A pipeline buried depth detection method and system based on image recognition

Abstract

The present application belongs to the field of pipeline construction technology, and specifically discloses a pipeline buried depth detection method and system based on image recognition. The detection method includes: obtaining the outline of the pipeline to be tested and the detection plate, adjusting the detection plate so that the center lines of the two coincide; placing the background plate parallel to the top of the detection plate, adjusting the horizontality of the detection plate based on color recognition technology and deep learning model, so that the detection plate is located on the horizontal section of the pipeline to be tested; adjusting the background plate according to the locator coordinates; then obtaining the first actual buried depth and the second actual buried depth respectively through the detection plate laser emitter and the detection beam, and taking the average value as the actual measurement value; finally, comparing the actual measurement value with the theoretical buried depth to determine whether it is qualified, and determining whether rework is required based on the difference between the two. Based on image recognition technology, the present application provides a new pipeline buried depth detection method that can effectively overcome the problem of accuracy, ensuring that the pipeline laying is accurate from the beginning of construction. Pipeline buried depth detection method and system.

Description

Technical Field

[0001] The present invention relates to the technical field of gas pipeline construction, and in particular to a pipeline burial depth detection method based on image recognition. Background Art

[0002] Pipeline engineering plays a crucial role in the construction and maintenance of modern infrastructure, encompassing numerous key areas, including urban water supply and drainage, gas transmission, and petrochemical pipelines. The precise depth of pipelines is not only crucial to construction quality but also a key factor in ensuring their long-term safety. Common methods for measuring pipeline depth during installation include leveling, total station trigonometric height measurement, and simple rope measurement.

[0003] The drawbacks of the above detection methods are: when the level measurement method is actually used, the cumbersome measurement and calculation process greatly increases the probability of human error. For example, the level rod reading is easily deviated due to the observer's parallax, and it is quite difficult to find a stable reference point. Although the total station triangulation height measurement method is efficient and convenient, it can face the complex and changing construction site environment. The atmospheric refraction coefficient causes the final result to deviate greatly from the true value. Especially in long-distance and large height difference measurement tasks, the error can even reach several centimeters, seriously deviating from the requirements of precise construction. As for the simple rope measurement method, because it relies too much on manual vertical placement operations, the verticality of the rope cannot be guaranteed in complex working conditions such as deep foundation pits and narrow trenches, and the scale reading error is significant. Therefore, there is an urgent need for a new pipeline depth detection method that can effectively overcome the accuracy problem, ensure that the pipeline laying is accurate from the beginning of construction, and provide a guarantee for subsequent long-term operation and maintenance. Summary of the Invention

[0004] In view of this, according to one aspect of the present application, a pipeline buried depth detection method based on image recognition is provided, the detection method comprising the following steps:

[0005] S1, placing the detection plate on top of the pipeline to be tested, obtaining the contours of the pipeline to be tested and the detection plate respectively, and adjusting the detection plate according to the distance between the contour edges of the pipeline to be tested and the detection plate so that the center line of the detection plate and the center line of the pipeline to be tested are on the same straight line;

[0006] S2: Place the background plate parallel to the detection plate. Based on color recognition technology and deep learning models, adjust the level of the detection plate so that the detection plate is located on the horizontal section of the pipeline to be tested.

[0007] S3, after the detection plate is adjusted to be horizontal, obtaining the spatial position coordinates of the detection plate using a first locator at the center of the bottom of the detection plate, and obtaining the spatial position coordinates of the background plate using a second locator at the center of the background plate; adjusting the background plate according to the difference between the spatial position coordinates so that the center line of the background plate and the center line of the detection plate are aligned;

[0008] S4, a processing module is in communication with the distance detection unit and the image recognition unit, and receives the first actual burial depth measured by the distance detection unit and the second actual burial depth calculated by the image recognition unit based on the mapping relationship between color and depth, and takes the average of the first actual burial depth and the second actual burial depth as the actual measurement value of the pipeline to be measured;

[0009] The distance detection unit includes a laser transmitter installed at the center of the top of the detection plate and a receiver installed at the center of the bottom of the background plate; the laser transmitter emits a laser beam to the receiver of the background plate to obtain the first actual buried depth of the pipeline to be measured;

[0010] The image recognition unit includes a light source of the detection plate and a collector installed at the bottom of the background plate. The light source of the detection plate emits a detection beam corresponding to the collector of the background plate to obtain the second actual buried depth of the pipeline to be measured;

[0011] S5: Determine whether the burial depth is qualified based on the difference between the actual measurement value and the theoretical burial depth. Specifically:

[0012] If the difference between the actual measured value and the theoretical burial depth is within the threshold range, it is judged as qualified, otherwise it is judged as unqualified, and whether rework is required is determined based on the difference between the two.

[0013] Preferably, the adjustment process of the levelness of the detection plate is:

[0014] Step 1: Build a deep learning model, collect the actual projection and color data of the emission light beam of the detection board on the background board under different conditions, preprocess the actual projection and actual color data to obtain the original data set, divide the original data set into a test set and a training set, use the training set to train the deep learning model, and evaluate the model performance with the test set;

[0015] Step 2: Adjust the shape and color of the light source of the detection board to emit light beams of different colors and shapes to the background board;

[0016] Step 3: The background plate collects the actual projection and actual color data of the light beam emitted by the detection plate each time;

[0017] Step 4: Input the actual projection and actual color data collected by the background plate into the trained deep learning model;

[0018] Step 5: Calculate the theoretical projection and color data of each emission on the background board according to the theoretical buried depth of the pipeline; and calculate the deviation between the actual projection and actual color data and the theoretical projection and theoretical color data respectively. Determine whether the horizontality of the test board is qualified based on the deviation value:

[0019] If the deviation value is within the threshold range, the levelness of the detection plate is determined to be qualified. If the deviation value is greater than the threshold, the detection plate is adjusted accordingly until the levelness of the detection plate is qualified.

[0020] Preferably, the actual projection and the theoretical projection respectively include the shape and area of ​​the projection;

[0021] The actual color data and the theoretical color data respectively include the saturation, chroma and brightness of the color.

