A surface defect imaging device and method for real-time tracking of large-span cold-rolled strip curved surface

Abstract

A surface defect imaging device and method for real-time tracking of a large-span cold-rolled steel strip curved surface, the device is used to adjust the spatial position and spatial posture of a set imaging detection bracket by means of a set industrial robot cooperating with a set ground rail; the method first determines the real-time position point and the spatial posture of the corresponding position point on the premise that the angle between the camera and the light source remains constant; then, according to the determined position point and the spatial posture of the corresponding position point, the scanning trajectory of the steel strip is set, and the imaging of the surface defects of the steel strip is completed according to the set scanning trajectory. A surface defect imaging device and method for real-time tracking of a large-span cold-rolled steel strip curved surface of the present invention, under the premise of ensuring that the angle between the camera and the linear light source remains constant, satisfies the effective imaging of surface defects of steel strips of different specifications by adjusting the height and posture of the imaging detection tool online.

Description

Technical Field

[0001] The invention belongs to the technical field of surface defect detection of strip steel before and after grinding, and in particular relates to a surface defect imaging device and method for real-time tracking of a large-span cold-rolled strip steel curved surface. Background Art

[0002] During the production process of cold-rolled strip, various defects such as "roller marks, lateral embossing, scratches, incoming steel defects, etc." are generated on the surface of the strip due to abnormal contact between the strip and the contacting rollers, or vibration of the rolling mill. These potential periodic defects are usually very subtle and difficult to detect. They need to be polished by the automatic strip polishing system before they can be identified. A defect detection system is used to complete the defect identification and detection work on the surface of the strip after grinding.

[0003] At present, the length of cold-rolled strip is generally 4 meters to 8 meters, and the maximum span can reach 9 meters. The main problem caused by long-span construction is that the strip sags due to its own weight, resulting in the steel plate surface not being in a completely horizontal state (see Figure 1 ), there will be a droop of ΔH1. When the defect detection system moves at a given speed along the surface of the strip at a constant speed, the surface defect imaging of the stationary strip surface is performed, and the imaging working distance ΔH2 will change. Due to the changes in the light source irradiation position, angle, imaging distance, and angle, the surface image will be blurred or the defect will not be obvious, which will seriously affect the defect imaging quality and bring great difficulties to the defect detection work.

[0004] Combined with the different specifications of cold-rolled strip and strip tensioning parameters, the strip sag ΔH1 after tensioning is different. The calculation method of ΔH1 is referenced Figure 1 As shown in the table, the maximum strip size is 8000mm*1650mm*2.3mm, the tension at both ends is 8t, and the deformation of the middle position of the strip along the direction of gravity under the action of gravity is calculated to be 30.8mm; how to track the curved surface of the strip in real time becomes a problem that needs to be solved.

[0005] The invention application with application number: CN201410120138.5 discloses "a line scanning camera imaging method for changing object thickness", wherein a camera (2) and a light source (1) are arranged at a set angle to form an imaging optical path of a detection system, and a plane mirror group is arranged on the reflection optical path of the light of the imaged object. The light is emitted from the light source, reflected by an imaging surface (3), and then enters the camera for imaging after passing through the plane mirror group; the plane mirror group includes a first plane mirror (4) and a second plane mirror (5), the reflection surfaces of the first plane mirror and the second plane mirror are opposite and parallel, the first plane mirror and the second plane mirror respectively rotate along a length direction rotation axis passing through the midpoint of the width, and the distance between them can be adjusted; when the height of the surface of the imaged object changes, by adjusting the distance and angle between the plane mirror groups, the original imaging light source and the camera position are maintained unchanged, and effective imaging can still be achieved.

[0006] The invention application with application number: CN 201810648486.8 discloses "a robot constant force surface tracking method based on reinforcement learning", which includes the following steps: (1) establishing a mapping relationship between the surface normal force and the known sensor coordinate system according to the characteristics of the industrial robot end effector when it contacts the surface contour; (2) designing an explicit force controller for the contact process between the robot and the surface; (3) optimizing the parameters of the controller using a reinforcement learning method based on Gaussian model inference; and (4) iterating the experiment until the force and the expected error are within the set range.

