Monocular vision tower span measuring method based on RTK correction

Abstract

The invention relates to a monocular vision tower span measurement method based on RTK correction, and belongs to the technical field of space distance measurement. Comprising the following steps: acquiring ambient light intensity and quality in real time based on an environment where a power transmission and distribution tower is located, and obtaining ambient light adaptation parameters by analyzing ambient light data and adjusting ISO sensitivity and shutter speed of a camera. According to the method, real-time environment light data analysis and camera parameter automatic adjustment are introduced in the monocular vision tower span measurement process, the image quality is effectively improved, tower recognition is more accurate, background interference is reduced, the geographic position and the shooting angle of a shooting point are corrected through the RTK technology, and the measurement accuracy is improved. And perspective transformation correction is carried out on the image in cooperation with a perspective projection theory, and the accuracy of distance measurement data is improved to centimeter level.

Description

Technical Field

[0001] The invention belongs to the technical field of space distance measurement, and in particular relates to a monocular vision tower distance measurement method based on RTK correction. Background Art

[0002] In the acceptance process of overhead power transmission and distribution line projects, span measurement is a key step. Existing span measurements usually use manual on-site surveys with GPS positioning equipment, which is inefficient and unsafe, and the test accuracy depends on personal skills and sense of responsibility. Using a drone equipped with an RTK positioning module to quickly scan the line remotely, the power transmission and distribution line can be remotely accepted and the span can be measured. With the traditional single RTK positioning method, the drone cannot accurately identify the height and center point of the tower, resulting in the usual distance measurement accuracy at the decimeter level. The present invention proposes a monocular vision tower span measurement method based on RTK correction, which can improve the span measurement to the centimeter level.

[0003] Spatial ranging technology involves the use of a variety of technical means to determine the position of an object in space and its relative or absolute distance. The core of the field of spatial ranging is accurate measurement and position determination. Commonly used technologies include laser ranging, sound wave ranging, infrared ranging, and vision-based ranging methods. Visual ranging is particularly dependent on image processing technology, which estimates the size, shape, position or distance of an object by analyzing the captured image. Spatial ranging technology is widely used in aerospace, industrial automation, robot navigation, construction engineering, agricultural monitoring, and vehicle autonomous driving.

[0004] Among them, the monocular vision tower span measurement method is a technology that uses a single camera to measure the distance between transmission and distribution towers. Using the image captured by the monocular camera, the towers are identified through image processing algorithms, and the distance between the towers is calculated. The main purpose of this technology is to maintain and inspect transmission and distribution lines in the power industry to ensure that the lines are safe and operate efficiently. The automated visual system can greatly reduce the need for manual measurement, improve the accuracy and efficiency of measurement, and reduce the danger during the measurement process. It is especially suitable for difficult-to-access high mountain complex outdoor environments.

[0005] Traditional measurement methods rely on fixed hardware configurations and prior settings, which are difficult to adapt to changing ambient light conditions, resulting in inconsistent image acquisition quality and affecting the final distance measurement accuracy. Especially in the power industry, the distance measurement between transmission and distribution towers needs to be carried out at different times and in various weather conditions. Fixed camera settings cannot take all situations into account, which may lead to inaccurate recognition of key features and affect the measurement results. Traditional methods lack corrections for shooting angles and geographic locations, which increases measurement errors in complex terrain or when multiple towers overlap, affecting the accuracy of distance measurement and making it difficult to achieve good distance measurement results. Summary of the invention

[0006] The purpose of the present invention is to solve the shortcomings of the prior art and provide a monocular vision tower span measurement method based on RTK correction.

[0007] To achieve the above object, the technical solution of the present invention is: a monocular vision tower span measurement method based on RTK correction, comprising:

[0008] Based on the environment of the power transmission and distribution towers, the ambient light data including the intensity and quality of the ambient light are collected in real time. By analyzing the ambient light data, the ISO sensitivity and shutter speed of the camera are adjusted to obtain the ambient light adaptation parameters;

[0009] Based on the ambient light adaptation parameters, the exposure time of the camera is adjusted to capture images of the transmission and distribution towers, and the images are screened to obtain optimized captured images;

[0010] Based on the optimized captured image, the image is sharpened, the contrast of the image is adjusted, and the tone of the image is optimized to obtain a feature-enhanced image;

[0011] Based on the feature enhanced image, according to the geographic location data of the shooting point provided by RTK, combined with the shooting angle information of the camera, the perspective deformation of the transmission and distribution tower in the image is analyzed, the image is perspective transformed, and the image is adjusted to correct the perspective deviation to obtain a perspective transformation corrected image;

[0012] Based on the perspective transformation corrected image, the shape and structural features of the power transmission and distribution tower are identified through image analysis, the power transmission and distribution tower is separated from the background image, the coordinate position of the power transmission and distribution tower in the image is marked, and the identification information of the power transmission and distribution tower is obtained;

[0013] Based on the transmission and distribution tower identification information, according to the geographical location data of the shooting point provided by RTK, combined with the focal length data during the shooting process, the distance between the transmission and distribution towers is calculated to obtain the tower span measurement result.

[0014] In one embodiment of the present invention, the ambient light adaptation parameters include ISO value adjustment parameters and shutter speed adjustment parameters of the camera, the optimized captured image includes color saturation adjustment information, brightness and contrast adjustment information of the image, the feature enhanced image includes edge clarity after image sharpening processing, contrast enhancement processing information and color balance adjustment information, the perspective transformation correction image includes image straight line horizontal and vertical correction and image plane correction, the power transmission and distribution tower identification information includes the marked power transmission and distribution tower position and the power transmission and distribution tower separation image, and the tower span measurement result includes the calculated value of the distance between towers and the measurement accuracy evaluation result.

