A multi-prism identification aiming method and system, a terminal device thereof and a computer readable storage medium

Abstract

The application discloses a multi-prism identification aiming method and system, a terminal device thereof and a computer readable medium, and belongs to the technical field of image processing and target positioning algorithm. The method is as follows: when it is determined that there is no multi-prism based on a starting pixel point gray value and a preset gray threshold, a preset reading square distance is taken as a reading radius, and a fluctuation type reading action is performed; when it is determined that there is no multi-prism, a current reading radius is obtained according to a preset fluctuation increasing method and the preset reading square distance, and the fluctuation type reading action is repeatedly performed based on the current reading radius to obtain a current region pixel point gray value, until it is determined that there is a multi-prism, a target pixel point position is obtained based on the current region pixel point gray value. The multi-prism identification aiming method and system, the terminal device thereof and the computer readable medium disclosed by the application solve the problems that the data grouping range is large and the nearest prism cannot be accurately positioned in the prior art, and high-precision multi-prism identification aiming is realized.

Description

Technical Field

[0001] This invention relates to the field of image processing and target localization algorithm technology, and in particular to a prism recognition aiming method, system, terminal device and computer-readable storage medium thereof. Background Technology

[0002] In existing technologies, total stations, as high-precision measuring instruments integrating optics, mechanics, and electronics, measure distance and angle by identifying the position of a prism and are widely used in surveying, subway tunnel construction, and other fields. Currently, the mainstream solution adopts the ZYNQ architecture image processing module, which consists of a PL (FPGA) end and a PS (ARM) end. The PL end utilizes the parallel processing advantages of the FPGA to preprocess the images acquired by the CMOS camera, performing grayscale judgment, pixel region division, etc. Pixels with grayscale values ​​exceeding the threshold are identified as bright spots, and data is divided according to pixel regions. However, due to the limitations of FPGA resources, data needs to be transmitted to the PS end in groups via DMA and FIFO. After multiple sets of grayscale data are transmitted from the ZYNQ PL end to the PS end, a small field-of-view algorithm performs a "spiral" search, combining gradient descent and spiral expansion strategies, scanning spirally outward from the center of the field of view. By calculating the average pixel coordinates within the region, the prism closest to the crosshairs is located, thus determining the approximate position of the prism for aiming.

[0003] However, due to the large range of the data set, it can cause "gradual" errors in angles, and it cannot distinguish adjacent prisms within a group in multi-prism scenarios. Furthermore, there is a calibration problem where the field of view center does not coincide with the CMOS physical center, and the spiral search, based on a preset center scan, further amplifies the positioning deviation. Therefore, this method has significant errors in multi-prism recognition, and there is an urgent need to provide a recognition method that solves this technical problem. Summary of the Invention

[0004] This invention provides a method, system, terminal device, and computer-readable storage medium for prism identification and aiming, which can solve the problems of large data grouping range and inability to accurately locate the nearest prism in the prior art, and achieve high-precision prism identification and aiming.

[0005] This invention provides a method for identifying and aiming using a prism, comprising:

[0006] Obtain the entire pixel image of the image to be identified;

[0007] The starting search address is obtained based on the whole-frame pixel image to be identified;

[0008] The grayscale value of the starting pixel is obtained based on the whole frame pixel image to be identified and the starting search address;

[0009] When it is determined that there is no prism based on the gray value of the starting pixel and the preset gray value threshold, the preset squared reading distance is used as the reading radius, and a fluctuating reading action is performed: the gray value of the first region pixel is obtained based on the reading radius, the whole frame pixel image to be identified and the starting search address;

[0010] When it is determined that there is no prism based on the gray value of the first region pixel and the preset gray value threshold, the current reading radius is obtained according to the preset fluctuation increase method and the preset reading square distance, and the fluctuation reading action is repeatedly executed based on the current reading radius to obtain the gray value of the current region pixel until it is determined that there is a prism based on the gray value of the current region pixel and the preset gray value threshold, and the target pixel position is obtained based on the gray value of the current region pixel.

[0011] The actual position of the target prism is obtained based on the position of the target pixel, so as to realize the automatic identification and aiming of the total station based on the actual position of the target prism.

[0012] This invention provides a method for identifying and aiming at multi-prisms. By using a preset reading square distance and a preset fluctuation increase method, the entire frame of pixel image to be identified is read in a fluctuating manner, gradually expanding the data reading range to achieve pixel-level positioning. It prioritizes locking onto position information that meets preset conditions, avoiding the misjudgment caused by detours in existing spiral search methods. This directly compresses the aiming angle error from graded to second-level, meeting the requirements of high-precision measurement. It solves the problems of large data grouping ranges and inability to accurately locate the nearest prism in existing technologies, achieving high-precision multi-prism identification and aiming.

