Automatic identification and positioning method based on machine vision binocular measurement system

Abstract

The invention discloses an automatic identification and positioning method based on a machine vision binocular measurement system, relates to the technical field of automatic identification and positioning, and solves the technical problem that the identification and monitoring accuracy of the machine vision binocular measurement system is reduced due to the fact that matched identification and positioning cannot be carried out through image identification in the prior art. Specifically, image recognition is matched with recognition and positioning, so that a shot object can be accurately recognized and positioned, the recognition and monitoring accuracy of a machine vision binocular measurement system is improved, and meanwhile, the surface shape defect of the shot object can be monitored according to the binocular measurement system; and shape defect generation detection can also be carried out in the moving stage of the shot object, abrasion tracing of the shot object is facilitated, shape defect repair can also be carried out in time when abrasion is generated, and the use quality of the shot object is guaranteed to the maximum extent.

Description

Technical Field

[0001] The present invention relates to the technical field of automatic identification and positioning, and in particular to an automatic identification and positioning method based on a machine vision binocular measurement system. Background Art

[0002] The machine vision binocular measurement system is a measurement system based on binocular stereo vision technology. It simulates the principle of human vision, uses two cameras to shoot objects from different angles, obtains stereo information of objects, and realizes three-dimensional measurement. By using high-precision calibration plates and advanced calibration algorithms, it accurately obtains the internal and external parameters of the camera, reduces calibration errors, and improves measurement and recognition accuracy.

[0003] However, in the prior art, the machine vision binocular measurement system cannot perform monocular camera detection and binocular camera detection during operation. It is impossible to avoid the deviation of the binocular image acquisition during analysis due to the deviation of the monocular camera's acquisition image. It is also impossible to avoid the shooting deviation of the binocular camera during shooting. This causes the shooting positioning of the binocular camera to change, resulting in the inability of the binocular camera's acquisition image to cooperate with image analysis. In addition, according to the acquired images of the real-time dual camera group, it is impossible to cooperate with recognition and positioning through image recognition, which reduces the recognition and monitoring accuracy of the machine vision binocular measurement system.

[0004] In view of the above technical defects, a solution is now proposed. Summary of the invention

[0005] The purpose of the present invention is to solve the above-mentioned problems and to propose an automatic recognition and positioning method based on a machine vision binocular measurement system.

[0006] The purpose of the present invention can be achieved by the following technical solutions: An automatic recognition and positioning method based on a machine vision binocular measurement system, the steps of the automatic recognition and positioning method are as follows: Monocular camera detection: two cameras in the machine vision binocular measurement system shoot at different angles and detect the image of any camera; Binocular camera detection: after completing the monocular camera detection and passing the detection, perform binocular camera detection; Binocular coordinated recognition and positioning: image recognition and analysis are performed based on the images collected by the binocular camera, and coordinated recognition and positioning are performed through image recognition; Modeling accuracy assessment: After identification and positioning by the machine vision binocular measurement system, three-dimensional modeling of the photographed object is performed, and three-dimensional modeling detection is used to infer whether there is a difference between the current photographed object and the actual object.

[0007] As a preferred embodiment of the present invention, the monocular camera detection process is as follows: The two cameras in the machine vision binocular measurement system are marked as a dual-camera group, and the cameras in the dual-camera group are uniformly marked as single cameras; the monocular camera information is collected, and the monocular camera detection coefficient is substituted into the calculation to obtain the monocular camera detection coefficient. If the monocular camera detection coefficient exceeds the monocular camera detection coefficient threshold, the current single camera is replaced in time and the current equipment is debugged, and the starting running time after the replacement is coordinated with the other single camera of the dual-camera group for debugging, and the collected image is segmented with the starting running time after the replacement as the dividing point; if the monocular camera detection coefficient does not exceed the monocular camera detection coefficient threshold, it is inferred that the current single camera is running normally; the collected images of all single cameras in the dual-camera group are statistically stored.

[0008] As a preferred embodiment of the present invention, the monocular camera information includes the number of repeated images corresponding to adjacent acquisition moments when the object is stationary during the operation of a single camera and the interval distance between the images corresponding to adjacent acquisition moments when the object is moving and showing the position of the object.

