Microspur measurement device and method

Abstract

The invention relates to the technical field of underwater sediment measurement, in particular to a microspur measurement device and method, and provides the following scheme: firstly, screening out an initial local selected area through overall image graying and feature extraction, and adjusting the local area through iteration to ensure that a microspur image can accurately reflect particle features in the overall image; then, decomposing and extracting high-frequency edge information by using a Laplacian pyramid, eliminating background noise through gray variance, and generating a particle region graph; further separating adjacent particles by using an adhesive particle cutting technology, and finally generating a high-quality sediment image through image correction; according to the method, the microspur image acquisition frequency can be reduced, the particle measurement accuracy is improved, the data processing complexity is reduced, and the method has relatively high measurement precision 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, and flood control and disaster reduction. In the prior art, common laser diffraction methods or ultrasonic scattering methods require expensive equipment and complex operations; screening methods, sedimentation methods, and microscopy 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 matter, 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 data volume 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 an image distortion correction algorithm to obtain a corrected first sediment particle size photo; determining first image 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 first riverbed sediment identification information from the corrected first sediment particle size photo, and obtaining underwater riverbed sediment grading information according to the first riverbed sediment identification information and the first image 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 identification of particle edges from low-resolution images and large data volume 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 by gray variance to generate a particle area map. Further, an adhesion particle cutting technology is 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: A macro measurement device, the macro measurement device comprising: 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; 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; 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; 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.

[0008] The laser emits a fan-shaped light beam for forming a sheet light curtain with a fixed thickness in the isolation chamber, and the irradiation plane of the sheet light curtain coincides with the imaging focal plane of the macro lens.

[0009] 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.

[0010] 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 range of the macro lens is the same as the size of the regional unit; The processing module includes: A determination unit, which determines a plurality of regional units from all the regional units as locally selected regions according to the pixel values of each pixel position in the overall image; An iteration unit, which performs an iterative operation to collect a 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, it merges the adjacent regional units of each current locally selected region into the corresponding locally selected region and replaces 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 outputs the currently collected macro image; A conversion unit, which 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.

[0011] A macro measurement method, which is applied to a macro measurement device. The macro measurement method includes: Obtaining 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; Determining a plurality of regional units from all the regional units as locally selected regions 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 region. If the particle edge continuity in the currently collected macro image is inconsistent with the particle edge continuity in the overall image, it merges the adjacent regional units of each current locally selected region into the corresponding locally selected region and replaces 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 outputs the currently collected macro image; Performing image segmentation on the macro image and outputting a sediment image through a connected component algorithm; 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.

[0012] The determining a plurality of regional units from all the regional units as locally selected regions includes: Performing grayscale processing on the overall image and calculating the grayscale mean and grayscale variance of each regional unit; Selecting an initial locally selected region according to a preset sediment area recognition standard in combination with the grayscale mean and grayscale variance; Performing secondary screening on the initial locally selected region to obtain the locally selected region.

[0013] Perform secondary screening on the initial locally selected area, including: Divide the initial locally selected area into multiple sub-blocks, and calculate the gray-scale contrast difference and gray-scale consistency between adjacent sub-blocks; Weight the initial locally selected area according to the gray-scale contrast difference and gray-scale consistency, and calculate the comprehensive score; 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 area as the locally selected area.

[0014] The calculation method of the particle edge continuity is as follows: Perform edge detection on the image to extract particle edge information; Calculate the connectivity of the particle edge information through an edge tracking algorithm, and calculate the edge continuity score according to the connectivity; Use the standard deviation of the edge continuity score as the particle edge continuity.

[0015] Perform image segmentation on the macro image, and output the sediment image through a connected component algorithm, including: Perform Laplacian pyramid decomposition on the macro image to obtain high-frequency edge information at different scales. According to the high-frequency edge information, generate a fused edge map; Calculate the regional gray-scale variance of the fused edge map, and eliminate low-variance regions through a background noise mask to generate a particle region map; Perform cutting on adhered particles in the particle region map to obtain multiple cut images; Perform correction on the multiple cut images to generate a sediment image.

[0016] Perform cutting on adhered particles in the particle region map to obtain multiple cut images, including: Calculate the gradient magnitude and gradient direction of the particle region map; Mark the region with sudden change in gradient direction as the adhesion boundary; Generate cutting lines along the adhesion boundary to divide the adhered particles into independent sub-regions.