[0022] According to another aspect of the present application, a pipeline burial depth detection system based on image recognition is provided. The detection system adopts a pipeline burial depth detection method based on image recognition. The detection system includes:

[0023] A detection plate is installed on the horizontal section of the top of the pipeline to be tested;

[0024] The background plate is used to assist the detection plate in completing the detection process and is located directly above the detection plate;

[0025] A level adjustment module is installed at the bottom of the detection plate, the level adjustment module is buckled on the top of the pipeline to be tested, and a contact sensor is provided on the side of the level adjustment module facing the pipeline to be tested;

[0026] The installation module is located on the ground of the pipeline to be tested and is used to adjust the position of the background plate and the detection plate;

[0027] The acquisition module includes: an image acquisition unit for acquiring the outlines of the pipeline to be tested and the detection plate, a spatial position acquisition unit for obtaining the spatial position coordinates of the detection plate and the background plate, a distance detection unit for measuring the first actual buried depth of the pipeline to be tested, and an image recognition unit for realizing color recognition and the second actual buried depth;

[0028] The image acquisition unit is located directly above the detection board;

[0029] The spatial position acquisition unit includes a first locator installed at the center of the bottom of the detection plate, and a second locator installed at the center of the background plate;

[0030] The distance detection unit and the image recognition unit are respectively installed on the detection plate and the background plate, and the image recognition unit is arranged on the peripheral side of the distance detection unit;

[0031] The processing control module is respectively connected to the detection board, the contact sensor, the installation module, the level adjustment module, the image acquisition unit, the first locator, the second locator, the distance detection unit and the image recognition unit.

[0032] Preferably, the detection board is an intelligent light-emitting board;

[0033] The center line of the detection plate coincides with the center line of the level adjustment module;

[0034] The distance detection unit includes a laser transmitter mounted at the center of the top of the detection plate and a receiver mounted at the center of the bottom of the background plate;

[0035] The image recognition unit includes a light source of the detection plate and a collector arranged on the background plate;

[0036] The laser emitter is located at the center of the light source;

[0037] The collector is located on the periphery of the receiver, and the collector is arranged corresponding to the light source of the detection board;

[0038] The laser transmitter, the receiver, the light source and the collector are respectively connected to the processing control module;

[0039] The contact sensor is mounted at the center of the bottom of the level adjustment module;

[0040] The second locator is installed at the center of the background plate.

[0041] Preferably, the mounting module is a mechanical gripper.

[0042] Preferably, the level adjustment module comprises a level adjustment portion, a clamping portion and a mounting plate connected sequentially from bottom to top;

[0043] The top and bottom of the mounting plate are respectively provided with a first concave mounting opening and a second concave mounting opening;

[0044] The first locator is located in the first concave mounting opening;

[0045] The contact sensor is installed in the second concave installation opening;

[0046] The detection plate is installed on the top of the mounting plate;

[0047] The clamping parts are respectively located on the outside of the contact sensor, and at least two clamping parts are provided. The two clamping parts are clamped on both sides of the pipeline to be tested by moving closer to or farther away from each other;

[0048] The level adjustment part is an automatic telescopic device, and one telescopic end faces underground.

[0049] Preferably, an adjustable telescopic rod is provided between the clamping portion and the mounting plate;

[0050] The adjustable telescopic rod is arranged along the radial direction of the pipeline to be tested, the telescopic ends of the adjustable telescopic rod are arranged facing each other and are respectively connected to the clamping parts, the opposite ends of the adjustable telescopic rod are respectively connected to the mounting plates, and the adjustable telescopic rod is an automatic telescopic rod.

[0051] Preferably, the clamping portion includes a first clamping portion and a second clamping portion with openings facing each other;

[0052] The first clamping portion and the second clamping portion respectively include a connecting plate and a clamping plate;

[0053] The connecting plates are respectively vertically arranged on the mounting plates, one end of each connecting plate is respectively connected to the telescopic end of the adjusting telescopic rod, and are driven by the adjusting telescopic rod to move along the radial direction of the pipeline to be measured on the mounting plate;

[0054] The clamping plates are respectively installed on opposite sides of the connecting plate, and the opposite sides of the clamping plates are respectively arc-shaped.

[0055] Compared with the prior art, the present invention has the following advantages:

[0056] During the measurement process, the present application accurately obtains the outlines of the pipeline to be measured and the detection plate through the image acquisition unit, and accurately adjusts the position of the detection plate according to the distance between the edges of the two contours to ensure that the center line of the detection plate completely coincides with the center line of the pipeline to be measured. This effectively avoids the burial depth error caused by the deviation of the measurement point. For example, the distance measured on the side of the pipeline will be smaller than the distance measured at the vertex, so that the distance measured by the laser emitter emitting laser from the top center of the detection plate can accurately reflect the actual burial depth of the pipeline to be measured, greatly improving the measurement accuracy. At the same time, the horizontality of the detection plate is finely adjusted using color recognition technology and deep learning models so that it is accurately located on the horizontal section of the pipeline to be measured, further ensuring the accuracy of the measurement benchmark and reducing the source of error.

[0057] This application uses a first locator at the bottom of the detection plate and a second locator at the center of the background plate to obtain the spatial position coordinates of the two. Based on the coordinate difference, the background plate is precisely adjusted to ensure that the background plate and the detection plate are parallel and their center lines coincide. This precise positioning method significantly improves the accuracy of collected projection and color data, providing a more reliable data foundation for deep learning models, thereby enhancing the accuracy of the model, ensuring that the buried depth measurement is based on an accurate relative position relationship, and effectively guaranteeing the reliability of the measurement results.

[0058] In this application, color recognition technology and laser measurement are combined to obtain multiple actual depth detection data (i.e., the first actual depth and the second actual depth), and the average is taken as the actual measurement value, effectively improving the accuracy of pipeline depth detection. Throughout the detection process, from adjusting the contact between the detection plate and the pipeline to calibrating the horizontality and centerline position, each link is closely connected and works together to ensure that each operation provides strong support for the accuracy of the final measurement result, effectively reducing cumulative errors and making the measurement closer to the true value.

