CN115661045A - 3D visual holographic detection method, device, equipment and medium for quality of automobile tire - Google Patents
3D visual holographic detection method, device, equipment and medium for quality of automobile tire Download PDFInfo
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Abstract
The invention relates to the field of three-dimensional images, and discloses a 3D visual holographic detection method, a device, equipment and a medium for the quality of an automobile tire, wherein the method comprises the following steps: acquiring three-dimensional contour data of an automobile tire, performing three-dimensional shape reconstruction on the automobile tire to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image; sampling a depth vision point set in a depth detection area, calculating the average gravity center of the point set of the depth vision point set, performing plane fitting on the average gravity center of the point set to obtain a fitted vision plane of the depth vision point set, and calculating the tire detection depth of an automobile tire; detecting edge vision points of the width detection area, performing edge line fitting on the edge vision points to obtain a fitted vision line, and calculating the tire detection width of the automobile tire; and determining a three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width. The invention can improve the defect detection comprehensiveness of the automobile tire.
Description
Technical Field
The invention relates to the field of three-dimensional images, in particular to a 3D visual holographic detection method, a device, equipment and a medium for the quality of an automobile tire.
Background
The 3D visual holographic detection of the quality of the automobile tire refers to the detection of three-dimensional visual sense of the automobile tire so as to detect whether the automobile tire has a quality problem.
At present, for a long time, the tire is only considered to be checked when an automobile is punctured and leaks, but the tire is only detected in a leaking place, and the depth, the width and the abrasion condition of the whole tire from a tire pattern are not completely and scientifically detected in a quantifiable manner; the automobile tire detection process comprises the following steps: after a vehicle is driven to a detection area, firstly, manually carrying out integral appearance inspection on a tire by naked eyes, and then randomly selecting certain parts of the tire by a caliper or a tire pattern depth detector for detection, thereby obtaining whether the tire needs to be replaced and maintained; on the other hand, the manual random selective inspection of the tire is limited, and the full tread is not detected, so that the quality of the automobile tire is not detected completely, and the common detection modes mainly comprise two types: one is to manually use calipers to randomly check the width and depth of the tire pattern at any positions of the tire, and use the values as references to judge whether the tire is worn excessively and whether the tire needs to be replaced, but in the actual use process of the tire, after a certain part is touched by a sharp foreign object, the abrasion at the part is often worn more easily than other parts of the tire, and the manual random check often causes missed check, so that the traffic safety risk exists in the running process of the automobile; except the manual detection through a caliper, the other hand-held tire detection equipment is also a technology of the conventional tire detection, the equipment is close to the tire tread, the depth of the tread is measured in real time through an internal distance measuring sensor, the efficiency and the detection precision of the scheme are improved compared with those of a manual mode, but the quality of the whole tire tread cannot be detected well, if the tire is required to be detected completely, a large amount of labor force is consumed to detect the tire one by one, through the analysis, the conventional automobile tire detection technology has the defects of low manual detection efficiency, unreliable detection flow, limited detection area of the tire, possibility of missing detection in a large probability, and the problems cause the defect of the quality detection of the automobile tire. Therefore, the overall performance of the automobile tire quality detection is insufficient.
Disclosure of Invention
In order to solve the problems, the invention provides a 3D visual holographic detection method, a device, equipment and a medium for the quality of an automobile tire, which can improve the defect detection comprehensiveness of the automobile tire.
In a first aspect, the invention provides a 3D visual holographic detection method for the quality of an automobile tire, comprising the following steps:
acquiring three-dimensional contour data of an automobile tire, performing three-dimensional shape reconstruction on the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image;
sampling a depth vision point set in the depth detection area, calculating the average gravity center of the point set of the depth vision point set, performing plane fitting on the average gravity center of the point set to obtain a fitting vision plane of the depth vision point set, and calculating the tire detection depth of the automobile tire according to the average gravity center of the point set and the fitting vision plane;
detecting the edge vision points of the width detection area, performing edge line fitting on the edge vision points to obtain a fitted vision line, and calculating the tire detection width of the automobile tire according to the edge vision points and the fitted vision line;
and determining a three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width.
In one possible implementation manner of the first aspect, the acquiring three-dimensional profile data of the automobile tire comprises:
configuring a rotary detection platform of the automobile tire by using a three-dimensional visual sensor and a motor encoder;
in the rotary detection platform, calculating a rotary encoding signal of the motor encoder by using the following formula:
wherein E represents the rotation coding signal, C represents the tire circumference of the automobile tire, and n represents the contour interval;
according to the rotary coding signal, constructing a transmitting laser signal and a corresponding reflecting laser signal of the automobile tire by using the three-dimensional vision sensor;
and determining the three-dimensional profile data of the automobile tire according to the transmitted laser signal and the corresponding reflected laser signal.
It can be seen that the embodiment of the invention can acquire the 3D point cloud data of the surface and the side surface of the whole 3D tire by using the three-dimensional vision sensor and the motor encoder to configure the rotary detection platform of the automobile tire to acquire the three-dimensional profile data of the automobile tire.
In a possible implementation manner of the first aspect, the reconstructing the three-dimensional shape of the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstructed image includes:
determining a two-dimensional depth image corresponding to the three-dimensional contour data;
calculating depth translation coordinates of the two-dimensional depth image using the following formula:
P(x′，0，z′)＝P(x，y，z+r)
wherein P (x ',0, z') represents depth translation coordinates of the two-dimensional depth image, r represents a tire radius of the auto tire, and x, y, z represent coordinates of auto tire point cloud data points collected in the two-dimensional depth image;
according to the depth translation coordinate, performing three-dimensional morphological reconstruction on the two-dimensional depth image by using the following formula to obtain a three-dimensional reconstructed image:
θ＝360°÷N
x′＝x
y′＝cosα*y-sinα*z
z′＝cosα*y+sinα*z
P(x″，y″，z″)＝P(x′，0，z′)→P(x，cosα*y-sinα*z，cosα*y+sinα*z)
wherein P (x ', y ', z ') represents point cloud data in the three-dimensional reconstructed image, x ', y ', z "represents the coordinates of the point cloud data in the three-dimensional reconstructed image, i represents the serial number of the contour line in the two-dimensional depth image, the range is [0, N ], x, y, z represents the coordinates of the auto tire point cloud data points collected in the two-dimensional depth image, and P (x ',0, z ') represents the depth translation coordinates of the two-dimensional depth image.