[0007] The invention application with application number: CN 201810454177.7 discloses "a method and system for tracking special-shaped surfaces based on moment feature learning neural network", including the following steps: obtaining a desired moment feature vector; obtaining an initial moment feature vector, a Jacobian matrix of the initial moment feature vector, and a target joint angular velocity vector of a robotic arm; performing deep offline training on a neural network controller based on a B-spline basis using the desired moment feature vector, the initial moment feature vector, and the robotic arm joint angular velocity matrix; the robotic arm joint angular velocity vector inputs the characteristic error between the current moment feature vector and the desired moment feature vector into the trained B-spline basis neural network controller to obtain the robotic arm joint angular velocity vector in the current posture; and controlling the movement of the robotic arm according to the robotic arm joint angular velocity vector in the current posture so that the camera at the end of the robotic arm moves accordingly. Summary of the invention

[0008] In order to solve the above problems, the present invention provides a surface defect imaging device and method for real-time tracking of a large-span cold-rolled strip curved surface, and the technical solution thereof is as follows:

[0009] A surface defect imaging device for real-time tracking of a large-span cold-rolled strip curved surface, characterized by comprising:

[0010] An industrial robot (1), a ground rail (2) for propelling the industrial robot along a track, and an imaging detection bracket (3) fixed to the arm of the industrial robot;

[0011] The industrial robot cooperates with the ground rail to adjust the spatial position and spatial posture of the imaging detection bracket;

[0012] A light source (4) and a camera (5) are respectively provided on the imaging detection bracket;

[0013] at the same time

[0014] A first sensor (6) and a second sensor (7) are also provided on the imaging detection bracket;

[0015] The first sensor and the second sensor are respectively arranged on the imaging detection bracket along the length direction of the strip according to the set interval, and are arranged so that under the ideal strip surface, the center point position of each detection point of the first sensor and the second sensor is consistent with the imaging point position.

[0016] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip curved surface, wherein the imaging is completed based on shooting by a set light source in cooperation with a set camera, and is characterized by comprising the following steps:

[0017] S1: Under the premise that the angle between the camera and the light source remains constant, the real-time position point and the spatial posture of the corresponding position point are determined;

[0018] S2: According to the determined position points and the spatial postures of the corresponding position points, the scanning trajectory of the strip is set, and the imaging of the surface defects of the strip is completed according to the set scanning trajectory.

[0019] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to the present invention is characterized in that:

[0020] The real-time position point is represented by a set target point;

[0021] The target points are arranged along the curved surface of the steel strip to form a discrete representation of the shape of the curved surface of the steel strip.

[0022] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to the present invention is characterized in that:

[0023] The determination of the position points and the spatial postures of the corresponding position points, the setting of the strip scanning trajectory and the shooting based on the setting are completed within the same scanning operation cycle.

[0024] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to the present invention is characterized in that:

[0025] First, the position point and the spatial posture of the corresponding position point are determined according to a scanning cycle.

[0026] Then the strip scanning trajectory is set accordingly.

[0027] Finally, the scanning of the strip surface defect imaging is completed according to the set scanning trajectory according to the next scanning cycle relative to this scanning cycle.

[0028] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to the present invention is characterized in that:

[0029] The first sensor and the second sensor are arranged along the length direction of the strip steel at a set interval;

[0030] At the same time, the arrangement is such that the center point positions of the respective detection points of the first sensor and the second sensor under the ideal strip steel surface are consistent with the imaging point positions;

[0031] The numerical representation of the real-time position point is completed through the above settings combined with the established constraint equation, as well as the real-time actual height detected by the first sensor and the real-time actual height detected by the second sensor.

