A macro measurement device and method

Abstract

The present invention relates to the technical field of underwater sediment measurement, and particularly to a macro measurement device and method. The present invention proposes the following solutions. First, through global image grayscale conversion and feature extraction, an initial locally selected area is screened out, and the local area is iteratively adjusted to ensure that the macro image can accurately reflect the particle characteristics in the global image. Subsequently, Laplacian pyramid decomposition is used to extract high-frequency edge information, and background noise is removed by gray variance to generate a particle area map. Further, an adhesion particle cutting technology is applied to separate adjacent particles, and finally, a high-quality sediment image is generated through image correction. This method can reduce the macro image acquisition frequency, improve the accuracy of particle measurement, reduce the complexity of data processing, and has high measurement accuracy and practical value.

Description

Technical Field

[0001] The present invention relates to the technical field of underwater sediment measurement, and particularly to a macro measurement device and method. Background Art

[0002] The particle size distribution of sediment particles in water bodies is of great significance for evaluating water quality, studying the movement law of sediment, flood control and disaster reduction, etc. In the prior art, common laser diffraction methods or ultrasonic scattering methods require expensive equipment and complex operations; screening methods, sedimentation methods, and microscopic methods are generally carried out in laboratories, and the efficiency of on-site real-time measurement is relatively low. In addition, factors such as insufficient underwater light, interference from suspended substances, and easy contamination of the lens make it quite difficult to perform stable high-resolution imaging on site.

[0003] Existing underwater sediment particle measurement methods usually rely on low-resolution overall images or directly collect high-resolution macro images for particle analysis. However, it is difficult to clearly identify the details of particles from low-resolution overall images, resulting in blurred particle edges, which in turn affects the accuracy of particle size measurement. On the other hand, although macro images can provide higher resolution and clearer particle edges, due to their large amount of data and high acquisition frequency, they often lead to excessive storage and calculation burdens. Moreover, in complex stacking and occlusion situations, particles may overlap or occlude each other, increasing the difficulty of measurement. Therefore, how to reduce the macro image acquisition frequency and optimize the image processing efficiency while ensuring the measurement accuracy has become an urgent technical problem in the field of underwater sediment particle measurement.

[0004] For example, the patent application with the publication number CN119044014A provides a method and device for measuring the particle size of underwater riverbed sediment. The specific solution is as follows: obtaining multiple first riverbed sediment photos; performing noise reduction processing on the multiple first riverbed sediment photos by using the median averaging method to obtain a first sediment particle size photo; correcting the first sediment particle size photo based on the image distortion correction algorithm to obtain the corrected first sediment particle size photo; determining the first picture scale information according to the side length information of the square bottom surface of the cuboid frame in the device for measuring the particle size of underwater riverbed sediment and the corrected first sediment particle size photo; identifying the first riverbed sediment identification information from the corrected first sediment particle size photo, and obtaining the underwater riverbed sediment grading information according to the first riverbed sediment identification information and the first picture scale information, which can improve the monitoring efficiency and accuracy of underwater riverbed sediment grading information.

[0005] The above technical solutions have the problems raised in this background art: existing underwater sediment particle measurement methods face the problems of unclear particle edges that cannot be clearly identified from low-resolution images and large amounts of data and low processing efficiency of high-resolution macro images. To solve the above problems, this application designs a macro measurement device and method. Summary of the Invention

[0006] The technical problem to be solved by the present invention is to provide a macro measurement device and method in view of the deficiencies of the prior art. First, through global image grayscale conversion and feature extraction, an initial locally selected area is screened out, and the local area is iteratively adjusted to ensure that the macro image can accurately reflect the particle characteristics in the global image. Subsequently, Laplacian pyramid decomposition is used to extract high-frequency edge information, and background noise is removed through gray variance to generate a particle area map. The technology of separating adhered particles is further applied to separate adjacent particles, and finally, high-quality sediment images are generated through image correction. This method can reduce the macro image acquisition frequency, improve the accuracy of particle measurement, reduce the complexity of data processing, and has high measurement accuracy and practical value.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A macro measurement device, the macro measurement device comprising:

[0009] A housing, including a circulation channel for the water sample to be measured, the circulation channel being provided with a water inlet and a water outlet for introducing and discharging the water sample to be measured;

[0010] An isolation chamber, provided inside the housing, wherein a camera module, a macro lens and a processing module are arranged in the isolation chamber, the macro lens faces the light-transmitting area of the housing of the isolation chamber, and the processing module is used to perform image preprocessing, particle segmentation and particle size statistics;

[0011] A laser, connected to the macro lens, forming a laser light curtain in the shooting area of the macro lens for enhancing the illumination of the water sample to be measured;

[0012] An automatic cleaning mechanism, provided at the water outlet of the circulation channel for the water sample to be measured, for cleaning the lens and the isolation chamber after shooting.

[0013] The laser emits a fan-shaped light beam for forming a sheet light curtain with a fixed thickness in the isolation chamber, and the illumination plane of the sheet light curtain coincides with the imaging focal plane of the macro lens.

[0014] The automatic cleaning mechanism includes a rotatable brush assembly, and the rotatable brush assembly is linked with the opening and closing of the water outlet during the cleaning process. When the water outlet is opened, the rotatable brush assembly rotates to clean the surface of the light-transmitting area of the isolation chamber and synchronously discharges the measured water sample.

