Wall surface clearing degree recognition device and use method thereof

Abstract

The invention relates to a wall clearance degree recognition device and a use method thereof.The wall clearance degree recognition device comprises an imaging system, an image processing module and a sensor control module, the imaging system comprises a structured light projection module, an infrared receiving camera and an auxiliary infrared receiving camera, and the structured light projection module, the infrared receiving camera and the auxiliary infrared receiving camera cooperate to form a 3D binocular structured light collection unit which is used for scanning a to-be-detected wall line by line; acquiring three-dimensional point cloud data; the image processing module is used for performing cutting processing on the three-dimensional point cloud data, segmenting continuous data into single-frame complete 3D images with 1.5 m sections, calculating wall flatness parameters based on a triangulation principle, and executing over-limit judgment; the sensor control module controls the triggering time of the imaging system, data acquisition is synchronous, and based on an overrun judgment result of the image processing module, a detection process is controlled in a linkage mode to advance to a next section or return to be re-cleared and then re-measured. According to the invention, manual point-by-point measurement is replaced by automatic scanning, rapid and accurate detection of a large-area wall surface is realized, and the limitation of strong light, high altitude and other scenes is broken through.

Description

Technical Field

[0001] This invention relates to the field of building engineering quality inspection technology, and in particular to a wall surface cleaning degree identification device and its usage method. Background Technology

[0002] With the acceleration of urban renewal, the flatness inspection of the exterior wall decoration layer after the old building has been removed has become a crucial step in ensuring the quality of subsequent construction. Currently, wall flatness inspection mainly relies on the following methods, but these methods have many technical shortcomings: Manual inspection method: measuring point by point with a straightedge and feeler gauge is inefficient and affected by the standardization of operation and human reading errors. The measurement accuracy is difficult to control and cannot meet the high precision requirements of large-scale projects. 2D visual inspection method: The method uses an industrial camera to capture 2D images of the wall surface and analyzes the surface undulation based on the grayscale comparison of the images. However, it cannot obtain three-dimensional spatial information, has low flatness measurement accuracy, and is easily affected by lighting conditions. Traditional 3D inspection method: It obtains point cloud data of the wall through 3D camera and directly calculates the distance from the point to the plane. Although it can obtain point cloud data, it requires fixed installation on the ground. When the ground is soft or uneven at the construction site, it is easy to shake, which will cause data distortion. Moreover, the error can reach 15% in strong light environment. High-altitude operation requires the construction of scaffolding or the use of suspended baskets, which poses safety hazards and consumes a lot of resources. Infrared thermal imaging detection method: It is mainly based on the principle that the heat transfer characteristics are different when the external wall decoration layer and the base layer are well bonded and when there are defects such as hollowness. It is only applicable to specific types of decoration. It has poor adaptability to walls with mixed-color decorative bricks, paints and uneven surface decorations, and cannot directly quantify flatness parameters.

[0003] The core drawbacks of existing technologies are that efficiency and accuracy are difficult to balance, they have weak adaptability to complex scenarios, and data visualization is insufficient, which cannot meet the needs of large-area, high-precision, and intelligent wall removal quality inspection in urban renewal projects. Summary of the Invention

[0004] This invention provides a wall surface cleaning degree identification device and its usage method to solve the above-mentioned technical problems.

[0005] To address the aforementioned technical problems, this invention provides a wall cleaning degree recognition device, comprising an imaging system, an image processing module, and a sensor control module. The imaging system includes a structured light projection module, an infrared receiving camera, and an auxiliary infrared receiving camera. The three work together to form a 3D binocular structured light acquisition unit, which is used to scan the wall surface to be detected line by line to acquire three-dimensional point cloud data. The image processing module is used to cut the three-dimensional point cloud data, dividing the continuous data into single-frame complete 3D images of 1.5m segments, calculating the wall flatness parameters based on the triangulation principle, and performing over-limit judgment. The sensor control module is used to control the triggering timing and data acquisition synchronization of the imaging system, and based on the over-limit judgment result of the image processing module, to control the detection process to advance to the next segment or to revert to re-testing after clearing.