[0009] As a preferred embodiment of the present invention, the binocular camera detection process is as follows: After ensuring that all single cameras in the dual-camera group have passed the operation test, perform binocular camera testing based on the statistically stored collected images, and infer the fit of the dual-camera group through image analysis; collect static coordination data and moving deviation data, and if the static coordination data exceeds the maximum value of the area excess value threshold range, adjust the dual-camera group shooting; if the static coordination data does not exceed the minimum value of the area excess value threshold range, adjust the dual-camera group shooting; If the moving deviation data exceeds the maximum floating span threshold, the dual-camera group will be controlled to ensure the coverage ratio of the shooting area set by all single cameras; if the static coordination data is within the area excess value threshold range and the moving deviation data does not exceed the maximum floating span threshold, it is inferred that the binocular camera detection is qualified.

[0010] As a preferred embodiment of the present invention, the static coordination data and the moving deviation data are respectively the excess value of the sum of the actual shooting area of ​​the object shot by the dual camera group when the object is in a static state and the area corresponding to the actual area of ​​the object shot, and the maximum floating span value of the overlapping area of ​​the object shot corresponding to different shooting moments of the dual camera group when the object is in a moving state.

[0011] As a preferred embodiment of the present invention, the binocular coordination recognition and positioning process is as follows: Acquire the captured images of the dual camera group, and perform coordinated recognition analysis on the captured images, determine the contour points of the captured object based on the captured images of the dual camera group in combination with the trajectory of the captured object, acquire the captured pictures corresponding to the capture time in the captured images, and perform contour point acquisition based on the display of all acquired captured pictures and set them as preset contour points, and perform preset contour point screening based on all captured pictures in the captured images, if the number of captured pictures that continuously appear corresponding to the preset contour point exceeds the set number threshold, then mark the preset contour point as a determined contour point, otherwise, mark it as a stored contour point; As a preferred embodiment of the present invention, the determined contour points are connected to each other to construct a three-dimensional model of the photographed object. If the real-time three-dimensional model has an abnormal contour shape, the stored contour points are substituted into the model to infer whether the contour shape is normal. If so, the contour points are retained, otherwise, the contour points are deleted. After determining the three-dimensional model of the photographed object, the morphological features and visual features of each display surface of the three-dimensional model are collected, where the morphological features are represented by the horizontality and deformation area of ​​the display surface; the visual features are represented by the color value of each position of the display surface.

[0012] As a preferred embodiment of the present invention, the floating span of the parameter values ​​in the corresponding morphological features and visual features of the photographed object during the shooting stage is obtained. If the floating span of the corresponding parameter values ​​exceeds the set floating span threshold, or the value of the corresponding type parameter cannot be restored to the value before the floating after the value span floats, it is inferred that the corresponding display surface is worn, and the corresponding position in the display surface is marked as a deformed position; if the floating span of the corresponding parameter values ​​does not exceed the set floating span threshold, it is inferred that the corresponding display surface is not worn, and the corresponding position in the display surface is marked as a normal position; the deformed position is highlighted in color on the three-dimensional model, and the color of the normal position remains constant.

[0013] As a preferred embodiment of the present invention, the modeling accuracy evaluation process is as follows: Obtain recognition deviation data and recognition delay data. If the recognition deviation data exceeds the area deviation threshold, or the recognition delay data does not exceed the span value ratio threshold, reset the current machine vision binocular measurement system to a dual-camera group; if the recognition deviation data does not exceed the area deviation threshold, and the recognition delay data exceeds the span value ratio threshold, continue shooting according to the current dual-camera group.