[0015] The camera module is used to obtain the global image of the underwater sediment deposition area to be measured, wherein the global image includes a plurality of regional units, and the single-shot field of view of the macro lens is the same as the size of the regional unit;

[0016] The processing module includes:

[0017] A determination unit that determines a plurality of regional units as locally selected regions from all regional units according to the pixel values of each pixel position in the overall image;

[0018] An iteration unit that performs an iterative operation, acquires a macro image of the current locally selected region. If the particle edge continuity in the currently acquired macro image is inconsistent with the particle edge continuity in the overall image, merge the adjacent regional units of each current locally selected region into the corresponding locally selected region and replace the current corresponding locally selected region until the particle edge continuity in the currently acquired macro image is consistent with the particle edge continuity in the overall image, and output the currently acquired macro image;

[0019] A conversion unit that performs image segmentation on the macro image, outputs a sediment image through a connected component algorithm, and converts the pixel size of the sediment image into an actual physical size based on a calibration reference object preset in the isolation chamber housing to measure the particle size distribution data of sediment particles.

[0020] A macro measurement method applied to a macro measurement device. The macro measurement method includes:

[0021] Obtain an overall image of the underwater sediment deposition area to be measured through the camera module, where the overall image includes a plurality of regional units, and the single-shot field of view range of the macro lens is the same as the size of the regional unit;

[0022] Determine a plurality of regional units as locally selected regions from all regional units according to the pixel values of each pixel position in the overall image;

[0023] Perform an iterative operation, acquire a macro image of the current locally selected region. If the particle edge continuity in the currently acquired macro image is inconsistent with the particle edge continuity in the overall image, merge the adjacent regional units of each current locally selected region into the corresponding locally selected region and replace the current corresponding locally selected region until the particle edge continuity in the currently acquired macro image is consistent with the particle edge continuity in the overall image, and output the currently acquired macro image;

[0024] Perform image segmentation on the macro image and output a sediment image through a connected component algorithm;

[0025] Based on a calibration reference object preset in the isolation chamber housing, convert the pixel size of the sediment image into an actual physical size to measure the particle size distribution data of sediment particles.

[0026] Determining multiple regional units as locally selected regions from all regional units includes:

[0027] Performing grayscale processing on the overall image, and calculating the grayscale mean and grayscale variance of each regional unit;

[0028] According to the preset sediment area recognition standard, combining the grayscale mean and grayscale variance, selecting the initial locally selected regions;

[0029] Performing secondary screening on the initial locally selected regions to obtain the locally selected regions.

[0030] Performing secondary screening on the initial locally selected regions includes:

[0031] Dividing the initial locally selected regions into multiple sub-blocks, and calculating the grayscale contrast difference and grayscale consistency between adjacent sub-blocks;

[0032] Weighting the initial locally selected regions according to the grayscale contrast difference and grayscale consistency, and calculating the comprehensive score;

[0033] Comparing the comprehensive score with a preset uniformity threshold, if it is greater than or equal to the uniformity threshold, taking the corresponding initial locally selected region as the locally selected region.

[0034] The calculation method of the particle edge continuity is as follows:

[0035] Performing edge detection on the image to extract particle edge information;

[0036] Calculating the connectivity of the particle edge information through an edge tracking algorithm, and calculating the edge continuity score according to the connectivity;

[0037] Taking the standard deviation of the edge continuity score as the particle edge continuity.

[0038] Performing image segmentation on the macro image, and outputting the sediment image through a connected component algorithm, including:

[0039] Performing Laplacian pyramid decomposition on the macro image to obtain high-frequency edge information at different scales, and generating a fused edge map according to the high-frequency edge information;

[0040] Calculating the regional grayscale variance of the fused edge map, and removing low-variance regions through a background noise mask to generate a particle region map;

[0041] Performing adhesion particle cutting on the particle region map to obtain multiple cut images;

[0042] Performing correction on the multiple cut images to generate the sediment image.

[0043] Perform cutting on the particle region map to obtain multiple cutting images, including:

[0044] Calculate the gradient magnitude and gradient direction of the particle region map;

[0045] Mark the region with sudden change in gradient direction as the adhesion boundary;

[0046] Generate cutting lines along the adhesion boundary to divide the adhered particles into independent sub-regions.

[0047] Perform correction on the multiple cutting images to generate sediment images, including:

[0048] Perform ellipse fitting on the segmented sub-regions and calculate the fitting error;

[0049] According to the fitting error, divide the cutting images into over-segmented and under-segmented ones, and correct the cutting images in sequence. Among them, when over-segmentation occurs, merge adjacent sub-regions and refit until the fitting error is less than the set fitting threshold. When under-segmentation occurs, perform iterative operations on the cutting images and continue to segment the cutting images until the fitting error is less than the set fitting threshold.