[0006] Preferably, the 3D binocular structured light acquisition unit of the imaging system is equipped with a red line laser to capture the three-dimensional shape of the wall surface through line-by-line scanning.

[0007] Preferably, the over-limit judgment condition of the image processing module is: the wall flatness value > p1, and the corresponding over-limit area > a1, where p1 is a preset flatness threshold and a1 is a preset over-limit area threshold.

[0008] Preferably, the flatness threshold is preset to 5mm for a clean surface and 8mm for a mixed surface.

[0009] Preferably, it also includes a data storage module, which is used to store at least the three-dimensional point cloud data, wall flatness parameters, location and parameters of the out-of-limit area, and detection time and segment information.

[0010] Preferably, the data storage module supports data traceability and visualization export.

[0011] The present invention also provides a method of using the wall cleaning degree identification device as described above, comprising the following steps: Step 1: After the initial wall cleaning is completed, the sensor control module triggers the imaging system to start, and the structured light projection module emits a linear laser to the wall to be inspected. The inspection area is the 1.5m wall that has just been cleaned, and the initial 3D data of the wall is obtained. Step 2: The image processing module segments the initial 3D data to generate a single-frame complete 3D image; Step 3: The infrared receiving camera and the auxiliary infrared receiving camera capture deformed structured light images reflected from the wall surface. After image preprocessing and structured light decoding, the first flatness calculation is performed based on the principle of triangulation. Step 4: Perform a recalculation of the wall flatness corresponding to the single-frame complete 3D image, and correct or verify the initial calculation results; Step 5: Determine whether there is an over-limit area based on the preset threshold. If the flatness is ≤p1 or the area of ​​the over-limit area is ≤a1, proceed to the next section cleaning operation; if the flatness is >p1 and the area of ​​the over-limit area is >a1, record the over-limit area information, wait for the re-cleaning operation to be completed, and repeat steps 1 to 5 for retesting. Step 6: After all sections have been detected, the data storage module saves all the detection data and provides a visual result.

[0012] Preferably, in step 3, the image preprocessing includes at least denoising and enhancement operations to remove outliers in the point cloud data; the structured light decoding includes restoring the encoded information of the deformed structured light image.

[0013] Preferably, the formula for calculating the flatness is: Where (x) i ,y i ,z i ) represents the point cloud coordinates, and A, B, C, and D are the parameters of the reference plane equation.

[0014] Preferably, in step 5, the information on the out-of-limit area includes the location of the out-of-limit area and the maximum deviation d. max Average deviation Root mean square deviation d rms And the percentage of exceeding the limit, η.

[0015] Compared with the prior art, the wall cleaning degree identification device and its usage method provided by the present invention have the following advantages: 1. This invention replaces manual point-by-point measurement with automated scanning, reducing manpower input and enabling rapid detection of large-area walls, greatly improving detection efficiency and coverage. At the same time, it eliminates the need for scaffolding, reducing the risk of working at heights. 2. This invention overcomes environmental limitations such as strong light and uneven ground, ensuring detection accuracy and stability in complex scenarios. It avoids damage to the wall surface through non-contact measurement, ensuring the objectivity and reliability of the detection data and meeting millimeter-level accuracy requirements. 3. This invention can quickly collect three-dimensional data of the wall surface, intuitively mark the flatness deviation area, realize the digitization and visualization of data, provide accurate basis for quality assessment and cleaning operations, and improve the intelligent level of project quality control. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the wall cleaning degree recognition device in a specific embodiment of the present invention; Figure 2 This is a flowchart illustrating the usage method of the wall cleaning degree recognition device in a specific embodiment of the present invention.

[0017] In the diagram: 1-structured light projection module, 2-infrared receiving camera, 3-auxiliary infrared receiving camera. Detailed Implementation

[0018] To illustrate the technical solutions of the invention in more detail, specific embodiments are listed below to demonstrate the technical effects; it should be emphasized that these embodiments are used to illustrate the invention and not to limit the scope of the invention.

