Crane installation auxiliary early warning method and system based on image analysis and application

Abstract

The invention discloses a crane installation auxiliary early warning method and system based on image analysis and application, and the crane installation auxiliary early warning method based on image analysis comprises the following steps: 1) setting a plurality of target spots as feature points on a target object needing to be installed on a crane; step 2), acquiring image data of the target object at different visual angles through an image acquisition unit; step 3), matching and splicing the images according to the feature point information in the images, and acquiring attitude information of the target object according to the spliced images and the feature point information; and 4) when the obtained attitude information of the target object exceeds a preset early warning threshold value, carrying out corresponding early warning prompt, and giving a corresponding adjustment strategy according to the current attitude information of the target object. Real-time monitoring and auxiliary early warning can be carried out on the target object attitude information in the installation process of the crane auxiliary high-rise structure, the safety of the crane installation process is ensured, and the method is suitable for further application and popularization.

Description

Technical Field

[0001] The present invention relates to the technical field of crane installation, and in particular to an image analysis-based crane installation auxiliary early warning method, system and application. Background Art

[0002] Tower crane is the most commonly used lifting equipment on construction sites. It is used to lift construction materials such as steel bars, wooden planks, scaffolding pipes, etc. It is an indispensable equipment for construction. In the installation of tower crane machinery, the hydraulic lifting tower is a key auxiliary high-rise structure, and its structural safety plays a decisive role in the safety of the entire construction. At present, there are few safety monitoring methods for the installation of auxiliary high-rise structures, and it is impossible to detect problems in the installation of high-rise structures in a timely manner.

[0003] Based on this, the purpose of the present invention is to provide a crane installation auxiliary warning method and system based on image analysis, which can play an effective auxiliary warning role in the installation of crane-assisted high-rise structures, can realize real-time monitoring of the installation process, and ensure the safety of life and property of inspection personnel and on-site construction personnel. Summary of the invention

[0004] To solve the above problems, the present invention provides a crane installation auxiliary warning method, system and application based on image analysis, which can perform real-time monitoring and auxiliary warning of the posture information of the target object during the crane-assisted high-rise structure installation process, avoid the risk of major accidents caused by improper installation of high-rise structures, and ensure the safety of the crane installation process.

[0005] In order to achieve the above technical objectives, the technical solution adopted by the present invention is: a crane installation auxiliary warning method based on image analysis, comprising:

[0006] Step 1): Set a number of target points on the target object where the crane needs to be installed, which will be used as feature points for subsequent image stitching;

[0007] Step 2): acquiring image data of the target object at different viewing angles through an image acquisition unit;

[0008] Step 3): Match and splice each image based on the feature point information in the image, and then obtain the posture information of the target object through the spliced ​​image and feature point information;

[0009] Step 4): When the acquired posture information of the target object exceeds the preset warning threshold, a corresponding warning prompt is given, and a corresponding adjustment strategy is given according to the current posture information of the target object.

[0010] As a possible implementation manner, further, in step 1), the number of target points is not less than two, and all target points are located on the same straight line, which is parallel to the axis of the target object or the length direction of the target object.

[0011] As a possible implementation, further, the image acquisition unit in step 2) is composed of n image acquisition cameras of the same model, where n≥2, and each image acquisition camera is calibrated by Zhang Zhengyou calibration method to calculate the intrinsic parameters, extrinsic parameters and distortion coefficient of each image acquisition camera;

[0012] After completing the calibration of each image acquisition camera, the image acquisition unit is calibrated and the calibration parameters of the image acquisition unit are obtained.

[0013] As a possible implementation, further, the calibration parameters of the image acquisition unit are obtained by sequentially numbering n image acquisition cameras and converting them using the calibration parameters of the n image acquisition cameras;

[0014] The calibration parameters of the image acquisition unit include a rotation matrix and a translation matrix;

[0015] The expression of the rotation matrix R is as follows:

[0016]

[0017] In the above formula, R i represents the rotation matrix of the i-th camera among n cameras, and T represents the transpose of the matrix;

[0018] Among them, the expression of the translation matrix t is as follows:

[0019]

[0020] In the above formula, t n Represents the translation matrix of the nth camera among n cameras, t n-i Represents the translation matrix of the ni-th camera among n cameras; R m Represents the rotation matrix corresponding to the image acquisition unit composed of the first m cameras among the n cameras, where m≤n;

[0021] Where R m The expression is as follows:

[0022]

[0023] In the above formula, R c represents the rotation matrix of the cth camera among m cameras, and T represents the transpose of the matrix.

