A mobile precision measurement method and device based on material surface texture features

Abstract

The application discloses a mobile precision measurement method and device based on material surface texture features, and the method comprises the following steps: a low-resolution image sensor outputs a displacement parameter of a rotating shaft in real time, speed determination is carried out based on the displacement parameter, after the speed meets the high-resolution clear imaging speed, a high-resolution image sensor starts to shoot the rotating shaft to obtain a high-resolution image; a region to be analyzed is extracted from the high-resolution image; invariant moments of the region to be analyzed and spatial coordinates of feature points in the region to be analyzed on the high-resolution image are calculated; comparison is made between the spatial coordinates and pre-stored position data to obtain offset values of the feature points in the region, and after correction, absolute position parameters of the feature points in the region are obtained. The application directly uses natural textures formed on the surface of the rotating shaft, does not need to use additional and expensive grating devices, meanwhile, the process of grating installation and debugging is omitted, and the application is convenient to use, especially for large-diameter shafts.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of precision measurement, in particular to a mobile precision measurement method and device based on material surface texture features. BACKGROUND

[0002] The rotation angle measurement of a large-diameter shaft generally requires the use of a large-aperture optical encoder, but it is difficult to manufacture a large-aperture code disc, which requires the use of large-scale precision machining equipment, and it is also difficult to ensure the flatness of the large-area substrate and the assembly flatness of the code disc.

[0003] At present, in addition to using a large-aperture photoelectric encoder, a precision metal grating belt + reflective displacement reading head can also be used, but at present this technology and product are dependent on foreign countries, and the preparation process of a long precision metal grating belt is complex, and it is difficult to overcome and achieve at low cost in a short time. SUMMARY

[0004] In view of this, the present application provides a mobile precision measurement method and device based on material surface texture features to solve the above technical problems.

[0005] The present application discloses a mobile precision measurement method based on material surface texture features, which comprises the following steps:

[0006] Step 1: A low-resolution image sensor outputs the displacement parameters of a rotating shaft in real time, and speed determination is performed based on the displacement parameters, and after the speed meets the high-resolution clear imaging speed, a high-resolution image sensor starts to shoot the rotating shaft to obtain a high-resolution image;

[0007] Step 2: Extracting a region to be analyzed from the high-resolution image;

[0008] Step 3: Calculating the moment invariants of the region to be analyzed and the spatial coordinates of the region feature points in the high-resolution image;

[0009] Step 4: Comparing the spatial coordinates with pre-stored position data to obtain the offset value of the region feature points, and after correction, the absolute position parameters of the region feature points are obtained.

[0010] Further, before the step 1, it further comprises:

[0011] A low-resolution image sensor uses the following improved digital image algorithm for displacement detection based on the surface texture image collected by the low-resolution image sensor;

[0012] The improved digital image algorithm comprises:

[0013] Step 01: The low-resolution image sensor shoots and keeps a surface texture image of a rotating shaft, and saves it as img_0;

[0014] Step 02: interval time dt, take a surface texture image of the rotating shaft again img_1;

[0015] Step 03: using digital image correlation algorithm, calculate the displacement difference dx, dy of img_1 relative to img_0 in x, y direction;

[0016] Step 04: if dx and dy are both 0, discard img_1; otherwise save img_1 as img_0;

[0017] Step 05: repeat steps 02 to 04.

[0018] Further, before the high-resolution image starts to take the rotating shaft and obtain the high-resolution image, further comprising:

[0019] Pre-calibrate the surface texture of the rotating shaft.

[0020] Further, the pre-calibration of the surface texture of the rotating shaft comprises:

[0021] Step 11: manually rotate the rotating shaft to zero position,

[0022] Step 12: high-resolution image sensor takes an image, binarizes the image and reverses the color to make a large area of white image, erodes the binary image first, then dilates it; based on the area threshold, select the required area as the candidate area;

[0023] Step 13: calculate the invariant moment and image coordinates of the region feature points of each candidate area respectively; wherein the region feature points include the corner points on the region edge and the pixel centroid;

[0024] Step 14: rotate the rotating shaft at a known specified angle from the origin, repeat steps 12 to 13 at each position;

[0025] Step 15: determine the optimal decision combination based on the significant difference of invariant moment of all candidate areas.

