inspection target.
  • each captured image included in the plurality of captured images contains information on pattern light characteristics when the corresponding captured image is acquired.
  • 1, 2, and 3 may be associated with each of the plurality of captured images as reference values.
  • a reference value of '1' may represent an image captured while irradiating pattern light having a color temperature of 4000K.
  • various data or program modules such as parameters related to the pre-learned defect detection model may be stored in the storage unit 140 as one or more packages.
  • the defect detection model is a machine learning model that receives a photographed image and outputs information on whether a defect exists or a defect grade, and has been pre-learned before being installed in the storage unit 140 or stored in the storage unit 140. This state may be a state in which learning continues.
  • the control unit 110 may determine whether or not there is a defect on the surface of the inspection target, the location of the defect, the type of defect, and the grade of the defect by using the defect detection model. That is, the defect detection model is configured to determine only whether or not there is a defect, or, as described above, calculates the grade of the product to be inspected according to the location of the defect, for example, the coating layer where the defect is formed, the type of defect, the number or size of the defect, etc. It can be designed in advance to output it.
  • the defect detection model receives a plurality of captured images one by one and is trained to output whether or not the corresponding captured image has a defect.
  • the controller 110 may check whether each of the plurality of captured images has a defect, and accordingly, if a defect is detected in one of the plurality of captured images, it may be determined that the inspection target has a defect.
  • the controller 110 when the color temperature is set so that the wavelengths of light according to the color temperature reach different paint layers, three captured images a, b, and c are obtained while irradiating pattern light of three different color temperatures.
  • the controller 110 outputs the first It can be determined that only the second coating layer and the second coating layer are defective.
  • the defect detection model identifies the surface of a product having surface defects as a product of the same kind as the inspection target, and the surface defects are on the image. It is possible to pre-learn each of the learning images obtained by photographing while irradiating pattern light whose characteristics are adjusted to be expressed in the image. That is, the defect detection model may pre-learn a learning image in which a reflected light pattern distorted due to the surface defect is captured by photographing the surface of a product having surface defects as a product of the same kind as the inspection target.
  • the learning image is an image taken in a state in which the characteristics of pattern light, such as color temperature, stripe pattern width, spacing, and arrangement direction, are adjusted so that distortion due to defects of the same defective product as the actual inspection target is expressed on the image.
  • the defect detection model may learn a plurality of training images including distortion obtained from a plurality of defective products.
  • the defect detection model receives a plurality of captured images obtained for one inspection object as a set and outputs whether or not there are defects or the number, location, size, type or grade of the product to be inspected due to the defect. may be learned.
  • the defect detection model sets a plurality of learning images obtained by photographing the surface of a product having surface defects as a product of the same kind as the inspection target while changing the characteristics of pattern light as one learning image set, A plurality of training image sets of defective products may be trained.
  • the defect detection model even if a plurality of training images included in one training image set includes a training image in which distortion due to a defect is not expressed, if distortion due to a defect is expressed in another training image, it is comprehensively It is possible to learn the distortion that the defects of the product show according to the characteristics of the patterned light.
  • a defect generated in a specific paint layer by a wavelength that varies according to a color temperature may not be expressed in one learning image in one learning image set but may be expressed in another learning image.
  • a specific type of defect is included in the learning image set
  • the defect detection model receives a plurality of images taken for the actual inspection target and detects defects as well as It can also be learned to classify the type of defect.
  • the defect detection result for each inspection target calculated by the controller 110 may be stored in the storage unit 140 of the surface defect detection apparatus 100 .
  • the surface defect detection device 100 may include an input/output unit 150 .
  • the input/output unit 120 may include an input unit for receiving an input from a user and an output unit for displaying information such as a job performance result or a state of the surface defect detection device 100 .
  • the input/output unit 120 may include an operation panel for receiving a user input and a display panel for displaying a screen.
  • the input unit may include devices capable of receiving various types of user inputs, such as a keyboard, a physical button, a touch screen, or a microphone.
  • the output unit may include a display panel or a speaker.
  • the input/output unit 120 is not limited thereto and may include a configuration supporting various input/output.
  • the input/output unit 150 may receive specific setting values for the lighting unit 120 or the camera unit 130 from a user and transmit them to the control unit 110 or store them in the storage unit 140 .
  • the input/output unit 150 may receive a type of examination target selected by a user, and the controller 110 may adjust settings of the lighting unit 120 or the camera unit 130 according to the selected examination target type.
  • the input/output unit 150 may output defect detection results for inspection targets. For example, when the control unit 110 determines that the grade of a specific inspection target is unqualified, the input/output unit 150 may generate an alarm so that the user can exclude the corresponding inspection target product from the inspection line.
  • the surface defect detection device 100 may include a communication unit 160 .