[0032] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to the present invention is characterized in that:

[0033] According to the inclination angle corresponding to the first-order derivative of the corresponding position point calculated in real time, a numerical representation of the spatial posture of the position point is established based on the quaternion expression.

[0034] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to the present invention is characterized in that:

[0035] An industrial robot, a ground track for propelling the industrial robot along a track, and an imaging detection bracket fixed to the arm of the industrial robot are provided;

[0036] The set light source and the set camera are arranged on an imaging detection bracket;

[0037] The strip scanning trajectory is set by setting the height and spatial posture of the imaging detection bracket corresponding to each position point;

[0038] Through the cooperation of the industrial robot with the ground rail, the imaging detection bracket is adjusted in real time based on the above-set strip scanning trajectory to complete the imaging and shooting of strip surface defects.

[0039] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to the present invention is characterized in that:

[0040] The constraint equation is specifically:

[0041]

[0042] in,

[0043] h0: ideal strip surface detection height, unit: mm;

[0044] h1: The height detected by the first sensor in real time based on the actual strip surface, unit: mm;

[0045] h2: The height detected by the second sensor in real time based on the actual strip surface, unit: mm;

[0046] Δh: The height of the real-time position point, unit: mm.

[0047] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to the present invention is characterized in that:

[0048] The first sensor and the second sensor are arranged along the length direction of the strip steel at a set interval;

[0049] At the same time, the arrangement is such that the center point positions of the respective detection points of the first sensor and the second sensor under the ideal strip steel surface are consistent with the imaging point positions;

[0050] Through the above settings, the position point corresponding to the actual strip steel surface height h1 detected in real time by the first sensor and the position point corresponding to the actual strip steel surface height h2 detected in real time by the second sensor are determined, and the straight line between the two points represents the tangent of the actual position point;

[0051] Based on this, the "angle of the inclination angle corresponding to the first-order derivative of the corresponding position point" is specifically:

[0052] θ1=arctan((h2-h1) / ΔX),

[0053] in,

[0054] θ1: the angle of the inclination corresponding to the first-order derivative of the corresponding position point;

[0055] h1: The height detected by the first sensor in real time based on the actual strip surface, unit: mm;

[0056] h2: The height detected by the second sensor in real time based on the actual strip surface, unit: mm;

[0057] ΔX: Horizontal distance between the first sensor and the second sensor, unit: mm.

[0058] The present invention provides a surface defect imaging device and method for real-time tracking of a large-span cold-rolled strip curved surface. Without changing the position of the imaging light source and the camera (i.e., under the premise of ensuring that the angle between the camera and the line light source remains unchanged), the height and posture of the imaging detection tool are adjusted online to meet the effective imaging of surface defects of strips of different specifications. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 A schematic diagram of the actual steel strip in the background technology section of the present invention;

[0060] Figure 2 according to Figure 1 The calculation method of ΔH1 refers to the table and figure;

[0061] Figure 3 It is a schematic diagram of the structure of the device in the present invention;

[0062] Figure 4 It is a schematic diagram of the method steps in the present invention;

[0063] Figure 5 Schematic diagram of the working process in an embodiment of the present invention.

[0064] In the figure,

[0065] 1-Industrial robots;

[0066] 2- Ground rails that propel the industrial robot along the trajectory;

[0067] 3- Imaging detection bracket fixed on the industrial robot arm;

[0068] 4- Light source;

[0069] 5- Camera;

[0070] 6- first sensor;

[0071] 7- Second sensor. DETAILED DESCRIPTION

[0072] Below, a surface defect imaging device and method for real-time tracking of a large-span cold-rolled strip curved surface of the present invention will be further specifically described based on the accompanying drawings and specific implementation methods of the specification.