Therefore, the embodiment of the invention can convert the two-dimensional depth image on the surface of the automobile tire into the three-dimensional model image of the automobile tire by reconstructing the three-dimensional shape of the two-dimensional depth image.
In a possible implementation manner of the first aspect, the dividing the depth detection region and the width detection region of the three-dimensional reconstructed image includes:
inquiring the pattern contour line of the automobile tire in the three-dimensional reconstruction image;
and taking a contour line area corresponding to the tire pattern contour line as a depth detection area and a width detection area of the three-dimensional reconstruction image.
It can be seen that, in the embodiment of the present invention, the contour line region corresponding to the contour line of the sipe is used as the depth detection region and the width detection region of the three-dimensional reconstructed image, so as to detect the depression depth and the sipe width of the sipe.
In one possible implementation manner of the first aspect, the calculating a point set mean centroid of the depth vision point set includes:
calculating the average gravity center of the point set of the depth vision point set by using the following formula:
wherein, P i A point set mean center of gravity representing the depth vision point set, i represents a sequence number in the depth vision point set, j represents a sequence number of a depth vision point in the depth vision point set, x j ,y j ,z j And m represents the total number of the depth vision points in the depth vision point set.
It can be seen that, in the embodiment of the present invention, the average value of the visual points in the depth visual point set is calculated, so as to be used for selecting the central point of the visual point in the depth visual point set.
In a possible implementation manner of the first aspect, the performing plane fitting on the mean barycenter of the point set to obtain a fitted visual plane of the depth visual point set includes:
determining a plane fitting surface parameter of the mean center of gravity of the point set by using the following formula:
wherein a, b and c represent plane fitting surface parameters of the mean gravity center of the point set, and x i ，y i ，z i The coordinates of the average gravity center of the point set are represented, and n represents the number of the point sets of the depth vision point set;
according to the plane fitting surface parameters, constructing a fitting visual plane of the depth visual point set by using the following formula:
z＝ax+by+c
wherein z = ax + by + c represents the fitted visual plane of the depth visual point set, z, x, y represent variables of the plane function, a represents parameters of variable x fitted to the visual plane function among plane fitted plane parameters of the mean center of gravity of the point set, b represents parameters of variable y fitted to the visual plane function among plane fitted plane parameters of the mean center of gravity of the point set, and c represents constants fitted to the visual plane function among plane fitted plane parameters of the mean center of gravity of the point set.
It can be seen that the embodiment of the present invention constructs a fitting visual plane of the depth visual point set, so as to construct a plane function relationship between visual points in the depth visual point set.
In one possible implementation manner of the first aspect, the calculating a tire detection depth of the automobile tire according to the point set mean gravity center and the fitted visual plane includes:
calculating the tire detection depth of the automobile tire by using the following formula:
wherein d is i The tire detection depth of the automobile tire is represented, a represents a parameter of a variable x of a fitted visual plane function in plane fitting plane parameters of the average gravity center of the point set, b represents a parameter of a variable y of the fitted visual plane function in plane fitting plane parameters of the average gravity center of the point set, c represents a constant of the fitted visual plane function in plane fitting plane parameters of the average gravity center of the point set, d represents a parameter of a variable z of the fitted visual plane function, and x represents the parameter of the variable z of the fitted visual plane function i ，y i ，z i Coordinates representing the mean center of gravity of the set of points.
It can be seen that the embodiment of the invention detects whether the automobile tire has defects by calculating the tire detection depth of the automobile tire to detect whether the detection depth deviates from the standard depth.
In one possible implementation manner of the first aspect, the detecting an edge visual point of the width detection area includes:
carrying out binarization operation on the width detection area by using the following formula to obtain a binarization area:
wherein p is i (x i ，y i ) Coordinate value, x, representing point cloud data in the binarized area i ，y i ，z i The coordinate of the average gravity center of the point set is represented, and k represents a preset image pixel parameter;
calculating the gradient of the edge point of the binarization region by using the following formula:
wherein | G (x, y) | represents the gradient of the edge point of the binarization region, sprt represents the square root calculation, f (x +1, y), f (x, y + 1) represent the pixel function of the binarization region,
representing a first derivative difference of pixels in the binarization region;
and determining the edge vision point of the width detection area according to the edge point gradient.
It can be seen that, in the embodiment of the present invention, by calculating the edge point gradient of the binarization region, a point with a large pixel gradient transformation is found in the binarization region as a point on an edge line of the width defect detection.
In a possible implementation manner of the first aspect, the fitting an edge line to the edge visual points to obtain a fitted visual line includes:
calculating a fit line parameter for the edge vision point using the following formula:
wherein A, B represent the fit line parameters, x, of the edge vision points j ，y j Representing the jth edge vision point, M representing the number of the edge vision points;
and fitting the edge line of the edge vision points by using the following formula to obtain a fitted vision line:
Y＝AX+B
where Y = AX + B represents the fitted visual line, a, B represent fitted line parameters for the edge visual points, and Y, X represent arguments of a straight-line function.
It can be seen that the embodiment of the present invention performs edge line fitting on the edge visual points to identify the edge portion of the area related to the width in the width detection area by using the edge line.
In a second aspect, the present invention provides a 3D visual holographic inspection device for the quality of a vehicle tyre, said device comprising:
the detection area dividing module is used for acquiring three-dimensional contour data of an automobile tire, reconstructing the three-dimensional shape of the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image;
the detection depth calculation module is used for sampling a depth vision point set in the depth detection area, calculating the average gravity center of the point set of the depth vision point set, performing plane fitting on the average gravity center of the point set to obtain a fitting vision plane of the depth vision point set, and calculating the tire detection depth of the automobile tire according to the average gravity center of the point set and the fitting vision plane;
the detection width calculation module is used for detecting the edge vision points of the width detection area, performing edge line fitting on the edge vision points to obtain a fitted vision line, and calculating the tire detection width of the automobile tire according to the edge vision points and the fitted vision line;
and the detection result determining module is used for determining the three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width.