[0014] As a preferred embodiment of the present invention, the recognition deviation data and the recognition delay data are respectively the range area deviation value between the real-time three-dimensional model color highlighted position and the actual verified position of the photographed object, and the span numerical ratio corresponding to the rising span of the quantity parameter of the real-time three-dimensional model color highlighted position corresponding to the photographed object position verification stage and the numerical deviation span of the actual quantity parameter in the verification stage.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the present invention, the camera of any camera is detected to infer whether the real-time image acquisition effect during the monocular camera process meets the preset effect, so as to avoid the decrease in the accuracy of the three-dimensional model construction of the photographed object by the entire measurement system due to the deviation of the collected image of the monocular camera during the analysis of the binocular collected image. At the same time, the operation stability of the binocular measurement system can be guaranteed according to the monocular camera detection, and the image acquisition of any camera can be guaranteed to be accurate, so that the photographed object can be accurately and efficiently identified and located; Through binocular camera detection, it is inferred whether the real-time shooting efficiency of the two cameras cooperating in shooting at different angles meets the actual needs, so as to avoid shooting deviation when the binocular cameras cooperate in shooting, resulting in abnormal movement of the binocular camera shooting positioning and the inability of the binocular camera to cooperate with image analysis. The binocular camera detection can ensure the accuracy of the binocular camera and ensure that the two cameras can cooperate synchronously at multiple angles when shooting objects, thereby improving the object recognition and positioning accuracy of the visual binocular measurement system.

[0016] 2. In the present invention, image recognition is used to coordinate identification and positioning, so as to accurately identify and locate the photographed object, thereby improving the recognition and monitoring accuracy of the machine vision binocular measurement system. At the same time, the binocular measurement system can monitor the surface defects of the photographed object, and can also detect the defects when the photographed object is moving, which is conducive to tracing the wear of the photographed object, and can also repair the defects in time when the wear occurs, thereby ensuring the use quality of the photographed object to the greatest extent; After identification and positioning by the machine vision binocular measurement system, 3D modeling of the photographed object is performed. Through 3D modeling detection, it is inferred whether there is a difference between the current photographed object and the actual object. Through the identification and positioning points of the binocular measurement system, it is inferred whether the binocular coordination recognition efficiency is qualified, thereby ensuring the accuracy of the 3D modeling efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to facilitate understanding by those skilled in the art, the present invention is further described below with reference to the accompanying drawings.

[0018] Figure 1 It is a schematic diagram of the process of the present invention. DETAILED DESCRIPTION