[0015] In one embodiment of the present invention, based on the environment in which the power transmission and distribution tower is located, ambient light data including ambient light intensity and quality are collected in real time, and the ISO sensitivity and shutter speed of the camera are adjusted by analyzing the ambient light data to obtain the ambient light adaptation parameters. Specifically, the steps are as follows:

[0016] Based on the environment of the transmission and distribution towers, sensors are used to monitor the light intensity and quality of the shooting points in real time, and the collected ambient light data is preliminarily screened to remove outliers to obtain an ambient light data set.

[0017] Based on the ambient light data set, analyzing the light intensity and light quality, calculating the average value and change trend of the light data set, evaluating the stability of the ambient light, and obtaining the ambient light analysis result;

[0018] Based on the ambient light analysis result, the ISO sensitivity and shutter speed adjustment value required for capturing the transmission and distribution tower image are calculated, the camera parameter settings are updated to match the current ambient light conditions, and the ambient light adaptation parameters are obtained.

[0019] In one embodiment of the present invention, based on the ambient light adaptation parameter, the exposure time of the camera is adjusted to capture the image of the power transmission and distribution tower, and the image is screened to obtain the optimized captured image. Specifically, the steps are as follows:

[0020] Based on the ambient light adaptation parameter, according to the ambient light data, the exposure time of the camera during shooting is adjusted to match the current ambient light conditions to obtain the exposure adjustment parameter;

[0021] Based on the exposure adjustment parameters, a real-time image of the power transmission and distribution tower is collected by a camera, and a plurality of images of the power transmission and distribution tower are continuously taken to obtain a group of images of the power transmission and distribution tower;

[0022] Based on the transmission and distribution tower image group, the clarity of multiple images is analyzed, and images with blur and lens occlusion are removed to obtain optimized captured images.

[0023] In one embodiment of the present invention, based on the optimized captured image, the image is sharpened, the contrast of the image is adjusted, and the tone of the image is optimized to obtain the feature enhanced image. Specifically, the steps are as follows:

[0024] Based on the optimized captured image, by changing the sharpening parameters of the image processing tool, the edge strength of the power transmission and distribution tower is adjusted to optimize the visibility and details of the edge of the power transmission and distribution tower, thereby obtaining a sharpened image;

[0025] Based on the sharpening processed image, by adjusting the brightness and contrast parameters of the image, modifying the brightness curve of the image, adjusting the grayscale difference between the bright and dark parts of the image, optimizing the image hierarchy, and obtaining a contrast-adjusted image;

[0026] Based on the contrast-adjusted image, the color balance adjustment is performed by adjusting the ratio of the red, green and blue channels of the image color, and the hue and saturation of the image are optimized to obtain a feature-enhanced image.

[0027] In one embodiment of the present invention, based on the feature enhanced image, according to the geographic location data of the shooting point provided by RTK, combined with the shooting angle information of the camera, the perspective deformation of the power transmission and distribution tower in the image is analyzed, the image is perspective transformed, and the image is adjusted to correct the perspective deviation. The steps of obtaining the perspective transformation corrected image are specifically as follows:

[0028] Based on the feature enhanced image, the geographical location data of the shooting point and the angle information of the camera provided by RTK are collected, and the data are integrated to obtain the image shooting parameter data;

[0029] Based on the image shooting parameter data, a perspective deformation analysis is performed on the power transmission and distribution tower in the image, and a deviation between the perspective of the tower in the image and the perspective representation of the target image is calculated to obtain a perspective deformation analysis result;

[0030] Based on the perspective deformation analysis results, the image is processed by perspective transformation, the deviation of each angle of the image is adjusted, the visual error caused by the shooting angle is corrected, and the perspective transformation corrected image is obtained.

[0031] In one embodiment of the present invention, based on the perspective transformation corrected image, the shape and structural features of the power transmission and distribution tower are identified through image analysis, the power transmission and distribution tower is separated from the background image, and the coordinate position of the power transmission and distribution tower in the image is marked. The steps of obtaining the identification information of the power transmission and distribution tower are specifically as follows:

[0032] Based on the perspective transformation corrected image, the characteristics of the power transmission and distribution tower are compared through image analysis, the power transmission and distribution tower is identified from the image, and the outline and structure of the power transmission and distribution tower are captured through the Sobel operator edge detection method to obtain shape recognition data;

[0033] Based on the shape recognition data, an image segmentation tool is applied to separate the transmission and distribution tower image from the background by adjusting the segmentation threshold, so as to obtain a transmission and distribution tower segmentation image;

[0034] Based on the power transmission and distribution tower segmented image, the power transmission and distribution tower is marked in the image, the coordinate position of the tower in the image is recorded, and the power transmission and distribution tower identification information is obtained.

[0035] In one embodiment of the present invention, the Sobel operator edge detection method is according to the formula:

[0036]

[0037] Calculate edge strength, capture the outline and structure of the transmission and distribution tower, and obtain shape recognition data, where G x is the first-order derivative of the image in the horizontal direction, G y is the first-order derivative of the image in the vertical direction, w x is the weight coefficient in the horizontal direction, w y is the weight coefficient in the vertical direction, α is the local contrast enhancement factor of the image, β is the local mean of the image, ∈ is a small positive number, and G′ is the edge strength.

[0038] In one embodiment of the present invention, based on the identification information of the transmission and distribution towers, according to the geographical location data of the shooting point provided by RTK, combined with the focal length data during the shooting process, the distance between the transmission and distribution towers is calculated, and the steps of obtaining the tower span measurement result are specifically as follows:

[0039] Based on the identification information of the transmission and distribution tower, the geographic location data of the shooting point provided by RTK is integrated, the focal length data used by the camera during shooting is collected, and the information is integrated to obtain geographic and focal length data;

[0040] Based on the geographic and focal length data, using the position coordinates of the power transmission and distribution towers on the image, by a triangulation method, the straight-line distance between the power transmission and distribution towers is calculated to obtain the calculation result of the distance between the towers;

[0041] Based on the calculation result of the distance between the pole towers, distance correction is performed to adjust the measurement error caused by environmental factors, optimize the measurement result, and obtain the pole tower spacing measurement result.