[0024] As a possible implementation, further, step 3): matching and splicing each image according to the feature point information in the image, and then obtaining the posture information of the target object according to the spliced ​​image and the feature point information, includes the following steps:

[0025] Step 3.1): using the camera internal parameters and distortion coefficients obtained in step 2), correcting each image acquired by the image acquisition unit;

[0026] Step 3.2): The target points obtained in each image are used as feature points to match and stitch the corrected images. Then, the point coordinates (Xcn, Ycn, Zcn) of each feature point in the stitched image relative to the image acquisition unit coordinate system are converted to the point coordinates (Xwn, Ywn, Zwn) in the world coordinate system through the calibration parameters of the acquired image acquisition unit. The specific expression is as follows:

[0027]

[0028] Among them, Xwn, Ywn, and Zwn represent the point coordinates of the nth feature point in the world coordinate system; Xcn, Ycn, and Zcn represent the point coordinates of the nth feature point in the image acquisition unit coordinate system; R -1 represents the inverse matrix of the rotation matrix R, and t represents the translation matrix;

[0029] Step 3.3): Obtain the posture information of the target object by combining the point coordinates of each feature point in the spliced ​​image in the world coordinate system and the contour information of the target object in the spliced ​​image.

[0030] As a possible implementation manner, further, the posture information includes inclination data of the target object and structural dimension data of the target object;

[0031] In step 3.3), the posture information of the target object is obtained by the point coordinates of each feature point in the spliced ​​image in the world coordinate system and the contour information of the target object in the spliced ​​image, which specifically includes the following steps:

[0032] Perform linear fitting on the point coordinates of each feature point in the spliced ​​image in the world coordinate system, and obtain the inclination data of the fitting line, and use the inclination data as the inclination data of the target object;

[0033] The stitched image is gray-scaled, and the contour information of the target object in the stitched image is obtained by using the Prewitt edge detection operator. Then, the structural dimension data of the target object is measured from the contour information of the target object.

[0034] As a possible implementation, further, in step 4), when the acquired posture information of the target object exceeds a preset warning threshold, a corresponding warning prompt is performed, as follows:

[0035] When the inclination data in the target object's posture information exceeds the corresponding preset warning threshold, a warning prompt about the inclination information is executed;

[0036] When the structural dimension data in the target object posture information exceeds the corresponding preset warning threshold, a warning prompt about the structural dimension is executed.

[0037] As a possible implementation, further, in step 4), a corresponding adjustment strategy is given according to the current posture information of the target object, and the specific steps are as follows:

[0038] When providing an early warning about the inclination information, provide an adjustment method and value for the inclination of the target object according to the current inclination data of the target object, so that the inclination data of the target object meets the angle requirement;

[0039] When an early warning prompt is given regarding the construction dimension, an adjustment method and a value regarding the target object dimension are provided according to the current construction dimension data of the target object, so that the construction dimension data of the target object meets the dimension requirement.

[0040] Based on the above, the present invention provides a crane installation auxiliary warning system based on image analysis, applying the above-mentioned crane installation auxiliary warning method based on image analysis, the system comprises:

[0041] An image acquisition unit, used to acquire image data of a target object from different viewing angles;

[0042] A calibration parameter calculation unit, used to calculate the calibration parameters of the image acquisition unit according to the calibration parameters of each image acquisition camera;

[0043] An image correction unit, used to correct each image acquired by the image acquisition unit according to the camera internal parameters and distortion coefficients;

[0044] An image stitching unit, used for matching and stitching the corrected images according to feature point information in the images;

[0045] A coordinate conversion unit, used to convert the point coordinates of each feature point in the spliced ​​image relative to the image acquisition unit coordinate system into the point coordinates in the world coordinate system by using the calibration parameters of the image acquisition unit;

[0046] The inclination data acquisition unit is used to perform linear fitting on the point coordinates of each feature point in the spliced ​​image in the world coordinate system and obtain the inclination data of the fitting line;

[0047] A structural dimension acquisition unit is used to perform grayscale processing on the spliced ​​image, and obtain contour information of the target object in the spliced ​​image through the Prewitt edge detection operator, and then measure and obtain structural dimension data of the target object from the contour information of the target object;

[0048] A logic unit is used to determine whether the inclination data and structural dimension data of the target object exceed the corresponding preset warning threshold. If so, the warning unit is controlled to execute the corresponding warning prompt according to the data type exceeding the preset warning threshold, and a corresponding adjustment strategy is given at the same time;

[0049] The early warning unit is used to perform corresponding early warning operations according to the judgment results output by the logic unit.