[0017] Perform correction on the multiple cut images to generate a sediment image, including: Perform elliptical fitting on the segmented sub-regions and calculate the fitting error; According to the fitting error, the cut image is divided into over-segmentation and under-segmentation, and the cut image is corrected in sequence. Among them, when over-segmentation occurs, adjacent sub-regions are merged and refitted until the fitting error is less than the set fitting threshold. When under-segmentation occurs, iterative operations are performed on the cut image, and the cut image is continuously segmented until the fitting error is less than the set fitting threshold.

[0018] Compared with the prior art, the beneficial effects of the present invention are as follows: 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 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; 2. The device design of the present invention introduces an isolation chamber, which, as a key functional component, mainly functions 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

[0019] By reading the detailed description of the non-restrictive embodiments with reference to the following drawings, other features, objects and advantages of the present invention will become more apparent: Figure 1 It is a schematic flow chart of a macro measurement method according to Embodiment 1 of the present invention; Figure 2 It is a schematic flow chart for confirming a locally selected area according to Embodiment 1 of the present invention; Figure 3 It is a schematic diagram of a locally selected area according to Embodiment 1 of the present invention; Figure 4 It is a schematic diagram of iteration of a locally selected area according to Embodiment 1 of the present invention; Figure 5 It is a schematic diagram of the iteration result of a locally selected area according to Embodiment 1 of the present invention; Figure 6 It is a schematic structural diagram of a macro measurement device according to Embodiment 2 of the present invention; Figure 7 It is a schematic structural diagram of a processing module in a macro measurement device according to Embodiment 2 of the present invention.

[0020] 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, water body measurement chamber; 210, processing module; 2101, determination unit; 2102, iteration unit; 2104, conversion unit. Detailed implementation manners

[0021] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to 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.

[0022] Embodiment 1 Please refer to Figure 1 , an embodiment provided by the present invention: a macro measurement method applied to a macro measurement device, the device includes a housing, an isolation chamber is configured in the housing, a camera module, a macro lens and a processing module are configured in the isolation chamber, the macro lens faces the light-transmitting area of the isolation chamber, and the specific steps of the macro measurement method are as follows: S1: Obtain an overall image of the underwater sediment area to be measured, where the overall image includes a plurality of regional units; In this embodiment, by taking the overall image, 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 collecting too many macro images in the initial stage, thereby effectively reducing the calculation amount and storage requirements.

[0023] S2: Determine a plurality of regional units from all the regional units as locally selected areas; In this embodiment, then analyze the overall image, calculate the gray mean value and gray variance of each regional unit, and based on a preset sediment area recognition standard, select a plurality of regional units as locally selected areas. The selected regional units usually contain areas where particle accumulation is more obvious and conform to the characteristics of sediment particles. Selecting these areas helps to reduce the area of subsequent macro image acquisition, ensure focusing on key particle areas, and further improve the measurement efficiency.

[0024] S3: Perform an iterative operation 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; In this embodiment, through iterative operations, macro images of a locally selected area are collected. If the continuity of the particle edges in the collected macro images is inconsistent with that in the overall image, the adjacent areas of the locally selected area are merged until the continuity of the particle edges in the currently collected macro image is consistent with that in the overall image. This ensures that the collected macro images can be consistent with the particle characteristics in the overall image, thereby ensuring the measurement accuracy.

[0025] S4: Perform image segmentation on the macro image and output the sediment image through the connected component algorithm; In this embodiment, the collected macro images are processed by an image segmentation method. The connected component algorithm is used to segment the particles, and the sediment image is output. The connected component algorithm can effectively separate the particle regions from the background, extract the shape and edge information of each particle, and thus help with subsequent particle size calculation.

[0026] S5: Based on the calibration reference object preset in the isolation chamber housing, convert the pixel size of the sediment image into the actual physical size to measure the particle size distribution data of the sediment; 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 display the details of particles finely, but this also means that the data volume will increase sharply, resulting in extremely heavy burdens on storage, processing, and calculation. Therefore, directly collecting 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 collection may lead to data explosion.