[0059] Various operations such as image acquisition, detection data collection, and adjustment of the levelness of the detection plate are automated with the help of deep learning models. Based on the deviation between the actual projection and actual color data and the theoretical data, the levelness of the detection plate is automatically determined and adjusted. The level adjustment unit and the adjustment telescopic rod of the clamping unit of the level adjustment module are both automatic devices, which greatly reduces manual operation errors and significantly improves detection efficiency. In addition, the clamping unit of the detection plate can flexibly change the spacing by adjusting the telescopic rod, and can firmly clamp pipelines of different diameters to be tested, making it widely adaptable to the buried depth detection needs of pipelines of various specifications. It can play an efficient and accurate detection role in different pipeline engineering scenarios, effectively reducing engineering costs and time costs, and improving the overall quality and efficiency of engineering construction and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] Figure 1 This is a schematic diagram of the positions of the detection plate, background plate, and pipeline to be tested in this application;

[0061] Figure 2 Schematic diagram of a vertical section of the test board in this application;

[0062] Figure 3 This is a bottom view of the test board in this application;

[0063] In the figure: 1. Pipe to be tested; 2. Level adjustment part; 3. Connecting plate; 4. Clamping plate; 5. Contact sensor; 6. Mounting plate; 7. First locator; 8. Detection plate; 9. Laser transmitter; 10. Background plate; 11. Receiver; 12. Second locator; 13. Slider; 14. Adjustment telescopic rod; 15. Mounting slot. DETAILED DESCRIPTION

[0064] As attached Figure 1 A pipeline buried depth detection method based on image recognition is shown, and the detection method includes the following steps:

[0065] S1, placing the detection plate on top of the pipeline to be tested, obtaining the contours of the pipeline to be tested and the detection plate respectively, and adjusting the detection plate according to the distance between the contour edges of the pipeline to be tested and the detection plate so that the center line of the detection plate and the center line of the pipeline to be tested are on the same straight line;

[0066] S2: Place the background plate parallel to the detection plate. Based on color recognition technology and deep learning models, adjust the level of the detection plate so that the detection plate is located on the horizontal section of the pipeline to be tested.

[0067] S3, after the detection plate is adjusted to be horizontal, obtaining the spatial position coordinates of the detection plate using a first locator at the center of the bottom of the detection plate, and obtaining the spatial position coordinates of the background plate using a second locator at the center of the background plate; adjusting the background plate according to the difference between the spatial position coordinates so that the center line of the background plate and the center line of the detection plate are aligned;

[0068] S4: The laser emitter at the top center of the detection plate emits a laser beam toward the receiver on the background plate to obtain a first actual buried depth of the pipeline to be measured. The detection plate then emits a detection beam toward the background plate to obtain a second actual buried depth of the pipeline to be measured. The average of the first and second actual buried depths is taken as the actual measured value.

[0069] S5: Determine whether the burial depth is qualified based on the difference between the actual measurement value and the theoretical burial depth. Specifically:

[0070] If the difference between the actual measured value and the theoretical burial depth is within the threshold range, it is judged as qualified, otherwise it is judged as unqualified, and whether rework is required is determined based on the difference between the two.

[0071] It should be noted that:

[0072] In this application, the outline of the pipeline to be tested refers to the outline of a certain section of the pipeline to be tested, rather than the outline of the entire pipeline to be tested. A fixed boundary is circled on the ground, and then the outline of the pipeline to be tested within the boundary is obtained by an image acquisition device. Generally, four points of a certain section of the pipeline to be tested are used, where two points form a group and are located on opposite sides of the pipeline to be tested. The outline is composed of four points.

[0073] In this application, the center line of the detection plate refers to the vertical perpendicular line of the geometric center of the detection plate; the center line of the pipeline to be tested refers to the vertical perpendicular line of the geometric center of the contour projection area of ​​a certain section of the pipeline to be tested; the center line of the background plate refers to the vertical perpendicular line of the geometric center of the background plate.

[0074] Furthermore, the adjustment process of the levelness of the detection plate is as follows:

[0075] Step 1: Build a deep learning model, collect the actual projection and actual color data of the emission light beam of the detection plate on the background plate under different conditions, preprocess the actual projection and actual color data to obtain the original data set, divide the original data set into a test set and a training set according to a reasonable ratio (such as 7:3 or 8:2), use the training set to train the deep learning model, and evaluate the model performance through the test set;

[0076] Step 2: Adjust the shape and color of the light source of the detection board to emit light beams of different colors and shapes to the background board;

[0077] Step 3: The background plate collects the actual projection and actual color data of the light beam emitted by the detection plate each time;

[0078] Step 4: Input the actual projection and actual color data collected by the background plate into the trained deep learning model;

[0079] Step 5: Calculate the theoretical projection and color data of each emission on the background board according to the theoretical buried depth of the pipeline; and calculate the deviation between the actual projection and actual color data and the theoretical projection and theoretical color data respectively. Determine whether the horizontality of the test board is qualified based on the deviation value:

[0080] If the deviation value is within the threshold range, the levelness of the detection plate is determined to be qualified. If the deviation value is greater than the threshold, the detection plate is adjusted accordingly until the levelness of the detection plate is qualified.

[0081] It should be noted that:

[0082] The acceptable depth threshold in this application is determined based on pipeline construction specifications and the allowable error range for the project. Since the allowable depth error in urban gas pipeline construction is within ±0.5 cm according to relevant standards, the threshold for the difference between the actual measured value and the theoretical depth is set at ±0.5 cm. Regarding the threshold for the levelness of the inspection plate, through extensive experiments and simulation analysis, and taking into account pipeline installation accuracy requirements and measurement error propagation, it was determined that the inspection plate is considered level when the shape deviation between the actual and theoretical projections is within 5%, the area deviation is within 3%, and the saturation, chroma, and brightness deviations of the color data are within 0.05, respectively.