[0042] In one embodiment of the present invention, the triangulation method is according to the formula:

[0043]

[0044] Calculate the straight-line distance between the transmission and distribution towers, where D is the straight-line distance between the towers, b is the pixel distance between the transmission and distribution towers in the image, f is the focal length of the camera, θ is the angular deviation between the towers, p is the distance conversion factor, and h is the observed height difference.

[0045] Compared with the prior art, the present invention has the following beneficial effects:

[0046] 1. In the present invention, the pole tower image is sharpened through image processing, the edge and details of the image are enhanced, the outline of the pole tower is made clearer, and the contrast and tone of the image are adjusted at the same time, the overall visual effect of the image is improved, the characteristics of the pole tower are highlighted, the misidentification caused by background interference is reduced, and the efficiency and accuracy of subsequent operations are enhanced.

[0047] 2. The present invention uses real-time dynamic positioning technology to accurately correct the geographical location and camera direction of the shooting device, ensure the accuracy of the shooting angle, improve the accuracy and repeatability of the measurement data, reduce measurement errors caused by position errors and angle deviations, and improve the reliability of data measurement.

[0048] 3. The present invention combines perspective projection theory to perform perspective transformation processing on the collected images, corrects shape distortion and proportion errors caused by shooting angles, ensures the authenticity of image geometric features, makes the image more consistent with the shape and size of actual objects, improves the accuracy and effect of distance measurement between poles and towers, and has important practical application value for maintenance and safety inspection of power lines. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] Figure 1 It is a schematic diagram of the main steps of the present invention;

[0050] Figure 2 It is a schematic diagram of the refinement of S1 of the present invention;

[0051] Figure 3 It is a schematic diagram of the refinement of S2 of the present invention;

[0052] Figure 4 It is a schematic diagram of the refinement of S3 of the present invention;

[0053] Figure 5 It is a schematic diagram of the refinement of S4 of the present invention;

[0054] Figure 6 It is a detailed schematic diagram of S5 of the present invention;

[0055] Figure 7 It is a detailed schematic diagram of S6 of the present invention. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0057] In the description of the present invention, it should be understood that the terms "length", "width", "up", "down", "front", "back", "left", "right", "vertical", "horizontal", "top", "bottom", "inside", "outside" and the like indicate positions or positional relationships based on the positions or positional relationships shown in the drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as limiting the present invention. In addition, in the description of the present invention, "multiple" means two or more, unless otherwise clearly and specifically defined.

[0058] The present invention provides a monocular vision tower span measurement method based on RTK correction, comprising:

[0059] Based on the environment of the power transmission and distribution towers, the ambient light data including the intensity and quality of the ambient light are collected in real time. By analyzing the ambient light data, the ISO sensitivity and shutter speed of the camera are adjusted to obtain the ambient light adaptation parameters;

[0060] Based on the ambient light adaptation parameters, the exposure time of the camera is adjusted to capture images of the transmission and distribution towers, and the images are screened to obtain optimized captured images;

[0061] Based on the optimized captured image, the image is sharpened, the contrast of the image is adjusted, and the tone of the image is optimized to obtain a feature-enhanced image;

[0062] Based on the feature enhanced image, according to the geographic location data of the shooting point provided by RTK, combined with the shooting angle information of the camera, the perspective deformation of the transmission and distribution tower in the image is analyzed, the image is perspective transformed, and the image is adjusted to correct the perspective deviation to obtain a perspective transformation corrected image;

[0063] Based on the perspective transformation corrected image, the shape and structural features of the power transmission and distribution tower are identified through image analysis, the power transmission and distribution tower is separated from the background image, the coordinate position of the power transmission and distribution tower in the image is marked, and the identification information of the power transmission and distribution tower is obtained;

[0064] Based on the transmission and distribution tower identification information, according to the geographical location data of the shooting point provided by RTK, combined with the focal length data during the shooting process, the distance between the transmission and distribution towers is calculated to obtain the tower span measurement result.

[0065] The following are specific implementation examples of the present invention.

[0066] See also Figure 1 An example of the present invention provides a monocular vision tower span measurement method based on RTK correction, comprising the following steps:

[0067] S1: Based on the environment of the power transmission and distribution tower, the light sensor collects the intensity and quality of ambient light in real time. By analyzing the ambient light data, the ISO sensitivity and shutter speed of the camera are adjusted to match the current ambient light conditions and obtain the ambient light adaptation parameters.

[0068] S2: Based on the ambient light adaptation parameters, the exposure time of the camera is adjusted, and according to the adjusted camera parameters, the image of the transmission and distribution tower is captured, and the image is screened to obtain an optimized captured image;

[0069] S3: Based on the optimized captured image, the image is sharpened to optimize the clarity of the transmission and distribution towers, and the image contrast is adjusted to optimize the image layering, the image color balance is adjusted, and the image tone is optimized to obtain a feature enhanced image;

[0070] S4: Based on the feature-enhanced image, according to the geographic location data of the shooting point provided by RTK and the shooting angle information of the camera, the perspective deformation of the transmission and distribution tower in the image is analyzed, the image is perspective transformed, and the image is adjusted to correct the perspective deviation to obtain the perspective transformation corrected image;

[0071] S5: Based on the perspective transformation correction image, the shape and structural features of the power transmission and distribution tower are identified through image analysis, the power transmission and distribution tower is separated from the background image, the coordinate position of the power transmission and distribution tower in the image is marked, and the identification information of the power transmission and distribution tower is obtained;

[0072] S6: Based on the identification information of the transmission and distribution towers, according to the geographical location data of the shooting point provided by RTK, combined with the focal length data during the shooting process, the distance between the transmission and distribution towers is calculated to obtain the tower span measurement result.