[0013] Further, acquiring the entire frame pixel image to be identified includes:

[0014] Acquire raw image data, line signals, and frame signals from the camera;

[0015] Preprocessing is performed on the original image data, line signals, and frame signals to obtain batch image data;

[0016] Frame-level processing is performed on the batched image data to obtain the whole frame pixel image to be identified.

[0017] The above solution ensures no data loss by acquiring raw image data, line signals, and frame signals, acquiring image data in batches, and performing frame-level processing. This solves the technical problem in existing technologies where grouped data transmission can only transmit a certain range of batch data and cannot cover all pixels of the entire frame image.

[0018] Further, obtaining the starting search address based on the whole frame pixel image to be identified includes:

[0019] Obtain crosshair coordinate data based on the full-frame pixel image to be identified;

[0020] The starting search address is obtained based on the crosshair coordinate data.

[0021] In the above scheme, the crosshair coordinate data in the entire frame of pixel image to be identified is used as the starting position of the search, which serves as the reference point for subsequent fluctuating reading and extraction. This allows for the gradual expansion of the pixel range reading and the identification of the prism that is actually closest to the crosshair position during the subsequent search and reading process, thus avoiding the technical problem of misjudging the closest prism.

[0022] Further, after the step of obtaining the grayscale value of the starting pixel based on the whole frame pixel image to be identified and the starting search address, the following steps are included:

[0023] When it is determined that a multi-prism exists based on the gray value of the starting pixel and the preset gray value threshold, the starting search address is used as the target pixel position, so as to obtain the actual position of the target prism based on the target pixel position.

[0024] Further, after the step of determining that no prism exists based on the grayscale value of the starting pixel and the preset grayscale threshold, and performing a fluctuating reading action by using the preset squared reading distance as the reading radius: obtaining the grayscale value of the first region pixel based on the reading radius, the entire frame pixel image to be identified, and the starting search address, the process includes:

[0025] When it is determined that a multi-prism exists based on the grayscale value of the pixels in the first region and a preset grayscale threshold, the position of the target pixel is obtained based on the grayscale value of the pixels in the first region, and the actual position of the target prism is obtained based on the position of the target pixel.

[0026] In the above scheme, the crosshair is used as the starting position, and a wave-like reading action is performed by combining a preset wave increase method to gradually expand the reading radius. Then, the target pixel position is determined based on the gray value when the prism is first identified. That is, the target prism that is actually closest to the crosshair is determined first, and the error can reach the second level. Then, it is converted into the actual position of the target prism to realize the aiming of the prism, thereby effectively improving the accuracy of prism recognition.

[0027] This invention provides a method for identifying and aiming at multi-prisms. It uses a preset squared reading distance as the initial reading radius, starts from the initial search address (the address corresponding to the crosshairs) as the reading starting point, and gradually expands the reading range according to a preset fluctuation increase method. At each step, the grayscale value of the corresponding pixel is read. The search range expands systematically from the nearest pixel to the farthest pixel. Initially, the region containing the multi-prism is determined by the grayscale value and a preset threshold; this is the prism spot closest to the crosshairs, achieving pixel-level positioning. This method avoids the problems of existing spiral-type searches that may lead to detours and misjudgments of distant prisms, reducing the aiming angle error from graded to second-level. During image processing, the raw data is preprocessed based on the time-series reference of line and frame signals to obtain batch image data. Then, frame-level processing integrates the batch data into a complete frame pixel image to be identified, covering all pixels in the total station's field of view. The system preserves complete image data without any data loss, overcoming the limitation of existing technologies that can only transmit partial batch data and cannot cover the entire frame of pixels. This provides blind-spot-free data for subsequent fluctuating searches, avoiding prism omissions or positioning deviations caused by missing data, and ensuring recognition reliability from the source. Based on the starting search address corresponding to the crosshair coordinate data, it prioritizes determining whether the starting pixel has multiple prisms (if the grayscale value meets the standard, it is directly locked). If not, the reading radius is expanded step by step for determination, and each step uses the first time the standard is met as the locking basis. Unlike the existing technology's coarse positioning mode based on group range, this system directly locks the prism that is truly closest to the crosshair through the nearest neighbor priority and successive expansion judgment rule, avoiding misselection caused by large group ranges containing multiple prisms, and effectively improving the accuracy of aiming.