  • the communication unit 160 is a means for mediating data exchange between other devices and the surface defect detection apparatus 100, and may perform wired/wireless communication with other devices or networks.
  • the communication unit 160 may include a communication module supporting at least one of various wired/wireless communication methods.
  • the communication module may be implemented in the form of a chipset.
  • the wireless communication supported by the communication unit 160 may be, for example, Wireless Fidelity (Wi-Fi), Wi-Fi Direct, Bluetooth, Ultra Wide Band (UWB), or Near Field Communication (NFC).
  • wired communication supported by the communication unit 140 may be, for example, USB or High Definition Multimedia Interface (HDMI).
  • HDMI High Definition Multimedia Interface
  • the communication unit 160 may transmit a defect detection result from the surface defect detection device 100 to another device, for example, a separate user device.
  • FIG. 3 is a diagram showing a surface defect detection method step by step according to an embodiment
  • FIG. 4 is an exemplary view showing types of defects occurring in a coating layer of a product to be inspected having a glossy surface
  • 5 is an exemplary view showing an example of defects detected by a surface defect detection method according to an embodiment.
  • the surface defect detection method according to the embodiment shown in FIG. 3 includes steps processed time-sequentially in the surface defect detection apparatus 100 shown in FIGS. 1 and 2 . Therefore, even if the contents are omitted below, the above description of the surface defect detection apparatus 100 shown in FIGS. 1 and 2 can also be applied to the surface defect detection method according to the embodiment shown in FIG. 3 .
  • the surface defect detection apparatus 100 acquires a photographed image of an object to be inspected, and detects a defect using the obtained photographed image.
  • steps S301 to S306 may be performed to obtain one or more captured images of the object to be inspected.
  • the surface defect detection apparatus 100 irradiates pattern light having preset characteristics to an inspection target (S301).
  • two or more characteristics of pattern light may be set for one inspection target, and thus, two or more captured images of the inspection target may be obtained.
  • three different pattern light characteristics for obtaining three photographed images may be preset. Accordingly, the surface defect detection apparatus 100 may sequentially change pattern light according to preset characteristics.
  • the surface defect detection apparatus 100 may capture the reflected light formed by reflecting the pattern light on the surface of the inspection target (S302). Subsequently, the surface defect detection apparatus 100 stores the photographed image (S303), and then determines whether or not the photographing has been completed a preset number of times, that is, a number corresponding to the preset number of pattern light characteristics (S304). ).
  • the surface defect detection device 100 In step S304, it is possible to check whether two times of image capturing according to each characteristic have been completed.
  • the surface defect detection apparatus 100 may detect defects of the inspection target based on one or more stored captured images (S307).
  • the surface defect detection apparatus 100 sequentially changes the characteristics of the pattern light according to a preset one (S305), and irradiates the pattern light with the changed characteristics to capture the captured image.
  • a series of processes (S301 to S304) of obtaining may be repeated.
  • the surface defect detection apparatus 100 can selectively change the setting value of the camera, that is, the photographing unit according to the characteristics of the pattern light set by changing in step S305 (S306). For example, you can change the camera's white balance settings.
  • the surface defect detection apparatus 100 may acquire one or more, depending on embodiments, a plurality of captured images of the inspection target.
  • the surface defect detection apparatus 100 may use a previously learned defect detection model.
  • the defect detection model may learn learning images obtained by photographing a defective product as a product of the same type as the inspection target in image units. In this case, it is preferable to train the defect detection model using only images in which distortion due to defects is expressed as training images.
  • the defect detection model may be trained to determine defects by using a plurality of training images acquired while changing the characteristics of pattern light identically to the actual inspection environment for one defective product as one unit set.
  • the defect detection model is labeled with the location or type of the defect, the shape, size, depth, etc. of the defect, and learns the characteristics of distortion that appear or do not appear in a plurality of learning images sequentially photographed with different characteristics of pattern light. By doing so, it may be learned to distinguish the type of defect.
  • various types of defects may be distributed on a glossy surface having a plurality of paint layers, such as an automobile.
  • the undercoating layer (L2), the color painting layer (L3), and the transparent painting layer (L4) are sequentially formed on the body panel (L1) of the vehicle, foreign matter 401, color aggregation 402, Defects such as poor gloss 403, bubbles 404, and scratches 405 and 406 may be distributed.
  • the surface defect detection apparatus 100 may calculate the number, size, type, etc. of defects from a plurality of captured images by photographing the surface of the vehicle while irradiating pattern light of different color temperatures in various directions.
  • the defect detection model comprehensively analyzes the plurality of captured images to further determine the type, depth, distribution, and the like of defects.
  • the surface defect detection apparatus 100 may classify the product grade of the inspection target according to the size, number, type, and depth of the detected defects.