[0027] In this embodiment, through preliminary analysis of the overall image, representative regional units are selected for macro image collection. Through the preliminary screening of the low resolution of the overall image, potential deposition areas are quickly determined, reducing the frequency of collecting macro images. This not only reduces the number of images required for collection but also intelligently identifies key areas through information such as the gray mean and gray variance of the images, thereby accurately selecting the local areas where macro images need to be collected. Unnecessary repeated collection is reduced, the processing efficiency is improved, and the pressure on computing power and storage space caused by excessive image data is avoided.

[0028] Specifically, in a macro image, due to the high resolution, the edges of the particles are very clear, while the overall image has blurred edges due to the low resolution. When comparing the edge continuity of the two, the difference between the macro image and the overall image can be effectively identified, and by iteratively adjusting the locally selected area, it is ensured that the particle features collected in the macro image can accurately represent the particle features in the overall image. The iterative mechanism not only improves the accuracy of the macro image but also can flexibly adapt to different particle morphologies, avoiding errors caused by too high a collection frequency or improper selection of the collection position.

[0029] Please refer to Figure 2 , the schematic diagram of the local selected area confirmation process in the embodiment of the present invention; The specific steps of S2 are as follows: S2.1: Perform grayscale processing on the overall image and calculate the grayscale mean and grayscale variance of each regional unit; Specifically, in the underwater sediment area, the distribution and size of the particles are usually relatively scattered and irregular, but the grayscale features of these particles can help identify their approximate positions. Although the overall image has a low resolution and the details of the particles are not clear, the grayscale image can better reflect the illumination and contrast characteristics of the sediment in the image. Sediment particles usually appear as areas with higher grayscale values, and these areas may have different grayscale values due to the density or morphology of the particles, while the background usually shows lower grayscale values. Therefore, through grayscale processing, the particle areas and background areas in the image can be distinguished, 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 a larger variance usually contain obvious physical feature changes, such as particles or particle boundaries.

[0030] It should be noted that the regional unit is preset during shooting and can be adjusted according to the specific shooting field of view of the macro lens.

[0031] S2.2: Select the initial local selected area according to the preset sediment area recognition criteria, in combination with the grayscale mean and grayscale variance; Specifically, sediment areas in the image usually have some common characteristics. First, the particles in these areas are more concentrated, showing a high grayscale mean and a large grayscale variance. Sediment areas usually have a high particle density, which means that the grayscale values within this area will change significantly, while the background area is more uniform with less grayscale change. Through this criterion, the areas with obvious particle characteristics can be more accurately identified from the overall image. Those skilled in the art can determine the specific recognition criteria through a large number of repeated experiments.

[0032] Furthermore, the purpose of selecting these regions is to ensure that subsequent macro-image acquisition can focus on the regions that truly contain deposited particles, avoiding ineffective acquisition of the background or regions with sparse particles. It effectively filters out those regions with relatively uniform gray levels and no obvious particle aggregation. Reducing the frequency of macro-image acquisition, thus alleviating the burden and saving computing and storage resources, while ensuring that the acquired regions are highly representative.

[0033] S2.3: Perform a secondary screening on the initial locally selected regions to obtain locally selected regions; Specifically, the core method of the secondary screening is based on the gray-scale mean and gray-scale variance calculated in the previous step. By further optimizing the selection of regions, it ensures that the finally selected regions meet the criteria of the deposition region. At this time, in addition to the basic screening based on the gray-scale mean and variance, those regions that do not conform to the deposition characteristics are eliminated by further analyzing the morphological characteristics and distribution rules of the regions.

[0034] The specific steps of S2.3 are as follows: S2.3.1: Divide the initial locally selected regions into multiple sub-blocks, and calculate the gray-scale contrast difference and gray-scale consistency between adjacent sub-blocks; Specifically, after the preliminary screening, the selected local regions may contain regions with different particle densities. Therefore, it is further divided into multiple sub-blocks to obtain more refined gray-scale information. The gray-scale 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 of different properties in this region. The gray-scale consistency reveals the stability of the gray levels between adjacent sub-blocks. When the consistency is high, it indicates that the region is relatively uniform in terms of brightness change and may be a relatively simple background region.