[0019] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present invention. The appearance of the phrase in various places in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0021] See also Figure 1 As shown, an automatic recognition and positioning method based on a machine vision binocular measurement system, the specific steps of the automatic recognition and positioning method are as follows: Monocular camera detection: the two cameras in the machine vision binocular measurement system shoot at different angles, and detect the camera of any camera to infer whether the real-time image acquisition effect during the monocular camera process meets the preset effect, so as to avoid the decrease in the accuracy of the three-dimensional model construction of the photographed object by the entire measurement system due to the deviation of the monocular camera's collected image when the binocular image is analyzed. At the same time, the monocular camera detection can ensure the operation stability of the binocular measurement system, ensure the accuracy of the image acquisition of any camera, and accurately and efficiently identify and locate the photographed object; Binocular camera detection: after completing the monocular camera detection and passing the detection, the binocular camera detection is carried out. Through the binocular camera detection, it is inferred whether the real-time shooting efficiency of the two cameras cooperating in shooting at different angles meets the actual needs, so as to avoid shooting deviations when the binocular cameras cooperate in shooting, causing the binocular camera shooting positioning to move abnormally, resulting in the binocular camera's collected images being unable to cooperate with image analysis. The binocular camera detection can ensure the accuracy of the binocular camera, ensure that the two cameras can cooperate synchronously at multiple angles when shooting objects, and improve the object recognition and positioning accuracy of the visual binocular measurement system; Binocular coordinated recognition and positioning: image recognition and analysis are performed based on the images collected by the binocular camera, and coordinated recognition and positioning are performed through image recognition, so as to accurately identify and locate the photographed object, thereby improving the recognition and monitoring accuracy of the machine vision binocular measurement system. At the same time, the binocular measurement system can monitor the surface defects of the photographed object, and can also detect the defects when the photographed object is moving, which is conducive to tracing the wear of the photographed object, and can also repair the defects in time when wear occurs, so as to ensure the use quality of the photographed object to the greatest extent; Modeling accuracy assessment: After the recognition and positioning by the machine vision binocular measurement system, the 3D modeling of the photographed object is carried out. Through the 3D modeling detection, it is inferred whether there is a difference between the current photographed object and the actual object. Through the recognition and positioning points of the binocular measurement system, it is inferred whether the binocular coordination recognition efficiency is qualified, thereby ensuring the accuracy of the 3D modeling efficiency; The monocular camera detection process is as follows: The two cameras in the machine vision binocular measurement system are marked as a dual-camera group, and the cameras in the dual-camera group are uniformly marked as a single camera; when the dual-camera group is running, monocular camera detection is performed, that is, camera detection is performed on the single camera; The number of repeated images corresponding to the captured images at adjacent capture moments when the object is stationary during the operation of a single camera and the interval distance between the captured images at adjacent capture moments when the object is moving are obtained, and the number of repeated images corresponding to the captured images at adjacent capture moments when the object is stationary during the operation of a single camera and the interval distance between the captured images at adjacent capture moments when the object is moving are marked as CD and JJ respectively; wherein the capture moment is represented by the time point corresponding to the focus buffer period set when the single camera is running, and the image in the captured image corresponding to the focus buffer moment of the single camera is directly eliminated if the clarity is too low; The above collected information is marked as monocular camera information, and substituted into the formula to obtain the monocular camera detection coefficient. The formula is: , where D is the monocular camera detection coefficient, h1 and h2 are preset proportional coefficients, both of which are greater than 1; cd represents the set number of overlapping images; jj represents the set allowed interval distance, e is a natural constant, and α is an error correction factor, which is 0.978; Compare the monocular camera detection coefficient with the monocular camera detection coefficient threshold: If the monocular camera detection coefficient exceeds the monocular camera detection coefficient threshold, it is inferred that the current single camera operation detection is abnormal, and the current single camera is replaced in time and the current equipment is debugged. The starting operation time after replacement is coordinated with the other single camera of the dual camera group for debugging, and the collected image is segmented with the starting operation time after replacement as the demarcation point; If the monocular camera detection coefficient does not exceed the monocular camera detection coefficient threshold, it is inferred that the current single camera is running normally; collect images of all single cameras in the dual-camera group and store them statistically; The binocular camera detection process is as follows: After ensuring that all single cameras in the dual-camera group have passed the operation test, perform binocular camera testing based on the statistically stored collected images, and infer the fit of the dual-camera group through image analysis; Obtaining an excess value between the sum of the actual shooting areas of the object shot by the dual camera group when the object is in a stationary state and the area corresponding to the actual area of ​​the object shot, and marking the sum of the actual shooting areas of the object shot by the dual camera group when the object is in a stationary state and the area corresponding to the actual area of ​​the object shot as stationary matching data; Obtaining the maximum floating span value of the overlapping area of ​​the photographed object corresponding to different shooting moments of the dual camera group when the photographed object is in a moving state, and marking the maximum floating span value of the overlapping area of ​​the photographed object corresponding to different shooting moments of the dual camera group when the photographed object is in a moving state as movement deviation data; The sum of the actual shooting areas of the dual-camera group when the shooting object is in a stationary state and the corresponding excess area value of the actual shooting area of ​​the shooting object, and the maximum floating span value of the overlapping area of ​​the shooting object corresponding to the different shooting moments of the dual-camera group when the shooting object is in a moving state are compared with the area excess value threshold and the maximum floating span threshold respectively: If the object being photographed is in a stationary state, the sum of the actual photographing area of ​​the object being photographed by the dual camera group and the area excess value corresponding to the actual area of ​​the object being photographed exceeds the maximum value of the area excess value threshold range, it is inferred that the binocular camera detection of the dual camera group is inefficient, and the dual camera group shooting adjustment is performed to reduce the overlapping area of ​​the object being photographed, avoid increasing the coordinated positioning recognition intensity of the image of the object being photographed, and perform overlapping area control without affecting the overall effect of photographing the object, that is, by adjusting the camera angle of a single camera; If the sum of the actual shooting areas of the shooting objects of the dual-camera group and the area excess value corresponding to the actual area of ​​the shooting objects when the shooting objects are in a stationary state does not exceed the minimum value of the area excess value threshold range, it is inferred that the binocular camera detection of the dual-camera group is abnormal, and the shooting of the dual-camera group is regulated to increase the shooting area coverage ratio of the shooting objects, so as to avoid the shooting area coverage ratio of the