[0026] Further, the step 15 comprises:

[0027] Step 151: first determine all candidate areas by invariant moment M1, that is, select a feature area at each position to form a tentative feature area set; in turn, judge whether the invariant moment M1 of each feature area is significantly different from other feature areas; if the set significant difference condition is met, the selection of feature area is completed; otherwise, continue to select the next combination, repeat step 151 until the invariant moment M1 of each feature area is significantly different from other feature areas.

[0028] Step 152: if all combinations of the alternative regions cannot be determined by the invariant moment M1, then the invariant moment M1 and the invariant moment M2 are used for determination according to step 151;

[0029] Step 153: the number of invariant moments used for determination is increased in sequence until a combination that can be determined significantly appears.

[0030] Further, the step 2 comprises:

[0031] The high-resolution image is binarized and inverted, so that a large area of white image is presented, and the binarized image is eroded and then dilated; based on an area threshold, a region meeting the requirements is selected as an alternative region; the alternative region is a region to be analyzed.

[0032] Further, the step 3 comprises:

[0033] Based on the number of invariant moments used for determining the optimal determination combination, the invariant moment of each region to be analyzed is calculated in sequence; based on the invariant moment data, each region to be analyzed is identified, if a region to be analyzed is identified, it is moved to the vicinity of a position known in advance, and the spatial coordinates of the feature points of the identified region to be analyzed on the current high-resolution image are calculated.

[0034] Further, after the step 4, the method further comprises:

[0035] The absolute position parameter is used to update the current position parameter of the system;

[0036] If the high-precision image sensor does not identify the region to be analyzed, no operation is performed.

[0037] The application further discloses a mobile precision measuring device based on material surface texture features, which comprises a rotating shaft, a high-resolution image sensor and a low-resolution image sensor.

[0038] The surface of the rotating shaft has a feature significant texture, and the texture of the surface is a region for mobile measurement.

[0039] The high-resolution image sensor and the low-resolution image sensor are arranged on the same side of the rotating shaft and are used for collecting surface texture images of the rotating shaft respectively.

[0040] The high-resolution image sensor and the low-resolution image sensor have the same structure and each comprise a light source, an imaging lens and a ccd chip.

[0041] The light source is arranged opposite to the imaging lens and the ccd chip and irradiates the surface of the rotating shaft.

[0042] The imaging lens is arranged opposite to the CCD chip, and the surface texture image of the rotating shaft is displayed on the CCD chip.

[0043] Further, the light source is selected as short wavelength blue light, which includes LED and semiconductor laser.

[0044] The pixel density of the CCD chip of the high-resolution image sensor is higher than the pixel density of the CCD chip of the low-resolution image sensor.

[0045] Due to the adoption of the above technical scheme, the present application has the following advantages:

[0046] 1. The present application can directly use the natural texture formed by the shaft surface, without using additional and expensive grating devices, and the process of grating installation and debugging is also saved, and the present application is convenient to use, especially for large-diameter shafts.

[0047] 2. The high / low two kinds of resolution image sensors are used, the low-resolution image sensor is used at high speed, which can measure the rapid displacement; the high-resolution image sensor automatically starts to work at low speed, which can generate zero position and absolute position data, and eliminate the error accumulation caused by micro displacement.

[0048] 3. The improved algorithm of the low-resolution image sensor can eliminate the accumulation of micro displacement errors to a certain extent.

[0049] 4. The high-resolution image sensor uses invariant moments as the identification of the texture. The invariant moment is a quantity independent of the position and rotation angle of the target image, so that the feature can be used in the scene of translation, rotation and compound motion of the feature area.

[0050] 5. According to the calculation complexity of various invariant moments, the M1 with the lowest calculation complexity is used preferentially to construct the identification feature space; when it cannot be distinguished significantly, the next lowest calculation complexity invariant moment is added in sequence to construct a high-dimensional identification feature space. This method enables the selected feature area to be identified with the lowest calculation complexity, thereby improving the identification speed of the high-resolution image sensor. BRIEF DESCRIPTION OF DRAWINGS

[0051] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiment description will be briefly introduced. Obviously, the drawings in the following description are only some embodiments described in the present application, and other drawings can also be obtained by those skilled in the art according to these drawings.

[0052] Figure 1 It is a structure schematic view of a mobile precision measurement device based on material surface texture characteristics.