In a third aspect, the present invention provides an electronic device comprising:
at least one processor; and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method for 3D visual holographic detection of quality of vehicle tyres according to any of the above-mentioned first aspects.
In a fourth aspect, the present invention provides a computer-readable storage medium storing a computer program which, when executed by a processor, implements the method for 3D visual holographic inspection of quality of automobile tires as set forth in any one of the above first aspects.
Compared with the prior art, the technical principle and the beneficial effects of the scheme are as follows:
the method comprises the steps of firstly collecting three-dimensional contour data of an automobile tire to perform 3D visual holographic detection on tire defects of the automobile tire, further, performing three-dimensional shape reconstruction on the automobile tire according to the three-dimensional contour data to construct a three-dimensional virtual model of the automobile tire, wherein the three-dimensional virtual model containing the shape characteristics of the automobile tire realizes comprehensive detection on the automobile tire, and further, the depth detection area and the width detection area of a three-dimensional reconstruction image are divided to detect the depth and the width of tire treads in the corresponding areas;
further, the depth visual point set in the depth detection area is sampled to integrate point cloud data in the depth detection area to realize comprehensive detection of the depth detection area, further, the average gravity center of the point set in the depth visual point set is calculated to determine the center of the point in the depth visual point set, further, plane fitting is performed on the average gravity center of the point set to obtain a functional relation according with data, the relation between the point cloud data is determined, the characteristic characterization capability of the point cloud data is improved, and the detection process of the unified standard for detecting the defects of the automobile tire is guaranteed;
further, the embodiment of the present invention detects edge visual points of the width detection area to identify points with obvious brightness change in the digital image, further, the embodiment of the present invention performs edge line fitting on the edge visual points to determine a functional relationship between each point in the edge visual points and other points by using a straight line function, and further, the embodiment of the present invention calculates the tire detection width of the automobile tire according to the edge visual points and the fitted visual lines to achieve the purpose of detecting tire defects by inquiring whether there is a deviation in width;
further, the embodiment of the invention determines the three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width, so as to determine whether the surface of the automobile tire has defects by using the detected depth and width data.
Therefore, the 3D visual holographic detection method, the device, the electronic equipment and the storage medium for the quality of the automobile tire provided by the embodiment of the invention can improve the defect detection comprehensiveness of the automobile tire.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a schematic flow chart of a 3D visual holographic detection method for quality of an automobile tire according to an embodiment of the present invention;
FIGS. 2-5 are schematic diagrams illustrating the acquisition of three-dimensional profile data provided in one embodiment of the present invention;
fig. 6-7 are schematic diagrams illustrating a three-dimensional reconstruction of a vehicle tire according to an embodiment of the present invention;
fig. 8 is a schematic block diagram of a 3D visual holographic inspection apparatus for quality of an automobile tire according to an embodiment of the present invention;
fig. 9 is a schematic internal structure diagram of an electronic device for implementing a 3D visual holographic detection method for quality of an automobile tire according to an embodiment of the present invention.
Detailed Description
It should be understood that the detailed description and specific examples, while indicating the preferred embodiment of the invention, are given by way of illustration only.
The embodiment of the invention provides a quality 3D visual holographic detection method for automobile tires, and an execution subject of the quality 3D visual holographic detection method for automobile tires comprises but is not limited to at least one of electronic devices such as a server and a terminal which can be configured to execute the method provided by the embodiment of the invention. In other words, the 3D visual holographic detection method for quality of automobile tires may be implemented by software or hardware installed in a terminal device or a server device, and the software may be a block chain platform. The server includes but is not limited to: a single server, a server cluster, a cloud server or a cloud server cluster, and the like. The server may be an independent server, or may be a cloud server that provides basic cloud computing services such as a cloud service, a cloud database, cloud computing, a cloud function, cloud storage, a Network service, cloud communication, a middleware service, a domain name service, a security service, a Content Delivery Network (CDN), and a big data and artificial intelligence platform.
Fig. 1 is a schematic flow chart of a 3D visual holographic detection method for quality of an automobile tire according to an embodiment of the present invention. The 3D visual holographic detection method for the quality of the automobile tire depicted in the figure 1 comprises the following steps:
s1, collecting three-dimensional contour data of an automobile tire, carrying out three-dimensional shape reconstruction on the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image.
The embodiment of the invention is used for carrying out 3D visual holographic detection on the tire defects of the automobile tires by acquiring the three-dimensional profile data of the automobile tires. The three-dimensional contour data refers to 3D point cloud contour data of the surface of an automobile tire acquired by a 3D vision sensor, the 3D vision sensor mainly measures the three-dimensional data by using a laser triangulation distance measuring principle, the 3D vision sensor emits a laser contour line from a laser emitting end, and a receiving end of the 3D vision sensor receives the laser beam reflected from the surface of the tire, so that the current single contour data of the surface of the tire can be acquired.
In one embodiment of the present invention, acquiring three-dimensional contour data of an automobile tire comprises: configuring a rotary detection platform of an automobile tire by using a three-dimensional visual sensor and a motor encoder; in the rotary detection platform, the rotary coding signal of the motor encoder is calculated by using the following formula:
wherein E represents a rotation coding signal, C represents the tire circumference of the automobile tire, and n represents the contour interval;
constructing a transmitting laser signal and a corresponding reflecting laser signal of the automobile tire by using a three-dimensional vision sensor according to the rotary coding signal; and determining the three-dimensional profile data of the automobile tire according to the transmitted laser signals and the corresponding reflected laser signals.
Illustratively, three 3D vision sensors are equipped to scan and detect the whole automobile tire from three angles, when the tire is in a rolling state, the positions of the three 3D vision sensors are respectively right above and at the left and right sides of the tire, after the tire rotates for one circle, the 3D vision sensors can collect the profile data of the crown surface and the profile data of the side surfaces of the two sides of the whole tire, when the tire rotates, the tire rotates under the driving of an external servo motor to passively rotate, after the tire is disassembled, the tire is installed on a tire detection device through a connecting rod, the other end of the connecting rod is connected with the servo motor, the servo motor rotates to drive the tire to rotate, when the tire is driven by the servo motor to rotate for one circle, an encoder in the servo motor also generates a time sequence corresponding to a circle of 3D tire contour line signal.