[0050] The present invention also provides a computer-readable storage medium, in which at least one instruction, at least one program, code set or instruction set is stored. The at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the above-mentioned crane installation auxiliary warning method based on image analysis.

[0051] By adopting the above technical solution, the present invention has the following beneficial effects compared with the prior art:

[0052] The crane installation auxiliary warning method and system based on image analysis provided by the present invention can realize real-time monitoring and auxiliary warning of the target object's posture information during the crane-assisted high-rise structure installation process, and give corresponding adjustment strategies according to the current posture information of the target object, which helps operators to promptly discover the safety risks in the auxiliary high-rise structure installation process and ensure the safety of the crane installation process, and is suitable for further promotion and application. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 It is a schematic diagram of a brief implementation process of the crane installation auxiliary warning method based on image analysis of the present invention;

[0055] Figure 2 A schematic diagram of a system implementation structure of a crane installation auxiliary warning method based on image analysis corresponding to the solution of the present invention;

[0056] Figure 3 A schematic diagram of a computer-readable storage medium provided for an embodiment of the present invention. DETAILED DESCRIPTION

[0057] The present invention will be further described in detail below in conjunction with the accompanying drawings and examples. It is particularly noted that the following examples are only used to illustrate the present invention, but are not intended to limit the scope of the present invention. Similarly, the following examples are only partial embodiments of the present invention rather than all embodiments, and all other embodiments obtained by those of ordinary skill in the art without creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] Refer to the attached Figure 1 As shown, this embodiment provides a crane installation auxiliary warning method based on image analysis, including:

[0060] Step 1): Set a number of target points on the target object on which the crane is to be installed, as subsequent feature points for image stitching; the number of target points is not less than two, and the target points are on the same straight line, which is parallel to the axis of the target object or the length direction of the target object.

[0061] Step 2): Acquire image data of the target object at different viewing angles through an image acquisition unit; specifically, in this embodiment, the image acquisition unit is composed of n (where n≥2) image acquisition cameras of the same model, and each image acquisition camera is calibrated by the Zhang Zhengyou calibration method to calculate the intrinsic parameters, extrinsic parameters and distortion coefficient of each image acquisition camera; after completing the calibration of each image acquisition camera, calibrate the image acquisition unit and obtain the calibration parameters of the image acquisition unit.

[0062] The calibration parameters of the image acquisition unit are obtained by sequentially numbering n image acquisition cameras and converting the calibration parameters of the n image acquisition cameras;

[0063] The calibration parameters of the image acquisition unit include a rotation matrix and a translation matrix;

[0064] The expression of the rotation matrix R is as follows:

[0065]

[0066] In the above formula, R i represents the rotation matrix of the ith camera among n cameras, and T represents the transpose of the matrix. In formula (1), T i+1 represents T raised to the power of i+1. For example, when n is an even number and i=2, When n is an odd number and i=2,

[0067] Among them, the expression of the translation matrix t is as follows:

[0068]

[0069] In the above formula, t m Represents the translation matrix of the nth camera among n cameras, t n-i Represents the translation matrix of the ni-th camera among n cameras; R m Represents the rotation matrix corresponding to the image acquisition unit composed of the first m cameras among the n cameras, where m≤n.

[0070] Where R m The expression is as follows:

[0071]

[0072] In the above formula, R c represents the rotation matrix of the cth camera among m cameras, and T represents the transpose of the matrix.

[0073] Step 3): Match and splice each image based on the feature point information in the image, and then obtain the posture information of the target object through the spliced ​​image and the feature point information, which specifically includes the following steps:

[0074] Step 3.1): Use the camera internal parameters and distortion coefficients obtained in step 2) to correct each image acquired by the image acquisition unit to ensure the accuracy of the image data.