[0028] This invention provides a prism recognition and aiming system for implementing the aforementioned prism recognition and aiming method, comprising an image processing module, a crosshair data acquisition module, and a data search module, wherein:

[0029] The image processing module is used to acquire a full-frame pixel image to be identified;

[0030] The crosshair data acquisition module is used to obtain the starting search address based on the whole frame pixel image to be identified;

[0031] The data search module is used for:

[0032] The grayscale value of the starting pixel is obtained based on the whole frame pixel image to be identified and the starting search address;

[0033] The starting search address is obtained based on the crosshair coordinate data;

[0034] The grayscale value of the starting pixel is obtained based on the whole frame pixel image to be identified and the starting search address;

[0035] When it is determined that there is no prism based on the gray value of the starting pixel and the preset gray value threshold, the preset squared reading distance is used as the reading radius, and a fluctuating reading action is performed: the gray value of the first region pixel is obtained based on the reading radius, the whole frame pixel image to be identified and the starting search address;

[0036] When it is determined that there is no prism based on the gray value of the first region pixel and the preset gray value threshold, the current reading radius is obtained according to the preset fluctuation increase method and the preset reading square distance, and the fluctuation reading action is repeatedly executed based on the current reading radius to obtain the gray value of the current region pixel until it is determined that there is a prism based on the gray value of the current region pixel and the preset gray value threshold, and the target pixel position is obtained based on the gray value of the current region pixel.

[0037] The actual position of the target prism is obtained based on the position of the target pixel, so as to realize the automatic identification and aiming of the total station based on the actual position of the target prism.

[0038] Furthermore, the image processing module includes a camera, an image data transmission submodule, and an image processing submodule, wherein:

[0039] The camera is used to acquire raw image data, line signals, and frame signals;

[0040] The image data transmission submodule is used to preprocess the original image data, line signals and frame signals to obtain batch image data.

[0041] The image processing submodule is used to perform frame-level processing based on the original image data, line signals, and frame signals to obtain a whole frame pixel image to be identified.

[0042] Furthermore, the crosshair data acquisition module includes a crosshair position recognition submodule and a position data conversion submodule, wherein:

[0043] The crosshair position recognition submodule is used to obtain crosshair coordinate data based on the whole frame pixel image to be recognized;

[0044] The location data conversion submodule is used to obtain the starting search address based on the crosshair coordinate data.

[0045] This invention provides a prism recognition and aiming system. The system uses a data search module with a preset squared reading distance as the initial reading radius. Starting from the initial search address (the address corresponding to the crosshairs), the reading range is gradually expanded using a preset fluctuation increase method. At each step, the grayscale value of the corresponding pixel is read. The search range expands systematically from the nearest pixel to more distant pixels. Initially, the system determines the area containing the prism by using the grayscale value and a preset threshold; this is the prism spot closest to the crosshairs, achieving pixel-level positioning. This avoids the problems of existing spiral-type searches that may lead to detours and misjudgments of distant prisms, reducing the aiming angle error from graded to second-level. The image processing module acquires the raw image data, line signals, and frame signals output from the camera. Based on the timing reference of the line and frame signals, the raw data is preprocessed to obtain batches of image data. Then, frame-level processing integrates the batches of data into a complete frame pixel image to be recognized, covering the total station. All pixels in the field of view retain complete image data without any data loss, solving the shortcomings of existing technologies that can only transmit partial batch data in group transmission and cannot cover the entire frame of pixels. This provides blind-spot-free data for subsequent fluctuating search, avoiding prism omissions or positioning deviations caused by missing data, and ensuring recognition reliability from the source. The crosshair data acquisition module acquires and processes crosshair coordinate data, allowing the data search module to use the starting search address corresponding to the crosshair coordinate data as a benchmark to first determine whether there is a multi-prism at the starting pixel (if the grayscale value meets the standard, it is directly locked). If not, the reading radius is expanded step by step for determination, and each step uses the first time the standard is met as the locking basis. Unlike the existing technology's coarse positioning mode based on group range, this method uses the nearest neighbor priority and successive expansion judgment rule to directly lock the prism that is truly closest to the crosshair, avoiding misselection caused by a large group range containing multiple prisms, and effectively improving the accuracy of aiming.

[0046] The present invention provides a terminal device, including a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the above-described prism recognition aiming method.

[0047] The present invention provides a computer-readable storage medium, comprising: a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the above-described prism recognition and aiming method. Attached Figure Description

[0048] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1 This is a schematic diagram of a prism recognition and aiming method provided in this embodiment;

[0050] Figure 2 This is a schematic diagram of the implementation circuit of a prism recognition aiming method provided in this embodiment;

[0051] Figure 3 This is a schematic diagram illustrating the implementation process of a prism recognition and aiming method provided in this embodiment. Detailed Implementation

[0052] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0053] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains; the terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the application; the terms “comprising” and “having”, and any variations thereof, in the specification, claims, and foregoing description of the drawings are intended to cover non-exclusive inclusion.

[0054] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0055] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0056] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0057] In the description of the embodiments of this application, the term "multiple" refers to two or more (including two), similarly, "multiple sets" refers to two or more (including two sets), and "multiple pieces" refers to two or more (including two pieces).

[0058] In the description of the embodiments of this application, unless otherwise expressly specified and limited, technical terms such as "installation," "connection," "joining," and "fixing" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral part; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; they can refer to the internal communication of two components or the interaction between two components. For those skilled in the art, the specific meaning of the above terms in the embodiments of this application can be understood according to the specific circumstances.