For further understanding the process of acquiring the three-dimensional profile data, reference may be made to fig. 2-5, which are schematic diagrams illustrating the acquisition of the three-dimensional profile data according to an embodiment of the present invention, where fig. 2 is used to show a principle that a three-dimensional vision sensor emits a laser signal, where a height of the laser signal emitted by the three-dimensional vision sensor to the surface of an automobile tire is represented by a z-axis, a height of the laser signal emitted by the three-dimensional vision sensor to the surface of the automobile tire is represented by a y-axis, a laser signal emitted along a rolling direction of the tire is represented by a y-axis, a laser signal emitted in a transverse direction of the tire is represented by an x-axis, a reflected laser signal corresponding to the laser signal is reflected from the surface of the tire to the three-dimensional vision sensor, and after the tire rotates one turn, 3D point cloud data of the entire surface profile of the tire can be obtained; fig. 3 is a diagram showing a placement position of a three-dimensional visual sensor built in the rotary detection platform, the three-dimensional visual sensor is respectively placed right above an automobile tire and on both sides of the tire, fig. 4 is a diagram showing the rotary detection platform including a motor encoder, a servo motor on the left side shows the motor encoder, during detection, the tire rotates by being driven by an external servo motor to rotate passively, after the tire is disassembled, the tire passes through a connecting rod to be installed on a tire detection device, the servo motor is connected to the other end of the connecting rod, the servo motor rotates to drive the tire to rotate, and when the servo motor drives the tire to rotate for one circle, the encoder in the servo motor also generates a time sequence of signals corresponding to one circle of 3D tire contour line; fig. 5 is a diagram for showing that the acquisition of the three-dimensional visual sensor triggered by the rotation coding signal is three-dimensional contour data, the invention adopts the phase a signal in the encoder signal as the trigger signal, when the servo motor rotates to make the phase a pulse be the rising edge, the 3D visual sensor triggers to acquire the contour line of the current position, the trigger mode can consider that one pulse triggers once, or multiple pulses trigger once, or when a finer trigger signal is needed, the invention is realized by combining the difference of the four signals of the encoder a +, a-, B +, and B-.
Further, according to the embodiment of the invention, the automobile tire is subjected to three-dimensional shape reconstruction according to the three-dimensional contour data so as to construct a three-dimensional virtual model of the automobile tire, and the three-dimensional virtual model containing the shape characteristics of the automobile tire realizes comprehensive detection of the automobile tire. The three-dimensional reconstructed image is a three-dimensional image obtained by converting a two-dimensional depth image originally composed of a two-dimensional image of the surface of the automobile tire and the depth of a tire pit, namely a three-dimensional virtual model structure diagram of the automobile tire.
In an embodiment of the present invention, reconstructing a three-dimensional shape of an automobile tire according to three-dimensional contour data to obtain a three-dimensional reconstructed image includes: determining a two-dimensional depth image corresponding to the three-dimensional contour data; calculating the depth translation coordinates of the two-dimensional depth image by using the following formula:
P(x′，0，z′)＝P(x，y，z+r)
wherein P (x ',0, z') represents depth translation coordinates of the two-dimensional depth image, r represents a tire radius of an automobile tire, and x, y, z represent coordinates of automobile tire point cloud data points collected in the two-dimensional depth image;
according to the depth translation coordinate, performing three-dimensional morphological reconstruction on the two-dimensional depth image by using the following formula to obtain a three-dimensional reconstructed image:
θ＝360°÷N
x′＝x
y′＝cosα*y-sinα*z
z′＝cosα*y+sinα*z
P(x″，y″，z″)＝P(x′，0，z′)→P(x，cosα*y-sinα*z，cosα*y+sinα*z)
wherein P (x ', y ', z ') represents point cloud data in a three-dimensional reconstructed image, x ', y ', z "represents the coordinates of the point cloud data in the three-dimensional reconstructed image, i represents the serial number of the contour line in the two-dimensional depth image, the range is [0, N ], x, y, z represent the coordinates of the auto tire point cloud data points collected in the two-dimensional depth image, and P (x ',0, z ') represents the depth translation coordinates of the two-dimensional depth image.
To further understand the process of reconstructing the three-dimensional shape of the automobile tire, reference may be made to fig. 6-7, which are schematic diagrams illustrating the process of reconstructing the three-dimensional shape of the automobile tire according to an embodiment of the present invention, where fig. 6 is used to represent a two-dimensional depth image corresponding to three-dimensional profile data, the number of profiles generated by scanning the whole tire tread is set to be N, and the distance between each profile is N (unit: mm), each contour line is composed of M space points, the distance between the points is M (unit: mm), in a three-dimensional space, the Y direction is the peripheral length direction of the contour line, the X direction is the point direction of a single contour line, and a 3D vision sensor provides height data in the Z direction for each point, so that a 2D height image with height information can be formed by stretching the contour line along an XOY projection plane, and each image pixel information P = (M) ((M)x i ，y i ，z i ) Is the coordinate value of the tire relative to the camera coordinate system, z i Is a height value of the tire surface to a camera coordinate system, and
y i ∈(0，N*n)，z i belongs to (-l, l), l belongs to the range of the 3D vision sensor, the original point of a system coordinate system is at the center of the tire, the range of X coordinates and z coordinates of a space point is in a positive and negative interval, the image 7 is used for representing a three-dimensional reconstruction image, a depth map is originally acquired on an XOY plane, a plurality of contour lines on the plane are firstly translated, and then the tire data are reconstructed after the coordinate transformation is rotated along the X-axis direction, so that the 3D form of the tire is restored.
Further, the embodiment of the invention divides a depth detection area and a width detection area of the three-dimensional reconstruction image to be used for detecting the depth and the width of the tyre thread in the corresponding areas. The depth detection area is an area in which the depth of depression of the tread of the tire needs to be calculated, and the width detection area is an area in which the width of the tread of the tire needs to be calculated.
In an embodiment of the present invention, dividing a depth detection region and a width detection region of a three-dimensional reconstructed image includes: inquiring a tyre pattern contour line of an automobile tyre in the three-dimensional reconstruction image; and taking a contour line area corresponding to the tire pattern contour line as a depth detection area and a width detection area of the three-dimensional reconstruction image.
The term "thread contour" is understood to mean, for example, a thread in a three-dimensional image perpendicular to the rolling direction in the rolling surface of a vehicle tire.