[0073] like Figure 3 The surface defect imaging device for real-time tracking of a large-span cold-rolled strip steel curved surface shown in the figure comprises:

[0074] An industrial robot (1), a ground rail (2) for propelling the industrial robot along a track, and an imaging detection bracket (3) fixed to the arm of the industrial robot;

[0075] The industrial robot cooperates with the ground rail to adjust the spatial position and spatial posture of the imaging detection bracket;

[0076] A light source (4) and a camera (5) are respectively provided on the imaging detection bracket;

[0077] at the same time

[0078] A first sensor (6) and a second sensor (7) are also provided on the imaging detection bracket;

[0079] The first sensor and the second sensor are respectively arranged on the imaging detection bracket along the length direction of the strip according to the set interval, and are arranged so that under the ideal strip surface, the center point position of each detection point of the first sensor and the second sensor is consistent with the imaging point position.

[0080] A surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface, wherein the imaging is completed based on the shooting of a set light source in cooperation with a set camera, such as Figure 4 As shown, the following steps are included:

[0081] S1: Under the premise that the angle between the camera and the light source remains constant, the real-time position point and the spatial posture of the corresponding position point are determined;

[0082] S2: According to the determined position points and the spatial postures of the corresponding position points, the scanning trajectory of the strip is set, and the imaging of the surface defects of the strip is completed according to the set scanning trajectory.

[0083] in,

[0084] The real-time position point is represented by a set target point;

[0085] The target points are arranged along the curved surface of the steel strip to form a discrete representation of the shape of the curved surface of the steel strip.

[0086] in,

[0087] The determination of the position points and the spatial postures of the corresponding position points, the setting of the strip scanning trajectory and the shooting based on the setting are completed within the same scanning operation cycle.

[0088] in,

[0089] First, the position point and the spatial posture of the corresponding position point are determined according to a scanning cycle.

[0090] Then the strip scanning trajectory is set accordingly.

[0091] Finally, the scanning of the strip surface defect imaging is completed according to the set scanning trajectory according to the next scanning cycle relative to this scanning cycle.

[0092] in,

[0093] The first sensor and the second sensor are arranged along the length direction of the strip steel at a set interval;

[0094] At the same time, the arrangement is such that the center point positions of the respective detection points of the first sensor and the second sensor under the ideal strip steel surface are consistent with the imaging point positions;

[0095] The numerical representation of the real-time position point is completed through the above settings combined with the established constraint equation, as well as the real-time actual height detected by the first sensor and the real-time actual height detected by the second sensor.

[0096] in,

[0097] According to the inclination angle corresponding to the first-order derivative of the corresponding position point calculated in real time, a numerical representation of the spatial posture of the position point is established based on the quaternion expression.

[0098] in,

[0099] An industrial robot, a ground track for propelling the industrial robot along a track, and an imaging detection bracket fixed to the arm of the industrial robot are provided;

[0100] The set light source and the set camera are arranged on an imaging detection bracket;

[0101] The strip scanning trajectory is set by setting the height and spatial posture of the imaging detection bracket corresponding to each position point;

[0102] Through the cooperation of the industrial robot with the ground rail, the imaging detection bracket is adjusted in real time based on the above-set strip scanning trajectory to complete the imaging and shooting of strip surface defects.

[0103] in,

[0104] The constraint equation is specifically:

[0105]

[0106] in,

[0107] h0: ideal strip surface detection height, unit: mm;

[0108] h1: The height detected by the first sensor in real time based on the actual strip surface, unit: mm;

[0109] h2: The height detected by the second sensor in real time based on the actual strip surface, unit: mm;

[0110] Δh: The height of the real-time position point, unit: mm.