[0083] The process of building a deep learning model is as follows:

[0084] 1) Data collection:

[0085] Set up the collection environment: Ensure that the detection plate and background plate are stable and under appropriate lighting conditions to reduce interference from external factors and make the collected data more accurate and consistent.

[0086] Multiple acquisitions: According to the operation in step 2, the detection board emits light beams (such as light beams of different shapes and colors) to the background board multiple times. The background board uses corresponding image acquisition equipment (such as a high-definition camera, etc.) to completely acquire the projection formed by each light beam emitted by the detection board and the corresponding color information, and records them as image data. These image data are accompanied by corresponding color feature descriptions, etc., to form an original data set.

[0087] 2) Data preprocessing:

[0088] Image normalization: For the collected image data, uniformly adjust the image size, pixel range, etc. For example, all images are normalized to a specific width and height pixel value (such as 500×500 pixels) to ensure that the data collected in different batches are consistent in size, which is convenient for subsequent processing.

[0089] Color correction: Since color deviation may occur due to factors such as lighting during the acquisition process, the image color is calibrated to a standard color space (such as the RGB standard color gamut) through a color correction algorithm to ensure the accuracy of the color data.

[0090] Data augmentation: To expand the dataset and improve the generalization ability of the model, operations such as rotation, flipping, and adding noise are used to appropriately transform the image to generate more image data with different forms but the same essential features. For example, the original image can be rotated 10 degrees clockwise, flipped horizontally, etc., and its corresponding color data records are updated accordingly.

[0091] Divide the dataset: Split the processed raw data into a test set and a training set according to a certain ratio (commonly used ratios such as 8:2 or 7:3). The test set is used to evaluate model performance, while the training set is used for model training.

[0092] 3) Feature extraction:

[0093] Choose an appropriate feature extraction algorithm: For example, you can use classic convolutional neural network (CNN) architectures (such as AlexNet and VGG) to automatically extract features from images. The convolutional layers in these networks use kernels of varying sizes to capture local features of varying scales in the image, such as edges and textures. The pooling layers further compress and refine these features, reducing data dimensionality while retaining key features.

[0094] Extracting color-related features: For color data, we can use methods such as statistical color histograms to obtain features such as the distribution ratio of each color channel (R, G, B) in the image. We can also use more complex methods such as color moments to describe color features. These features, together with visual features such as image shape, can be used as input for subsequent model learning.

[0095] 4) Model training:

[0096] Use a common deep learning model for image classification or regression, such as the ResNet deep residual network. Residual connections allow the model to better learn the mapping relationship between image features and target outputs (i.e., the feature representation corresponding to the theoretical projection and theoretical color data).

[0097] Define the loss function: Depending on the specific task requirements, if the goal is to predict the exact position and color of the projection, the mean square error (MSE) can be used as the loss function to measure the error between the theoretical projection and color data predicted by the model and the actual projection and color data.

[0098] Set the optimization algorithm: Commonly used algorithms include stochastic gradient descent (SGD), which dynamically adjusts the model's weight update strategy based on set parameters such as the learning rate, so that the model continuously optimizes and converges in the direction of reducing the loss function.

[0099] Training process: The training set data is input into the selected model, and it is iterated in multiple training rounds (epochs) according to the set batch size (such as 32 images and their corresponding color data each time). The backpropagation algorithm is used to calculate the gradient and update the model weight parameters, so that the model gradually learns the mapping rules from input images and color data to output theoretical projections and theoretical color data.

[0100] 5) Model evaluation and verification (can be performed during training):

[0101] Test on the test set: After a certain number of training rounds, the test set data is input into the trained model to obtain the model's prediction results (i.e., the predicted theoretical projection and theoretical color data).

[0102] Evaluation Metric Calculation: Calculate relevant evaluation metrics. For example, for regression tasks, if predicting continuous values ​​like projected position coordinates, calculate the root mean square error (RMSE) to measure the deviation between the predicted value and the true value. A similar approach is used for color data prediction. Use these metrics to assess model performance. If performance falls short of expectations, adjust model parameters, increase training frequency, optimize data preprocessing, and retrain the model.

[0103] Among them, in one embodiment of the present application, the ResNet-50 architecture was finally selected because it has good performance in processing image feature extraction and regression tasks. After multiple verifications, it can effectively learn the mapping relationship between image features and buried depth data in this pipeline buried depth detection scenario. During training, the learning rate is set to 0.001, the training rounds (epochs) are 100, and the batch size is 64. During the model verification process, if the root mean square error (RMSE) is greater than the set threshold (such as 0.5 cm), the model is optimized by increasing the amount of training data, adjusting the number of network layers, or optimizing the convolution kernel parameters, and retraining and verification until a satisfactory accuracy index is achieved.

[0104] Furthermore, the actual projection and theoretical projection respectively include projection shape and area;

[0105] The actual color data and the theoretical color data respectively include the saturation, chroma and brightness of the color.

[0106] A pipeline buried depth detection system based on image recognition, the detection system comprising:

[0107] The detection plate 8 is installed on the horizontal section of the top of the pipeline 1 to be tested;

[0108] The background plate 10 is used to assist the detection plate 8 in completing the detection process and is located directly above the detection plate 8;

[0109] A level adjustment module is installed at the bottom of the detection plate 8 and is buckled onto the top of the pipeline to be tested 1. A contact sensor 5 is provided on the side of the level adjustment module facing the pipeline to be tested;

[0110] The installation module is located on the ground of the pipeline to be tested and is used to adjust the positions of the background plate 10 and the detection plate 8;

[0111] The acquisition module includes: an image acquisition unit for acquiring the outlines of the pipeline to be tested and the detection plate, a spatial position acquisition unit for obtaining the spatial position coordinates of the detection plate and the background plate, a distance detection unit for measuring the first actual buried depth of the pipeline to be tested, and an image recognition unit for realizing color recognition and the second actual buried depth;

[0112] The image acquisition unit is located directly above the detection board;

[0113] The spatial position acquisition unit includes a first locator 7 installed at the center of the bottom of the detection plate 8, and a second locator 12 installed at the top center of the background plate 10;

[0114] The distance detection unit and the image recognition unit are respectively mounted on the detection plate 8 and the background plate 10, and the image recognition unit is arranged on the peripheral side of the distance detection unit;

[0115] The processing control module is respectively connected to the detection board 8, the contact sensor 5, the installation module, the level adjustment module, the image acquisition unit, the first locator 7, the second locator 12, the distance detection unit and the image recognition unit.