[0053] Figure 2 Structure diagram of high / low resolution image sensor of the embodiment of the present application;

[0054] Figure 3 Flowchart of a mobile precision measurement method based on material indicating texture features of the embodiment of the present application;

[0055] Reference signs:

[0056] 1-rotating shaft, 2-high resolution image sensor, 3-low resolution image sensor, 4-light source, 5-imaging lens, 6-ccd chip. DETAILED DESCRIPTION

[0057] The present application is further illustrated in conjunction with the accompanying drawings and embodiments. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. All other embodiments obtained by those skilled in the art should belong to the scope of protection of the embodiments of the present application.

[0058] The surface texture of the rotating shaft is naturally formed along with the manufacturing and processing of the shaft. The area used for mobile measurement should have a relatively rough surface to form a distinctive texture.

[0059] Referring to Figure 3 The present application provides an embodiment of a mobile precision measurement method based on material indicating texture features, which includes the following steps:

[0060] S1: The low resolution image sensor 3 outputs the displacement parameters of the rotating shaft 1 in real time, and performs speed determination based on the displacement parameters. After the speed meets the high resolution clear imaging speed, the high resolution image sensor 2 starts to take pictures of the rotating shaft 1 to obtain high resolution images.

[0061] S2: Extracting the area to be analyzed from the high resolution images.

[0062] S3: Calculating the moment invariants of the area to be analyzed and the spatial coordinates of the area feature points in the high resolution images.

[0063] S4: Comparing the spatial coordinates with the pre-stored position data to obtain the offset value of the area feature points. After correction, the absolute position parameters of the area feature points are obtained.

[0064] In the embodiment, before S1, it also includes:

[0065] The low resolution image sensor 3 uses the following improved digital image algorithm for displacement detection based on the surface texture images collected by it, so as to enhance the micro-creeper detection capability.

[0066] The improved digital image algorithm includes:

[0067] S01: The low-resolution image sensor 3 takes and keeps a surface texture image of the rotating shaft 1, saved as img_0;

[0068] S02: After an interval time dt, a surface texture image img_1 of the rotating shaft 1 is taken again;

[0069] S03: Using the digital image correlation algorithm, the displacement difference dx, dy of img_1 relative to img_0 in the x, y direction is calculated;

[0070] S04: If dx and dy are both 0, img_1 is discarded; otherwise, img_1 is saved as img_0;

[0071] S05: Repeat S02 to S04.

[0072] Based on the improved digital image algorithm S, when a very small displacement occurs, that is, a small displacement is actually generated, but the digital imaging space resolution is low, and the small displacement change cannot be reflected on the image, resulting in the algorithm recognizing the displacement as 0, the small error will not be accumulated (when the small displacement error is continuously accumulated and reaches the image resolvable scale, the previous error can be recognized and compensated at one time).

[0073] The low-resolution image (digital image scale ≤ 512x512 pixels) is used to reduce image data acquisition and transmission time, and less pixel calculation amount, so as to realize fast correlation calculation. It is required to perform at least 1000 correlation operations per second. The sensor outputs displacement information in an incremental manner, that is, the displacement amount relative to the last position. It is mainly used for real-time displacement measurement in fast significant motion.

[0074] In this embodiment, before the high-resolution image starts to take pictures of the rotating shaft 1, the following steps are further included before the high-resolution image is acquired:

[0075] The surface texture of the rotating shaft 1 is calibrated in advance.

[0076] The high-resolution image sensor 2 has the same structure as the low-resolution image sensor 3, except that a higher pixel density imaging chip is used to form a more detailed shaft surface texture image. It is mainly used for zero point determination, elimination of micro-displacement cumulative error, and provision of low-speed absolute positioning.

[0077] In this embodiment, the surface texture of the rotating shaft 1 is calibrated in advance, including:

[0078] S11: The rotating shaft 1 is manually rotated to the zero position;

[0079] S12: The high-resolution image sensor 2 takes an image, binarizes the taken image and inverts the color, so that a large area of white image is presented, and erodes the binary image first and then dilates it; based on an area threshold, a region meeting the requirement is selected as a candidate region;

[0080] S13: The moment invariants of each candidate region and the image coordinates of the region feature points are calculated respectively; wherein the region feature points include the corner points on the region edges and the pixel centroids;

[0081] S14: Starting from the origin, the rotation shaft 1 is rotated at a known specified angle, and S12 to S13 are repeated at each position;

[0082] S15: Based on the moment-invariant difference significance of all candidate regions, the optimal decision combination is determined.