[0050] Compared with the prior art, the beneficial effects of the present invention are:

[0051] 1. By combining the advantages of the overall image and the macro image, the present invention reduces the acquisition frequency of the macro image, reduces the complexity of data processing and the storage pressure. Through the preliminary screening of the overall image and the fine capture of the macro image, sediment particles can be efficiently identified, avoiding the accumulation of redundant data caused by collecting too many macro images;

[0052] 2. The device design of the present invention introduces an isolation chamber, which is a key functional component. Its main function is to isolate the water sample to be measured and the macro lens, thus avoiding the direct influence of the water sample on the lens during the measurement process. The isolation chamber can effectively maintain the stability of the water sample and provide a clear light-transmitting area, which is beneficial to reducing the interference of impurities in the water sample on the measurement and ensuring the acquisition quality of the macro image. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] By reading the detailed description of the non-limiting embodiments with reference to the following drawings, other features, objectives and advantages of the present invention will become more obvious:

[0054] Figure 1 It is a schematic flow chart of a macro measurement method according to Embodiment 1 of the present invention;

[0055] Figure 2 It is a schematic flow chart for confirming the locally selected area according to Embodiment 1 of the present invention;

[0056] Figure 3 Schematic diagram of a partially selected area in Embodiment 1 of the present invention;

[0057] Figure 4 Schematic diagram of iteration of a partially selected area in Embodiment 1 of the present invention;

[0058] Figure 5 Schematic diagram of the iteration result of a partially selected area in Embodiment 1 of the present invention;

[0059] Figure 6 Schematic diagram of the structure of a macro measurement device in Embodiment 2 of the present invention;

[0060] Figure 7 Schematic diagram of the structure of a processing module in a macro measurement device in Embodiment 2 of the present invention.

[0061] Reference numerals: 201, housing; 202, isolation chamber; 203, camera module; 204, macro lens; 205, laser; 206, water inlet; 207, water outlet; 208, automatic cleaning mechanism; 209, measurement water body chamber; 210, processing module; 2101, determination unit; 2102, iteration unit; 2104, conversion unit. Detailed implementation manners

[0062] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments.

[0063] Embodiment 1

[0064] Please refer to Figure 1 , an embodiment provided by the present invention: a macro measurement method, which is applied to a macro measurement device. The device includes a housing, and an isolation chamber is configured inside the housing. A camera module, a macro lens, and a processing module are configured inside the isolation chamber. The macro lens faces the light-transmitting area of the isolation chamber. The specific steps of the macro measurement method are as follows:

[0065] S1: Obtain an overall image of the underwater sediment area to be measured, where the overall image includes a plurality of area units;

[0066] In this embodiment, by taking the overall image, the preliminary data of the underwater sediment area can be comprehensively obtained, covering the particle information of the entire area, providing a reference for subsequent macro image acquisition, and avoiding excessive macro images being collected in the initial stage, thereby effectively reducing the calculation amount and storage requirements.

[0067] S2: Determine a plurality of area units from all the area units as the partially selected areas;

[0068] In this embodiment, next, the overall image is analyzed to calculate the grayscale mean and grayscale variance of each regional unit. Based on a preset deposition area recognition criterion, multiple regional units are selected as locally selected areas. The selected regional units usually contain areas where particle accumulation is more obvious and conform to the characteristics of deposited particles. Selecting these areas helps to reduce the area for subsequent macro-image acquisition, ensuring focus on key particle areas and further improving the measurement efficiency.

[0069] S3: Perform iterative operations until the particle edge continuity in the currently acquired macro-image is consistent with the particle edge continuity in the overall image, and output the currently acquired macro-image.

[0070] In this embodiment, through iterative operations, macro-images of the locally selected areas are acquired. If the particle edge continuity in the acquired macro-image is inconsistent with the particle edge continuity in the overall image, the adjacent areas of the locally selected areas are merged until the particle edge continuity in the currently acquired macro-image is consistent with that in the overall image. Ensure that the acquired macro-image can be consistent with the particle characteristics in the overall image to ensure the measurement accuracy.

[0071] S4: Perform image segmentation on the macro-image and output a sediment image through a connected-component algorithm.

[0072] In this embodiment, the acquired macro-image is processed by an image segmentation method, and a connected-component algorithm is used to segment the particles to output a sediment image. The connected-component algorithm can effectively separate the particle areas from the background, extract the shape and edge information of each particle, and thus help with subsequent particle size calculation.

[0073] S5: Based on a calibration reference object preset inside the isolation chamber housing, convert the pixel size of the sediment image into an actual physical size to measure the particle size distribution data of sediment particles.

[0074] In traditional macro-measurement methods, in order to obtain accurate particle size distributions, a large number of macro-images usually need to be taken. Macro-images usually have a high resolution and can finely display particle details, but this also means that the data volume will increase sharply, resulting in extremely heavy burdens for storage, processing, and calculation. Therefore, directly acquiring macro-images for measurement not only poses a huge pressure on data management but also significantly reduces the efficiency of particle size identification. Especially in complex underwater deposition environments, frequent image acquisition may lead to data explosion.

[0075] In this embodiment, a preliminary analysis of the entire image is performed to select representative area units for macro image acquisition. This low-resolution preliminary screening of the entire image allows for rapid identification of potential deposition areas, reducing the frequency of macro image acquisition. This not only reduces the number of images required for acquisition, but also intelligently identifies key areas based on information such as the image's grayscale mean and grayscale variance, allowing precise selection of local areas for macro image acquisition. This reduces unnecessary repeated acquisitions, improves processing efficiency, and avoids the pressure on computing power and storage space caused by excessive image data.

[0076] Specifically, in macro images, particle edges are very clear due to their high resolution, while in the overall image, edges are blurred due to their lower resolution. By comparing the edge continuity between the two, the difference between the macro and overall images can be effectively identified. By iteratively adjusting the selected area, the particle features captured in the macro image accurately represent those in the overall image. This iterative mechanism not only improves the accuracy of macro images but also flexibly adapts to different particle morphologies, avoiding errors caused by excessive acquisition frequency or inappropriate acquisition location.