[0059] Example 1:

[0060] This embodiment provides a method for identifying and aiming using a multi-prism. (See also...) Figure 1 ,include:

[0061] S1. Obtain the entire frame pixel image to be identified;

[0062] S2. Obtain the starting search address based on the whole-frame pixel image to be identified;

[0063] S3. Obtain the grayscale value of the starting pixel based on the whole frame pixel image to be identified and the starting search address;

[0064] S4. When it is determined that there is no prism based on the gray value of the starting pixel and the preset gray value threshold, the preset squared reading distance is used as the reading radius, and a fluctuating reading action is performed: the gray value of the first region pixel is obtained based on the reading radius, the whole frame pixel image to be identified and the starting search address.

[0065] S5. When it is determined that there is no prism based on the gray value of the first region pixel and the preset gray value threshold, the current reading radius is obtained according to the preset fluctuation increase method and the preset reading square distance, and the fluctuation reading action is repeatedly executed based on the current reading radius to obtain the gray value of the current region pixel until it is determined that there is a prism based on the gray value of the current region pixel and the preset gray value threshold, and the target pixel position is obtained based on the gray value of the current region pixel.

[0066] S6. Obtain the actual position of the target prism based on the position of the target pixel, so as to realize the automatic identification and aiming of the total station based on the actual position of the target prism.

[0067] This embodiment provides a multi-prism recognition and aiming method that uses a preset reading square distance and a preset fluctuation increase method to perform fluctuating readings of the entire frame of pixel image to be identified, gradually expanding the data reading range to achieve pixel-level positioning. It prioritizes locking onto position information that meets preset conditions, avoiding the misjudgment problem that may occur in existing spiral search methods due to detours. This directly compresses the aiming angle error from graded to second-level, meeting the requirements of high-precision measurement. It solves the problems of large data grouping ranges and inability to accurately locate the nearest prism in existing technologies, achieving high-precision multi-prism recognition and aiming.

[0068] Optionally, acquiring the entire frame of pixel image to be identified includes:

[0069] Acquire raw image data, line signals, and frame signals from the camera;

[0070] Preprocessing is performed on the original image data, line signals, and frame signals to obtain batch image data;

[0071] Frame-level processing is performed on the batched image data to obtain the whole frame pixel image to be identified.

[0072] In the above scheme, during specific implementation, this embodiment uses DDR to cache image frame data, i.e., the entire frame of pixel image to be identified. By acquiring the original image data, line signals, and frame signals, acquiring image data in batches, and performing frame-level processing, it ensures no data omissions and solves the technical problem in the prior art that group transmission of data can only transmit a certain range of batch data and cannot cover all pixels of the entire frame image.

[0073] Optionally, obtaining the starting search address based on the whole frame pixel image to be identified includes:

[0074] Obtain crosshair coordinate data based on the full-frame pixel image to be identified;

[0075] The starting search address is obtained based on the crosshair coordinate data.

[0076] In the specific implementation process, the crosshair coordinate data in the whole frame pixel image to be identified is used as the starting position of the search, which serves as the reference point for subsequent fluctuating reading and extraction. This allows for the gradual expansion of the pixel range reading, and in the subsequent search and reading process, the prism that is truly closest to the crosshair position is found, thus avoiding the technical problem of misjudging the nearest prism.

[0077] Optionally, after the step of obtaining the grayscale value of the starting pixel based on the whole frame pixel image to be identified and the starting search address, the following steps are included:

[0078] When it is determined that a multi-prism exists based on the gray value of the starting pixel and the preset gray value threshold, the starting search address is used as the target pixel position, so as to obtain the actual position of the target prism based on the target pixel position.

[0079] Optionally, after the step of determining that no prism exists based on the grayscale value of the starting pixel and the preset grayscale threshold, and performing a fluctuating reading action by using a preset squared reading distance as the reading radius: obtaining the grayscale value of the first region pixel based on the reading radius, the entire frame pixel image to be identified, and the starting search address, the method includes:

[0080] When it is determined that a multi-prism exists based on the grayscale value of the pixels in the first region and a preset grayscale threshold, the position of the target pixel is obtained based on the grayscale value of the pixels in the first region, and the actual position of the target prism is obtained based on the position of the target pixel.