shooting objects failing to meet the standard, so that the point recognition efficiency of the subsequent shooting objects is reduced; If the maximum floating span value of the overlapping area of ​​the dual camera group corresponding to the photographed object at different shooting moments when the photographed object is in a moving state exceeds the maximum floating span threshold, it is inferred that the coordination performance of the dual camera group is abnormal, and the coverage ratio of the photographed area of ​​the photographed object cannot be guaranteed when the moving state of the photographed object changes, and the dual camera group is controlled to ensure the coverage ratio of the photographed area set by all single cameras, and the shooting angle is coordinated and adjusted according to the real-time shooting area when the photographed object moves. When the shooting area of ​​any single camera is reduced during the moving stage, the shooting angle of the other single camera is adjusted to expand the shooting area; If the sum of the actual shooting areas of the dual camera groups when the shooting object is in a stationary state and the excess area value corresponding to the actual area of ​​the shooting object are within the excess area value threshold range, and the maximum floating span value of the overlapping areas of the shooting objects corresponding to the dual camera groups at different shooting moments when the shooting object is in a moving state does not exceed the maximum floating span threshold, it is inferred that the binocular camera detection is qualified; The binocular coordination recognition and positioning process is as follows: Acquire the captured images of the dual camera group, and perform coordinated recognition analysis on the captured images. Determine the contour points of the captured object based on the captured images of the dual camera group combined with the trajectory of the captured object, that is, acquire the captured pictures corresponding to the capture time in the captured image according to the moving trajectory or static position of the captured object, and perform contour point acquisition based on all the acquired captured pictures and set them as preset contour points, and screen the preset contour points based on all the captured pictures in the captured image. If the number of captured pictures that continuously appear corresponding to the preset contour point exceeds the set number threshold, the preset contour point is marked as a determined contour point, otherwise, it is marked as a stored contour point; connect the determined contour points to each other to construct a three-dimensional model of the captured object. If the real-time three-dimensional model has an abnormal contour shape, substitute the stored contour points into the model to infer whether the contour shape is normal. If it is, retain it, otherwise delete it. The abnormal contour shape indicates that the contour shape is obviously unreasonable, such as contour overlap when adjacent contour points are connected. The normal contour shape indicates that the contour shape is reasonable, and the contour shape also has a similar structure to the same type of captured objects. After determining the three-dimensional model of the photographed object, the morphological features and visual features of each display surface of the three-dimensional model are collected, wherein the morphological features are represented by the horizontality of the display surface and the area of ​​the deformation region; the visual features are represented by the color value of each position of the display surface; the floating span of the parameter values ​​in the corresponding morphological features and visual features of the photographed object at the shooting stage is obtained. If the floating span of the corresponding parameter value exceeds the set floating span threshold, or the value of the corresponding type parameter cannot be restored to the value before the floating after the value span floats, it is inferred that the corresponding display surface is worn, and the corresponding position in the display surface is marked as a deformation position; If the floating span of the corresponding parameter value does not exceed the set floating span threshold, it is inferred that the corresponding display surface is not worn, and the corresponding position in the display surface is marked as a normal position; the deformed position is highlighted in color on the three-dimensional model, and the color of the normal position remains constant; The modeling accuracy evaluation process is as follows: After the coordinated recognition and positioning is completed, the modeling accuracy of the constructed 3D model is evaluated, and the range area deviation value between the color highlighted position of the real-time 3D model and the actual verified position of the photographed object is obtained, and the range area deviation value between the color highlighted position of the real-time 3D model and the actual verified position of the photographed object is marked as recognition deviation data; Obtain the span value ratio of the rising span of the quantity parameter of the color highlighted position of the real-time three-dimensional model corresponding to the shooting object position verification stage and the numerical deviation span of the actual quantity parameter in the verification stage, and mark the span value ratio of the rising span of the quantity parameter of the color highlighted position of the real-time three-dimensional model corresponding to the shooting object position verification stage and the numerical deviation span of the actual quantity parameter in the verification stage as recognition delay data, wherein the quantity parameter is represented by the numerical parameters such as the area and quantity of the color highlighted position; And the range area deviation value between the real-time 3D model color highlighted position and the actual verification position of the photographed object, and the span value ratio corresponding to the rising span of the quantity parameter of the real-time 3D model color highlighted position corresponding to the photographed object position verification stage and the numerical deviation span of the actual quantity parameter in the verification stage are compared with the area deviation threshold and the span value ratio threshold respectively: If the range area deviation value between the real-time 3D model color highlighted position and the actual verified position of the photographed object exceeds the area deviation threshold, or the span value ratio of the rising span of the real-time 3D model color highlighted position quantity parameter corresponding to the photographed object position verification stage and the numerical deviation span of the actual quantity parameter in the verification stage does not exceed the span value ratio threshold, it is inferred that the 3D modeling accuracy of the photographed object is poor, and the current machine vision binocular measurement system is reset for dual camera groups, that is, the specifications and shooting angles are reset; If the range area deviation value between the real-time 3D model color highlighted position and the actual verified position of the photographed object does not exceed the area deviation threshold, and the span value ratio corresponding to the rising span of the real-time 3D model color highlighted position quantity parameter in the photographed object position verification stage and the numerical deviation span of the actual quantity parameter in the verification stage exceeds the span value ratio threshold, it is inferred that the 3D modeling accuracy of the photographed object is high, and the shooting is continued according to the current dual camera group; The above formulas are obtained by collecting a large amount of data and performing software simulation, and a formula close to the actual value is selected. The coefficients in the formula are set by technicians in this field according to actual conditions; When the present invention is in use, monocular camera detection is performed, and two cameras in the machine vision binocular measurement system shoot at different angles, and the camera image of any camera is detected; binocular camera detection is performed, and binocular camera detection is performed after the monocular camera detection is completed and the detection is qualified; binocular coordinated recognition and positioning is performed, and image recognition and analysis are performed according to the collected images of the binocular camera, and coordinated recognition and positioning are performed through image recognition; modeling accuracy evaluation is performed, and three-dimensional modeling of the photographed object is performed after recognition and positioning by the machine vision binocular measurement system, and whether there is a difference between the current photographed object and the actual object is inferred through three-dimensional modeling detection.