[0035] S2.3.2: Weight the initial locally selected regions according to the gray-scale contrast difference and gray-scale consistency, and calculate the comprehensive score; Specifically, the gray-scale contrast difference and gray-scale consistency are complementary. The former helps to identify the boundaries of particle regions, while the latter can ensure the stability of the region. In order to accurately evaluate the characteristics of the local region, combining the characteristics of both, different weights are set for the gray-scale contrast difference and gray-scale consistency respectively to balance the significance of particles and the stability of the region. In actual operation, regions with a large gray-scale contrast difference and high consistency are more likely to represent regions containing deposited particles, and these regions are the most preferred for subsequent macro-image acquisition.

[0036] Furthermore, the priorities of each region can be sorted according to their actual characteristics. For example, if the grayscale contrast difference of a certain sub-block is large but the grayscale consistency is low, then the score of this region will be relatively low, indicating that this region may be too complex or irregular and not suitable for further processing; while if the grayscale contrast difference and consistency of a certain region are both high, it means that the particle distribution in this region is clear and the structure is stable, which is suitable as the target region for subsequent acquisition.

[0037] 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 weighting, a region with a large grayscale contrast difference will obtain a relatively high 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 differences 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. In order to ensure that such regions can be preferentially selected, a higher weighting value will be assigned to the 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.

[0038] 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.

[0039] Please refer to Figure 3 , the schematic diagram of the locally selected region in the embodiment of the present invention. The overall image is divided into 10×10 sizes, a total of 100 region units. Each region unit is a small square, and the locally selected region is marked as a gray block, such as Figure 3 The locally selected region 1, locally selected region 2, and locally selected region 3 in. These regions represent the parts that need to be further analyzed in the overall image. The range of the overall image is large, and these locally selected regions are the regions 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 regions and region units in actual applications.

[0040] The specific steps of S3 are as follows: S3.1: Collect a macro image of the currently locally selected area; In this embodiment, the locally selected area is a target area screened out through the aforementioned features such as the average gray value, gray variance, and gray contrast difference, and has high particle saliency and stability. The high resolution of the macro image enables the 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 in 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.

[0041] S3.2: Obtain the particle edge continuity of the corresponding regional units in the macro image and the overall image; Specifically, the macro image usually has a high resolution and can clearly display 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 this 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 in line 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. The 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.

[0042] S3.3: If the particle edge continuity in the currently collected macro image is inconsistent with the particle edge continuity of the overall image, merge the adjacent regional units of each currently locally selected area into the corresponding locally selected area and replace the currently corresponding locally selected area; Specifically, when the particle edge continuity in the macro image is inconsistent with the particle edge continuity in the overall image, it means that there may be a situation of particle information loss or inaccurate area selection in the current locally selected area. To ensure that the collection 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.

[0043] Further, when it is found that the continuity of the particle edges in the macro image does not conform to the continuity of the particle edges in the overall image, the adjacent regional units of the locally selected area will be automatically merged, and a new locally selected area that is more in line with the particle distribution in the overall image will be reselected to replace the original locally selected area. This process will be iterated until the continuity of the particle edges in the macro image is consistent with the continuity of the particle edges in the overall image. By continuously adjusting the selected area, the particle characteristics of the macro image can be gradually optimized to make it more in line with the particle distribution in the overall image. The iterative adjustment ensures the consistency of the particle information and avoids inaccurate measurements caused by acquisition errors or improper area selection.

[0044] It should be noted that when merging adjacent regional units, since the range that needs to be photographed for the newly generated locally selected area is larger than the field of view of a single shot of the macro lens, the iterative process will obtain multiple continuously shot macro images according to the size of the area. 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 that the merged macro image is seamlessly connected and the particle information of each part is completely retained.

[0045] S3.4: Until the continuity of the particle edges in the currently acquired macro image is consistent with the continuity of the particle edges in the overall image, output the currently acquired macro image; Specifically, when the edge continuities of the two are consistent, it means that the macro image accurately reflects the particle characteristics in the overall image. At this time, output the currently acquired macro image. This image can provide accurate particle information for subsequent particle size measurement and ensure that the matching degree between the macro image and the overall image is maximized during the measurement of underwater sediment deposition.