[0077] See also Figure 2 , a schematic diagram of a partially selected area confirmation process according to an embodiment of the present invention;

[0078] The specific steps of S2 are as follows:

[0079] S2.1: grayscale the entire image and calculate the grayscale mean and grayscale variance of each regional unit;

[0080] Specifically, in underwater sedimentary areas, the distribution and size of particles are typically dispersed and irregular, but the grayscale characteristics of these particles can help identify their approximate location. Although the overall image resolution is low and the details of the particles are not clear, the grayscale image can better reflect the lighting and contrast characteristics of the sediment in the image. Sediment particles typically appear as areas with higher grayscale values, which may vary depending on the density or morphology of the particles, while the background usually appears as lower grayscale values. Therefore, through grayscale processing, the particle areas in the image can be distinguished from the background areas, although the edges of the particles are relatively blurred. The grayscale mean reflects the overall brightness level of the area, while the grayscale variance reveals the degree of grayscale fluctuation within the area. Areas with large variance often contain obvious changes in physical characteristics, such as particles or particle boundaries.

[0081] It should be noted that the area unit is pre-set during shooting and can be adjusted according to the specific shooting field of view of the macro lens.

[0082] S2.2: According to the preset sedimentation area identification criteria, combined with the grayscale mean and grayscale variance, select the initial locally selected area;

[0083] Specifically, sedimentation areas in images usually have some common characteristics. Firstly, the particles in these areas are relatively concentrated, showing a high grayscale mean and a large grayscale variance. Sedimentation areas usually have a high particle density, which means that the grayscale values in this area will change significantly, while the background area is relatively uniform with small grayscale changes. Through this criterion, areas with obvious particle characteristics can be identified more accurately from the overall image. Those skilled in the art can determine the specific identification criteria through a large number of repeated experiments.

[0084] Furthermore, the purpose of selecting these areas is to ensure that subsequent macro-image acquisition can focus on the areas that truly contain sediment particles, avoiding ineffective acquisition of the background or areas with sparse particles. Effectively filter out those areas with relatively uniform grayscale and no obvious particle aggregation. Reduce the frequency of macro-image acquisition, thereby reducing the burden and saving computing and storage resources, while ensuring that the acquired areas are highly representative.

[0085] S2.3: Perform a secondary screening on the initial locally selected area to obtain the locally selected area;

[0086] Specifically, the core method of the secondary screening is based on the grayscale mean and grayscale variance calculated in the previous step. By further optimizing the selection of areas, ensure that the finally selected areas meet the sedimentation area criteria. At this time, in addition to the basic screening of the grayscale mean and variance, further analyze the morphological characteristics and distribution laws of the areas, and eliminate those areas that do not conform to the sedimentation characteristics.

[0087] The specific steps of S2.3 are as follows:

[0088] S2.3.1: Divide the initial locally selected area into multiple sub-blocks, and calculate the grayscale contrast difference and grayscale consistency between adjacent sub-blocks;

[0089] Specifically, after the preliminary screening, the selected local area may contain areas with different particle densities. Therefore, further divide it into multiple sub-blocks to obtain more refined grayscale information. The grayscale contrast difference reflects the degree of brightness change between adjacent sub-blocks. If the contrast difference is large, it may mean that there are obvious particle boundaries or objects with different properties in this area. The grayscale consistency reveals the stability of the grayscale between adjacent sub-blocks. When the consistency is high, it indicates that the area is relatively uniform in terms of brightness change and may be a relatively simple background area.

[0090] S2.3.2: Weight the initial locally selected regions based on the grayscale contrast difference and grayscale consistency, and calculate the comprehensive score.

[0091] Specifically, the grayscale contrast difference and grayscale consistency are complementary. The former helps to identify the boundaries of particle regions, while the latter can ensure the stability of the regions. To accurately evaluate the characteristics of local regions, different weights are set for the grayscale contrast difference and grayscale consistency respectively, combining their characteristics, to balance the saliency of particles and the stability of regions. In actual operation, regions with a larger grayscale contrast difference and higher consistency are more likely to represent regions containing deposited particles, and these regions are the most preferred for subsequent macro image acquisition.

[0092] Furthermore, the priority of each region can be sorted according to its actual characteristics. For example, if a sub-block has a large grayscale contrast difference but low grayscale consistency, then the score of this region will be relatively low, indicating that this region may be too complex or irregular and is not suitable for further processing; while if a region has both a large grayscale contrast difference and high consistency, it means that the particle distribution in this region is clear and the structure is stable, and it is suitable as the target region for subsequent acquisition.

[0093] Preferably, a region with a large grayscale contrast difference usually means that there is a relatively obvious boundary between the particles and the background in this region, and the particles can be clearly distinguished from the surrounding environment. However, if such a region lacks a stable grayscale distribution or the grayscale changes too violently, it may affect the accuracy of subsequent image processing and particle recognition. Therefore, when calculating the weight, a region with a large grayscale contrast difference will obtain a higher preliminary score, but if its grayscale consistency is low, the weight will be appropriately reduced. On the contrary, a region with a high grayscale consistency usually shows that the particles are evenly and stably distributed in this region, which can effectively reduce the interference of noise and improve the stability of particle recognition. For these regions, although their grayscale contrast difference may not be as obvious as the former, the high consistency means that the particle distribution in the region is uniform and the structure is stable, and it is easy to extract the characteristic information of the particles. To ensure that such regions can be preferentially selected, a higher weighting value will be given to regions with a high grayscale consistency. Finally, the comprehensive score formed by combining the grayscale contrast difference and grayscale consistency will sort each region. A region with a higher comprehensive score indicates that this region has both a relatively obvious particle boundary and a stable grayscale distribution, and is most suitable as the target region for subsequent macro image acquisition. The specific weighting value can be determined by those skilled in the art through a large number of repeated experiments to determine the specific weighting weights.