[0081] In the specific implementation process, this embodiment starts from the DDR position corresponding to the crosshair when performing wave-like search. In this embodiment, the preset reading square distance is set to 1 pixel, and the preset wave increase method is to increase the reading radius by 1 pixel each time. That is, first read the gray value of the crosshair position, i.e., the starting position, and determine whether the number of white points is large according to the preset gray value threshold, and then determine whether there is a prism at the starting position. Then, first read the gray value of the first region of pixels with a radius squared of 1 pixel (preset reading square distance) from the crosshair position. If there is no position with a large number of white points, then take the position with a radius squared of 2 pixels from the crosshair, ..., until a position with a large number of white points is identified in the current region of pixels, i.e., the pixel gray value exceeds the preset gray value threshold. The position with a large number of white points is determined as the target pixel position, thereby determining the actual position of the closer prism, i.e., the actual position of the target prism. Then, the drive motor is turned to this position to perform ATR aiming.

[0082] This embodiment provides a prism recognition aiming method. It uses a preset squared reading distance as the initial reading radius, starts from the initial search address (the address corresponding to the crosshairs) as the reading starting point, and gradually expands the reading range according to a preset fluctuation increase method. At each step, it reads the grayscale value of the corresponding pixel in the area. The search range expands systematically from the nearest pixel to the farthest pixel. Initially, the region containing the prism is determined by the grayscale value and a preset threshold; this is the prism spot closest to the crosshairs. The position of the prism closest to the crosshairs is preferentially determined, with an error down to the second level, greatly improving the accuracy of prism recognition and achieving pixel-level positioning. It avoids the problem of existing spiral search methods that may lead to detours and misjudgments of distant prisms, compressing the aiming angle error from graded to second-level. During image processing, the original data is preprocessed based on the time-series reference of line and frame signals to obtain batch image data, which is then processed at the frame level to integrate the batch data. The image is combined into a complete frame of pixels to be identified, covering all pixels in the total station's field of view, preserving complete image data without any data loss. This solves the shortcomings of existing technologies that can only transmit partial batch data in groups and cannot cover the entire frame of pixels. It provides data without blind spots for subsequent fluctuating searches, avoiding prism omissions or positioning deviations caused by missing data, and ensuring the reliability of identification from the source. Based on the starting search address corresponding to the crosshair coordinate data, it first determines whether there is a multi-prism at the starting pixel point (if the gray value meets the standard, it is directly locked). If not, the reading radius is expanded step by step to determine, and each step uses the first time it meets the standard as the locking basis. Unlike the existing technology that roughly locates by group range, it directly locks the prism that is truly closest to the crosshair through the judgment rule of nearest neighbor priority and successive expansion, avoiding misselection caused by large group ranges containing multiple prisms, and effectively improving the accuracy of aiming. This embodiment improves the prism recognition algorithm by using the prism recognition aiming method. Under the recognition algorithm provided in this embodiment, the accuracy of prism recognition is greatly enhanced, and it can be controlled by parameters on the ZYNQ PS terminal, making it easy to operate.

[0083] Example 2:

[0084] This embodiment provides a prism recognition and aiming system for implementing the aforementioned prism recognition and aiming method, including an image processing module, a crosshair data acquisition module, and a data search module, wherein:

[0085] The image processing module is used to acquire a full-frame pixel image to be identified;

[0086] The crosshair data acquisition module is used to obtain the starting search address based on the whole frame pixel image to be identified;

[0087] The data search module is used for:

[0088] The grayscale value of the starting pixel is obtained based on the whole frame pixel image to be identified and the starting search address;

[0089] The starting search address is obtained based on the crosshair coordinate data;

[0090] The grayscale value of the starting pixel is obtained based on the whole frame pixel image to be identified and the starting search address;

[0091] When it is determined that there is no prism based on the gray value of the starting pixel and the preset gray value threshold, the preset squared reading distance is used as the reading radius, and a fluctuating reading action is performed: the gray value of the first region pixel is obtained based on the reading radius, the whole frame pixel image to be identified and the starting search address;

[0092] When it is determined that there is no prism based on the gray value of the first region pixel and the preset gray value threshold, the current reading radius is obtained according to the preset fluctuation increase method and the preset reading square distance, and the fluctuation reading action is repeatedly executed based on the current reading radius to obtain the gray value of the current region pixel until it is determined that there is a prism based on the gray value of the current region pixel and the preset gray value threshold, and the target pixel position is obtained based on the gray value of the current region pixel.

[0093] The actual position of the target prism is obtained based on the position of the target pixel, so as to realize the automatic identification and aiming of the total station based on the actual position of the target prism.

[0094] Optionally, the image processing module includes a camera, an image data transmission submodule, and an image processing submodule, wherein:

[0095] The camera is used to acquire raw image data, line signals, and frame signals;

[0096] The image data transmission submodule is used to preprocess the original image data, line signals and frame signals to obtain batch image data.

[0097] The image processing submodule is used to perform frame-level processing based on the original image data, line signals, and frame signals to obtain a whole frame pixel image to be identified.

[0098] Optionally, the crosshair data acquisition module includes a crosshair position recognition submodule and a position data conversion submodule, wherein:

[0099] The crosshair position recognition submodule is used to obtain crosshair coordinate data based on the whole frame pixel image to be recognized;

[0100] The location data conversion submodule is used to obtain the starting search address based on the crosshair coordinate data.

