WO2026027461A1 - Procédé de détection automatisée par image de nœuds dans une grille de lignes de contraste - Google Patents

Procédé de détection automatisée par image de nœuds dans une grille de lignes de contraste

Info

Publication number
WO2026027461A1
WO2026027461A1 PCT/EP2025/071623 EP2025071623W WO2026027461A1 WO 2026027461 A1 WO2026027461 A1 WO 2026027461A1 EP 2025071623 W EP2025071623 W EP 2025071623W WO 2026027461 A1 WO2026027461 A1 WO 2026027461A1
Authority
WO
WIPO (PCT)
Prior art keywords
nodes
contrast
grid
line grid
contrast line
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.)
Pending
Application number
PCT/EP2025/071623
Other languages
German (de)
English (en)
Inventor
Stefanos Serefoglou
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Carl Zeiss GOM Metrology GmbH
Original Assignee
Carl Zeiss GOM Metrology GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Carl Zeiss GOM Metrology GmbH filed Critical Carl Zeiss GOM Metrology GmbH
Publication of WO2026027461A1 publication Critical patent/WO2026027461A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/543—Depth or shape recovery from line drawings
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M7/00—Vibration-testing of structures; Shock-testing of structures
    • G01M7/08—Shock-testing
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/30—Determination of transform parameters for the alignment of images, i.e. image registration
    • G06T7/33—Determination of transform parameters for the alignment of images, i.e. image registration using feature-based methods
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06T2207/30124—Fabrics; Textile; Paper
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06T2207/30204—Marker
    • G06T2207/30208—Marker matrix