[0111] in,

[0112] The first sensor and the second sensor are arranged along the length direction of the strip steel at a set interval;

[0113] At the same time, the arrangement is such that the center point positions of the respective detection points of the first sensor and the second sensor under the ideal strip steel surface are consistent with the imaging point positions;

[0114] Through the above settings, the position point corresponding to the actual strip steel surface height h1 detected in real time by the first sensor and the position point corresponding to the actual strip steel surface height h2 detected in real time by the second sensor are determined, and the straight line between the two points represents the tangent of the actual position point;

[0115] Based on this, the "angle of the inclination angle corresponding to the first-order derivative of the corresponding position point" is specifically:

[0116] θ1=arctan((h2-h1) / ΔX),

[0117] in,

[0118] θ1: the angle of the inclination corresponding to the first-order derivative of the corresponding position point;

[0119] h1: The height detected by the first sensor in real time based on the actual strip surface, unit: mm;

[0120] h2: The height detected by the second sensor in real time based on the actual strip surface, unit: mm;

[0121] ΔX: Horizontal distance between the first sensor and the second sensor, unit: mm.

[0122] Working principle, original process and implementation examples

[0123] Combine the following Figure 5 And two embodiments are used to illustrate the principle and working process, such as Figure 5 As shown in the figure

[0124] L1: vertical normal of imaging point 1;

[0125] L2: vertical normal of imaging point 2;

[0126] h0: Detection height based on ideal strip surface;

[0127] h1, h2: the heights detected by the first sensor and the second sensor according to the actual strip surface;

[0128] ∠A0: the angle between the light source and the normal at imaging point 1;

[0129] ∠A1: the angle between the light source and the normal at imaging point 2;

[0130] ∠B0: the angle between the camera and the normal at imaging point 1;

[0131] ∠B1: the angle between the camera and the normal at imaging point 2;

[0132] ∠θ1: the angle between L1 and L2, that is, the inclination angle of the tangent line at imaging point 2;

[0133] ΔX: The horizontal distance between the first sensor and the second sensor.

[0134] Example 1

[0135] refer to Figure 3 , a robot and a ground rail are selected as a movable device, and an imaging detection bracket and a laser sensor are installed at the end of the robot. Considering that on a production line with high production cycle requirements, when the robot moves at a fast speed, it is necessary to perform an imaging scan on the surface of the strip, and the height and angle of the imaging detection bracket need to be corrected in real time. The specific implementation method is as follows:

[0136] In order to improve the detection accuracy of the strip surface height, the design method is to install sensor a and sensor b on the imaging detection bracket along the length direction of the strip, with the center distance between the two within 20 mm, and the installation position of sensor a and sensor b must meet the requirements that imaging point 1 is located at the center of the two sensor detection points.

[0137] The robot moves along the preset trajectory to the initial detection position above the strip surface and starts scanning the strip surface. The more target points are set on the initial trajectory, the better the linear approximation is. The target point spacing is within 20 mm. The laser sensor synchronously starts to detect the strip surface in real time. The actual height values ​​detected by sensor a and sensor b on the strip surface are h1 and h2.

[0138] The robot takes the average value of the height values ​​h1 and h2 fed back by the sensor and the initially set h0 value, and then performs conversion calculations with reference to the robot's current coordinate system.

[0139] To ensure that ∠A0=∠A1, ∠B0=∠B1, the calculation formula is as follows:

[0140] Tanθ2=(h2-h1) / ΔX,

[0141] Δh1=h1-h0,

[0142] Δh2=h2-h0,

[0143] Δh=(Δh1+Δh2) / 2,

[0144] Here, considering that the strip sag is ≤30mm, and when ΔX≤20mm, the tangent line of imaging point 2 and the straight line between points HS1 and HS2 are approximately the same line, that is, θ1≈θ2.

[0145] θ1=arctan((h2-h1) / ΔX),

[0146] In the formula: h0 is the height between the ideal imaging detection bracket and the strip surface,

[0147] h1 is the detection height value of sensor a,

[0148] h2 is the detection height value of sensor b,

[0149] Δh1 is the difference between the actual height value and the ideal height value of the imaging detection bracket and the strip surface at the sensor position a.

[0150] Δh2 is the difference between the actual height value and the ideal height value of the imaging detection bracket and the strip surface at the sensor position b.

[0151] ΔX is the distance between sensor a and sensor b,

[0152] θ1 is the angle of change of the imaging bracket from the original position to the new position,

[0153] θ2 is the inclination of the strip at HS2.