[0046] Please refer to Figure 4 and Figure 5 , during the iterative process, after the macro image is acquired, if the continuity of its particle edges is inconsistent with the continuity of the particle edges in the overall image, the selected area will be readjusted. For example, in the iterative process, it is found that the macro images 1 and 3 do not conform to the continuity of the particle edges in the overall image for the locally selected area 1 and the locally selected area 3. The adjacent grids of the locally selected area 1 and the locally selected area 3 are merged to better represent the particle distribution in the overall image, and finally ensure that the macro image can accurately reflect the particle characteristics in the overall image. It is found that the macro image 2 conforms to the continuity of the particle edges in the overall image for the locally selected area 2 during the iterative process. Therefore, there is no need to adjust the locally selected area 2. By continuously iteratively adjusting the locally selected area until the edge continuities are consistent, the accuracy and effectiveness of data acquisition are ensured. Further, in Figure 5It is found that the local selected area 1 and the local selected area 3 are already adjacent. If any area still does not meet the particle edge continuity during the subsequent iteration process, the two areas will be further merged into a new local selected area.

[0047] The specific steps of S3.2 are as follows: S3.2.1: Perform edge detection on the image to extract particle edge information; Specifically, edge detection identifies the edge parts in the image by calculating the gradient of the gray-scale change in the image. In the underwater sediment image, the edges of the particles often show 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.

[0048] S3.2.2: Calculate the connectivity of the particle edge information through an edge tracking algorithm, and calculate the edge continuity score according to the connectivity; Specifically, the continuity of the edge is an important indicator for evaluating the stability of the particle morphology. A continuous edge indicates that the particle morphology is relatively clear and not easily affected by noise or background interference, thus improving the reliability of the measurement. Calculating the connectivity of the edge can help identify and eliminate irregular or missing edges, ensuring more accurate subsequent particle measurements.

[0049] In this embodiment, connectivity refers to whether the particle edge is a continuous area 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 lead to errors in subsequent particle size measurement.

[0050] Furthermore, through the edge tracking algorithm, continuous edge pixel points can be traced and marked along the edge line in the image to determine whether the particle edge is complete and continuous. If the edge is continuous, it means that the external shape characteristics of the particle are relatively complete and suitable for further measurement; if the edge is broken, it means that the particle morphology is incomplete and may affect the measurement result. According to the score calculated from the connectivity, a higher score indicates that the particle edge is relatively smooth and has no breaks, while a lower score indicates that there may be missing or irregular edges.

[0051] S3.2.3: Use the standard deviation of the edge continuity score as the particle edge continuity.

[0052] It should be noted that in this embodiment, the continuity of the particle edges in the corresponding regional units of the overall image is relatively low, mainly because the resolution of the overall image is relatively 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 rather provides a rough reference framework for the macro image. By comparing the continuity of the particle edges 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 continuity of the particle edges 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 morphology of the particles is stable and consistent at different resolutions. At this time, the iterative 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 continuity of the particle edges by approaching the particle edges through continuous iteration, the final macro image can serve as an effective sample area to provide a basis for subsequent particle size analysis and particle morphology measurement.

[0053] The specific steps of S4 are as follows: 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 according to the high-frequency edge information; 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 possible complex details of the particle morphology and edges, Laplacian pyramid decomposition helps to capture the detail and structural 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.

[0054] S4.2: Calculate the regional gray variance of the fused edge map, and generate a particle region map by removing the low-variance regions through a background noise mask; 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 the particle region. 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 the relevant regions, and avoiding errors caused by background noise or low-contrast regions.

[0055] In this embodiment, first, the gray variance of the fused edge map calculation region is calculated 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 a higher variance usually indicate that the region contains more variation information, such as the boundaries and contours of particles; while regions with a lower variance typically represent the background or uniform regions lacking particle information. By calculating the gray variance, it is possible to eliminate those background regions with small gray changes from the image, ensuring that only the regions containing particle information are retained.

[0056] Furthermore, a background noise mask is used to further eliminate the low-variance regions. The gray changes in the low-variance regions are small, usually representing meaningless backgrounds or regions without obvious particle distributions. Through the background noise mask technology, these background regions can be accurately filtered out, retaining the regions containing particles and generating a particle region map. The particle region map can accurately identify the positions and shapes of the particles.