Definitions

  • the present invention relates to a method for automated image-based detection of nodes of a contrast line grid, as well as a method for detecting the surface geometry and in particular changes in the surface geometry of an object with a contrast line grid.
  • Such well-known methods are used, for example, in the analysis of forming processes for pressed or deep-drawn components.
  • a corresponding contrast line grid is applied to a test sheet, and the deep-drawn or pressed component is imaged from several positions after forming in order to determine the component's geometry from the various images.
  • the processing times can be considerable.
  • the 3D coordinates of the nodes of a contrast line grid can be tactilely recorded using a coordinate measuring machine, but this is also complex.
  • the contrast line grid has a first node as its origin.
  • the origin is, for example, the node of the contrast line grid from which the search for further nodes is to begin, and can be part of a first grid cell defined by the contrast lines running through the origin of the contrast line grid and adjacent nodes.
  • the contrast line grid is characterized by the fact that a marker is located adjacent to the node at the grid's origin. This marker can be detected by image recognition and serves to uniquely identify the origin. The marker can, in particular, be located within the first grid cell. This automates and simplifies the capture of the nodes of the contrast line grid. Manual user input to define the origin, for example by clicking on images, is eliminated, thus reducing processing time.
  • the marker at the origin can be a circle, as simple methods for capturing such shapes are known in image recognition. However, other marker shapes, such as squares, ellipses, or other forms, are also possible.
  • the marker and the adjacent contrast lines should be clearly aligned with each other, for example by placing the circular marker closer to the origin than to other nodes.
  • the marker can be positioned eccentrically in the first grid cell at the origin of the contrast line grid.
  • various parameters can be used by an algorithm or evaluation software to, for example, adjust the size and position of the marker relative to the origin of the contrast line grid.
  • These parameters can include, for example, the size of the marker, in particular the radius of a circular marker or at least one radius of an elliptical marker, or the side length of a square marker, and/or one or more distances of the marker to the contrast lines through the origin of the grid, and/or the asymmetry of the distances of the marker to the contrast lines, and/or the width of one or more contrast lines.
  • Another possible parameter could be the selection of the color or brightness relation of the contrast line grid and/or marker to the background, such as a light contrast line grid on a dark background or vice versa.
  • These parameters facilitate the automated detection of contrast line grid nodes that are adjacent to the origin and/or to other previously detected nodes.
  • the parameters can be determined automatically or provided by the user. When determining the parameters automatically, a process can be implemented in which the parameters are evaluated through incremental adjustments and repeated analysis. Regardless of whether the contrast line grid parameters are provided manually or automatically, they can be saved as needed. to be available, for example, for the reuse of a corresponding contrast line grid.
  • the adjacent nodes can be successively detected, using the parameters mentioned above, such as line width and/or brightness relation.
  • the detection of nodes can continue until a predetermined number of nodes have been detected or no further nodes can be found.
  • recording the nodes can be understood in particular as determining the coordinates of the nodes in a coordinate system.
  • the captured nodes of the contrast line grid can be assigned to a reference grid, for example a planar two-dimensional or undistorted contrast line grid, in order to make it easier to compare changes or deviations of a surface geometry.
  • the contrast line grid can be a rectangular grid, especially with square grid cells, although other grid shapes, such as grids with triangular or other polygonal grid cells, are also conceivable.
  • the contrast line grid can also be applied to a three-dimensional surface while preserving its topology. Its nodes can be determined based on the spatially oriented images and the parameters set by the user.
  • a grid of contrast lines is applied to the object's surface.
  • several images of the object's surface with the grid are taken from multiple different positions. These surface images are then analyzed using photogrammetry methods to determine their inner and outer orientation. Subsequently, the nodes of the grid can be automatically captured using the method described above.
  • the contrast line grid can be applied to or attached to the undeformed surface. At least several images of the deformed surface of the object can be taken from multiple positions using the contrast line grid to determine the geometry.
  • the contrast line grid applied to the surface of the undeformed object can serve as a reference grid against which the changes or deformations can be determined. Deformation detection focuses on the change from an initial state (undeformed state) to a deformed state. Accordingly, the initial state itself could have been the result of a deformation.
  • the contrast line grid applied to a surface can also be recorded in two or more deformation states and used for deformation analysis.
  • coordinates of the nodes and/or normal vectors on the surface can be determined at the nodes and/or vectors along the directions of the intersecting contrast lines in order to characterize the surface geometry.
  • a captured, orthonormalized coordinate system for each node can be output. This is derived from the position and directions of the intersecting line segments.
  • the resulting surface normal can also be output.
  • the calculation results can be dynamically displayed to the user on a graphical user interface, for example, as a 3D scene or in a dialog box, as soon as they are available.
  • Figure 1 shows an example of a contrast line grid according to the invention.
  • Figure 2 shows a representation of several images for the automated determination of a geometry
  • Figure 3 shows a representation of an object with a contrast line grid in the form of a crash test dummy with a vest on which the contrast line grid is arranged.
  • Figure 4 shows a detailed representation of the origin of the contrast line grid from Fig. 3, with a representation of the input of parameters for the contrast line grid to capture the nodes.
  • Figure 5 shows a further detailed representation of the origin of the contrast line grid, similar to Figure 4, with a representation of the input of different parameters for capturing the nodes of another contrast line grid and in
  • Figure 6 shows a representation of an image of part of the object from Figure 3, showing the determined surface normals at the nodes and vectors with directions to the adjacent nodes.
  • Figure 1 shows a contrast line grid 1 according to the invention, as it can be used in a method for the automated determination of a geometry or for the detection of deformations of a surface using a corresponding algorithm or evaluation software.
  • the corresponding contrast line grid 1 is applied to the surface to be detected, for example by printing, etching, laser engraving or the like.
  • the contrast line grid 1 is printed onto a vest 10 of a crash test dummy 9 in order to detect the change in surface shape or the deformation of the vest 10 after the crash test.
  • the vest 10 can also be a sensor vest equipped with sensors to additionally record, for example, forces acting on the dummy.
  • the contrast line grid 1 has a plurality of vertical contrast lines 2 and horizontal contrast lines 3, which define nodes 7 at their intersection points.
  • the contrast lines 2, 3 and nodes 7 define grid cells 4.
  • the contrast lines intersect at right angles. Accordingly, the contrast line grid 1 is a rectangular grid, in this example even a square grid.
  • the contrast line grid 1 can also be shaped differently, for example, with triangular, hexagonal, or polygonal grid cells 4. In such cases, the contrast lines 2, 3 no longer intersect at right angles at the nodes 7, but at other angles, such as 60° or the like.
  • the contrast line grid 1 has an origin 6, which can be referred to as the first node 6.
  • the origin 6 is the node of the contrast line grid 1 from which the search for further nodes 7 is to be started.
  • the origin 6 can be the upper left corner of the contrast line grid 1.
  • the first grid cell 5 with the first node 6 or origin 6 can be referred to as the origin cell, wherein the contrast line grid 1 is formed by the corresponding arrangement of adjacent grid cells 4.
  • a marker 8 in the form of a circle is arranged in the first grid cell 5 adjacent to the origin 6, and is positioned eccentrically to the center of the grid cell 5. The marker is located closer to the origin 6 than to the other nodes 7.
  • This marker 8 serves for the automated detection of the origin 6 of a contrast line grid 1 and, consequently, of the further nodes 7 of the contrast line grid.
  • a contrast line grid 1 is printed onto the sensor vest 10 of the crash test dummy 9.
  • this surface represents the reference surface relative to which the deformation of the sensor vest 10 after the crash test is to be detected.
  • the reference geometry can first be determined.
  • the crash test dummy 9 with the sensor vest 10 and the contrast line grid 1 can first be captured by a large number of photographs to determine the geometry, as shown, for example, in Figure 2. By taking multiple photographs from different positions, the geometry of the sensor vest 10 of the crash test dummy 9 can be determined.
  • the nodes 7 of the contrast line grid 1 are first detected and their position in space determined.
  • the origin 6 can be easily detected, particularly automatically, using the contrast line grid 1 according to the invention by capturing the marker 8 via image recognition.
  • the contrast lines 2, 3 can then be traced to the next nodes 7 and these can be determined.
  • the size or radius of the marker 8, the distance of the marker 8 to the adjacent contrast lines 2, 3, and the width of the contrast lines 2, 3 can be used to detect the origin 6 and the further nodes 7.
  • relative values can also be used, such as the asymmetry of the marker to the adjacent contrast lines 2, 3 passing through the origin 6, i.e., the ratio of the distances of the marker 8 to the adjacent contrast lines 2, 3.
  • contrast information for example, light to dark or vice versa
  • the user can enter the relevant data into the evaluation software, for example, via a dialog box in a graphical user interface.
  • the evaluation software can determine these parameters itself from the images and evaluate them iteratively by modifying the parameters and comparing the acquired data.
  • the successive determination of the remaining nodes 7 of the contrast line grid 1 can be carried out according to the known number of horizontal and vertical contrast lines. However, it is advantageous to search for nodes 7 using successive procedural steps until no further nodes 7 can be found.
  • Figure 3 shows an image of the crash test dummy 9 with sensor vest 10 and a contrast line grid 1 printed on it, taken from the majority of the images shown in Figure 2.
  • Figure 4 shows an enlarged view of the origin 6 of a contrast line grid 1 with the marker 8 near the origin 6.
  • the marker 8 is visible at the origin 6, and the parameters of the circle radius of the marker 8 and the width of the contrast lines 2, 3 have been adjusted by moving the sliders.
  • a further control (“Offset") can be used to adjust any The given asymmetry of the distances between marker 8 and contrast lines 2 and 3 can be adjusted. This allows the evaluation software to identify and detect the origin 6.
  • These parameters are adjustable relative to each other.
  • the user can define the pattern contrast (light to dark or vice versa).
  • Figure 5 shows another illustration corresponding to the illustration of Figure 4 with a different contrast line grid 1, so that the parameters are chosen accordingly.
  • Figure 6 shows a representation of an image with a highly deformed area after complete evaluation, i.e., after determining the geometry or deformation by comparison with the reference geometry.
  • the corresponding surface normals at the nodes 7, which characterize the geometry are shown.
  • the evaluation shows vectors from the respective nodes 7 directed in the direction of the intersecting line segments, thus providing a comprehensive characterization of the geometry.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Theoretical Computer Science (AREA)
  • Length Measuring Devices By Optical Means (AREA)