[0075] Step 3.2): The target points obtained in each image are used as feature points to match and stitch the corrected images. Then, the point coordinates (Xcn, Ycn, Zcn) of each feature point in the stitched image relative to the image acquisition unit coordinate system are converted to the point coordinates (Xwn, Ywn, Zwn) in the world coordinate system through the calibration parameters of the acquired image acquisition unit. The specific expression is as follows:

[0076]

[0077] Among them, Xwn, Ywn, and Zwn represent the point coordinates of the nth feature point in the world coordinate system; Xcn, Ycn, and Zcn represent the point coordinates of the nth feature point in the image acquisition unit coordinate system; R -1 represents the inverse matrix of the rotation matrix R, and t represents the translation matrix. Through the calibration parameters of the image acquisition unit, the point coordinates of each feature point in the spliced ​​image relative to the image acquisition unit coordinate system are converted into point coordinates in the world coordinate system, effectively improving the consistency and accuracy of the feature point coordinates in the world coordinate system.

[0078] Step 3.3): Obtain the posture information of the target object (wherein the posture information includes the inclination data of the target object and the structural dimension data of the target object) by using the point coordinates of each feature point in the spliced ​​image in the world coordinate system and the contour information of the target object in the spliced ​​image, which specifically includes the following steps:

[0079] Perform linear fitting on the point coordinates of each feature point in the spliced ​​image in the world coordinate system, and obtain the inclination data of the fitting line, and use the inclination data as the inclination data of the target object;

[0080] The stitched image is gray-scaled, and the contour information of the target object in the stitched image is obtained by using the Prewitt edge detection operator. Then, the structural dimension data of the target object is measured from the contour information of the target object.

[0081] Step 4): When the acquired posture information of the target object exceeds the preset warning threshold, a corresponding warning prompt is given, and a corresponding adjustment strategy is given according to the current posture information of the target object, which specifically includes the following steps:

[0082] When the inclination data in the target object's posture information exceeds the corresponding preset warning threshold, an early warning prompt about the inclination information is executed (i.e., a warning prompt in the form of text, image, voice, etc. about the inclination data exceeding the threshold is issued to prompt the operator that the inclination data of the target object exceeds the preset warning threshold); when an early warning prompt about the inclination information is performed, an adjustment method and value for the inclination of the target object are provided according to the current inclination data of the target object, so that the inclination data of the target object meets the angle requirements. For example: when the detected inclination angle is greater than the preset warning threshold, a prompt in the form of text, image, voice, etc. is issued to prompt the operator to reduce the inclination, and a specific inclination adjustment value is given so that the inclination data of the target object meets the preset angle requirements.

[0083] When the structural dimension data in the target object's posture information exceeds the corresponding preset warning threshold, an early warning prompt about the structural dimension is executed (i.e., a warning prompt in the form of text, image, voice, etc. about the structural dimension data exceeding the threshold is issued to prompt the operator that the structural dimension data of the target object exceeds the preset warning threshold); when an early warning prompt about the structural dimension is performed, an adjustment method and value about the target object size are provided according to the current structural dimension data of the target object, so that the structural dimension data of the target object meets the size requirements. For example: when the detected structural dimension is greater than the preset warning threshold, a prompt in the form of text, image, voice, etc. is issued to prompt the operator to reduce the structural dimension, and a specific structural dimension adjustment value is given to make the structural dimension data of the target object meet the preset size requirements.

[0084] Refer to the attached Figure 3As shown, an embodiment of the present invention further provides a computer-readable storage medium, in which at least one instruction, at least one program, code set or instruction set is stored, and the at least one instruction, at least one program, code set or instruction set is loaded and executed by a processor to implement the above-mentioned crane installation auxiliary warning method based on image analysis.

[0085] In addition, each functional unit in this embodiment can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above integrated unit can be implemented in the form of hardware or software functional unit.