[0057] S4.3: Perform cutting on the agglomerated particles in the particle region map to obtain multiple cut images; Specifically, the cutting of agglomerated particles is to ensure that each particle can be measured independently without being interfered by other particles. Sediment deposits often form agglomeration phenomena due to the accumulation and overlap of particles, which can lead to inaccurate measurement of particle sizes. Through effective cutting methods, these agglomerated particles can be separated to ensure that each particle can be independently identified and measured.

[0058] In this embodiment, by analyzing the connected regions in the particle region map, the mutually agglomerated particles are detected and cut. During the cutting process, the system automatically identifies the independent boundaries of each particle based on the morphology, edges, and region characteristics of the particles, and divides them into multiple individual particle images.

[0059] S4.4: Correct the multiple cut images to generate a sediment image; 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.

[0060] The specific steps of S4.3 are as follows: S4.3.1: Calculate the gradient magnitude and gradient direction of the particle region map; Mark the regions with sudden changes in gradient direction as agglomeration boundaries; Generate cutting lines along the agglomeration boundaries to divide the agglomerated particles into independent sub-regions; The specific steps of S4.4 are as follows: S4.4.1: Perform elliptical fitting on the segmented sub-regions and calculate the fitting error; S4.4.2: According to the fitting error, divide the cut image into over-segmented and under-segmented parts, and correct the cut 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 cut image and continue to segment the cut image until the fitting error is less than the set fitting threshold.

[0061] Embodiment 2 Please refer to Figure 6 , the present invention provides an embodiment: a macro measurement device, and the macro measurement device includes: A housing 201, including a circulation channel for the water sample to be measured, and 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; An isolation chamber 202 is arranged inside the housing 201. A camera module 203, a macro lens 204 and a processing module 210 are configured 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; 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; A measurement water body chamber 209 is arranged inside the isolation chamber 202 and is connected to the water inlet 206 and the water outlet 207 for placing the water body containing sediment deposits to be measured; An automatic cleaning mechanism 208 is arranged at the water outlet 207 of the circulation channel of the water sample to be measured for cleaning the lens and the isolation chamber 202 after shooting.

[0062] The laser 205 emits a fan-shaped light beam for forming a sheet light curtain with a fixed thickness in the isolation chamber 202, and the illumination plane of the sheet light curtain coincides with the imaging focal plane of the macro lens 204.

[0063] The automatic cleaning mechanism 208 includes a rotatable brush assembly, and 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 chamber 202 and synchronously discharges the measured water sample.

[0064] The camera module 203 is used to obtain an overall image of the underwater sediment deposition area to be measured, and the overall image includes a plurality of regional units, and the single-shot field of view range of the macro lens 204 is the same as the size of the regional unit; Please refer to Figure 7 , a schematic structural diagram of the processing module 210 according to an embodiment of the present invention, where the dashed line indicates the data flow direction, and the processing module 210 includes: A determination unit 2101, which 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 2102, which performs an iterative operation to collect a macro image of the current locally selected region. If the continuity of the particle edges in the currently collected macro image is inconsistent with the continuity of the particle edges in the overall image, the adjacent regional units of the current each locally selected region are merged into the corresponding locally selected region and replace the current corresponding locally selected region until the continuity of the particle edges in the currently collected macro image is consistent with the continuity of the particle edges in the overall image, and then outputs the currently collected macro image; A conversion unit 2104, which performs image segmentation on the macro image, outputs a sediment image through a connected component algorithm, and based on a calibration reference object preset in the housing 201 of the isolation chamber 202, converts the pixel size of the sediment image into an actual physical size to measure the particle size distribution data of sediment particles.

[0065] 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 disposed in the housing, a camera module, a macro lens and a processing module disposed in the isolation chamber, the macro lens facing the light-transmitting area of ​​the isolation chamber, characterized in that: The macro measurement method comprises: Acquire an overall image of the underwater sediment area to be measured, wherein the overall image includes a plurality of area units, and the single-shot field of view of the macro lens is the same as the size of the area unit; According to the pixel value of each pixel in the overall image, a plurality of area units are determined from all area units as local selected areas; Perform an iterative operation to collect 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 of the overall image, merge adjacent area units of each current local selected area 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 of the overall image, and output the currently collected macro image; Based on all the macro images currently collected, the particle size distribution data of the sediment particles are measured.