[0116] It should be noted that:

[0117] In this application, the detection plate 8 is placed on the horizontal section of the top of the pipeline 1 to be tested to provide a detection reference;

[0118] The background plate 10 is placed directly above the detection plate 8 to assist in detection.

[0119] The level adjustment module is buckled on the top of the pipeline to be tested 1 and is used to carry the detection plate 8. The level adjustment module is used to adjust the levelness of the detection plate 8, monitor the fitting state with the pipeline to be tested 1 through the contact sensor 5 set at the bottom, and accurately obtain the spatial position coordinates of the detection plate 8 through the first locator 7 set on the inner side of the top.

[0120] The installation module is located on the ground of the pipeline to be tested, and is used to install the detection plate 8 and the level adjustment module on the top of the pipeline to be tested 1, and to adjust the position of the background plate so that it is placed parallel to and directly above the detection plate 8;

[0121] The image acquisition unit in the acquisition module is located directly above the pipeline 1 to be tested and is used to acquire contour images of the pipeline 1 to be tested and the detection plate 8. In this application, the image acquisition unit is any one of an industrial 3D scanner or a 3D line laser profiler.

[0122] The spatial position acquisition unit in the acquisition module includes a first locator 7 located at the center of the bottom of the detection plate 8 and a second locator 12 located at the center of the background plate 10, which are used to obtain the spatial coordinates of the detection plate 8 and the background plate 10 respectively;

[0123] The distance detection unit in the acquisition module is set at the top center of the detection plate 8 and is used to measure the first actual buried depth of the pipeline 1 to be tested; the image recognition unit is used to recognize the color and calculate the second actual buried depth;

[0124] The processing and control module serves as the "intelligent center" of the entire system. In this application, the processing and control module is a programmable controller. It is connected to the image acquisition unit to receive the contour image information of the pipeline to be tested and the detection plate collected by the image acquisition unit. Based on this raw material, the controller uses the image analysis algorithm in S1 to calculate the distance between the contour edges of the pipeline to be tested 1 and the detection plate 8, and then accurately adjusts the position of the detection plate 8 so that the center line of the detection plate 8 coincides with the center line of the pipeline to be tested 1;

[0125] The processing and control module is connected to the image recognition unit to obtain the color recognition data collected by the collector on the background plate 10. In S2, the processing and control module uses the color change information fed back by the image recognition unit in combination with the deep learning model to issue dynamic adjustment instructions for the shape and color of the detection plate 8 to calibrate the horizontal state of the detection plate 8 so that it is located on the horizontal section of the pipeline to be tested.

[0126] The processing and control module is connected to the spatial position acquisition unit to obtain the spatial position coordinates of the detection plate 8 and background plate 10, as reported by the first locator 7 (located at the center of the leveling module facing the detection plate / at the center of the detection plate's bottom) and the second locator 12 (located at the center of the background plate). In S3, based on the difference between these two coordinates, the processing module issues a command to adjust the background plate's posture so that the background plate 10 is parallel to the detection plate 8 and their centerlines coincide.

[0127] The processing control module is in communication with the distance detection unit, receives the first actual buried depth data of the pipeline to be measured, and combines it with the second actual buried depth data inferred from the image recognition unit based on the mapping relationship between color and depth, and completes the average calculation in S4 to obtain the actual measurement value of the pipeline 1 to be measured.

[0128] The processing and control module is connected to the contact sensor 5 of the leveling module, providing real-time sensing of the tightness of contact between the leveling module and the pipeline 1 under test. This ensures the accuracy of the test benchmark during the leveling of the test plate 8 and assists in meeting the test method's requirements for leveling the test plate. By closely collaborating with these units, the processing and control module ensures the orderly advancement and precise execution of each step in the image recognition-based pipeline depth detection method, ensuring the efficient and stable operation of the entire detection system.

[0129] Furthermore, the detection board 8 is an intelligent light-emitting board;

[0130] The center line of the detection plate 8 coincides with the center line of the level adjustment module;

[0131] The distance detection unit includes a laser transmitter 9 installed at the center of the top of the detection plate and a receiver 11 installed at the center of the bottom of the background plate;

[0132] The image recognition unit includes a light source of the detection plate 8 and a collector installed at the bottom of the background plate 10;

[0133] The laser emitter 9 is located at the center of the light source;

[0134] The collector is located on the peripheral side of the receiver 11;

[0135] The laser transmitter 9, the receiver 11, the light source and the collector are respectively connected to the processing control module;

[0136] The contact sensor 5 is installed at the center of the bottom of the level adjustment module;

[0137] The second locator 12 is installed at the center of the background plate 10 .

[0138] It should be noted that:

[0139] In one embodiment of the present application, the intelligent lighting of the detection board 10 (i.e., the intelligent lighting board) is primarily based on light-emitting diode (LED) technology and intelligent control, with the LED being the core component. The color can be adjusted by a program. For example, in the RGB (red, green, and blue) color mode, various colors can be mixed by varying the ratio of the three colors.

[0140] In terms of shape adjustment, the intelligent light-emitting board has multiple small light-emitting units (i.e., light-emitting diodes (LEDs)) built into the board body. These light-emitting units are combined into different shapes through program control, such as circles, squares or other custom shapes.

[0141] In terms of intelligent control, the smart light-emitting panel has an internal control circuit. This circuit is connected to the processing control module and receives signals from it. Upon receiving instructions to adjust the color or shape, the control circuit can control the lighting units to light up or off to create different shapes. This control also controls the LED chips, causing electrons and holes to recombine, releasing energy in the form of photons, thereby emitting light. LED chips made of different materials can emit different colors of light. By adjusting the brightness of different colored LED beads, a variety of colors can be mixed.