[0154] According to the θ1 calculated above, the tool orientation data of the next target point of the robot is adjusted, which is expressed as q(w,x,y,z) in the form of quaternion. In the present invention, the imaging detection bracket is vertically installed at the end of the robot, and its initial tool orientation data is as follows: the rotation angle of the imaging detection tool corresponding to the x, y, and z axes of the robot end is (0,0,0), and q=(1,0,0,0) is calculated. In fact, the rotation angle of the imaging detection tool corresponding to the x, y, and z axes of the robot end is defined as (X,Y,Z), where X=0,Y=0,Z=θ1, so as to calculate the actual q(w,x,y,z), and the calculation formula 2 is as follows:

[0155] x=sin(Y / 2)*sin(Z / 2)*cos(X / 2)+cos(Y / 2)*cos(Z / 2)*sin(X / 2),

[0156] y=sin(Y / 2)*cos(Z / 2)*cos(X / 2)+cos(Y / 2)*sin(Z / 2)*sin(X / 2),

[0157] z=cos(Y / 2)*sin(Z / 2)*cos(X / 2)-sin(Y / 2)*cos(Z / 2)*sin(X / 2),

[0158] w=cos(Y / 2)*cos(Z / 2)*cos(X / 2)-sin(Y / 2)*sin(Z / 2)*sin(X / 2),

[0159] The position of the tool center point of the robot's next target point is recalculated based on the Δh value, and the robot's motion trajectory is planned in real time relative to the defined current coordinate system (x, y, z) to ensure that the imaging working distance of the imaging detection tool at each imaging point is h0.

[0160] The robot moves along each newly planned target point, and the angle between the camera and the imaging light source on the imaging detection bracket does not need to be changed, thus realizing unmanned and intelligent operation.

[0161] Example 2

[0162] refer to Figure 3 , considering that the time takt requirement is not so high on the production line, the robot moving speed can be appropriately reduced, and the robot performs two imaging scans on the strip surface. The specific implementation method is as follows:

[0163] In order to improve the detection accuracy of the strip surface height, the design method is to install sensor a and sensor b on the imaging detection bracket along the length direction of the strip, with the distance between the two within 20mm, and the installation position of sensor a and sensor b should meet the imaging point 1 located at the center of the two sensor detection points.

[0164] The robot moves along the preset trajectory to the initial position above the strip surface and performs the first strip curvature scan for the specified strip length. The laser sensor synchronously starts to detect the strip surface in real time. The actual height values ​​detected by sensor a and sensor b on the strip surface are h1 and h2, which are recorded in real time in the robot controller.

[0165] Referring to the calculation method and Formula 1 and Formula 2 in Example 1, the tool orientation data in each target point position data of the robot is corrected according to the n θ1 values ​​calculated during the entire imaging scan of the strip surface, and the position of the tool center point in each target point position data of the robot is recalculated according to the n Δh values ​​calculated during the entire imaging scan of the strip surface. All target points on the robot's motion trajectory are corrected, and the motion trajectory path is replanned.

[0166] The robot re-scans the strip surface for a second time according to the planned new motion trajectory, and finally completes the defect imaging of the entire strip surface.

[0167] The present invention provides a surface defect imaging device and method for real-time tracking of a large-span cold-rolled strip curved surface. Without changing the position of the imaging light source and the camera (i.e., under the premise of ensuring that the angle between the camera and the line light source remains unchanged), the height and posture of the imaging detection tool are adjusted online to meet the effective imaging of surface defects of strips of different specifications.