[0094] S2.3.3: Compare the comprehensive score with a preset uniform threshold. If it is greater than or equal to the uniform threshold, use the corresponding initial locally selected region as the locally selected region.

[0095] Please refer to Figure 3 , a schematic diagram of a locally selected area in an embodiment of the present invention. The overall image is divided into 10×10 sizes, a total of 100 area units. Each area unit is a small square, and the locally selected area is marked as a gray block, such as Figure 3 the locally selected area 1, the locally selected area 2, and the locally selected area 3 in. These areas represent the parts that need to be further analyzed in the overall image. The range of the overall image is relatively large, and these locally selected areas are the areas selected from the overall image for more precise image acquisition or analysis. It should be noted that the overall image in this embodiment is only a schematic diagram for easy understanding, and there will be more locally selected areas and area units in actual applications.

[0096] The specific steps of S3 are as follows:

[0097] S3.1: Collect a macro image of the current locally selected area;

[0098] In this embodiment, the locally selected area is a target area selected through the foregoing features such as gray mean value, gray variance, and gray contrast difference, and has high particle saliency and stability. The high resolution of the macro image enables precise capture of particle details, especially the edge information of the particles, which provides an important basis for subsequent particle size analysis. Collecting the macro image is to be able to record the particle morphology of the local area in detail. Especially when the resolution of the overall image is low, the macro image can provide more details, making the particle morphology clearer and more accurate.

[0099] S3.2: Obtain the particle edge continuity of the corresponding area unit in the macro image and the overall image;

[0100] Specifically, the macro image usually has a high resolution and can clearly show details, while the resolution of the overall image is low and the particle edges are blurred. When comparing, the particle edges in the macro image are more precise, and the overall image is a more rough representation. Therefore, comparing the details of the macro image with the rough features of the overall image can verify whether the macro image can represent the true features of the area. If the edge continuities of the two are inconsistent, by iteratively adjusting the selected area, the selected local area can be gradually optimized to make it more consistent with the particle features in the overall image. Because of the resolution difference between the overall image and the macro image, it means that the particles in the macro image may not exactly correspond to the particles in the overall image. Particle edge continuity, as a measure of morphological consistency, can reflect whether the geometric morphology of the particles is consistent. When the edge consistency between the macro image and the locally selected area is high, it indicates that the morphological features of this part of the particles are stable at different resolutions, so it represents a reliable sample area.

[0101] S3.3: If the edge continuity of the particles in the currently captured macro image is inconsistent with that of the overall image, merge the adjacent regional units of each currently locally selected area into the corresponding locally selected area and replace the current corresponding locally selected area;

[0102] Specifically, when the edge continuity of the particles in the macro image is inconsistent with that of the overall image, it indicates that there may be a loss of particle information or inaccurate area selection in the current locally selected area. To ensure that the capture of the macro image can represent the true particle distribution in the overall image, this embodiment adjusts the locally selected area in an iterative manner.

[0103] Furthermore, when it is found that the edge continuity of the particles in the macro image does not conform to that of the overall image, the adjacent regional units of the locally selected area will be automatically merged, and a new, more conforming local area to the particle distribution of the overall image will be reselected and replace the original locally selected area. This process will be continuously iterated until the edge continuity of the particles in the macro image is consistent with that of the overall image. By continuously adjusting the selected area, the particle characteristics of the macro image can be gradually optimized to make it more conform to the particle distribution in the overall image. The iterative adjustment ensures the consistency of particle information and avoids inaccurate measurements caused by acquisition errors or improper area selection.

[0104] It should be noted that when merging adjacent regional units, since the shooting range required for the newly generated locally selected area is larger than the field of view of a single shot of the macro lens, multiple continuously captured macro images will be obtained according to the size of the area during the iterative process. At the same time, during the iterative process, the macro images need to be stitched. During the stitching process, the images will be aligned and fused according to the overlapping parts between different areas to ensure seamless connection of the merged macro image and complete retention of the particle information of each part.

[0105] S3.4: Until the edge continuity of the particles in the currently captured macro image is consistent with that of the overall image, output the currently captured macro image;

[0106] Specifically, when the edge continuity of the two is consistent, it indicates that the macro image accurately reflects the particle characteristics in the overall image. At this time, output the currently captured macro image. This image can provide accurate particle information for subsequent particle size measurement and ensure the maximization of the matching degree between the macro image and the overall image during the measurement of underwater sediment deposition.