S2, sampling a depth vision point set in the depth detection area, calculating the average gravity center of the point set of the depth vision point set, performing plane fitting on the average gravity center of the point set to obtain a fitting vision plane of the depth vision point set, and calculating the tire detection depth of the automobile tire according to the average gravity center of the point set and the fitting vision plane.
According to the embodiment of the invention, the depth visual point set in the depth detection area is sampled to integrate point cloud data in the depth detection area so as to realize comprehensive detection of the depth detection area. The depth visual point set refers to a set of data points of point cloud data in a depth detection area.
In an embodiment of the present invention, a depth vision point set in a sampling depth detection area includes: collecting depth visual point coordinates in a depth detection area; and taking a coordinate set corresponding to the depth vision point coordinates as a depth vision point set.
Further, the embodiment of the invention calculates the average gravity center of the point set of the depth vision point set so as to be used for determining the center of the point in the depth vision point set. The point set average gravity center refers to the gravity center, which is the resultant force action point of the earth on each tiny gravity in the object, and represents the center of the point in the depth vision point set in the invention.
In an embodiment of the present invention, the average gravity center of the point set of the depth vision point set is calculated by using the following formula:
wherein, P i Mean gravity center of point set representing depth visual point set, i represents sequence number of depth visual point set, j represents sequence number of depth visual point in depth visual point set, x j ,y j ,z j And m represents the total number of the depth vision points in the depth vision point set.
Further, the embodiment of the invention performs plane fitting on the average gravity center of the point set to obtain a functional relation according with data, determines the relation between point cloud data, improves the characteristic characterization capability of the point cloud data, and ensures a unified detection flow for detecting the automobile tire defects. Wherein, fitting the visual plane refers to a depth visual point set characterized by a plane function.
In an embodiment of the present invention, performing plane fitting on the average gravity center of the point set to obtain a fitted visual plane of the depth visual point set, includes: determining the plane fitting surface parameters of the mean center of gravity of the point set by using the following formula:
wherein a, b and c represent plane fitting surface parameters of the mean gravity center of the point set, and x i ，y i ，z i The coordinates of the average gravity center of the point set are represented, and n represents the number of the point sets of the depth vision point set;
according to the parameters of the plane fitting surface, constructing a fitting visual plane of the depth visual point set by using the following formula:
z＝ax+by+c
wherein z = ax + by + c represents the fitting visual plane of the depth visual point set, z, x, y represent variables of the plane function, a represents a parameter of a variable x of the fitting visual plane function in the plane fitting plane parameters of the average gravity center of the point set, b represents a parameter of a variable y of the fitting visual plane function in the plane fitting plane parameters of the average gravity center of the point set, and c represents a constant of the fitting visual plane function in the plane fitting plane parameters of the average gravity center of the point set.
Further, the embodiment of the invention calculates the tire detection depth of the automobile tire according to the point set average gravity center and the fitted visual plane, so as to determine whether the surface pattern depression of the automobile tire has defects. The tire detection depth refers to a degree of depression of a tread pattern on the tire surface.
In an embodiment of the invention, according to the average gravity center of the point set and the fitted visual plane, the tire detection depth of the automobile tire is calculated by using the following formula:
wherein d is i A plane fitting surface for representing the tire detection depth of the automobile tire, a representing the average gravity center of the point setThe parameter of variable x fitting the visual plane function in the parameters, b represents the parameter of variable y fitting the visual plane function in the plane fitting plane parameters of the average gravity center of the point set, c represents the constant of the visual plane function in the plane fitting plane parameters of the average gravity center of the point set, d represents the parameter of variable z fitting the visual plane function, x i ，y i ，z i Coordinates representing the mean center of gravity of the set of points.
And S3, detecting the edge vision points of the width detection area, fitting edge lines to the edge vision points to obtain a fitted vision line, and calculating the tire detection width of the automobile tire according to the edge vision points and the fitted vision line.
The embodiment of the invention detects the edge vision points of the width detection area to identify the points with obvious brightness change in the digital image. Here, the edge visual point refers to an edge point in the width detection region.
In an embodiment of the present invention, detecting an edge vision point of a width detection area includes: carrying out binarization operation on the width detection area by using the following formula to obtain a binarization area:
wherein p is i (x i ,y i ) Coordinate value, x, representing point cloud data in a binarized area i ，y i ，z i The coordinate of the average gravity center of the point set is represented, and k represents a preset image pixel parameter;
calculating the gradient of the edge point of the binarization area by using the following formula:
wherein | G (x, y) | represents the edge point gradient of the binarization region, sprt represents the square root calculation, f (x +1, y), f (x, y + 1) represent the pixel function of the binarization region,
representing a first derivative difference of pixels in a binarization region;
and determining the edge vision point of the width detection area according to the edge point gradient.
Optionally, the edge visual point in the width detection area may be determined according to the edge point gradient by comparing the edge point gradient with a preset threshold, and if the edge point gradient is greater than the preset threshold, it indicates that the gradient change is large, and the point corresponding to the gradient is the edge point.
Further, the embodiment of the present invention performs edge line fitting on the edge vision points to determine a functional relationship between each point of the edge vision points and other points by using a straight line function. Wherein the fitted visual line is represented by a straight line function.
In an embodiment of the present invention, fitting an edge line to the edge visual points to obtain a fitted visual line includes: calculating the fitted line parameters of the edge vision points by using the following formula:
wherein A, B represent the fit line parameters of the edge vision points, x j ，y j Representing the jth edge vision point, and M representing the number of the edge vision points;
and fitting the edge line to the edge vision points by using the following formula to obtain a fitted vision line:
Y＝AX+B
where Y = AX + B represents the fitted visual line, a, B represent fitted line parameters of the peripheral visual points, and Y, X represent the arguments of the straight-line function.
Further, the tire detection width of the automobile tire is calculated according to the edge visual points and the fitting visual line, so that the purpose of detecting the tire defects is achieved by inquiring whether the width has deviation or not. The tire test width is a tire tread width.
In one embodiment of the invention, the tire detection width of the automobile tire is calculated by using the following formula according to the edge visual points and the fitting visual line:
wherein, W represents the tire detection width of the automobile tire, x and y represent the edge points P (x and y) to be obtained, and A and B represent the fitting line parameters of the edge visual points.