[0083] In this embodiment, S15 includes:

[0084] S151: First, the moment invariants M1 are used to determine all candidate regions, that is, a feature region is selected at each position to form a tentative feature region set; in turn, it is judged whether the moment invariants M1 of each feature region are significantly different from those of other feature regions; if the set significant difference condition is met, the selection of the feature region is completed; otherwise, the next combination is selected, and S151 is repeated until the moment invariants M1 of each feature region are significantly different from those of other feature regions;

[0085] S152: If the combination of all candidate regions cannot be determined by the moment invariants M1, the moment invariants M1 and M2 are used together for determination according to S151;

[0086] S153: The number of moment invariants used for determination is increased in turn until a combination that can be significantly determined appears.

[0087] In this embodiment, S2 includes:

[0088] The high-resolution image is binarized and inverted in color, so that a large area of white image is presented, and the binary image is eroded first and then dilated; based on an area threshold, a region meeting the requirement is selected as a candidate region; the candidate region is the region to be analyzed.

[0089] In this embodiment, S3 includes:

[0090] Based on the number of moment invariants used to determine the optimal decision combination, the moment invariants of each region to be analyzed are calculated in turn; based on the moment-invariant data, each region to be analyzed is identified, such as a region to be analyzed is identified, then the motion is moved to the vicinity of the previously known position; and the spatial coordinates of the region feature points of the identified region to be analyzed on the current high-resolution image are calculated.

[0091] In this embodiment, S4 is followed by:

[0092] updating the current position parameter of the system with the absolute position parameter;

[0093] If the high-precision image sensor does not identify the region to be analyzed, no operation is performed.

[0094] Referring to Figure 1 and Figure 2 , the application also provides an embodiment of a mobile precision measurement device based on material surface texture features, which comprises a rotating shaft 1, a high-resolution image sensor 2 and a low-resolution image sensor 3.

[0095] The surface of the rotating shaft 1 has a feature-distinctive texture, and the surface texture is the region for mobile measurement.

[0096] The high-resolution image sensor 2 and the low-resolution image sensor 3 are arranged on the same side of the rotating shaft 1 and are used to collect surface texture images of the rotating shaft 1, respectively.

[0097] The high-resolution image sensor 2 and the low-resolution image sensor 3 have the same structure and each comprise a light source 4, an imaging lens 5 and a ccd chip 6.

[0098] The light source 4 is arranged opposite to the imaging lens 5 and the ccd chip 6 and irradiates the surface of the rotating shaft 1.

[0099] The imaging lens 5 is arranged opposite to the ccd chip 6 and displays the surface texture image of the rotating shaft 1 on the ccd chip 6.

[0100] In this embodiment, the light source 4 uses short-wavelength blue light, which includes an LED and a semiconductor laser.

[0101] The pixel density of the ccd chip 6 of the high-resolution image sensor 2 is higher than that of the ccd chip 6 of the low-resolution image sensor 3.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the application and not to limit it. Although the application has been described in detail with reference to the above embodiments, those skilled in the art should understand that the specific embodiments of the application can be modified or replaced by equivalents without departing from the spirit and scope of the application, and any modification or equivalent replacement without departing from the spirit and scope of the application should be covered in the protection scope of the claims of the application.