[0073] The ambient light adaptation parameters include the ISO value adjustment parameters and shutter speed adjustment parameters of the camera. The optimized captured image includes the color saturation adjustment information, brightness and contrast adjustment information of the image. The feature enhanced image includes the edge clarity after image sharpening, contrast enhancement processing information and color balance adjustment information. The perspective transformation correction image includes the horizontal and vertical correction of the image line and the image plane correction. The transmission and distribution pole tower identification information includes the marked transmission and distribution pole tower position and the transmission and distribution pole tower separation image. The pole tower span measurement results include the calculated value of the distance between the pole towers and the measurement accuracy evaluation results.

[0074] See also Figure 2 Based on the environment of the power transmission and distribution tower, the light sensor collects the intensity and quality of ambient light in real time. By analyzing the ambient light data, the ISO sensitivity and shutter speed of the camera are adjusted to match the current ambient light conditions. The specific steps for obtaining the ambient light adaptation parameters are as follows:

[0075] S101: Based on the environment where the power transmission and distribution tower is located, the light intensity and quality of the shooting point are monitored in real time through sensors, and the collected ambient light data are preliminarily screened to remove abnormal values, and the specific process of obtaining the ambient light data set is as follows;

[0076] In S101, based on the environment of the transmission and distribution tower, the light intensity and quality of the shooting point are monitored in real time through sensors, and the abnormal threshold is applied to filter and eliminate the values ​​that deviate greatly from the average light level. The formula for identifying abnormal values ​​is as follows: T iA =μ A +kA ·σ A , where T iA is the abnormal threshold of the data point, μ A is the average value of the obtained data, σ A is the standard deviation of the data, k A To adjust the sensitivity coefficient of the threshold, it is adjusted according to the actual monitoring conditions, allowing a quantitative definition of what is an outlier. By precisely controlling the sensitivity coefficient, the reliability and validity of the data set are improved, and an ambient light data set is obtained.

[0077] S102: Based on the ambient light data set, analyze the light intensity and light quality, calculate the average value and change trend of the light data set, evaluate the stability of the ambient light, and obtain the ambient light analysis result. The specific process is as follows;

[0078] In S102, based on the optimized ambient light data set, the specific situation of light intensity and quality is understood through analysis. The average light intensity and standard deviation in each time window are calculated using a sliding window method, and the long-term light change trend is determined by applying the following trend analysis formula: Among them, β A Indicates the slope of the light intensity change trend, x iA Represents each measurement time point, is the average value of the measurement time points, y iA is the light intensity at the corresponding time point, is the average value of light intensity. The formula helps to identify and analyze the stability and change pattern of light, evaluate the stability of ambient light, and obtain the ambient light analysis results.

[0079] S103: Based on the ambient light analysis result, the ISO sensitivity and shutter speed adjustment value required for capturing the transmission and distribution tower image are calculated, and the camera parameter settings are updated to match the current ambient light conditions. The specific process of obtaining the ambient light adaptation parameters is as follows;

[0080] In S103, based on the ambient light analysis result, the ISO sensitivity required for capturing the transmission and distribution tower image is calculated to ensure that high-quality images can be captured under changing lighting conditions. The required ISO value is calculated using the following sensitivity adjustment formula: ISO A =α A ·log(1+β A ·η A ), where ISO A is the calculated sensitivity, α A is the adjustment coefficient preset based on equipment characteristics, β A is the slope of the light intensity change trend obtained from data analysis, η AIt represents the coefficient of variation of light intensity. Log is the natural logarithm function, which is used to adjust the nonlinear response of sensitivity. By accurately adjusting ISO sensitivity and shutter speed to adapt to the current ambient light conditions and optimize the image shooting effect, the ambient light adaptation parameters are obtained.

[0081] See also Figure 3 , based on the ambient light adaptation parameters, the exposure time of the camera is adjusted, and according to the adjusted camera parameters, the image of the transmission and distribution tower is captured, and the image is screened to obtain the optimized captured image. The specific steps are:

[0082] S201: Based on the ambient light adaptation parameter, according to the ambient light data, the exposure time of the camera during shooting is adjusted to match the current ambient light conditions, and the process of obtaining the exposure adjustment parameter is specifically as follows;

[0083] In S201, based on the ambient light adaptation parameter and the ambient light data, a dynamic exposure time adjustment formula is used to match the current ambient light condition and adjust the exposure time of the camera. The reference formula in the process is as follows: Among them, E tB is the required exposure time, τ B is the time constant adjusted based on ambient light changes, ISO B is ISO sensitivity, L B is the light brightness level measured in real time. The exposure adjustment parameter is obtained by formula to ensure that the exposure time can be flexibly adjusted to adapt to rapidly changing light conditions.

[0084] S202: Based on the exposure adjustment parameters, the real-time image of the power transmission and distribution tower is collected by a camera, and a plurality of images of the power transmission and distribution tower are continuously taken to obtain the image group of the power transmission and distribution tower. Specifically, the process is as follows;

[0085] In S202, based on the exposure adjustment parameters, the enhanced shooting mode is used to continuously collect real-time images of the transmission and distribution towers. The continuous shooting mode uses the following formula to determine the optimal shooting interval to ensure image quality: Among them, Δt B is the shooting interval, f B is the focal length adjustment factor, taking into account the aperture and focal length of the lens, R B is the expected refresh rate of the image. The formula helps set the time interval of continuous shooting to adapt to environmental changes and maximize the image acquisition efficiency. The real-time image of the power transmission and distribution tower is collected by the camera, and multiple images of the power transmission and distribution tower are continuously shot to obtain a group of power transmission and distribution tower images.

[0086] S203: Based on the transmission and distribution tower image group, the clarity of multiple images is analyzed, and images with blur and lens occlusion are removed to obtain an optimized captured image. The specific process is as follows;

[0087] In S203, based on the transmission and distribution tower image group, the clarity analysis is performed on the collected transmission and distribution tower image group, and the image with the best quality is screened out through the image quality evaluation formula, and the images with large blur and occlusion effects are removed: Among them, Q B is the overall image quality score, C iB is the clarity score of the i-th image, N B is the total number of images in the image group. The formula provides a quantitative criterion for selecting high-quality images. By evaluating the clarity score of each image, it ensures that the images in the selected image group have the highest usability and quality, and obtains optimized captured images.