[0101] This embodiment provides a prism recognition and aiming system. The data search module uses a preset squared reading distance as the initial reading radius, starting from the initial search address (the address corresponding to the crosshairs) and gradually expanding the reading range according to a preset fluctuation increase method. At each step, the grayscale value of the corresponding pixel is read. The search range expands systematically from the nearest pixel to the more distant pixels. Initially, the region containing the prism is determined by the grayscale value and a preset threshold; this is the prism spot closest to the crosshairs, achieving pixel-level positioning. This avoids the problems of existing spiral-type searches that may lead to detours and misjudgments of distant prisms, reducing the aiming angle error from graded to second-level. During image processing, the image processing module acquires the raw image data, line signals, and frame signals output by the camera. Based on the timing reference of the line and frame signals, the raw data is preprocessed to obtain batch image data. Then, frame-level processing integrates the batch data into a complete frame pixel image to be identified, covering the total station. All pixels in the field of view retain complete image data without any data loss, solving the shortcomings of existing technologies that can only transmit partial batch data in group transmission and cannot cover the entire frame of pixels. This provides blind-spot-free data for subsequent fluctuating search, avoiding prism omissions or positioning deviations caused by missing data, and ensuring recognition reliability from the source. The crosshair data acquisition module acquires and processes crosshair coordinate data, allowing the data search module to use the starting search address corresponding to the crosshair coordinate data as a benchmark to first determine whether there is a multi-prism at the starting pixel (if the grayscale value meets the standard, it is directly locked). If not, the reading radius is expanded step by step for determination, and each step uses the first time the standard is met as the locking basis. Unlike the existing technology's coarse positioning mode based on group range, this method uses the nearest neighbor priority and successive expansion judgment rule to directly lock the prism that is truly closest to the crosshair, avoiding misselection caused by a large group range containing multiple prisms, and effectively improving the accuracy of aiming.

[0102] Example 3:

[0103] Based on the above-described method and system embodiments, one embodiment of the present invention provides an implementation circuit for a multi-prism recognition aiming method, such as... Figure 2 As shown, it includes: a camera, an image data processing module, DDR, a ZYNQ PS terminal, a data search module, an AXI bus connector, and a motor, wherein:

[0104] The first end of the ZYNQ PS terminal is electrically connected to the first end of the AXI bus connector for transmitting relevant parameters; the drive end of the ZYNQ PS terminal is electrically connected to the motor, the configuration end of the ZYNQ PS terminal is electrically connected to the camera, the write end of the ZYNQ PS terminal is electrically connected to the DDR, and the receiver end of the ZYNQ PS terminal is electrically connected to the data search module; the second end of the AXI bus connector is electrically connected to the first end of the image data processing module, and the second end of the image data processing module is electrically connected to the camera feedback end.

[0105] The ZYNQ PS terminal includes an IIC (Inter-Integrated Circuit) bus for configuring camera parameters.

[0106] The camera is responsible for capturing images and providing raw image data, line signals, and frame signals to the image data processing module in line and frame order.

[0107] The image data processing module is responsible for transmitting the interrupt signal collected from the camera to the ZYNQ PS terminal via the AXI bus connector. Based on this interrupt signal, the module initiates the prism's wave-like search process and transmits image data frame by frame according to the line and frame signals from the camera. This allows the ZYNQ PS terminal to receive the data signals collected from the camera by the image data processing module in batches, obtaining batch image data. The image data processing module includes an image data transmission submodule as described in the prism recognition aiming system above, used for preprocessing the original image data, line signals, and frame signals to obtain batch image data.

[0108] The ZYNQ PS end writes the entire frame pixel image to be identified into the DDR, obtains the crosshair coordinate data corresponding to the crosshair position and transmits it to the data search module; the ZYNQ PS end is also used to send motor drive signals, target angle position and other aiming data based on the actual position of the target prism fed back by the data search module, and control the motor to achieve ATR aiming.

[0109] The DDR is primarily responsible for caching batches of image data collected by the ZYNQ PS terminal as frame-by-frame data to obtain a complete frame pixel image to be identified, facilitating data processing. The DDR includes an image processing submodule and a crosshair position recognition submodule, as described in the prism recognition aiming system above: the image processing submodule performs frame-level processing based on the original image data, line signals, and frame signals to obtain the complete frame pixel image to be identified; the crosshair position recognition submodule obtains crosshair coordinate data based on the complete frame pixel image to be identified.