And S4, determining a three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width.
According to the embodiment of the invention, the three-dimensional visual quality detection result of the automobile tire is determined according to the tire detection depth and the tire detection width, so that whether the surface of the automobile tire has defects or not is determined by using the detected depth and width data. The three-dimensional visual quality detection result refers to the combination of the tire detection depth and the tire detection width.
It can be seen that, in the embodiment of the present invention, three-dimensional profile data of an automobile tire is collected to perform 3D visual holographic detection on a tire defect of the automobile tire, further, according to the three-dimensional profile data, three-dimensional shape reconstruction is performed on the automobile tire to construct a three-dimensional virtual model of the automobile tire, the three-dimensional virtual model including shape characteristics of the automobile tire realizes comprehensive detection on the automobile tire, and further, a depth detection area and a width detection area of the three-dimensional reconstruction image are divided to detect a depth and a width of a tire tread in a corresponding area;
further, the depth visual point set in the depth detection area is sampled to integrate point cloud data in the depth detection area to realize comprehensive detection of the depth detection area, further, the average gravity center of the point set of the depth visual point set is calculated to determine the center of the point in the depth visual point set, further, plane fitting is performed on the average gravity center of the point set to obtain a functional relation according with data, the relation between point cloud data is determined, the characteristic characterization capability of the point cloud data is improved, and the detection process of the unified standard for detecting the defects of the automobile tires is guaranteed;
further, the embodiment of the present invention detects edge visual points of the width detection area to identify points with obvious brightness change in the digital image, further, the embodiment of the present invention performs edge line fitting on the edge visual points to determine a functional relationship between each point in the edge visual points and other points by using a straight line function, and further, the embodiment of the present invention calculates the tire detection width of the automobile tire according to the edge visual points and the fitted visual lines to achieve the purpose of detecting tire defects by inquiring whether there is a deviation in width;
further, the embodiment of the invention determines the three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width, so as to determine whether the surface of the automobile tire has defects by using the detected depth and width data.
Therefore, the 3D visual holographic detection method for the quality of the automobile tire, provided by the embodiment of the invention, can improve the defect detection comprehensiveness of the automobile tire.
Fig. 8 is a functional block diagram of the 3D visual holographic detection device for quality of automobile tires according to the present invention.
The 3D visual holographic detection device 800 for the quality of the automobile tire can be installed in electronic equipment. According to the realized functions, the 3D visual holographic detection device for the quality of the automobile tire can comprise a detection area dividing module 801, a detection depth calculation module 802, a detection width calculation module 803 and a detection result determination module 804. The module of the invention, which may also be referred to as a unit, refers to a series of computer program segments that can be executed by a processor of an electronic device and that can perform a fixed function, and that are stored in a memory of the electronic device.
In the embodiment of the present invention, the functions of the modules/units are as follows:
the detection area division module 801 is used for acquiring three-dimensional contour data of an automobile tire, performing three-dimensional shape reconstruction on the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image;
the detection depth calculation module 802 is used for sampling a depth visual point set in a depth detection area, calculating the average gravity center of the point set of the depth visual point set, performing plane fitting on the average gravity center of the point set to obtain a fitting visual plane of the depth visual point set, and calculating the tire detection depth of an automobile tire according to the average gravity center of the point set and the fitting visual plane;
the detection width calculation module 803 is configured to detect edge visual points in the width detection area, perform edge line fitting on the edge visual points to obtain a fitted visual line, and calculate a tire detection width of the automobile tire according to the edge visual points and the fitted visual line;
and the detection result determining module 804 is used for determining a three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width.
In detail, when the modules in the device 800 for 3D visual holographic detection of quality of an automobile tire according to the embodiment of the present invention are used, the same technical means as the above-mentioned method for 3D visual holographic detection of quality of an automobile tire in fig. 1 to 7 are used, and the same technical effects can be produced, which are not described herein again. Fig. 9 is a schematic structural diagram of an electronic device for implementing a 3D visual holographic detection method for quality of an automobile tire according to the present invention.
The electronic device may comprise a processor 90, a memory 91, a communication bus 92 and a communication interface 93, and may further comprise a computer program, such as a 3D visual holographic inspection program for quality of car tyres, stored in the memory 91 and executable on the processor 90.
In some embodiments, the processor 90 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or may be composed of a plurality of integrated circuits packaged with the same or different functions, and includes one or more Central Processing Units (CPUs), microprocessors, digital Processing chips, graphics processors, and combinations of various control chips. The processor 90 is a Control Unit (Control Unit) of the electronic device, connects various components of the whole electronic device by using various interfaces and lines, executes or executes programs or modules stored in the memory 91 (for example, executes a 3D visual holographic detection program for quality of automobile tires, etc.), and calls data stored in the memory 91 to perform various functions of the electronic device and process the data.
The memory 91 includes at least one type of readable storage medium including flash memory, removable hard disks, multimedia cards, card-type memory (e.g., SD or DX memory, etc.), magnetic memory, magnetic disks, optical disks, etc. The memory 91 may in some embodiments be an internal storage unit of the electronic device, for example a removable hard disk of the electronic device. The memory 91 may also be an external storage device of the electronic device in other embodiments, such as a plug-in removable hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card), and the like provided on the electronic device. Further, the memory 91 may also include both an internal storage unit and an external storage device of the electronic device. The memory 91 may be used not only to store application software installed in the electronic device and various types of data, such as codes of a database configuration connection program, but also to temporarily store data that has been output or is to be output.
The communication bus 92 may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is arranged to enable connection communication between the memory 91 and at least one processor 90 or the like.
The communication interface 93 is used for communication between the electronic apparatus 9 and other apparatuses, and includes a network interface and a user interface. Alternatively, the network interface may include a wired interface and/or a wireless interface (e.g., WI-FI interface, bluetooth interface, etc.), which are typically used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a Display (Display), an input unit such as a Keyboard (Keyboard), and optionally a standard wired interface, a wireless interface. Alternatively, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, an OLED (Organic Light-Emitting Diode) touch device, or the like. The display, which may also be referred to as a display screen or display unit, is suitable, among other things, for displaying information processed in the electronic device and for displaying a visualized user interface.