[0107] Please refer to Figure 4 and Figure 5, during the iteration process, when a macro image is acquired, if the continuity of the particle edges in it is inconsistent with that in the overall image, the selected area will be readjusted. For example, if it is found during the iteration process that the macro images 1 and 3 do not conform to the particle edge continuity of the overall image in the locally selected areas 1 and 3, the adjacent grids of the locally selected areas 1 and 3 will be merged to better represent the distribution of particles in the overall image, and finally ensure that the macro image can accurately reflect the particle characteristics in the overall image. It is found during the iteration process that the macro image 2 conforms to the particle edge continuity of the overall image in the locally selected area 2, so there is no need to adjust the locally selected area 2. By continuously iteratively adjusting the locally selected areas until the particle edge continuity is consistent, the accuracy and effectiveness of data acquisition are ensured. Further, in Figure 5 it is found that the locally selected areas 1 and 3 are already adjacent. If any area still does not conform to the particle edge continuity during subsequent iteration processes, the two areas will be further merged into a new locally selected area.

[0108] The specific steps of S3.2 are as follows:

[0109] S3.2.1: Perform edge detection on the image to extract particle edge information;

[0110] Specifically, edge detection identifies the edge parts in the image by calculating the gradient of the gray-scale change in the image. In underwater sediment images, the edges of particles often exhibit relatively prominent gray-scale changes, while the gray-scale changes in the background part are relatively stable. Therefore, particle contours and boundaries can be effectively extracted from the image through edge detection.

[0111] S3.2.2: Calculate the connectivity of the particle edge information through an edge tracking algorithm, and calculate an edge continuity score based on the connectivity;

[0112] Specifically, the continuity of the edge is an important indicator for evaluating the stability of particle morphology. A continuous edge indicates that the particle morphology is relatively clear and is not easily affected by noise or background interference, thereby improving the reliability of measurement. Calculating the connectivity of the edge can help identify and eliminate those irregular or missing edges, ensuring more accurate subsequent particle measurements.

[0113] In this embodiment, connectivity refers to whether the particle edge is a continuous region in the image or whether there are interruptions or breaks. In particle measurement, the continuity of the edge directly reflects the integrity and stability of the particle morphology. If the particle edge is broken in the image, it may cause errors in subsequent particle size measurement.

[0114] Furthermore, through the edge tracking algorithm, continuous edge pixel points can be traced and marked along the edge line in the image, thereby determining whether the particle edge is complete and continuous. If the edge is continuous, it indicates that the shape characteristics of the particle are relatively complete and suitable for further measurement; if the edge is broken, it means that the particle shape is incomplete, which may affect the measurement results. According to the score calculated by connectivity, a higher score indicates that the particle edge is smoother and has no breaks, while a lower score indicates that there may be missing or irregular edges.

[0115] S3.2.3: Take the standard deviation of the edge continuity score as the particle edge continuity.

[0116] It should be noted that in this embodiment, the particle edge continuity of the corresponding regional unit in the overall image is relatively low, mainly because the resolution of the overall image is low and the particle edges are relatively blurred in the overall image. This blurriness does not mean that the particle information in the overall image is completely invalid, but provides a rough reference framework for the macro image. By comparing the edge continuity of the particles in the macro image with that in the overall image, it is possible to determine whether the macro image can accurately represent the particle distribution in the overall image. When the edge continuity of the particles in the macro image is highly consistent with that in the overall image, it indicates that the macro image can accurately reflect the particle characteristics in the overall image, and the particle morphology is stable and consistent at different resolutions. At this time, the iteration process ends, and reliable particle information can be provided for subsequent particle size analysis. The core lies in that sediment particles often show a piled-up state, and this piled-up characteristic will cause the particle edges to appear blurred or discontinuous in the overall image. Although the macro image reduces the particle edge continuity by approaching the particle edge through continuous iteration, the final macro image can be used as an effective sample area to provide a basis for subsequent particle size analysis and particle morphology measurement.

[0117] The specific steps of S4 are as follows:

[0118] S4.1: Perform Laplacian pyramid decomposition on the macro image to obtain high-frequency edge information at different scales, and generate a fused edge map based on the high-frequency edge information;

[0119] Specifically, the Laplacian pyramid is a multi-scale image processing method. By gradually downsampling and calculating the difference layers of the image, the image can be decomposed into representations at multiple scales. The high-frequency information in each layer of the pyramid represents the details and edge features of the image. In the macro image, due to the complex details that may exist in the particle morphology and edges, Laplacian pyramid decomposition helps to capture the detail and structure information separately. By fusing the edge information at different scales, it is possible to obtain the details of the particles at multiple levels of the image, ensuring that the edge features of the particles can be completely extracted regardless of the particle size.

[0120] S4.2: Calculate the regional gray variance of the fused edge map, and remove the low-variance regions through the background noise mask to generate a particle region map;

[0121] Specifically, calculating the gray variance and removing the low-variance regions helps to remove the regions without particle information, reduce the interference of background noise, and thus improve the recognition accuracy of particle regions. Through this screening process, it is possible to effectively focus on the regions with high particle density, ensuring that subsequent image cutting and particle size measurement are only carried out in relevant regions, and avoiding errors caused by background noise or low-contrast regions.

[0122] In this embodiment, first calculate the regional gray variance of the fused edge map to further analyze the particle distribution characteristics in the image. The gray variance reflects the degree of brightness fluctuation within the image region. Regions with higher variance usually indicate that the region contains more changing information, such as the boundaries and contours of particles; while regions with lower variance usually represent the background or uniform regions, lacking particle information. By calculating the gray variance, it is possible to remove those background regions with small gray changes from the image, ensuring that only the regions containing particle information are retained.