Claims

1. A surface defect imaging method for real-time tracking of a large-span cold-rolled strip curved surface, wherein the imaging is completed based on the shooting of a set light source in cooperation with a set camera, characterized in that The steps include: S1: Under the premise that the angle between the camera and the light source is constant, the real-time position point and the spatial posture of the corresponding position point are determined; S2: According to the determined position points and the spatial postures of the corresponding position points, the strip scanning trajectory is set, and the strip surface defect imaging is completed according to the set scanning trajectory. The real-time position point is represented by a set target point; The target points are arranged along the curved surface of the strip to form a discrete representation of the curved surface shape of the strip. The first sensor and the second sensor are arranged along the length direction of the strip steel at a set interval; At the same time, the arrangement is such that the center point positions of the respective detection points of the first sensor and the second sensor under the ideal strip steel surface are consistent with the imaging point positions; Through the above settings combined with the established constraint equation, as well as the real-time actual height detected by the first sensor and the real-time actual height detected by the second sensor, the numerical representation of the real-time position point is completed. The constraint equation is specifically: in, h0: ideal strip surface detection height, unit: mm; h1: The height detected by the first sensor in real time based on the actual strip surface, unit: mm; h2: The height detected by the second sensor in real time based on the actual strip surface, unit: mm; Δh: the height of the real-time position point, unit: mm, Through the above settings, the position point corresponding to the actual strip steel surface height h1 detected in real time by the first sensor and the position point corresponding to the actual strip steel surface height h2 detected in real time by the second sensor are determined, and the straight line between the two points represents the tangent of the actual position point; The angle of the inclination angle corresponding to the first-order derivative of the corresponding position point is specifically: θ1=arctan((h2-h1) / ΔX), in, θ1: the angle of the inclination corresponding to the first-order derivative of the corresponding position point; h1: The height detected by the first sensor in real time based on the actual strip surface, unit: mm; h2: The height detected by the second sensor in real time based on the actual strip surface, unit: mm; ΔX: Horizontal setting distance between the first sensor and the second sensor, unit: mm; According to the inclination angle corresponding to the first-order derivative of the corresponding position point calculated in real time, a numerical representation of the spatial posture of the position point is established based on the quaternion expression.

2. The surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to claim 1, characterized in that: The determination of the position points and the spatial postures of the corresponding position points, the setting of the strip scanning trajectory and the shooting based on the setting are completed within the same scanning operation cycle.

3. The surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to claim 1, characterized in that: First, the position point and the spatial posture of the corresponding position point are determined according to a scanning cycle. Then the strip scanning trajectory is set accordingly. Finally, the scanning of the strip surface defect imaging is completed according to the set scanning trajectory according to the next scanning cycle relative to this scanning cycle.

4. The surface defect imaging method for real-time tracking of a large-span cold-rolled strip steel curved surface according to claim 1, characterized in that: An industrial robot, a ground track for propelling the industrial robot along a track, and an imaging detection bracket fixed to the arm of the industrial robot are provided; The set light source and the set camera are arranged on an imaging detection bracket; The strip scanning trajectory is set by setting the height and spatial posture of the imaging detection bracket corresponding to each position point; Through the cooperation of the industrial robot with the ground rail, the imaging detection bracket is adjusted in real time based on the above-set strip scanning trajectory to complete the imaging and shooting of strip surface defects.

5. A surface defect imaging device for real-time tracking of a curved surface of a large-span cold-rolled steel strip used in the surface defect imaging method for real-time tracking of a curved surface of a large-span cold-rolled steel strip as claimed in claim 1, characterized in that; Included are: An industrial robot (1), a ground rail (2) for propelling the industrial robot along a track, and an imaging detection bracket (3) fixed to the arm of the industrial robot; The industrial robot cooperates with the ground rail to adjust the spatial position and spatial posture of the imaging detection bracket; A light source (4) and a camera (5) are respectively provided on the imaging detection bracket; At the same time, a first sensor (6) and a second sensor (7) are also provided on the imaging detection bracket; The first sensor and the second sensor are respectively arranged on the imaging detection bracket along the length direction of the strip according to the set interval, and are arranged so that under the ideal strip surface, the center point position of each detection point of the first sensor and the second sensor is consistent with the imaging point position.

Images