Fig. 9 shows only an electronic device with components, and those skilled in the art will appreciate that the structure shown in fig. 9 does not constitute a limitation of the electronic device, and may include fewer or more components than shown, or some components may be combined, or a different arrangement of components.
For example, although not shown, the electronic device may further include a power supply (such as a battery) for supplying power to the various components, and preferably, the power supply may be logically connected to the at least one processor 90 through a power management device, so that functions such as charge management, discharge management, and power consumption management are implemented through the power management device. The power supply may also include any component of one or more dc or ac power sources, recharging devices, power failure detection circuitry, power converters or inverters, power status indicators, and the like. The electronic device may further include various sensors, a bluetooth module, a Wi-Fi module, etc., which are not described herein again.
It is to be understood that the embodiments are illustrative only and that the scope of the invention is not limited to the structures described.
The database configuration interface program stored in the memory 91 of the electronic device is a combination of computer programs that, when executed in the processor 90, implement:
acquiring three-dimensional contour data of an automobile tire, performing three-dimensional shape reconstruction on the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image;
sampling a depth vision point set in a depth detection area, calculating the average gravity center of the point set of the depth vision point set, performing plane fitting on the average gravity center of the point set to obtain a fitting vision plane of the depth vision point set, and calculating the tire detection depth of an automobile tire according to the average gravity center of the point set and the fitting vision plane;
detecting edge vision points of the width detection area, performing edge line fitting on the edge vision points to obtain a fitted vision line, and calculating the tire detection width of the automobile tire according to the edge vision points and the fitted vision line;
and determining a three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width.
Specifically, the processor 90 may refer to the description of the relevant steps in the embodiment corresponding to fig. 1 for a specific implementation method of the computer program, which is not described herein again.
Further, the electronic device integrated module/unit, if implemented in the form of a software functional unit and sold or used as a separate product, may be stored in a non-volatile computer-readable storage medium. The storage medium may be volatile or nonvolatile. For example, the computer-readable medium may include: any entity or device capable of carrying computer program code, recording medium, U.S. disk, removable hard disk, magnetic disk, optical disk, computer Memory, read-Only Memory (ROM).
The present invention also provides a storage medium, which is readable to store a computer program, wherein the computer program, when executed by a processor of an electronic device, can implement:
acquiring three-dimensional contour data of an automobile tire, performing three-dimensional shape reconstruction on the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image;
sampling a depth vision point set in a depth detection area, calculating the average gravity center of the point set of the depth vision point set, performing plane fitting on the average gravity center of the point set to obtain a fitting vision plane of the depth vision point set, and calculating the tire detection depth of an automobile tire according to the average gravity center of the point set and the fitting vision plane;
detecting edge vision points of the width detection area, performing edge line fitting on the edge vision points to obtain a fitted vision line, and calculating the tire detection width of the automobile tire according to the edge vision points and the fitted vision line;
and determining a three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width.
In the several embodiments provided in the present invention, it should be understood that the disclosed apparatus, device and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, a module may be divided into only one logical function, and may be divided into other ways in actual implementation.
Modules described as separate parts may or may not be physically separate, and parts displayed as modules may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, functional modules in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional module.
It will be evident to those skilled in the art that the invention is not limited to the details of the foregoing illustrative embodiments, and that the present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof.
The present embodiments are therefore to be considered in all respects as illustrative and not restrictive, the scope of the invention being indicated by the appended claims rather than by the foregoing description, and all changes which come within the meaning and range of equivalency of the claims are therefore intended to be embraced therein. Any reference signs in the claims shall not be construed as limiting the claim concerned.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a component of' 8230; \8230;" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.
The above are merely exemplary embodiments of the present invention, which can be understood and implemented by those skilled in the art. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (12)
1. A3D visual holographic detection method for quality of automobile tires is characterized by comprising the following steps:
acquiring three-dimensional contour data of an automobile tire, performing three-dimensional shape reconstruction on the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image;
sampling a depth vision point set in the depth detection area, calculating the average gravity center of the point set of the depth vision point set, performing plane fitting on the average gravity center of the point set to obtain a fitting vision plane of the depth vision point set, and calculating the tire detection depth of the automobile tire according to the average gravity center of the point set and the fitting vision plane;
detecting the edge vision points of the width detection area, performing edge line fitting on the edge vision points to obtain a fitted vision line, and calculating the tire detection width of the automobile tire according to the edge vision points and the fitted vision line;
and determining a three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width.
2. The method according to claim 1, wherein said acquiring three-dimensional profile data of vehicle tires comprises:
configuring a rotary detection platform of the automobile tire by using a three-dimensional visual sensor and a motor encoder;
in the rotary detection platform, calculating a rotary encoding signal of the motor encoder by using the following formula:
wherein E represents the rotation coding signal, C represents the tire circumference of the automobile tire, and n represents the contour interval;
constructing a transmitting laser signal and a corresponding reflecting laser signal of the automobile tire by using the three-dimensional vision sensor according to the rotary coding signal;
and determining the three-dimensional profile data of the automobile tire according to the transmitted laser signal and the corresponding reflected laser signal.
3. The method according to claim 1, wherein the three-dimensional morphological reconstruction of the vehicle tire from the three-dimensional contour data to obtain a three-dimensional reconstructed image comprises:
determining a two-dimensional depth image corresponding to the three-dimensional contour data;
calculating depth translation coordinates of the two-dimensional depth image using the following formula:
P(x′，0，z′)＝P(x，y，z+r)
wherein P (x ',0, z') represents depth translation coordinates of the two-dimensional depth image, r represents a tire radius of the auto tire, and x, y, z represent coordinates of auto tire point cloud data points collected in the two-dimensional depth image;
according to the depth translation coordinate, performing three-dimensional morphological reconstruction on the two-dimensional depth image by using the following formula to obtain a three-dimensional reconstructed image:
θ＝360°÷N
x′＝x
y′＝cosα*y-sinα*z
z′＝cosα*y+sinα*z
P(x″，y″，z″)＝P(x′，0，z′)→P(x，cosα*y-sinα*z，cosα*y+sinα*z)
wherein P (x ', y ', z ') represents point cloud data in the three-dimensional reconstructed image, x ', y ', z "represents coordinates of the point cloud data in the three-dimensional reconstructed image, i represents a serial number of a contour line in the two-dimensional depth image, the range is [0, N ], x, y, z represents the coordinates of the auto tire point cloud data points collected in the two-dimensional depth image, and P (x ',0, z ') represents the depth translation coordinates of the two-dimensional depth image.