[0142] Furthermore, the installation module is a mechanical gripper, and the controllers of the mechanical grippers are respectively connected to the processing control modules.

[0143] It should be noted that the synchronous movement of the detection plate and the level adjustment module, as well as the movement of the background plate, are achieved by mechanical grippers, which are commercially available.

[0144] Furthermore, the level adjustment module includes a level adjustment portion, a clamping portion, and a mounting plate 6 connected sequentially from bottom to top;

[0145] The top and bottom of the mounting plate 6 are respectively provided with a first concave mounting opening and a second concave mounting opening;

[0146] The first locator 7 is located in the first concave mounting opening;

[0147] The contact sensor 5 is installed in the second concave installation opening;

[0148] The depth of the first concave mounting opening matches the overall height of the first locator 7 to prevent affecting the installation of the detection board 8;

[0149] The depth of the second concave mounting opening matches the overall height of the contact sensor 5 , that is, after the contact sensor 5 is installed, it is parallel to the bottom surface of the mounting plate 6 ;

[0150] The laser emitter 9 is mounted on the top of the detection plate 8 in the exact center;

[0151] The receiver 11 is installed at the exact center of the bottom of the background plate 10 , and the second locator 12 is installed at the exact center of the top of the background plate 10 .

[0152] The detection plate 8 is installed on the top of the mounting plate 6, and when the detection plate 8 is installed on the top of the mounting plate 6, the first locator 7 is inserted into the first concave mounting opening;

[0153] The clamping parts are respectively located on the outside of the contact sensor 5. At least two clamping parts are provided. The two clamping parts are clamped on both sides of the pipeline to be tested 1 by moving closer to or farther away from each other, so as to fix the detection plate 8 on the horizontal section of the top of the pipeline to be tested 1 through the level adjustment module.

[0154] The level adjustment part 2 is an automatic telescopic device, and one end of the telescopic device is facing underground. In one embodiment of the present application, the level adjustment part 2 is an electric telescopic rod, and its controller is electrically connected to the processing control module;

[0155] Furthermore, an adjustable telescopic rod 14 is provided between the clamping portion and the mounting plate 6;

[0156] The adjustable telescopic rod 14 is arranged along the radial direction of the pipeline to be tested 1, and the telescopic ends of the adjustable telescopic rod 14 are arranged facing each other and are respectively connected to the clamping parts. The opposite ends of the adjustable telescopic rod 14 are respectively connected to the mounting plate 6. The adjustable telescopic rod 14 is an automatic telescopic rod, and the controller of the adjustable telescopic rod 14 is electrically connected to the processing control module;

[0157] Furthermore, the clamping portion includes a first clamping portion and a second clamping portion with openings facing each other;

[0158] The first clamping portion and the second clamping portion respectively include a connecting plate 3 and a clamping plate 4;

[0159] The connecting plates 3 are respectively vertically arranged on the mounting plates 6, and are respectively connected to the telescopic ends of the adjustable telescopic rods 14, and are driven by the adjustable telescopic rods 14 to move along the radial direction of the pipeline 1 to be measured on the mounting plates 6;

[0160] The clamping plates are respectively installed on opposite sides of the connecting plate, and the opposite sides of the clamping plates are respectively arc-shaped.

[0161] It should be noted that:

[0162] In this application, the size of the mounting plate 6 is larger than that of the detection plate 8; the outer surface of the mounting plate 6 is subjected to anti-corrosion treatment;

[0163] The mounting plate 6 is a metal plate, and the bottom of the detection plate 8 is an electromagnetic plate. The control switch of the electromagnetic plate is electrically connected to the processing control module. The processing control module controls whether the detection plate 8 has magnetism by controlling the power supply status, so as to achieve magnetic connection with the mounting plate 6;

[0164] In this application, in order to realize that the connecting plate 3 can move on the mounting plate 6 and ensure its stability, as shown in the attached Figure 2 and 3 As shown, in one embodiment of the present application, two groups of clamping portions (i.e., a total of four) are spaced apart along the extension direction of the mounting plate 6 (i.e., the axial direction of the pipeline to be measured 1), and two mounting grooves 15 are correspondingly provided at the bottom of the mounting plate 6. The mounting grooves 15 are arranged along the longitudinal direction of the mounting plate 6 (i.e., the radial direction of the pipeline to be measured 1), and two adjustable telescopic rods 14 are provided in one of the mounting grooves 15. The two adjustable telescopic rods 14 are respectively arranged along the extension direction of the mounting groove 15, and the opposite ends of the two adjustable telescopic rods 14 are respectively fixed to the ends of the mounting groove 15. The opposite ends of the two adjustable telescopic rods 14 are telescopic ends, which are respectively fixedly connected to the connecting plate 3, and the top end of the connecting plate 3 is perpendicularly connected to the adjustable telescopic rod 14;

[0165] The two side walls of the mounting groove 15 are provided with corresponding sliding grooves, and the two sides of the connecting plate 3 at one end of the mounting groove are provided with corresponding sliders 13. The sliders 13 are respectively connected to the opposite sides of the same connecting plate 3, and the sliders 13 are slidably connected to the sliding grooves.

[0166] The bottom end of the connecting plate 3 is fixedly connected to the top end of the levelness adjusting part 2, wherein the center lines of the levelness adjusting part 2 and the connecting plate 3 are located on the same straight line; the opposite sides of the two connecting plates 3 located in the same mounting groove 15 are respectively connected to the clamping plates 4, and the opposite sides of the two clamping plates 4 are arc-shaped surfaces.