[0110] The data search module includes a position data conversion submodule in the multi-prism recognition aiming system described above. It is used to obtain the starting search address based on the crosshair coordinate data, that is, to convert the crosshair coordinate data into a DDR storage address (i.e., the starting search address). Then, it performs a fluctuating search reading starting from this starting search address, gradually expanding the search range until a position with a gray value greater than a preset gray value threshold is found. The position is then transmitted to the ZYNQ PS terminal, which in turn controls the motor to rotate to the specified position to achieve aiming.

[0111] When using the implementation circuit of this prism recognition aiming method for wave-like search:

[0112] First, raw image data is acquired through the camera. The acquired raw image data signal, as well as the line signal and frame signal, are sent to the image data processing module so that the PS end can process the RGB data of the image frame by frame. Then, the PS end input is buffered frame by frame through DDR. The data search module starts from the DDR address corresponding to the crosshair and determines whether the gray value at that location is greater than the threshold. If not, the reading square distance is increased by 1, and the reading square distance is increased by 2, and the reading square distance is increased by 3, and so on, until the location of the pixel with a gray value greater than the threshold is read. This location is recorded as the target pixel location and converted into the corresponding actual location, i.e., the actual location of the target prism.

[0113] The process of converting the target pixel position into the actual target prism position includes: adding the row number * 960 (960 being the number of data points corresponding to the row signal) to the base address of the data read from the target pixel position, and then adding the corresponding column number. The sum obtained is the actual target prism position. When the ZYNQ PS end receives the position of the crosshair, it calculates its position in the DDR (DDR position = 960 * row number + column number + DDR base address) based on the row and column of that position. The data search module starts from this address and gradually expands the search radius until it finds a position with a gray value greater than the threshold. This position must be the light spot corresponding to the prism closer to the crosshair position. This position is transmitted to the ZYNQ PS end, which controls the motor to rotate to the corresponding position for ATR aiming, thereby completing the accurate identification of the multi-prism with an error controlled within the second level.

[0114] Example 4:

[0115] Based on the above embodiment of the prism recognition and aiming method, another embodiment of the present invention provides an implementation flow of the prism recognition and aiming method, as follows: Figure 3 As shown, it includes:

[0116] S31. Obtain the entire frame pixel image to be identified and the starting search address;

[0117] S32. Obtain the grayscale value of the starting pixel based on the whole frame pixel image to be identified and the starting search address;

[0118] S33. Determine whether a prism exists based on the gray value of the starting pixel and the preset gray threshold. If it exists, proceed to step S37; if it does not exist, proceed to step S34.

[0119] S34. Using the preset squared reading distance as the reading radius r, perform a fluctuating reading action: obtain the grayscale value of the pixels in the reading area based on the reading radius, the whole frame pixel image to be identified, and the starting search address;

[0120] S35. Determine whether a prism exists based on the grayscale value of the pixels in the reading area and the preset grayscale threshold. If it exists, proceed to step S38; if it does not exist, proceed to step S36.

[0121] S36, r = r + 1: Obtain the current reading radius according to the preset fluctuation increase method and the preset reading square distance, that is, assign the value r = r + 1, and return to step S34;

[0122] S37. Obtain the target pixel position based on the grayscale value searched from the initial image;

[0123] S38. Obtain the position of the target pixel based on the grayscale value of the pixels in the reading area;

[0124] S39. Obtain the actual position of the target prism based on the position of the target pixel, so as to realize the automatic identification and aiming of the total station based on the actual position of the target prism.

[0125] The fluctuating reading action is repeatedly executed based on the current reading radius to obtain the grayscale value of the current region's pixels until a prism is determined to exist based on the grayscale value of the current region's pixels and a preset grayscale threshold. Then, the position of the target pixel is obtained based on the grayscale value of the current region's pixels.

[0126] Example 5:

[0127] Based on the above-described embodiment of the prism recognition and aiming method, another embodiment of the present invention provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements a prism recognition and aiming method according to any embodiment of the present invention.

[0128] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete the present invention. The one or more modules may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0129] The terminal device may be a desktop computer, laptop, handheld computer, or cloud server, etc. The terminal device may include, but is not limited to, a processor and a memory.

[0130] The processor can be a Central Processing Unit (CPU), or other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor can be a microprocessor or any conventional processor. The processor is the control center of the terminal device, connecting all parts of the terminal device via various interfaces and lines.

[0131] Example 6:

[0132] Based on the above-described method embodiments, another embodiment of the present invention provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute a prism recognition and aiming method as described in any of the above-described method embodiments of the present invention.