Abstract

La présente invention concerne un procédé de détection de la géométrie de surface d'un objet et en particulier de modifications de la géométrie de surface d'un objet au moyen d'une grille de lignes de contraste (1), ainsi qu'un procédé de détection automatisée de nœuds (7) à partir d'une pluralité d'images d'une grille de lignes de contraste (1).
PCT/EP2025/071623 2024-07-29 2025-07-28 Procédé de détection automatisée par image de nœuds dans une grille de lignes de contraste Pending WO2026027461A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102024121562.3 2024-07-29
DE102024121562.3A DE102024121562A1 (de) 2024-07-29 2024-07-29 Verfahren zur automatisierten bildbasierten erfassung von knotenpunkten eines kontrastliniengitters

Publications (1)

Publication Number Publication Date
WO2026027461A1 true WO2026027461A1 (fr) 2026-02-05

Family

ID=96774194

Family Applications (1)

Application Number Title Priority Date Filing Date
PCT/EP2025/071623 Pending WO2026027461A1 (fr) 2024-07-29 2025-07-28 Procédé de détection automatisée par image de nœuds dans une grille de lignes de contraste

Country Status (2)

Country Link
DE (1) DE102024121562A1 (fr)
WO (1) WO2026027461A1 (fr)

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1793901B (zh) * 2005-12-23 2011-06-29 上海宝钢工业检测公司 大型铸件网格定位跟踪检测方法
US11257245B1 (en) * 2017-09-11 2022-02-22 Apple Inc. Method and device for detection and tracking of low-texture objects

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN1793901B (zh) * 2005-12-23 2011-06-29 上海宝钢工业检测公司 大型铸件网格定位跟踪检测方法
US11257245B1 (en) * 2017-09-11 2022-02-22 Apple Inc. Method and device for detection and tracking of low-texture objects

Also Published As

Publication number Publication date
DE102024121562A1 (de) 2026-01-29

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