[0167] This application is also applicable to the later burial depth monitoring of the pipeline to be tested after the construction is completed. It is realized by electromagnetic wave transmitter and matching electromagnetic wave receiver. The specific implementation method is as follows:

[0168] During the construction process, after the difference between the actual buried depth of the pipeline to be tested and the theoretical buried depth is detected to be within the qualified buried depth threshold, the processing module controls the magnetic plate at the bottom of the detection plate to be powered off, and then the detection plate and the first positioner are taken out together by the mechanical gripper, and the electromagnetic wave transmitter is placed in the first concave installation opening. After that, the entire level adjustment module and the electromagnetic wave transmitter are buried together with the pipeline to be tested, and the electromagnetic wave receiver is installed on the ground near the pipeline to be tested. Pay attention to the waterproof and anti-corrosion treatment of the electromagnetic wave receiver and the electromagnetic wave; the battery transmitter sends electromagnetic waves to the corresponding electromagnetic wave receiver, records the electromagnetic waves, and uses the electromagnetic waves as a reference;

[0169] In the later monitoring process, when extreme weather occurs or there are major environmental changes in the area where the pipeline to be tested is located, the processing and control module sends electromagnetic waves to the corresponding electromagnetic wave receiver through the electromagnetic wave transmitter. When the electromagnetic wave data transmitted by the electromagnetic wave transmitter to the electromagnetic wave receiver deviates greatly from the reference electromagnetic wave and exceeds the safety range, the processing and control module issues an early warning prompt to promptly discover whether the buried depth of the pipeline to be tested has changed significantly.

[0170] The specific testing process of this application is as follows:

[0171] 1. Make the center line of the pipe to be tested 1 and the center line of the detection plate on the same straight line. The specific process is as follows:

[0172] The outline of the pipeline to be tested is acquired through the image acquisition unit, and its vertical center line is determined through algorithm processing in the processing and control module; the detection plate 8 is powered on, so that the detection plate 8 is installed on the top of the level adjustment module, and the detection plate 8 and the level adjustment module are lowered. After the level adjustment module contacts the pipeline to be tested 1, the contact sensor 5 is connected to the processing and control module and sends a signal to the processing and control module. At this time, the processing and control module controls the image acquisition unit to capture the outline of the detection plate 8 and the outline of the pipeline to be tested 1. According to the distance between the outline of the detection plate 8 and each side of the outline of the pipeline to be tested 1, the corresponding control installation module adjusts the detection plate 8 and the level adjustment module so that the center line of the detection plate 8 and the level adjustment module is on the same straight line with the center line of the pipeline to be tested 1; to ensure the accuracy of the detection benchmark, this process is achieved by using a high-precision image analysis algorithm;

[0173] 2. Adjust the levelness of the detection plate so that the detection plate 8 is located on the horizontal section of the pipeline 1 to be tested. Specifically:

[0174] The processing control module emits light beams of different shapes and colors to the background plate 10 by repeatedly changing the luminous shape and color of the detection plate 8. The collector on the background plate 10 responds by collecting data on the luminous shape, projection area, and synchronized color data for each time. The collected projection and color data are pre-processed and divided into a training set, a test set, and a training learning model. The new collected data is then input to obtain the actual projection and actual color. The theoretical projection and theoretical color data are calculated based on the theoretical burial depth. The detection plate 8 is adjusted according to the deviation value until the deviation value is within the threshold range, and the horizontality of the detection plate 8 is determined to be qualified. At this time, the detection plate 8 is located on the horizontal section of the pipeline 1 to be tested.

[0175] 3. Based on the coordinate deviation of the spatial position coordinates of the first locator 7 and the spatial position coordinates of the second locator 12, the processing and control module controls the installation module to adjust the background plate 10 accordingly so that the center line of the background plate 10, the center line of the detection plate 8, and the center line of the pipeline to be tested 1 are on the same straight line;

[0176] 4. The laser transmitter 9 installed at the top center of the detection board emits a laser beam to the receiver 11 to obtain the first actual burial depth. The detection board emits a detection beam to obtain the second actual burial depth. The average of the two is the actual measurement value to improve data reliability.

[0177] 5. Measurement result judgment: Compare the actual measurement value with the theoretical burial depth. If the difference is within the threshold, it is considered qualified. Otherwise, it is unqualified and rework is determined based on the difference, achieving efficient evaluation and quality control of the test results.

[0178] 6. If the buried depth of the pipeline to be tested is determined to be qualified, the detection board 8 is powered off through the processing control module, and the detection board 8 is taken out through the electromagnetic clamp, and the level adjustment module is buried synchronously with the pipeline to be tested.

Claims

1. A pipeline buried depth detection method based on image recognition, characterized in that: The detection method comprises the following steps: S1, placing the detection plate on the top of the pipeline to be tested, respectively obtaining the contours of the pipeline to be tested and the detection plate, and adjusting the detection plate according to the distance between the contour edges of the pipeline to be tested and the detection plate, so that the center line of the detection plate and the center line of the pipeline to be tested are on the same straight line; S2, place the background plate parallel to the detection plate, and adjust the horizontality of the detection plate based on color recognition technology and deep learning model so that the detection plate is located on the horizontal section of the pipeline to be tested; S3, after the detection plate is adjusted to be horizontal, the spatial position coordinates of the detection plate are obtained by a first locator at the center of the bottom of the detection plate, and the spatial position coordinates of the background plate are obtained by a second locator at the center of the background plate; the background plate is adjusted according to the difference between the spatial position coordinates of the two, so that the center line of the background plate and the center line of the detection plate are located on the same straight line; S4, the processing module is in communication with the distance detection unit and the image recognition unit respectively, the processing module receives the first actual burial depth measured by the distance detection unit and the second actual burial depth inferred from the image recognition unit according to the mapping relationship between color and depth, and takes the average of the first actual burial depth and the second actual burial depth as the actual measurement value of the pipeline to be measured; The distance detection unit includes a laser transmitter installed at the center of the top of the detection plate and a receiver installed at the center of the bottom of the background plate; the laser transmitter correspondingly emits a laser beam to the receiver of the background plate to obtain the first actual buried depth of the pipeline to be detected; The image recognition unit includes a light source of the detection plate and a collector installed at the bottom of the background plate. The light source of the detection plate emits a detection beam to the collector of the background plate to obtain the second actual buried depth of the pipeline to be detected. S5, judging whether the burial depth is qualified according to the difference between the actual measured value and the theoretical burial depth, specifically: If the difference between the actual measured value and the theoretical burial depth is within the threshold range, it is judged as qualified, otherwise it is judged as unqualified, and whether rework is required is determined based on the difference between the two.