[0123] Furthermore, use the background noise mask to further remove the low-variance regions. The gray changes in the low-variance regions are small, usually representing meaningless backgrounds or regions without obvious particle distribution. Through the background noise mask technology, these background regions can be accurately filtered out, retaining the regions containing particles to generate a particle region map. The particle region map can accurately identify the positions and shapes of the particles.

[0124] S4.3: Cut the adhered particles in the particle region map to obtain multiple cut images;

[0125] Specifically, cutting the adhered particles is to ensure that each particle can be measured independently without being interfered by other particles. Sediment deposits often form adhesion phenomena due to the accumulation and overlap of particles, which will lead to inaccurate measurement of particle size. Through effective cutting methods, these adhered particles can be separated to ensure that each particle can be independently identified and measured.

[0126] In this embodiment, by analyzing the connected regions in the particle region map, detect and cut out the adhered particles. During the cutting process, the system automatically identifies the independent boundaries of each particle based on the morphology, edges, and regional characteristics of the particles, and divides them into multiple individual particle images.

[0127] S4.4: Correct the multiple cut images to generate a sediment image;

[0128] Specifically, the cut particle images may have certain deviations due to noise, irregular shapes, or segmentation errors. Through the correction process, the true morphology of each particle image can be accurately presented, avoiding measurement errors caused by image problems. The correction process can optimize the image quality of the particles, ensuring more accurate subsequent particle size measurement and particle distribution analysis.

[0129] The specific steps of S4.3 are as follows:

[0130] S4.3.1: Calculate the gradient magnitude and gradient direction of the particle region map;

[0131] Mark the gradient direction mutation region as the adhesion boundary;

[0132] Generate a cutting line along the adhesion boundary to divide the adhered particles into independent sub-regions;

[0133] The specific steps of S4.4 are as follows:

[0134] S4.4.1: Perform elliptical fitting on the segmented sub-regions and calculate the fitting error;

[0135] S4.4.2: According to the fitting error, divide the cutting image into over-segmented and under-segmented parts, and correct the cutting image in sequence. Among them, when over-segmentation occurs, merge adjacent sub-regions and refit until the fitting error is less than the set fitting threshold. When under-segmentation occurs, perform iterative operations on the cutting image and continue to segment the cutting image until the fitting error is less than the set fitting threshold.

[0136] Embodiment 2

[0137] Please refer to Figure 6 , the present invention provides an embodiment: a macro measurement device, the macro measurement device includes:

[0138] A housing 201, including a circulation channel for the water sample to be measured, the circulation channel is provided with a water inlet 206 and a water outlet 207 for introducing and discharging the water sample to be measured;

[0139] An isolation chamber 202 is arranged inside the housing 201, wherein a camera module 203, a macro lens 204, and a processing module 210 are arranged in the isolation chamber 202. The macro lens 204 faces the light-transmitting area of the housing 201 of the isolation chamber 202, and the processing module 210 is used to perform image preprocessing, particle segmentation, and particle size statistics;

[0140] A laser 205 is connected to the macro lens 204 to form a laser light curtain in the shooting area of the macro lens 204 for enhancing the illumination of the water sample to be measured;

[0141] The water body measurement bin 209 is disposed inside the isolation bin 202 and is connected to the water inlet 206 and the water outlet 207, and is used for placing the water body containing sediment deposits to be measured;

[0142] The automatic cleaning mechanism 208 is disposed at the water outlet 207 of the water sample circulation channel to be measured, and is used for cleaning the lens and the isolation bin 202 after the shooting is completed.

[0143] The laser emitted by the laser 205 is a fan-shaped light beam, which is used to form a light curtain with a fixed thickness in the isolation bin 202, and the illumination plane of the light curtain coincides with the imaging focal plane of the macro lens 204.

[0144] The automatic cleaning mechanism 208 includes a rotatable brush assembly. The rotatable brush assembly is linked with the opening and closing of the water outlet 207 during the cleaning process. When the water outlet 207 is opened, the rotatable brush assembly rotates to clean the surface of the light-transmitting area of the isolation bin 202 and synchronously discharges the measured water sample.

[0145] The camera module 203 is used to obtain an overall image of the underwater sediment deposition area to be measured. The overall image includes a plurality of regional units, and the single-shot field of view of the macro lens 204 is the same as the size of the regional unit;

[0146] Please refer to Figure 7 , the structural schematic diagram of the processing module 210 in the embodiment of the present invention, where the dotted line indicates the data flow direction. The processing module 210 includes:

[0147] The determination unit 2101 determines a plurality of regional units as local selected areas from all the regional units according to the pixel values of each pixel position in the overall image;

[0148] The iteration unit 2102 performs an iteration operation, collects a macro image of the current local selected area. If the particle edge continuity in the currently collected macro image is inconsistent with the particle edge continuity in the overall image, the adjacent regional units of the current local selected area are merged into the corresponding local selected area and replace the current corresponding local selected area until the particle edge continuity in the currently collected macro image is consistent with the particle edge continuity in the overall image, and outputs the currently collected macro image;

[0149] The conversion unit 2104 performs image segmentation on the macro image, outputs a sediment image through a connected component algorithm, and converts the pixel size of the sediment image into an actual physical size based on a calibration reference object preset in the outer shell 201 of the isolation bin 202 to measure the particle size distribution data of the sediment particles.

[0150] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present invention.