[0088] See also Figure 4 , based on the optimized captured image, the image is sharpened, the clarity of the transmission and distribution tower is optimized, the contrast of the image is adjusted, the image layering is optimized, the color balance of the image after contrast adjustment is performed, the image tone is optimized, and the steps of obtaining the feature enhanced image are as follows:

[0089] S301: Based on the optimized captured image, the edge strength of the transmission and distribution tower is adjusted by changing the sharpening parameters of the image processing tool, and the visibility and details of the edge of the transmission and distribution tower are optimized to obtain the sharpened processed image.

[0090] In S301, based on the optimized captured image, the edge strength of the transmission and distribution tower is adjusted by changing the sharpening parameters of the image processing tool to enhance the visibility and details of the edge of the transmission and distribution tower. The following sharpening strength adjustment formula is used to accurately control the edge enhancement process: kC =α C ·(1+β C ·L eC ), where S kC is the sharpening factor, α C is the basic sharpening strength, β C is the edge detection sensitivity coefficient, L eC The formula dynamically adjusts the sharpening strength according to the edge contrast in the image to enhance the details and clarity of the image and obtain a sharpened image.

[0091] S302: Based on the sharpening processing of the image, by adjusting the brightness and contrast parameters of the image, modifying the brightness curve of the image, adjusting the grayscale difference between the bright and dark parts of the image, optimizing the image level, and obtaining the contrast-adjusted image, the specific process is as follows;

[0092] In S302, based on the sharpening process, the brightness and contrast parameters of the image are adjusted to modify the brightness curve of the image. The grayscale difference between the bright and dark parts of the image is adjusted by the following brightness adjustment formula to optimize the image level: dC =γ C ·(L mC -L avgC )+L avgC , where B dC is the adjusted brightness value, γ C is the contrast adjustment coefficient, L mC is the local brightness value of the original image, L avgC is the average brightness of the entire image. The formula is used to balance the overall and local brightness of the image, enhance the image depth and detail level, and obtain a contrast-adjusted image.

[0093] S303: Based on contrast adjustment, the image is adjusted by adjusting the ratio of red, green and blue channels of the image color to perform color balance adjustment, optimize the hue and saturation of the image, and obtain a feature enhanced image. The specific process is as follows;

[0094] In S303, the image is adjusted based on contrast, and the color balance is adjusted by adjusting the ratio of the red, green and blue channels of the image color to optimize the hue and saturation of the image. The following color balance adjustment formula is used to carefully control the output ratio of each color channel: rC =R oC ·r C , C gC =G oC ·g C and C bC =B oC b C Among them, C rC , C gC , C bC are the adjusted red, green, and blue channel values, respectively. oC , G oC , B oC are the values ​​of the red, green, and blue channels in the original image, r C , g C , b C is the adjustment coefficient for each channel, used to fine-tune color balance and saturation. The formula allows the output of each color channel to be adjusted independently, accurately controlling the color balance to achieve a natural and full visual effect, and obtain a feature-enhanced image.

[0095] See also Figure 5Based on the feature enhanced image, according to the geographic location data of the shooting point provided by RTK, combined with the shooting angle information of the camera, the perspective deformation of the transmission and distribution tower in the image is analyzed through the perspective projection theory, the image is perspective transformed, and the image is adjusted to correct the perspective deviation. The specific steps of obtaining the perspective transformation corrected image are as follows:

[0096] S401: Based on the feature enhanced image, the geographic location data of the shooting point and the angle information of the camera provided by the RTK system are collected, and the data is integrated to obtain the image shooting parameter data. Specifically, the process is as follows;

[0097] In S401, based on the feature enhanced image, the shooting point geographic location data and the camera angle information provided by the RTK system are used to integrate the information to generate accurate image shooting parameters. The geographic and angle data are combined by the following formula: locD =λ D ·φ D +θ D , where P locD represents the image capture parameter data, λ D Indicates the longitude coordinate of the geographic location, φ D Represents the latitude coordinate of the geographic location, θ D Represents the angle information of the camera. Ensure that the collected geographic and angle information can be accurately mapped to each shooting point to obtain image shooting parameter data.

[0098] S402: Based on the image shooting parameter data, a perspective deformation analysis is performed on the power transmission and distribution tower in the image, and the deviation between the perspective of the tower in the image and the perspective representation of the target image is calculated to obtain the perspective deformation analysis result. The specific process is as follows;

[0099] In S402, based on the image shooting parameter data, the perspective deformation analysis of the transmission and distribution tower is performed to calculate the deviation between the perspective of the tower in the image and the perspective representation of the target image. The following perspective deviation calculation formula is used: Among them, δ D is the total deviation in the perspective deformation analysis results, P tiD is the actual tower position at each angle in the image, P piD is the ideal perspective position of the target image. The formula helps to identify the perspective deviation in the image and quantify the deviation for subsequent correction processing to obtain the perspective deformation analysis result.

[0100] S403: Based on the perspective deformation analysis result, the image is processed for perspective transformation, the deviation of each angle of the image is adjusted, the visual error caused by the shooting angle is corrected, and the process of obtaining the perspective transformation corrected image is as follows;

[0101] In S403, according to the perspective deformation analysis result, the image is processed for perspective transformation, the deviation of each angle of the image is adjusted, and the visual error caused by the shooting angle is corrected. The following perspective correction formula is used for image adjustment: adjD =I origD +k D ·Δθ D ), where I adjD is the image corrected by perspective transformation, I origD is the original uncorrected image, k D is the adjustment coefficient, set according to the perspective analysis results, Δθ D Indicates the angle deviation that needs to be adjusted. The formula ensures that the visual error in the image is effectively corrected, and by accurately adjusting the deviation of each angle, a more visually accurate perspective transformation corrected image is obtained.