4. The method of claim 1, wherein the dividing the depth detection area and the width detection area of the three-dimensional reconstructed image comprises:
inquiring the pattern contour line of the automobile tire in the three-dimensional reconstruction image;
and taking a contour line area corresponding to the tire pattern contour line as a depth detection area and a width detection area of the three-dimensional reconstruction image.
5. The method of claim 1, wherein the computing a point set mean center of gravity for the depth vision point set comprises:
calculating a point set mean center of gravity for the depth vision point set using the following formula:
wherein, P i A point set mean center of gravity representing the depth vision point set, i represents a sequence number in the depth vision point set, j represents a sequence number of a depth vision point in the depth vision point set, x j ，y j ，z j And m represents the total number of the depth vision points in the depth vision point set.
6. The method of claim 1, wherein the performing a plane fit to the mean center of gravity of the set of points to obtain a fitted visual plane of the set of depth vision points comprises:
determining the plane fitting surface parameters of the mean center of gravity of the point set by using the following formula:
wherein a, b and c represent plane fitting surface parameters of the mean gravity center of the point set, and x i ，y i ，z i The coordinates of the average gravity center of the point set are represented, and n represents the number of the point sets of the depth vision point set;
according to the plane fitting surface parameters, constructing a fitting visual plane of the depth visual point set by using the following formula:
z＝ax+by+c
wherein z = ax + by + c represents the fitted visual plane of the depth visual point set, z, x, y represent variables of the plane function, a represents parameters of variable x fitted to the visual plane function among plane fitted plane parameters of the mean center of gravity of the point set, b represents parameters of variable y fitted to the visual plane function among plane fitted plane parameters of the mean center of gravity of the point set, and c represents constants fitted to the visual plane function among plane fitted plane parameters of the mean center of gravity of the point set.
7. The method of claim 1, wherein said calculating a tire detection depth for the vehicle tire from the point set mean center of gravity and the fitted visual plane comprises:
calculating the tire detection depth of the automobile tire by using the following formula:
wherein, d i The tire detection depth of the automobile tire is represented, a represents a parameter of a variable x of a fitted visual plane function in plane fitting plane parameters of the average gravity center of the point set, b represents a parameter of a variable y of the fitted visual plane function in plane fitting plane parameters of the average gravity center of the point set, c represents a constant of the fitted visual plane function in plane fitting plane parameters of the average gravity center of the point set, d represents a parameter of a variable z of the fitted visual plane function, and x represents the parameter of the variable z of the fitted visual plane function i ，y i ，z i Coordinates representing the mean center of gravity of the set of points.
8. The method of claim 1, wherein the detecting the edge vision point of the width detection region comprises:
carrying out binarization operation on the width detection area by using the following formula to obtain a binarization area:
wherein p is i (x i ，y i ) Coordinate value, x, representing point cloud data in the binarization region i ，y i ，z i The coordinate of the average gravity center of the point set is represented, and k represents a preset image pixel parameter;
calculating the gradient of the edge point of the binarization area by using the following formula:
wherein | G (x, y) | represents the gradient of the edge point of the binarization region, sprt represents the square root calculation, f (x +1, y), f (x, y + 1) represents the pixel function of the binarization region,
representing a first derivative difference of pixels in the binarization region;
and determining the edge vision point of the width detection area according to the edge point gradient.
9. The method according to claim 1, wherein said fitting the edge vision points to obtain a fitted vision line comprises:
calculating fit line parameters for the edge vision points using the following formula:
wherein A, B represent the fit line parameters, x, of the edge vision points j ，y j Representing the jth edge vision point, M representing the number of the edge vision points;
and fitting the edge line of the edge vision point by using the following formula to obtain a fitted vision line:
Y＝AX+B
where Y = AX + B represents the fitted visual line, a, B represent fitted line parameters of the edge visual points, and Y, X represent arguments of a straight-line function.
10. 3D visual holographic detection device of the quality of automobile tires, characterized in that, the device includes:
the detection area dividing module is used for acquiring three-dimensional contour data of an automobile tire, reconstructing the three-dimensional shape of the automobile tire according to the three-dimensional contour data to obtain a three-dimensional reconstruction image, and dividing a depth detection area and a width detection area of the three-dimensional reconstruction image;
the detection depth calculation module is used for sampling a depth vision point set in the depth detection area, calculating the average gravity center of the point set of the depth vision point set, performing plane fitting on the average gravity center of the point set to obtain a fitting vision plane of the depth vision point set, and calculating the tire detection depth of the automobile tire according to the average gravity center of the point set and the fitting vision plane;
the detection width calculation module is used for detecting the edge visual points of the width detection area, performing edge line fitting on the edge visual points to obtain a fitted visual line, and calculating the tire detection width of the automobile tire according to the edge visual points and the fitted visual line;
and the detection result determining module is used for determining the three-dimensional visual quality detection result of the automobile tire according to the tire detection depth and the tire detection width.
11. An electronic device, characterized in that the electronic device comprises:
at least one processor; and the number of the first and second groups,
a memory communicatively coupled to the at least one processor; wherein the content of the first and second substances,
the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the method for 3D visual holographic detection of quality of automotive tires according to any one of claims 1 to 9.
12. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out a method for 3D visual holographic detection of the quality of a vehicle tyre according to one of claims 1 to 9.
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| CN116007526A (en) * | 2023-03-27 | 2023-04-25 | 西安航天动力研究所 | Automatic measuring system and measuring method for diaphragm notch depth |
| CN117291893A (en) * | 2023-09-28 | 2023-12-26 | 广州市西克传感器有限公司 | Tire tread wear degree detection method based on 3D image |
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN116007526A (en) * | 2023-03-27 | 2023-04-25 | 西安航天动力研究所 | Automatic measuring system and measuring method for diaphragm notch depth |
| CN117291893A (en) * | 2023-09-28 | 2023-12-26 | 广州市西克传感器有限公司 | Tire tread wear degree detection method based on 3D image |
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