2. The pipeline buried depth detection method based on image recognition according to claim 1 is characterized in that: The process of adjusting the levelness of the test plate is as follows: Step 1: Build a deep learning model, collect the actual projection and actual color data of the emission light beam of the detection board on the background board under different conditions, pre-process the actual projection and actual color data to obtain the original data set, divide the original data set into a test set and a training set, use the training set to train the deep learning model, and evaluate the model performance through the test set; Step 2: Adjust the shape and color of the light source of the detection board to emit light beams of different colors and shapes to the background board; Step 3: The background plate correspondingly collects the actual projection and actual color data of the light beam emitted by the detection plate each time; Step 4: Input the actual projection and actual color data collected by the background plate into the trained deep learning model; Step 5: According to the theoretical buried depth of the pipeline, calculate the theoretical projection and theoretical color data of each emission on the background board; and calculate the deviation values ​​of the actual projection and actual color data from the theoretical projection and theoretical color data respectively, and judge whether the horizontality of the detection board is qualified according to the deviation value: If the deviation value is within the threshold range, the horizontality of the detection plate is determined to be qualified. If the deviation value is greater than the threshold, the detection plate is adjusted accordingly until the horizontality of the detection plate is qualified.

3. The pipeline buried depth detection method based on image recognition according to claim 2 is characterized in that: The actual projection and the theoretical projection respectively include the shape and area of ​​the projection; The actual color data and the theoretical color data include the saturation, chroma and brightness of the color respectively.

4. A pipeline buried depth detection system based on image recognition, characterized in that: The detection system adopts the pipeline burial depth detection method based on image recognition as described in any one of claims 1 to 3, and the detection system includes: A detection plate is installed on a horizontal section of the top of the pipeline to be tested; The background plate is used to assist the detection plate in completing the detection process and is located directly above the detection plate; A level adjustment module is installed at the bottom of the detection plate, the level adjustment module is buckled on the top of the pipeline to be tested, and a contact sensor is provided on the side of the level adjustment module facing the pipeline to be tested; The installation module is located on the ground of the pipeline to be tested and is used to adjust the positions of the background plate and the detection plate; The acquisition module includes: an image acquisition unit for acquiring the contours of the pipeline to be tested and the detection plate, a spatial position acquisition unit for acquiring the spatial position coordinates of the detection plate and the background plate, a distance detection unit for measuring the first actual buried depth of the pipeline to be tested, and an image recognition unit for realizing color recognition and a second actual buried depth; The image acquisition unit is located directly above the detection board; The spatial position acquisition unit includes a first locator installed at the center of the bottom of the detection plate, and a second locator installed at the center of the background plate; The distance detection unit and the image recognition unit are respectively installed on the detection plate and the background plate, and the image recognition unit is arranged on the peripheral side of the distance detection unit; The processing control module is respectively connected to the detection board, the contact sensor, the installation module, the level adjustment module, the image acquisition unit, the first locator, the second locator, the distance detection unit and the image recognition unit.

5. The pipeline buried depth detection system based on image recognition according to claim 4 is characterized in that: The detection board is an intelligent light-emitting board; The center line of the detection plate coincides with the center line of the level adjustment module; The distance detection unit includes a laser transmitter installed at the center of the top of the detection plate and a receiver installed at the center of the bottom of the background plate; The image recognition unit includes a light source of the detection plate and a collector arranged on the background plate; The laser emitter is located at the center of the light source; The collector is located on the periphery of the receiver, and the collector is arranged corresponding to the light source of the detection board; The laser transmitter, the receiver, the light source and the collector are respectively connected to the processing control module; The contact sensor is mounted at the center of the bottom of the level adjustment module; The second locator is installed at the center of the background plate.

6. The pipeline buried depth detection system based on image recognition according to claim 4 is characterized in that: The mounting module is a mechanical gripper.

7. The pipeline buried depth detection system based on image recognition according to claim 4 is characterized in that: The level adjustment module comprises a level adjustment part, a clamping part and a mounting plate connected in sequence from bottom to top; The top and bottom of the mounting plate are respectively provided with a first concave mounting opening and a second concave mounting opening; The first locator is located in the first concave mounting opening; The contact sensor is installed in the second concave installation opening; The detection plate is installed on the top of the mounting plate; The clamping parts are respectively located on the outside of the contact sensor, and at least two clamping parts are correspondingly provided. The two clamping parts are clamped on both sides of the pipeline to be tested by moving closer to or farther away from each other; The level adjustment part is an automatic telescopic device, and one telescopic end faces underground.

8. The pipeline buried depth detection system based on image recognition according to claim 7 is characterized in that: Adjustable telescopic rods are respectively arranged between the clamping portion and the mounting plate; The adjustable telescopic rod is arranged along the radial direction of the pipeline to be tested, the telescopic ends of the adjustable telescopic rod are arranged facing each other and are respectively connected to the clamping parts, the opposite ends of the adjustable telescopic rod are respectively connected to the mounting plates, and the adjustable telescopic rod is an automatic telescopic rod.

9. The pipeline buried depth detection system based on image recognition according to claim 7 is characterized in that: The clamping portion includes a first clamping portion and a second clamping portion with openings facing each other; The first clamping portion and the second clamping portion respectively include a connecting plate and a clamping plate; The connecting plates are respectively vertically arranged on the mounting plates, one end of the connecting plates is respectively connected to the telescopic end of the adjusting telescopic rod, and are driven by the adjusting telescopic rod to move along the radial direction of the pipeline to be tested on the mounting plate; The clamping plates are respectively installed on opposite sides of the connecting plate, and the opposite sides of the clamping plates are respectively arc-shaped.

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