[0102] See also Figure 6 Based on the perspective transformation correction image, the shape and structural characteristics of the power transmission and distribution tower are identified through image analysis, the power transmission and distribution tower is separated from the background image, and the coordinate position of the power transmission and distribution tower in the image is marked. The specific steps of obtaining the power transmission and distribution tower identification information are as follows:

[0103] S501: Based on the perspective transformation correction image, through image analysis, the characteristics of the power transmission and distribution tower are compared, the power transmission and distribution tower is identified from the image, and the outline and structure of the power transmission and distribution tower are captured through the Sobel operator edge detection method to obtain the shape recognition data. The specific process is as follows;

[0104] In S501, based on the perspective transformation correction image, the characteristics of the power transmission and distribution tower are compared through image analysis, the power transmission and distribution tower is identified from the image, the outline and structure of the power transmission and distribution tower are extracted through the Sobel operator, the precise outline and structure of the power transmission and distribution tower are captured, and the shape recognition data is obtained.

[0105] Sobel operator edge detection method, according to the formula:

[0106]

[0107] The edge strength G' is calculated to capture the outline and structure of the transmission and distribution towers and obtain shape recognition data.

[0108] Parameter details and how to obtain them:

[0109] G x is the first-order inverse of the image in the horizontal direction, obtained by applying a horizontal Sobel filter to the image, G yIt is the first-order inverse of the image in the vertical direction, obtained by applying a vertical Sobel filter to the image. The Sobel filter is used to capture changes in the edge of the image by performing a convolution operation on the image in the horizontal or vertical direction. The Sobel operator is a discrete differential operator for edge detection, which calculates the derivative by applying a specific kernel (or filter) around each pixel of the image.

[0110] w x is the weight coefficient in the horizontal direction, w y is the weight coefficient in the vertical direction, and the weight coefficient can be adjusted according to the specific content of the image or the edge detection requirements to optimize the strength and direction of the edge response.

[0111] α is the local contrast enhancement factor of the image, which can be obtained by measuring the difference between the highest brightness value and the lowest brightness value in the local area of ​​the image. The parameter is used to increase the contrast of the image edge and make the edge more obvious.

[0112] β is the local mean of the image, which is usually obtained by calculating the average value of the pixel values ​​in a small area of ​​the image. The parameter is used to adjust the impact of the enhancement factor on image edge detection and maintain the stability of edge detection.

[0113] ∈ is a small positive number used to prevent division by zero errors. It is usually taken to be a very small value, such as 0.01, to ensure the stability of the formula and the feasibility of the calculation.

[0114] Calculation example:

[0115] In a specific image area, G is calculated by Sobel filter. x =120 and G y =80.

[0116] Set the weight coefficient w x = 0.5 and w y =0.7, local contrast enhancement factor α=30, local mean β=100, and ∈=0.01.

[0117] The steps to calculate the edge strength G' are as follows:

[0118] Compute the weighted first-order derivative:

[0119] w x ·G x =0.5·120=60

[0120] w y ·G y =0.7·80=56

[0121] Compute the sum of squares of weighted derivatives:

[0122] (w x ·G x ) 2 +(w y ·G y ) 2 =60 2 +56 2 =3600+3136=6736

[0123] Calculate the ratio of the local contrast enhancement factor to the local mean:

[0124]

[0125] The above parts are combined to calculate G':

[0126]

[0127] The calculated edge strength G'≈82.07 indicates that there is a significant edge in this image area. This high value indicates that the edge detection effect is significant.

[0128] S502: Based on the shape recognition data, an image segmentation tool is applied to separate the transmission and distribution tower image from the background by adjusting the segmentation threshold, and the process of obtaining the transmission and distribution tower segmentation image is specifically as follows;

[0129] In S502, based on the shape recognition data, an image segmentation tool is applied to separate the transmission and distribution tower image from the background by adjusting the segmentation threshold. The image segmentation formula is used to determine the appropriate threshold: T E =μ E +k segE ·σ E , where T E is the segmentation threshold, μ E is the average value of the image pixel intensity, σ E is the standard deviation of pixel intensity, k segE It is an adjustment factor, which is set according to the changes in image content. The formula helps to achieve efficient image segmentation, clearly separate the transmission and distribution towers from the background, and obtain the transmission and distribution tower segmentation image.

[0130] S503: Based on the power transmission and distribution tower segmentation image, the power transmission and distribution tower is marked in the image, and the coordinate position of the tower in the image is recorded to obtain the power transmission and distribution tower identification information. Specifically, the process is as follows;

[0131] In S503, the image is segmented based on the power transmission and distribution towers, the power transmission and distribution towers are marked in the image, and the coordinate positions of the towers in the image are recorded. The tower positions are marked and saved using the following coordinate recording formula: Among them, P locE represents the coordinate position of the tower in the image, R EIndicates the area identified as power transmission and distribution towers in the image, ensuring that each identified tower is accurately marked to obtain the power transmission and distribution tower identification information.

[0132] See also Figure 7 Based on the identification information of the transmission and distribution towers, the geographical location data of the shooting point provided by RTK, combined with the focal length data during the shooting process, the distance between the transmission and distribution towers is calculated, and the steps to obtain the tower span measurement results are as follows:

[0133] S601: Based on the identification information of the transmission and distribution tower, the geographic location data of the shooting point provided by the RTK system is integrated, the focal length data used by the camera during shooting is collected, and the information is integrated to obtain the geographic and focal length data. Specifically, the process is as follows;

[0134] In S601, based on the identification information of the transmission and distribution tower, the geographical location data of the shooting point provided by the RTK system is integrated to collect the focal length data used by the camera when shooting. These data are combined using the following information integration formula: geoF =λ F ·φ F +f F , where P geoF represents the integrated geographic and focal length data, λ F Indicates the longitude coordinate of the geographic location, φ F Indicates the latitude coordinate of the geographic location, f F Represents the focal length used by the camera. The formula ensures that the geolocation and focal length data are correctly integrated to form a comprehensive data set for distance calculations.