[0086] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including a number of instructions for a computer device (which can be a personal computer, server, or network device, etc.) or a processor (processor) to perform all or part of the steps of the method of the embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0087] Example 2

[0088] See attached Figure 2 As shown, an embodiment of the present invention provides a crane installation auxiliary warning system based on image analysis, which applies the crane installation auxiliary warning method based on image analysis in the above embodiment 1, and the system includes:

[0089] An image acquisition unit, used to acquire image data of a target object from different viewing angles;

[0090] A calibration parameter calculation unit, used to calculate the calibration parameters of the image acquisition unit according to the calibration parameters of each image acquisition camera;

[0091] An image correction unit, used to correct each image acquired by the image acquisition unit according to the camera internal parameters and distortion coefficients;

[0092] An image stitching unit, used for matching and stitching the corrected images according to feature point information in the images;

[0093] A coordinate conversion unit, used to convert the point coordinates of each feature point in the spliced ​​image relative to the image acquisition unit coordinate system into the point coordinates in the world coordinate system by using the calibration parameters of the image acquisition unit;

[0094] The inclination data acquisition unit is used to perform linear fitting on the point coordinates of each feature point in the spliced ​​image in the world coordinate system and obtain the inclination data of the fitting line;

[0095] A structural dimension acquisition unit is used to perform grayscale processing on the spliced ​​image, and obtain contour information of the target object in the spliced ​​image through the Prewitt edge detection operator, and then measure and obtain structural dimension data of the target object from the contour information of the target object;

[0096] A logic unit is used to determine whether the inclination data and structural dimension data of the target object exceed the corresponding preset warning threshold. If so, the warning unit is controlled to execute the corresponding warning prompt according to the data type exceeding the preset warning threshold, and a corresponding adjustment strategy is given at the same time;

[0097] The early warning unit is used to perform corresponding early warning operations according to the judgment results output by the logic unit.

[0098] The above descriptions are only some embodiments of the present invention, and are not intended to limit the protection scope of the present invention. Any equivalent device or equivalent process transformation made using the contents of the present invention specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present invention.

Claims

1. A crane installation auxiliary warning method based on image analysis, characterized in that: include: Step 1): Set a number of target points on the target object where the crane needs to be installed, which will be used as feature points for subsequent image stitching; Step 2): acquiring image data of the target object at different viewing angles through an image acquisition unit; Step 3): Match and splice each image based on the feature point information in the image, and then obtain the posture information of the target object through the spliced ​​image and feature point information; Step 4): When the acquired posture information of the target object exceeds the preset warning threshold, a corresponding warning prompt is given, and a corresponding adjustment strategy is given according to the current posture information of the target object.

2. The crane installation auxiliary warning method based on image analysis according to claim 1 is characterized in that: The number of target points in step 1) is not less than two, and all target points are located on the same straight line, which is parallel to the axis of the target object or the length direction of the target object.

3. The crane installation auxiliary warning method based on image analysis according to claim 2 is characterized in that: The image acquisition unit in step 2) is composed of n image acquisition cameras of the same model, where n≥2, and each image acquisition camera is calibrated by Zhang Zhengyou calibration method to calculate the intrinsic parameters, extrinsic parameters and distortion coefficient of each image acquisition camera; After completing the calibration of each image acquisition camera, the image acquisition unit is calibrated and the calibration parameters of the image acquisition unit are obtained.

4. The crane installation auxiliary warning method based on image analysis according to claim 3 is characterized in that: The calibration parameters of the image acquisition unit are obtained by sequentially numbering n image acquisition cameras and converting them using the calibration parameters of the n image acquisition cameras; The calibration parameters of the image acquisition unit include a rotation matrix and a translation matrix; The expression of the rotation matrix R is as follows: In the above formula, R i represents the rotation matrix of the i-th camera among n cameras, and T represents the transpose of the matrix; Among them, the expression of the translation matrix t is as follows: In the above formula, t n Represents the translation matrix of the nth camera among n cameras, t n-i Represents the translation matrix of the ni-th camera among n cameras; R m Represents the rotation matrix corresponding to the image acquisition unit composed of the first m cameras among the n cameras, where m≤n; Where R m The expression is as follows: In the above formula, R c represents the rotation matrix of the cth camera among m cameras, and T represents the transpose of the matrix.