2. A macro measurement method according to claim 1, characterized in that: The step of determining a plurality of area units from all area units as local selected areas includes: Grayscale the whole image and calculate the grayscale mean and grayscale variance of each regional unit; According to a preset deposition area identification standard, combined with the grayscale mean and grayscale variance, an initial local selected area is selected; The initial local selected area is screened a second time to obtain the local selected area.

3. A macro measurement method according to claim 2, characterized in that: The initial local selected area is subjected to secondary screening, including: Divide the initial local selected area into multiple sub-blocks, and calculate the grayscale contrast difference and grayscale consistency between adjacent sub-blocks; Weighting the initial local selected area according to the grayscale contrast difference and grayscale consistency to calculate a comprehensive score; The comprehensive score is compared with a preset uniform threshold, and if the comprehensive score is greater than or equal to the uniform threshold, the corresponding initial local selected area is used as the local selected area.

4. A macro measurement method according to claim 1, characterized in that: The particle edge continuity is calculated as follows: Perform edge detection on the image and extract particle edge information; Calculating the connectivity of the particle edge information by an edge tracking algorithm, and calculating the edge continuity score according to the connectivity; The standard deviation of the edge continuity scores was taken as the particle edge continuity.

5. A macro measurement method according to claim 1, characterized in that: The particle size distribution data of the sediment particles is measured based on all the currently collected macro images, including: Performing image segmentation on the macro image, and outputting a sediment image by using a connected domain algorithm; Based on the calibration reference object preset in the isolation chamber shell, the pixel size of the sediment image is converted into the actual physical size to measure the particle size distribution data of the sediment particles.

6. A macro measurement method according to claim 5, characterized in that: Performing image segmentation on the macro image and outputting a sediment image through a connected domain algorithm includes: Performing Laplace 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 grayscale variance of the fused edge image, removing low variance areas through a background noise mask, and generating a particle area map; Performing adhesion particle cutting on the particle region map to obtain a plurality of cutting images; The multiple cut images are corrected to generate a sediment image.

7. A macro measurement method according to claim 6, characterized in that: The particle region map is subjected to adhesion particle cutting to obtain a plurality of cutting images, including: Calculating the gradient amplitude and gradient direction of the particle area map; The region with abrupt changes in gradient direction is marked as the adhesion boundary; A cutting line is generated along the adhesion boundary to divide the adhesion particles into independent sub-regions.

8. A macro measurement device, characterized in that: include: The housing comprises a flow channel for the water sample to be tested, wherein the flow channel is provided with a water inlet and a water outlet for introducing and discharging the water sample to be tested; An isolation chamber is arranged inside the shell, wherein a camera module, a macro lens and a processing module are arranged inside the isolation chamber, the macro lens faces the light-transmitting area of ​​the shell of the isolation chamber, and the processing module is used to perform image preprocessing, particle segmentation and particle size statistics; A laser, connected to the macro lens, forms a laser light curtain in the shooting area of ​​the macro lens, which is used to enhance the illumination of the water sample to be tested; The automatic cleaning mechanism is arranged at the water outlet of the water sample flow channel to be tested, and is used to clean the lens and the isolation chamber after shooting is completed.

9. A macro measurement device according to claim 8, characterized in that: The automatic cleaning mechanism includes a rotatable brush assembly, which is linked to 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 simultaneously discharges the tested water sample.

10. A macro measurement device according to claim 8, characterized in that: The camera module is used to obtain an overall image of the underwater sediment area to be measured, wherein the overall image includes a plurality of area units, and the single-shot field of view of the macro lens is the same as the size of the area unit; The processing module comprises: A determination unit, which determines a plurality of area units from all area units as local selected areas according to the pixel value of each pixel in the overall image; an iterative unit, performing an iterative operation, acquiring a macro image of the current local selected area, and if the particle edge continuity in the currently acquired macro image is inconsistent with the particle edge continuity of the overall image, merging adjacent area units of each current local selected area into the corresponding local selected area, and replacing the current corresponding local selected area, until the particle edge continuity in the currently acquired macro image is consistent with the particle edge continuity of the overall image, and outputting the currently acquired macro image; The conversion unit performs image segmentation on the macro image, outputs a sediment image through a connected domain 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 shell to measure the particle size distribution data of the sediment particles.

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