[0019] The wall cleaning degree recognition device provided by this invention, such as Figure 1 As shown, it includes an imaging system, an image processing module, and a sensor control module. The image processing module and the sensor control module can be integrated into the imaging system to form an integrated detection device, which is convenient for on-site deployment and operation.

[0020] The imaging system includes a structured light projection module 1, an infrared receiving camera 2, and an auxiliary infrared receiving camera 3. These three components work together to form a 3D binocular structured light acquisition unit, used to scan the wall surface to be inspected line by line to acquire three-dimensional point cloud data. In some embodiments, the 3D binocular structured light acquisition unit of the imaging system is paired with a red line laser to capture the three-dimensional shape of the wall surface through line-by-line scanning. A single image can cover a wall area up to 1.5 meters long, ensuring the spatial continuity and integrity of the point cloud data.

[0021] The image processing module is used to segment the three-dimensional point cloud data, dividing the continuous data into single-frame complete 3D images of 1.5m segments. It calculates the wall flatness parameters based on the triangulation principle and performs an over-limit judgment. In some embodiments, the over-limit judgment condition of the image processing module is: the wall flatness value > p1, and the corresponding over-limit area > a1, where p1 is a preset flatness threshold and a1 is a preset over-limit area threshold. In this embodiment, the flatness threshold is preset to 5mm for fair-faced walls and 8mm for plastered walls, and can be flexibly adjusted according to different construction scenarios.

[0022] The sensor control module is used to control the triggering timing and data acquisition synchronization of the imaging system. Based on the over-limit judgment result of the image processing module, it can also control the detection process to advance to the next segment or revert to re-testing after clearing, thereby realizing the automated closed loop of the detection process.

[0023] This invention replaces manual point-by-point measurement with automated scanning, reducing manpower input and enabling rapid detection of large-area walls, significantly improving detection efficiency and coverage. At the same time, it eliminates the need for scaffolding, reducing the risk of working at heights. This invention overcomes environmental limitations such as strong light and uneven ground, ensuring detection accuracy and stability in complex scenarios. Non-contact measurement avoids damage to the wall, ensuring the objectivity and reliability of the detection data and meeting millimeter-level accuracy requirements.

[0024] In some embodiments, the device further includes a data storage module for storing at least the three-dimensional point cloud data and wall flatness parameters (including the maximum deviation d). max Average deviation Root mean square deviation d rms The data storage module includes the location and parameters of the out-of-limit areas, as well as the detection time and section information. In some embodiments, the data storage module supports data traceability and visualization export, and can generate 3D point cloud maps, out-of-limit area marking maps, and flatness statistical reports, providing a basis for quality assessment and engineering traceability.

[0025] The present invention also provides a method of using the wall cleaning degree identification device as described above, such as... Figure 2 As shown, it includes the following steps: Step 1: After the initial wall cleaning is completed, the sensor control module triggers the imaging system to start. The structured light projection module 1 emits a linear laser to the wall to be inspected. The inspection area is the 1.5m wall that has just been cleaned. The 3D binocular structured light acquisition unit simultaneously acquires the initial 3D data of the wall. At this time, the cleaning equipment remains stationary to avoid vibration interfering with the accuracy of data acquisition.

[0026] Step 2: The image processing module segments the initial 3D data and generates a single complete 3D image according to a 1.5m segment length, ensuring that each frame of data corresponds to an independent detection unit, which facilitates accurate location of defects.

[0027] Step 3: The infrared receiving camera 2 and the auxiliary infrared receiving camera 3 capture deformed structured light images reflected from the wall surface. After image preprocessing and structured light decoding, an initial flatness calculation is performed based on the triangulation principle. The flatness calculation requires a preset evaluation standard (such as statistical analysis of the deviation values ​​of each point on the wall relative to a reference plane) to obtain the overall flatness data of that section of the wall. In some embodiments, the image preprocessing includes at least denoising and enhancement operations to remove outliers from the point cloud data. The structured light decoding includes restoring the encoded information of the deformed structured light image to achieve rapid localization of laser feature points.