Claims

1. A method for mobile precision measurement based on material indicative texture features, characterized in that, The method comprises the following steps: Step 1: A low-resolution image sensor outputs a displacement parameter of the rotating shaft in real time, a speed determination is made based on the displacement parameter, and after a high-resolution clear imaging speed is reached, a high-resolution image sensor starts to capture the rotating shaft to obtain a high-resolution image; Step 2: An area to be analyzed is extracted from the high-resolution image; Step 3: Invariant moments of the area to be analyzed and spatial coordinates of region feature points of the area to be analyzed on the high-resolution image are calculated; Step 4: A comparison is made between the spatial coordinates and pre-stored position data to obtain an offset value of the region feature points, and after correction, an absolute position parameter of the region feature points is obtained; Before the high-resolution image sensor starts to capture the rotating shaft to obtain a high-resolution image, the method further comprises the following steps: A surface texture of the rotating shaft is calibrated in advance; The calibration of the surface texture of the rotating shaft comprises the following steps: Step 11: The rotating shaft is manually rotated to a zero position; Step 12: A high-resolution image sensor captures an image, performs a binaryzation processing on the captured image and reverses the color to make a large-area white image appear, corrodes the binary image first and then expands it, selects a region meeting a requirement as a candidate region based on an area threshold; Step 13: Invariant moments of each candidate region and image coordinates of region feature points of each candidate region are calculated respectively; wherein the region feature points include corner points on a region edge and pixel centers; Step 14: The rotating shaft is rotated at a known specified angle from an origin, and steps 12 to 13 are repeated at each position; Step 15: An optimal determination combination is determined based on a significant difference of invariant moments of all candidate regions; The step 3 comprises the following steps: Based on a number of invariant distances used for determining the optimal determination combination, the invariant moments of each area to be analyzed are calculated in sequence, each area to be analyzed is identified based on the invariant distance data, if a certain area to be analyzed is identified, it means that the rotating shaft is moving to a position close to a previously known position, and spatial coordinates of region feature points of the identified area to be analyzed on the current high-resolution image are calculated.

2. The method of claim 1, wherein, Before the step 1, the method further comprises the following steps: A low-resolution image sensor uses an improved digital image algorithm to detect displacement based on a surface texture image collected by the low-resolution image sensor; The improved digital image algorithm comprises the following steps: Step 01: A low-resolution image sensor captures and keeps a surface texture image of a rotating shaft as img_0; Step 02: After an interval time dt, the surface texture image img_1 of the rotating shaft is captured again; Step 03: A digital image correlation algorithm is used to calculate displacement differences dx and dy of img_1 relative to img_0 in x and y directions; Step 04: If both dx and dy are 0, img_1 is discarded; otherwise, img_1 is saved as img_0; Step 05: Steps 02 to 04 are repeatedly executed.

3. The method of claim 1, wherein, The step 15 comprises the following steps: Step 151: first, judge all candidate regions with invariant moment M1, that is, select a feature region at each position to form a set of proposed feature regions; in turn, judge whether the invariant moment M1 of each feature region is significantly different from other feature regions; if the set significant difference condition is met, the selection of the feature region is completed; otherwise, continue to select the next combination, repeat step 151 until the invariant moment M1 of each feature region is significantly different from other feature regions; Step 152: if all combinations of candidate regions cannot be judged by invariant moment M1, then use invariant moment M1 and invariant moment M2 to judge according to step 151; Step 153: increase the number of invariant moments used for judgment in turn until a combination that can be significantly judged appears.

4. The method of claim 1, wherein, The step 2 comprises: The high-resolution image is binarized and inverted to present a large area of white image, and the binary image is first eroded and then expanded; based on the area threshold, the region meeting the requirements is selected as a candidate region; the candidate region is the region to be analyzed.

5. The method of claim 1, wherein, After the step 4, it further comprises: Updating the current position parameter of the system with the absolute position parameter; If the high-resolution image sensor does not identify the region to be analyzed, no operation is performed.

6. A mobile precision measuring device based on material indicating texture features, adapted for use in the method of any one of claims 1-5, characterized in that, It comprises: A rotating shaft, a high-resolution image sensor and a low-resolution image sensor; The surface of the rotating shaft has a feature significant texture, and the texture of the surface is a region for movement measurement; The high-resolution image sensor and the low-resolution image sensor are arranged on the same side of the rotating shaft and are used to collect the surface texture image of the rotating shaft respectively; The high-resolution image sensor and the low-resolution image sensor have the same structure and each comprises a light source, an imaging lens and a ccd chip; The light source is arranged opposite to the imaging lens and the ccd chip and irradiates to the surface of the rotating shaft; The imaging lens is arranged opposite to the ccd chip and displays the surface texture image of the rotating shaft on the ccd chip.

7. The apparatus of claim 6, wherein, The light source selects short-wavelength blue light, which includes LED and semiconductor laser; The pixel density of the ccd chip of the high-resolution image sensor is higher than that of the ccd chip of the low-resolution image sensor.

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