[0022] The preferred embodiments of the present invention disclosed above are only used to help explain the present invention. The preferred embodiments do not describe all the details in detail, nor do they limit the invention to only specific implementation methods. Obviously, many modifications and changes can be made according to the content of this specification. This specification selects and specifically describes these embodiments in order to better explain the principles and practical applications of the present invention, so that those skilled in the art can understand and use the present invention well. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An automatic identification and positioning method based on a machine vision binocular measurement system, characterized in that: The steps of automatic identification and positioning method are as follows: Monocular camera detection: two cameras in the machine vision binocular measurement system shoot at different angles and detect the image of any camera; Binocular camera detection: after completing the monocular camera detection and passing the detection, perform binocular camera detection; Binocular coordinated recognition and positioning: image recognition and analysis are performed based on the images collected by the binocular camera, and coordinated recognition and positioning are performed through image recognition; Modeling accuracy assessment: After identification and positioning by the machine vision binocular measurement system, three-dimensional modeling of the photographed object is performed, and three-dimensional modeling detection is used to infer whether there is a difference between the current photographed object and the actual object.

2. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 1 is characterized in that: The monocular camera detection process is as follows: The two cameras in the machine vision binocular measurement system are marked as a dual-camera group, and the cameras in the dual-camera group are uniformly marked as single cameras; the monocular camera information is collected, and the monocular camera detection coefficient is obtained by substitution in the calculation. If the monocular camera detection coefficient exceeds the monocular camera detection coefficient threshold, the current single camera is replaced in time and the current equipment is debugged, and the starting operation time after the replacement is coordinated with the other single camera of the dual-camera group for debugging, and the collected image is segmented with the starting operation time after the replacement as the dividing point; if the monocular camera detection coefficient does not exceed the monocular camera detection coefficient threshold, it is inferred that the current single camera is running normally; The images collected by all single cameras of the dual-camera group are statistically stored.

3. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 2 is characterized in that: The monocular camera information includes the number of repeated images corresponding to the captured images at adjacent capture times when the object is stationary during the operation of the single camera, and the interval distance between the captured images corresponding to the adjacent capture times when the object is moving.

4. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 1 is characterized in that: The binocular camera detection process is as follows: After ensuring that all single cameras in the dual-camera group have passed the operation test, perform binocular camera testing based on the statistically stored collected images, and infer the fit of the dual-camera group through image analysis; Collect static coordination data and moving deviation data. If the static coordination data exceeds the maximum value of the area excess value threshold range, perform dual camera group shooting adjustment. If the static matching data does not exceed the minimum value of the area excess value threshold range, the dual camera group shooting is regulated; If the moving deviation data exceeds the maximum floating span threshold, the dual-camera group will be controlled to ensure the coverage ratio of the shooting area set by all single cameras; if the static coordination data is within the area excess value threshold range and the moving deviation data does not exceed the maximum floating span threshold, it is inferred that the binocular camera detection is qualified.

5. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 4 is characterized in that: The static coordination data and the moving deviation data are respectively the excess value of the sum of the actual shooting area of ​​the object shot by the dual camera group when the object is in a static state and the area corresponding to the actual area of ​​the object shot, and the maximum floating span value of the overlapping area of ​​the object shot at different shooting moments of the dual camera group when the object is in a moving state.

6. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 1, characterized in that: The binocular coordination recognition and positioning process is as follows: The captured images of the dual camera group are obtained, and the captured images are coordinated for recognition and analysis. The contour points of the captured object are determined based on the captured images of the dual camera group in combination with the trajectory of the captured object. The captured pictures corresponding to the capture time in the captured images are acquired, and the contour points are collected according to the display of all the acquired captured pictures and set as preset contour points. The preset contour points are screened according to all the captured pictures in the captured images. If the number of captured pictures that continuously appear corresponding to the preset contour points exceeds the set number threshold, the preset contour points are marked as determined contour points, otherwise, they are marked as stored contour points.

7. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 6 is characterized in that: The determined contour points are connected to each other to construct a 3D model of the photographed object. If the real-time 3D model has an abnormal contour shape, the stored contour points are substituted into the model to infer whether the contour shape is normal. If it is normal, it is retained, otherwise it is deleted; After determining the 3D model of the photographed object, the morphological and visual features of each display surface of the 3D model are collected. The morphological features are represented by the horizontality of the display surface and the area of ​​the deformation region; the visual features are represented by the color value of each position on the display surface.

8. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 7 is characterized in that: The floating span of the parameter values ​​in the corresponding morphological features and visual features of the photographed object during the photographing stage is obtained. If the floating span of the corresponding parameter values ​​exceeds the set floating span threshold, or the value of the corresponding type parameter cannot be restored to the value before the floating after the value span floats, it is inferred that the corresponding display surface is worn, and the corresponding position in the display surface is marked as a deformed position; if the floating span of the corresponding parameter values ​​does not exceed the set floating span threshold, it is inferred that the corresponding display surface is not worn, and the corresponding position in the display surface is marked as a normal position; the deformed position is highlighted in color on the three-dimensional model, and the color of the normal position remains constant.

9. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 1, characterized in that: The modeling accuracy evaluation process is as follows: Obtain recognition deviation data and recognition delay data. If the recognition deviation data exceeds the area deviation threshold, or the recognition delay data does not exceed the span value ratio threshold, reset the current machine vision binocular measurement system to a dual-camera group; if the recognition deviation data does not exceed the area deviation threshold, and the recognition delay data exceeds the span value ratio threshold, continue shooting according to the current dual-camera group.

10. The automatic identification and positioning method based on a machine vision binocular measurement system according to claim 9, characterized in that: The recognition deviation data and recognition delay data are respectively the range area deviation value between the real-time three-dimensional model color highlighted position and the actual verified position of the photographed object, and the span numerical ratio corresponding to the rising span of the quantity parameters of the real-time three-dimensional model color highlighted position corresponding to the photographed object position verification stage and the numerical deviation span of the actual quantity parameters in the verification stage.

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