Claims

1. A macro measurement method, applied to a macro measurement device, the device comprising a housing, an isolation chamber is configured inside the housing, a camera module, a macro lens and a processing module are configured inside the isolation chamber, the camera module is used to obtain an overall image of the underwater sediment area to be measured, the macro lens faces the light-transmitting area of the isolation chamber, and the processing module is used to perform image preprocessing, particle segmentation and particle size statistics, characterized in that, The described macro measurement method includes: Obtaining an overall image of the underwater sediment area to be measured, where the overall image includes a plurality of regional units, and the single-shot field of view of the macro lens is the same as the size of the regional unit; Determining a plurality of regional units as locally selected areas from all the regional units according to the pixel values of each pixel position in the overall image; Performing an iterative operation to collect a macro image of the current locally selected area. If the particle edge continuity in the currently collected macro image is inconsistent with the particle edge continuity in the overall image, merge the adjacent regional units of the current locally selected area into the corresponding locally selected area and replace the current corresponding locally selected area until the particle edge continuity in the currently collected macro image is consistent with the particle edge continuity in the overall image, and output the currently collected macro image; Measuring the particle size distribution data of sediment particles based on all the currently collected macro images, including: Performing image segmentation on the macro image and outputting a sediment image through a connected component algorithm, including: Performing Laplacian pyramid decomposition on the macro image to obtain high-frequency edge information at different scales, and generating a fused edge map according to the high-frequency edge information; Calculating the regional gray variance of the fused edge map, removing low-variance regions through a background noise mask, and generating a particle region map; Performing cutting on the agglomerated particles in the particle region map to obtain multiple cut images, including: Calculating the gradient magnitude and gradient direction of the particle region map; Marking the regions with sudden changes in gradient direction as agglomeration boundaries; Generating cutting lines along the agglomeration boundaries to divide the agglomerated particles into independent sub-regions; Correcting the multiple cut images to generate a sediment image; Based on a calibration reference object preset in the isolation chamber housing, converting the pixel size of the sediment image into an actual physical size to measure the particle size distribution data of sediment particles.

2. The macro measurement method according to claim 1, wherein The determining a plurality of regional units as locally selected areas from all the regional units includes: Performing grayscale processing on the overall image and calculating the grayscale mean and grayscale variance of each regional unit; Selecting initial locally selected areas according to the preset sediment area recognition criteria in combination with the grayscale mean and grayscale variance; Performing secondary screening on the initial locally selected areas to obtain locally selected areas.

3. The macro measurement method according to claim 2, wherein Performing secondary screening on the initial locally selected areas includes: Dividing the initial locally selected areas into multiple sub-blocks, and calculating the grayscale contrast difference and grayscale consistency between adjacent sub-blocks; Weighting the initial locally selected areas according to the grayscale contrast difference and grayscale consistency, and calculating a comprehensive score; Comparing the comprehensive score with a preset uniformity threshold. If it is greater than or equal to the uniformity threshold, taking the corresponding initial locally selected area as a locally selected area.

4. The macro measurement method according to claim 1, wherein The calculation method of the particle edge continuity is as follows: Performing edge detection on the image to extract particle edge information; Calculating the connectivity of the particle edge information through an edge tracking algorithm, and calculating an edge continuity score according to the connectivity; Taking the standard deviation of the edge continuity score as the particle edge continuity.

5. A macro measurement device for implementing a macro measurement method according to any one of claims 1-4, characterized in that, Including: The outer shell includes a flowing channel for the water sample to be measured, and the flowing channel is provided with a water inlet and a water outlet for introducing and discharging the water sample to be measured. The isolation chamber is arranged inside the outer shell. A camera module, a macro lens and a processing module are arranged in the isolation chamber. The macro lens faces the light-transmitting area of the outer shell of the isolation chamber, and the processing module is used to perform image preprocessing, particle segmentation and particle size statistics. The laser is connected to the macro lens and forms a laser light curtain in the shooting area of the macro lens for enhancing the illumination of the water sample to be measured. The automatic cleaning mechanism is arranged at the water outlet of the flowing channel for the water sample to be measured and is used to clean the lens and the isolation chamber after shooting.

6. The macro measurement device according to claim 5, characterized in that, The automatic cleaning mechanism includes a rotatable brush assembly. The rotatable brush assembly is linked with the opening and closing of the water outlet during the cleaning process. When the water outlet is opened, the rotatable brush assembly rotates to clean the surface of the light-transmitting area of the isolation chamber and synchronously discharges the measured water sample.

7. The macro measurement device according to claim 5, wherein The camera module is used to obtain the overall image of the underwater sediment deposition area to be measured. The overall image includes a plurality of regional units, and the single-shot field of view of the macro lens is the same as the size of the regional unit. The processing module includes: A determination unit that determines a plurality of regional units as locally selected regions from all regional units according to the pixel values of each pixel position in the overall image. An iteration unit that performs an iterative operation to collect the macro image of the current locally selected region. If the particle edge continuity in the currently collected macro image is inconsistent with the particle edge continuity in the overall image, the adjacent regional units of the current locally selected region are merged into the corresponding locally selected region and replace the current corresponding locally selected region until the particle edge continuity in the currently collected macro image is consistent with the particle edge continuity in the overall image, and then outputs the currently collected macro image. A conversion unit that performs image segmentation on the macro image, outputs a sediment image through a connected component algorithm, and converts the pixel size of the sediment image into an actual physical size based on a calibration reference object preset in the outer shell of the isolation chamber to measure the particle size distribution data of sediment particles.

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