[0133] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the above embodiments of the present invention can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0134] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A method for identifying and aiming using a prism, characterized in that, include: Obtain the entire pixel image of the image to be identified; The starting search address is obtained based on the whole-frame pixel image to be identified; The grayscale value of the starting pixel is obtained based on the whole frame pixel image to be identified and the starting search address; When it is determined that there is no prism based on the gray value of the starting pixel and the preset gray value threshold, the preset squared reading distance is used as the reading radius, and a fluctuating reading action is performed: the gray value of the first region pixel is obtained based on the reading radius, the whole frame pixel image to be identified and the starting search address; When it is determined that there is no prism based on the gray value of the first region pixel and the preset gray value threshold, the current reading radius is obtained according to the preset fluctuation increase method and the preset reading square distance, and the fluctuation reading action is repeatedly executed based on the current reading radius to obtain the gray value of the current region pixel until it is determined that there is a prism based on the gray value of the current region pixel and the preset gray value threshold, and the target pixel position is obtained based on the gray value of the current region pixel. The actual position of the target prism is obtained based on the position of the target pixel, so as to realize the automatic identification and aiming of the total station based on the actual position of the target prism.

2. The method for identifying and aiming using a prism as described in claim 1, characterized in that, The process of acquiring the entire frame pixel image to be identified includes: Acquire raw image data, line signals, and frame signals from the camera; Preprocessing is performed on the original image data, line signals, and frame signals to obtain batch image data; Frame-level processing is performed on the batched image data to obtain the whole frame pixel image to be identified.

3. The method for identifying and aiming with a multi-prism as described in claim 1, characterized in that, The step of obtaining the starting search address based on the whole frame pixel image to be identified includes: Obtain crosshair coordinate data based on the full-frame pixel image to be identified; The starting search address is obtained based on the crosshair coordinate data.

4. The method for identifying and aiming with a prism as described in claim 1, characterized in that, After the step of obtaining the grayscale value of the starting pixel based on the whole frame pixel image to be identified and the starting search address, the following is included: When it is determined that a multi-prism exists based on the gray value of the starting pixel and the preset gray value threshold, the starting search address is used as the target pixel position, so as to obtain the actual position of the target prism based on the target pixel position.

5. The method for identifying and aiming with a prism as described in claim 1, characterized in that, When it is determined that no prism exists based on the grayscale value of the starting pixel and the preset grayscale threshold, a fluctuating reading action is performed using the preset squared reading distance as the reading radius: after the step of obtaining the grayscale value of the first region pixel based on the reading radius, the entire frame pixel image to be identified, and the starting search address, the following steps are included: When it is determined that a multi-prism exists based on the grayscale value of the pixels in the first region and a preset grayscale threshold, the position of the target pixel is obtained based on the grayscale value of the pixels in the first region, and the actual position of the target prism is obtained based on the position of the target pixel.

6. A prism recognition and aiming system, characterized in that, A method for implementing a multi-prism recognition and aiming as described in any one of claims 1 to 5, comprising an image processing module, a crosshair data acquisition module, and a data search module, wherein: The image processing module is used to acquire a full-frame pixel image to be identified; The crosshair data acquisition module is used to obtain the starting search address based on the whole frame pixel image to be identified; The data search module is used for: The grayscale value of the starting pixel is obtained based on the whole frame pixel image to be identified and the starting search address; The starting search address is obtained based on the crosshair coordinate data; The grayscale value of the starting pixel is obtained based on the whole frame pixel image to be identified and the starting search address; When it is determined that there is no prism based on the gray value of the starting pixel and the preset gray value threshold, the preset squared reading distance is used as the reading radius, and a fluctuating reading action is performed: the gray value of the first region pixel is obtained based on the reading radius, the whole frame pixel image to be identified and the starting search address; When it is determined that there is no prism based on the gray value of the first region pixel and the preset gray value threshold, the current reading radius is obtained according to the preset fluctuation increase method and the preset reading square distance, and the fluctuation reading action is repeatedly executed based on the current reading radius to obtain the gray value of the current region pixel until it is determined that there is a prism based on the gray value of the current region pixel and the preset gray value threshold, and the target pixel position is obtained based on the gray value of the current region pixel. The actual position of the target prism is obtained based on the position of the target pixel, so as to realize the automatic identification and aiming of the total station based on the actual position of the target prism.

7. The prism recognition and aiming system as described in claim 6, characterized in that, The image processing module includes a camera, an image data transmission submodule, and an image processing submodule, wherein: The camera is used to acquire raw image data, line signals, and frame signals; The image data transmission submodule is used to preprocess the original image data, line signals and frame signals to obtain batch image data. The image processing submodule is used to perform frame-level processing based on the original image data, line signals, and frame signals to obtain a whole frame pixel image to be identified.

8. The prism recognition and aiming system as described in claim 6, characterized in that, The crosshair data acquisition module includes a crosshair position recognition submodule and a position data conversion submodule, wherein: The crosshair position recognition submodule is used to obtain crosshair coordinate data based on the whole frame pixel image to be recognized; The location data conversion submodule is used to obtain the starting search address based on the crosshair coordinate data.

9. A terminal device, characterized in that, The device includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements a prism recognition aiming method as described in any one of claims 1-5.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform a prism recognition aiming method as described in any one of claims 1-5.

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