[0028] In some embodiments, the flatness calculation algorithm directly converts the three-dimensional coordinates into flatness deviation (calculated using the core formula of associated triangulation to the normal distance to the reference plane; the specific formula for flatness calculation is as follows:) Where (x) i ,y i ,z i ) represents the point cloud coordinates, and A, B, C, and D are the parameters of the reference plane equation.

[0029] Step 4: Perform a verification calculation on the wall flatness corresponding to the single-frame complete 3D image. For the wall flatness calculated initially, perform the wall flatness calculation operation again. The purpose of this step is to verify the initial calculation results and ensure the accuracy of the flatness data. If there are errors in the initial calculation, the verification calculation can correct the results and provide reliable data for subsequent wall flatness determination; if the initial calculation results are accurate, the verification calculation can further verify the validity of the data.

[0030] Step 5: Determine if there is an over-limit area based on a preset threshold. Based on the flatness calculation result, determine if the wall area corresponding to a single frame image exceeds the limit. If the flatness is ≤ p1 or the area of ​​the over-limit area is ≤ a1, it indicates that the wall area corresponding to the current single frame image does not exceed the limit, and the current segment is deemed qualified, proceeding to the next segment clearing operation. If the flatness is > p1 and the area of ​​the over-limit area is > a1, record the over-limit area information (including the specific location, flatness value, and area of ​​the over-limit area), wait for the re-clearing operation to complete, and repeat steps 1-5 for retesting. In some embodiments, the over-limit area information includes the location of the over-limit area and the maximum deviation d. max Average deviation Root mean square deviation d rms The percentage of deviations exceeding the limit, η, is used to quantify extreme deviations, overall trends, and dispersion of the wall surface. A passing threshold is set (p=5mm for fair-faced walls and p=8mm for plastered walls), and the area corresponding to the point cloud with deviations exceeding the threshold is statistically analyzed. Calculate the percentage of exceeding limits η: S 超限 S represents the area of ​​the point cloud region where the deviation exceeds the threshold. 总 This represents the total area of ​​the detection region in a single frame.

[0031] Step 6: After all sections have been cut, the flatness calculation (including the initial calculation and the verification calculation) and the judgment of exceeding the limit of the single frame 3D image have been completed. The data storage module saves all the detection data (including normal area data and out-of-limit area record data) and provides feedback on the visualization results. The operator can quickly grasp the quality of wall cleaning through the exported detection report and guide the subsequent construction.

[0032] In summary, the wall surface cleaning degree identification device and its usage method provided by the present invention include an imaging system, an image processing module, and a sensor control module. The imaging system includes a structured light projection module 1, an infrared receiving camera 2, and an auxiliary infrared receiving camera 3, which work together to form a 3D binocular structured light acquisition unit for scanning the wall surface to be detected line by line to acquire three-dimensional point cloud data. The image processing module is used to segment the three-dimensional point cloud data, dividing the continuous data into single-frame complete 3D images in 1.5m segments, calculating the wall surface flatness parameters based on the triangulation principle, and performing over-limit judgment. The sensor control module is used to control the triggering timing and data acquisition synchronization of the imaging system, and based on the over-limit judgment result of the image processing module, to control the detection process to advance to the next segment or retreat to re-cleaning and retesting, realizing an automated closed loop of the detection process. This invention replaces manual point-by-point measurement with automated scanning, reducing manpower input and enabling rapid detection of large-area walls, significantly improving detection efficiency and coverage. At the same time, it eliminates the need for scaffolding, reducing the risk of working at heights. This invention overcomes environmental limitations such as strong light and uneven ground, ensuring detection accuracy and stability in complex scenarios. Non-contact measurement avoids damage to the wall, ensuring the objectivity and reliability of the detection data and meeting millimeter-level accuracy requirements.