[0135] S602: Based on the geographic and focal length data, using the position coordinates of the power transmission and distribution towers on the image, by using a triangulation method, the straight-line distance between the power transmission and distribution towers is calculated, and the specific process of obtaining the calculation result of the distance between the towers is as follows;

[0136] In S602, based on geographic and focal length data, using the position coordinates of the power transmission and distribution towers on the image, the straight-line distance between the power transmission and distribution towers is calculated by triangulation method, and the geometric distance between the towers is calculated according to the image coordinate difference to obtain the calculation result of the distance between the towers.

[0137] The triangulation method is based on the formula:

[0138]

[0139] Calculate the straight-line distance D between transmission and distribution towers.

[0140] Parameter details and how to obtain them:

[0141] b: Pixel distance between transmission and distribution towers in the image. This parameter can be directly measured using image analysis software and is determined based on the pixel position of the transmission and distribution towers on the image.

[0142] f: The focal length of the camera, usually given in the camera's technical specifications or available through the camera settings.

[0143] θ: Angle deviation between towers. Usually calculated through field measurements or data obtained using inclination sensors, or can be calculated through geometric relationships based on the relative position between the camera and the target.

[0144] p: The number of pixels per millimeter, which is determined by the sensor size and resolution of the camera. For example, if the sensor width is 20 mm and the image width is 4000 pixels, then the number of pixels per millimeter is 200 pixels / mm.

[0145] h: Observed height difference. Obtained through field measurement or using equipment such as an altimeter, used to calculate the height difference between the observation point and the target point.

[0146] Calculation example:

[0147] Set the parameters as follows:

[0148] b = 800 pixels, p = 200 pixels / mm, f = 35 mm, θ = 5°, h = 10 m

[0149] Calculation process:

[0150] Calculate the cosine:

[0151] cos(5°)≈0.9962

[0152] Compute the product b f:

[0153] b·f=800·35=28000

[0154] Multiply by the cosine:

[0155] (b·f)·cos(θ)=28000·0.9962

[0156] ≈27913.6

[0157] Compute the logarithmic part, using the natural logarithm:

[0158] log(1+h)=log(1+10)≈2.3979

[0159] Calculate d':

[0160]

[0161] The calculation results show that considering the camera focal length, pixel density and observation height difference, the straight-line distance between the two transmission and distribution towers is 58.14 meters, providing a more accurate distance estimation method.

[0162] S603: Based on the calculation result of the distance between the towers, distance correction is performed to adjust the measurement error caused by environmental factors, optimize the measurement result, and obtain the tower span measurement result. The specific process is as follows;

[0163] In S603, based on the calculation result of the distance between the towers, distance correction is performed to adjust the measurement error caused by environmental factors. The following distance correction formula is used to optimize the measurement result: corrF =D F +∈ F Among them, D corrF is the corrected tower distance, D F is the initial calculated tower distance, ∈ F It is the error correction value caused by environmental factors, which is used to adjust the distance obtained by preliminary calculation. It takes possible errors into consideration to ensure the accuracy of the measurement results and obtain accurate tower span measurement results.

[0164] The above are only preferred embodiments of the present invention and are not intended to limit the present invention in other forms. Any technician familiar with the profession may use the technical contents disclosed above to change or modify them into equivalent embodiments with equivalent changes and apply them to other fields. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the technical solution of the present invention still falls within the protection scope of the technical solution of the present invention.

Claims

1. A monocular vision tower span measurement method based on RTK correction, characterized in that: include: Based on the environment of the power transmission and distribution towers, the ambient light data including the intensity and quality of the ambient light are collected in real time. By analyzing the ambient light data, the ISO sensitivity and shutter speed of the camera are adjusted to obtain the ambient light adaptation parameters; Based on the ambient light adaptation parameters, the exposure time of the camera is adjusted to capture images of the transmission and distribution towers, and the images are screened to obtain optimized captured images; Based on the optimized captured image, the image is sharpened, the contrast of the image is adjusted, and the tone of the image is optimized to obtain a feature-enhanced image; Based on the feature enhanced image, according to the geographic location data of the shooting point provided by RTK, combined with the shooting angle information of the camera, the perspective deformation of the transmission and distribution tower in the image is analyzed, the image is perspective transformed, and the image is adjusted to correct the perspective deviation to obtain a perspective transformation corrected image; Based on the perspective transformation corrected image, the shape and structural features of the power transmission and distribution tower are identified through image analysis, the power transmission and distribution tower is separated from the background image, the coordinate position of the power transmission and distribution tower in the image is marked, and the identification information of the power transmission and distribution tower is obtained; Based on the transmission and distribution tower identification information, according to the geographical location data of the shooting point provided by RTK, combined with the focal length data during the shooting process, the distance between the transmission and distribution towers is calculated to obtain the tower span measurement result.

2. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 1, characterized in that: The ambient light adaptation parameters include ISO value adjustment parameters and shutter speed adjustment parameters of the camera, the optimized captured image includes color saturation adjustment information, brightness and contrast adjustment information of the image, the feature enhanced image includes edge clarity after image sharpening, contrast enhancement processing information and color balance adjustment information, the perspective transformation correction image includes image straight line horizontal and vertical correction and image plane correction, the power transmission and distribution tower identification information includes the marked power transmission and distribution tower position and the power transmission and distribution tower separation image, and the tower span measurement result includes the calculated value of the distance between towers and the measurement accuracy evaluation result.

3. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 1 is characterized in that: Based on the environment of the power transmission and distribution tower, the ambient light data including the intensity and quality of the ambient light are collected in real time. By analyzing the ambient light data, the ISO sensitivity and shutter speed of the camera are adjusted to obtain the ambient light adaptation parameters. The specific steps are as follows: Based on the environment of the transmission and distribution towers, sensors are used to monitor the light intensity and quality of the shooting points in real time, and the collected ambient light data is preliminarily screened to remove outliers to obtain an ambient light data set. Based on the ambient light data set, analyzing the light intensity and light quality, calculating the average value and change trend of the light data set, evaluating the stability of the ambient light, and obtaining the ambient light analysis result; Based on the ambient light analysis result, the ISO sensitivity and shutter speed adjustment value required for capturing the transmission and distribution tower image are calculated, the camera parameter settings are updated to match the current ambient light conditions, and the ambient light adaptation parameters are obtained.

4. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 1, characterized in that: Based on the ambient light adaptation parameters, the exposure time of the camera is adjusted to capture the image of the transmission and distribution tower, and the image is screened to obtain the optimized captured image. Specifically, the steps are as follows: Based on the ambient light adaptation parameter, according to the ambient light data, the exposure time of the camera during shooting is adjusted to match the current ambient light conditions to obtain the exposure adjustment parameter; Based on the exposure adjustment parameters, a real-time image of the power transmission and distribution tower is collected by a camera, and a plurality of images of the power transmission and distribution tower are continuously taken to obtain a group of images of the power transmission and distribution tower; Based on the transmission and distribution tower image group, the clarity of multiple images is analyzed, and images with blur and lens occlusion are removed to obtain optimized captured images.

5. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 1, characterized in that: Based on the optimized captured image, the image is sharpened, the contrast of the image is adjusted, and the tone of the image is optimized to obtain the feature enhanced image. Specifically, the steps are as follows: Based on the optimized captured image, by changing the sharpening parameters of the image processing tool, the edge strength of the power transmission and distribution tower is adjusted to optimize the visibility and details of the edge of the power transmission and distribution tower, thereby obtaining a sharpened image; Based on the sharpening processed image, by adjusting the brightness and contrast parameters of the image, modifying the brightness curve of the image, adjusting the grayscale difference between the bright and dark parts of the image, optimizing the image hierarchy, and obtaining a contrast-adjusted image; Based on the contrast-adjusted image, the color balance adjustment is performed by adjusting the ratio of the red, green and blue channels of the image color, and the hue and saturation of the image are optimized to obtain a feature-enhanced image.

6. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 1, characterized in that: Based on the feature enhanced image, according to the geographic location data of the shooting point provided by RTK, combined with the shooting angle information of the camera, the perspective deformation of the transmission and distribution tower in the image is analyzed, the image is perspective transformed, and the image is adjusted to correct the perspective deviation. The steps of obtaining the perspective transformation corrected image are specifically as follows: Based on the feature enhanced image, the geographical location data of the shooting point and the angle information of the camera provided by RTK are collected, and the data are integrated to obtain the image shooting parameter data; Based on the image shooting parameter data, a perspective deformation analysis is performed on the power transmission and distribution tower in the image, and a deviation between the perspective of the tower in the image and the perspective representation of the target image is calculated to obtain a perspective deformation analysis result; Based on the perspective deformation analysis results, the image is processed by perspective transformation, the deviation of each angle of the image is adjusted, the visual error caused by the shooting angle is corrected, and the perspective transformation corrected image is obtained.

7. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 1, characterized in that: Based on the perspective transformation corrected image, the shape and structural features of the power transmission and distribution tower are identified through image analysis, the power transmission and distribution tower is separated from the background image, and the coordinate position of the power transmission and distribution tower in the image is marked. The steps of obtaining the identification information of the power transmission and distribution tower are specifically as follows: Based on the perspective transformation corrected image, the characteristics of the power transmission and distribution tower are compared through image analysis, the power transmission and distribution tower is identified from the image, and the outline and structure of the power transmission and distribution tower are captured through the Sobel operator edge detection method to obtain shape recognition data; Based on the shape recognition data, an image segmentation tool is applied to separate the transmission and distribution tower image from the background by adjusting the segmentation threshold, so as to obtain a transmission and distribution tower segmentation image; Based on the power transmission and distribution tower segmented image, the power transmission and distribution tower is marked in the image, the coordinate position of the tower in the image is recorded, and the power transmission and distribution tower identification information is obtained.

8. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 7 is characterized in that: The Sobel operator edge detection method is based on the formula: Calculate edge strength, capture the outline and structure of the transmission and distribution tower, and obtain shape recognition data, where G x is the first-order derivative of the image in the horizontal direction, G y is the first-order derivative of the image in the vertical direction, w x is the weight coefficient in the horizontal direction, w y is the weight coefficient in the vertical direction, α is the local contrast enhancement factor of the image, β is the local mean of the image, ∈ is a small positive number, and G′ is the edge strength.

9. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 1, characterized in that: Based on the identification information of the transmission and distribution tower, according to the geographical location data of the shooting point provided by RTK, combined with the focal length data during the shooting process, the distance between the transmission and distribution towers is calculated, and the steps of obtaining the tower span measurement result are specifically as follows: Based on the identification information of the transmission and distribution tower, the geographic location data of the shooting point provided by RTK is integrated, the focal length data used by the camera during shooting is collected, and the information is integrated to obtain geographic and focal length data; Based on the geographic and focal length data, using the position coordinates of the power transmission and distribution towers on the image, by a triangulation method, the straight-line distance between the power transmission and distribution towers is calculated to obtain the calculation result of the distance between the towers; Based on the calculation result of the distance between the pole towers, distance correction is performed to adjust the measurement error caused by environmental factors, optimize the measurement result, and obtain the pole tower spacing measurement result.

10. The method for measuring the distance between towers using monocular vision based on RTK correction according to claim 9, characterized in that: The triangulation method is according to the formula: Calculate the straight-line distance between the transmission and distribution towers, where D is the straight-line distance between the towers, b is the pixel distance between the transmission and distribution towers in the image, f is the focal length of the camera, θ is the angular deviation between the towers, p is the distance conversion factor, and h is the observed height difference.

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