  • the outermost layer of the glossy surface of the inspection object for example, by irradiating the inspection object with patterned light having a color temperature corresponding to the wavelength reflected from the transparent coating layer of FIG.
  • the detection performance does not deteriorate according to the color of the product to be inspected by using the glossy characteristics of the painted surface, and defects found on the surface can be easily detected.
  • the lower layer for example, the undercoating layer (L2 of FIG. ) or an image reflected from the color coating layer L3, etc., it is possible to improve the accuracy of defect detection by maximizing the visibility of defects generated inside the coating.
  • ' ⁇ unit' used in the above embodiments means software or a hardware component such as a field programmable gate array (FPGA) or ASIC, and ' ⁇ unit' performs certain roles.
  • ' ⁇ part' is not limited to software or hardware.
  • ' ⁇ bu' may be configured to be in an addressable storage medium and may be configured to reproduce one or more processors. Therefore, as an example, ' ⁇ unit' refers to components such as software components, object-oriented software components, class components, and task components, processes, functions, properties, and procedures. , subroutines, segments of program patent code, drivers, firmware, microcode, circuitry, data, databases, data structures, tables, arrays, and variables.
  • components and ' ⁇ units' may be implemented to play one or more CPUs in a device or a secure multimedia card.
  • the surface defect detection method according to the embodiment described with reference to FIG. 3 may be implemented in the form of a computer-readable medium storing instructions and data executable by a computer.
  • instructions and data may be stored in the form of program codes, and when executed by a processor, a predetermined program module may be generated to perform a predetermined operation.
  • computer-readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media.
  • a computer-readable medium may be a computer recording medium, which is a volatile and non-volatile memory implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules, or other data.
  • Computer storage media include magnetic storage media such as HDDs and SSDs, optical media such as CDs, DVDs and Blu-ray discs, or media accessible through a network. It may be a memory included in the server.
  • the surface defect detection method according to the embodiment described with reference to FIG. 3 may be implemented as a computer program (or computer program product) including instructions executable by a computer.
  • a computer program includes programmable machine instructions processed by a processor and may be implemented in a high-level programming language, object-oriented programming language, assembly language, or machine language.
  • the computer program may be recorded on a tangible computer-readable recording medium (eg, a memory, a hard disk, a magnetic/optical medium, or a solid-state drive (SSD)).
  • SSD solid-state drive
  • a computing device may include at least some of a processor, a memory, a storage device, a high-speed interface connected to the memory and a high-speed expansion port, and a low-speed interface connected to a low-speed bus and a storage device.
  • a processor may include at least some of a processor, a memory, a storage device, a high-speed interface connected to the memory and a high-speed expansion port, and a low-speed interface connected to a low-speed bus and a storage device.
  • Each of these components are connected to each other using various buses and may be mounted on a common motherboard or mounted in any other suitable manner.
  • the processor may process commands within the computing device, for example, to display graphic information for providing a GUI (Graphic User Interface) on an external input/output device, such as a display connected to a high-speed interface.
  • GUI Graphic User Interface
  • Examples include instructions stored in memory or storage devices.
  • multiple processors and/or multiple buses may be used along with multiple memories and memory types as appropriate.
  • the processor may be implemented as a chipset comprising chips including a plurality of independent analog and/or digital processors.
  • Memory also stores information within the computing device.
  • the memory may consist of a volatile memory unit or a collection thereof.
  • the memory may be composed of a non-volatile memory unit or a collection thereof.
  • Memory may also be another form of computer readable medium, such as, for example, a magnetic or optical disk.
  • a storage device may provide a large amount of storage space to the computing device.
  • a storage device may be a computer-readable medium or a component that includes such a medium, and may include, for example, devices in a storage area network (SAN) or other components, such as a floppy disk device, a hard disk device, an optical disk device, or a tape device, flash memory, or other semiconductor memory device or device array of the like.
  • SAN storage area network

Abstract

L'invention propose un dispositif et un procédé de détection de défaut de surface. Le procédé de détection de défaut de surface réalisé par un dispositif de détection de défaut de surface comprend les étapes consistant à : obtenir une image capturée d'un objet à inspecter ; et détecter un défaut à l'aide de l'image capturée obtenue. L'étape d'obtention d'une image capturée comprend les étapes consistant à : irradier, sur la surface de l'objet à inspecter, une lumière de motif ayant des motifs de bande à intervalles réguliers ; et obtenir une image capturée en capturant la lumière réfléchissante réfléchie par la surface de l'objet à inspecter.
PCT/KR2022/004988 2021-09-10 2022-04-06 Dispositif et procédé de détection de défaut de surface WO2023038214A1 (fr)

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