[0033] Obviously, those skilled in the art can make various modifications and variations to the invention without departing from the spirit and scope of the invention. Therefore, if these modifications and variations fall within the scope of the claims of the invention and their equivalents, the invention is also intended to include these modifications and variations.

Claims

1. A wall surface cleaning degree recognition device, characterized in that, Includes an imaging system, an image processing module, and a sensor control module. The imaging system includes a structured light projection module, an infrared receiving camera, and an auxiliary infrared receiving camera. The three work together to form a 3D binocular structured light acquisition unit, which is used to scan the wall surface to be detected line by line to acquire three-dimensional point cloud data. The image processing module is used to cut the three-dimensional point cloud data, dividing the continuous data into single-frame complete 3D images of 1.5m segments, calculating the wall flatness parameters based on the triangulation principle, and performing over-limit judgment. The sensor control module is used to control the triggering timing and data acquisition synchronization of the imaging system, and based on the over-limit judgment result of the image processing module, to control the detection process to advance to the next segment or to revert to re-testing after clearing.

2. The wall surface cleaning degree identification device as described in claim 1, characterized in that, The imaging system's 3D binocular structured light acquisition unit, paired with a red line laser, captures the three-dimensional shape of the wall surface through line-by-line scanning.

3. The wall cleaning degree identification device as described in claim 1, characterized in that, The over-limit judgment condition of the image processing module is: the wall flatness value > p1, and the corresponding over-limit area > a1, where p1 is a preset flatness threshold and a1 is a preset over-limit area threshold.

4. The wall surface cleaning degree identification device as described in claim 3, characterized in that, The flatness threshold is preset to 5mm for a clean surface and 8mm for a mixed surface.

5. The wall surface cleaning degree identification device as described in claim 1, characterized in that, It also includes a data storage module, which is used to store at least the three-dimensional point cloud data, wall flatness parameters, location and parameters of the out-of-limit area, and detection time and section information.

6. The wall surface cleaning degree identification device as described in claim 5, characterized in that, The data storage module supports data traceability and visualization export.

7. A method of using a wall surface cleaning degree identification device as described in any one of claims 1 to 5, characterized in that, Includes the following steps: Step 1: After the initial wall cleaning is completed, the sensor control module triggers the imaging system to start, and the structured light projection module emits a linear laser to the wall to be inspected. The inspection area is the 1.5m wall that has just been cleaned, and the initial 3D data of the wall is obtained. Step 2: The image processing module segments the initial 3D data to generate a single-frame complete 3D image; Step 3: The infrared receiving camera and the auxiliary infrared receiving camera capture deformed structured light images reflected from the wall surface. After image preprocessing and structured light decoding, the first flatness calculation is performed based on the principle of triangulation. Step 4: Perform a recalculation of the wall flatness corresponding to the single-frame complete 3D image, and correct or verify the initial calculation results; Step 5: Determine whether there is an over-limit area based on the preset threshold. If the flatness is ≤p1 or the area of ​​the over-limit area is ≤a1, proceed to the next section cleaning operation; if the flatness is >p1 and the area of ​​the over-limit area is >a1, record the over-limit area information, wait for the re-cleaning operation to be completed, and repeat steps 1 to 5 for retesting. Step 6: After all sections have been detected, the data storage module saves all the detection data and provides a visual result.

8. The method of use as described in claim 7, characterized in that, In step 3, the image preprocessing includes at least denoising and enhancement operations to remove outliers in the point cloud data; the structured light decoding includes restoring the encoded information of the deformed structured light image.

9. The method of use as described in claim 7, characterized in that, The formula for calculating the flatness is: Where (x) i ,y i ,z i ) represents the point cloud coordinates, and A, B, C, and D are the parameters of the reference plane equation.

10. The method of use as described in claim 7, characterized in that, In step 5, the information on the out-of-limit area includes the location of the out-of-limit area and the maximum deviation d. max Average deviation Root mean square deviation d rms And the percentage of exceeding the limit, η.

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