5. The crane installation auxiliary warning method based on image analysis according to claim 4 is characterized in that: Step 3): Match and splice each image based on the feature point information in the image, and then obtain the posture information of the target object through the spliced ​​image and the feature point information, including the following steps: Step 3.1): using the camera internal parameters and distortion coefficients obtained in step 2), correcting each image acquired by the image acquisition unit; Step 3.2): The target points obtained in each image are used as feature points to match and stitch the corrected images. Then, the point coordinates (Xcn, Ycn, Zcn) of each feature point in the stitched image relative to the image acquisition unit coordinate system are converted to the point coordinates (Xwn, Ywn, Zwn) in the world coordinate system through the calibration parameters of the acquired image acquisition unit. The specific expression is as follows: Among them, Xwn, Ywn, and Zwn represent the point coordinates of the nth feature point in the world coordinate system; Xcn, Ycn, and Zcn represent the point coordinates of the nth feature point in the image acquisition unit coordinate system; R -1 represents the inverse matrix of the rotation matrix R, and t represents the translation matrix; Step 3.3): Obtain the posture information of the target object by combining the point coordinates of each feature point in the spliced ​​image in the world coordinate system and the contour information of the target object in the spliced ​​image.

6. The crane installation auxiliary warning method based on image analysis according to claim 5 is characterized in that: The posture information includes the inclination data of the target object and the structural dimension data of the target object; In step 3.3), the posture information of the target object is obtained by the point coordinates of each feature point in the spliced ​​image in the world coordinate system and the contour information of the target object in the spliced ​​image, which specifically includes the following steps: Perform linear fitting on the point coordinates of each feature point in the spliced ​​image in the world coordinate system, and obtain the inclination data of the fitting line, and use the inclination data as the inclination data of the target object; The stitched image is gray-scaled, and the contour information of the target object in the stitched image is obtained by using the Prewitt edge detection operator. Then, the structural dimension data of the target object is measured from the contour information of the target object.

7. The crane installation auxiliary warning method based on image analysis according to claim 6 is characterized in that: In step 4), when the acquired posture information of the target object exceeds the preset warning threshold, a corresponding warning prompt is given, as follows: When the inclination data in the target object's posture information exceeds the corresponding preset warning threshold, a warning prompt about the inclination information is executed; When the structural dimension data in the target object posture information exceeds the corresponding preset warning threshold, a warning prompt about the structural dimension is executed.

8. The crane installation auxiliary warning method based on image analysis according to claim 7 is characterized in that: In step 4), a corresponding adjustment strategy is given according to the current posture information of the target object. The specific steps are as follows: When providing an early warning about the inclination information, provide an adjustment method and value for the inclination of the target object according to the current inclination data of the target object, so that the inclination data of the target object meets the angle requirement; When an early warning prompt is given regarding the construction dimension, an adjustment method and a value regarding the target object dimension are provided according to the current construction dimension data of the target object, so that the construction dimension data of the target object meets the dimension requirement.

9. A crane installation auxiliary warning system based on image analysis, characterized in that: The crane installation auxiliary warning method based on image analysis as claimed in any one of claims 1 to 8 is applied, and the system comprises: An image acquisition unit, used to acquire image data of a target object at different viewing angles; A calibration parameter calculation unit, used to calculate the calibration parameters of the image acquisition unit according to the calibration parameters of each image acquisition camera; An image correction unit, used to correct each image acquired by the image acquisition unit according to the camera internal parameters and distortion coefficients; An image stitching unit, used for matching and stitching the corrected images according to feature point information in the images; A coordinate conversion unit, used to convert the point coordinates of each feature point in the spliced ​​image relative to the image acquisition unit coordinate system into the point coordinates in the world coordinate system by using the calibration parameters of the image acquisition unit; The inclination data acquisition unit is used to perform linear fitting on the point coordinates of each feature point in the spliced ​​image in the world coordinate system and acquire the inclination data of the fitting line; A structural dimension acquisition unit is used to perform grayscale processing on the spliced ​​image, and obtain the contour information of the target object in the spliced ​​image through the Prewitt edge detection operator, and then measure and obtain the structural dimension data of the target object from the contour information of the target object; A logic unit is used to determine whether the inclination data and structural dimension data of the target object exceed the corresponding preset warning threshold. If so, the warning unit is controlled to execute the corresponding warning prompt according to the data type exceeding the preset warning threshold, and a corresponding adjustment strategy is given at the same time; The early warning unit is used to perform corresponding early warning operations according to the judgment results output by the logic unit.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the crane installation auxiliary warning method based on image analysis as described in any one of claims 1 to 8 is implemented.

Images