Sediment floc structure parameter measurement method, system, device and medium based on three-dimensional reconstruction

Abstract

The application discloses a kind of based on three-dimensional reconstruction's silt flocculation structure parameter measurement method, system, equipment and medium, belong to flocculation monitoring technical field.The method comprises: by at least two image acquisition devices, the two-dimensional image of silt flocculation is synchronously collected from different visual angle, and its imaging geometric parameter is obtained;The two-dimensional image of silt flocculation is segmented to obtain the binary mask of each visual angle;Based on imaging geometric parameter, each mask is reversely projected to three-dimensional space to form visual cone, and the three-dimensional visual convex hull model of silt flocculation is obtained by intersection operation;Based on the three-dimensional visual convex hull model, the structure parameter of silt flocculation is output.Therefore, by implementing the present application, the problem that the calculation error of irregular silt flocculation structure parameter is large in the prior art based on single visual angle two-dimensional projection and sphere assumption can be solved, and the accurate measurement of the real three-dimensional morphology of silt flocculation is realized.

Description

Technical Field

[0001] This application relates to the field of floc monitoring technology, and in particular to a method, system, equipment and medium for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction. Background Technology

[0002] In water treatment engineering, estuarine and coastal dynamics, and environmental monitoring, the flocculation and sedimentation of viscous fine particulate matter (such as sediment and pollutants) is a core physical process. During sedimentation, suspended matter collides and agglomerates to form loosely structured flocs, and their three-dimensional morphology determines wastewater treatment efficiency, sediment transport patterns, and pollutant fate. Therefore, observing the three-dimensional morphology of fine particulate matter flocs formed in water is crucial for studying their sedimentation patterns and optimizing water treatment processes.

[0003] Currently, mainstream non-contact observation technologies mainly include laser diffraction and single-view image analysis. Laser diffraction, based on the assumption of spherical scattering, infers particle size distribution but cannot obtain the actual shape information of fine-grained sediment flocs. Single-view image analysis acquires a two-dimensional projection image of the sediment floc using a single camera and generally employs an "equivalent sphere" or "standard fractal" model to convert the projected area into a three-dimensional volume. However, naturally formed fine-grained sediment flocs are often highly irregular sheet-like, strip-like, or porous structures. The aforementioned conversion methods based on simple geometric assumptions suffer from severe distortion, leading to significant errors in volume and surface area calculations and failing to accurately reconstruct the spatial morphology of fine-grained sediment flocs. Although methods exist for acquiring images from different angles using multi-camera hardware, there is still a lack of supporting automated three-dimensional reconstruction algorithms, relying on manual processing, which is inefficient and difficult to widely apply. Therefore, the core deficiency of existing technologies lies in the inability of volume calculation methods based on single-view two-dimensional projection and the spherical assumption to accurately obtain the true three-dimensional morphology and structural parameters of irregular sediment flocs. Summary of the Invention

[0004] This application provides a method, system, device, and medium for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction, which can solve the problem of how to accurately obtain the structural parameters of irregular sediment flocs with true three-dimensional morphology.

[0005] Firstly, this application provides a method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction, including: Multiple sets of two-dimensional images of sediment flocs are obtained by simultaneously acquiring two images of sediment flocs within the observation area from at least two image acquisition devices that are aimed at the observation area from different perspectives; wherein each set of images of sediment flocs contains one frame of image acquired by each of the image acquisition devices at the same time; and the imaging geometric parameters of each of the image acquisition devices are obtained by pre-determined joint calibration. Each of the multiple sets of two-dimensional images of sediment flocs is segmented to obtain a corresponding binarized mask of sediment flocs from the perspective of each image acquisition device. Based on the imaging geometric parameters of each image acquisition device, the binarized mask of the mud and sand flocs from the perspective of each image acquisition device is projected backwards into three-dimensional space to obtain multiple view frustums; the intersection operation of all view frustums is performed to obtain the three-dimensional visual convex hull model of the mud and sand flocs. Based on the three-dimensional visual convex hull model of the sediment flocs, the structural parameters of the sediment flocs are output.

[0006] This application provides multi-faceted morphological information for irregular sediment flocs by simultaneously acquiring multi-view two-dimensional images, breaking the limitations of single-view observation. Through multi-view spatial intersection technology, it abandons geometric assumptions such as "equivalent sphere" in traditional methods, reconstructing a three-dimensional convex hull model of the sediment flocs from multiple two-dimensional contours, thereby achieving accurate calculation of the true volume of the sediment flocs.

[0007] Furthermore, the process of simultaneously acquiring two-dimensional images of sediment flocs within the observation area using at least two image acquisition devices aligned with the observation area from different perspectives, resulting in multiple sets of two-dimensional images of sediment flocs, specifically includes: Three image acquisition devices are set up such that the optical axes of the three image acquisition devices are distributed at a 120-degree angle on the horizontal plane and focus together on the observation area; The observation area is illuminated using a background light source, and the three image acquisition devices are simultaneously triggered to acquire multiple sets of two-dimensional images of sediment flocs.

[0008] This arrangement of three image acquisition devices, evenly distributed at 120-degree intervals, enables comprehensive, complementary, and seamless coverage of the observation area with minimal hardware cost. This ensures the capture of morphological information of irregular sediment flocs from multiple perspectives, providing an optimal data foundation for subsequent high-fidelity 3D reconstruction. Background illumination and synchronized triggering guarantee consistent image quality and strict temporal alignment, eliminating reconstruction errors caused by asynchrony or lighting differences.

[0009] Furthermore, after obtaining the three-dimensional visual convex hull model of the sediment flocs, the process further includes: The cross-sectional profile of the three-dimensional visual convex hull model is extracted, and the cross-sectional profile is fitted using a B-spline curve to obtain a second three-dimensional visual convex hull model of smooth mud and sand flocs.

[0010] By using B-spline curves to smoothly fit the initial model obtained from the intersection of multiple views, which may have sharp edges or jagged edges on the surface, the surface roughness problem caused by limited viewpoints and discretization during the reconstruction process can be effectively eliminated, generating a smooth three-dimensional model that is closer to the physical morphology of real sediment flocs, thereby further improving the accuracy of parameters such as volume and surface area calculated based on the model.

[0011] Furthermore, based on the imaging geometric parameters of each of the image acquisition devices, the binarized mask of the mud and sand flocs from the perspective of each image acquisition device is projected backwards into three-dimensional space to obtain multiple view frustums, specifically as follows: Based on the projection matrix defined by the imaging geometric parameters of each of the image acquisition devices, the binarized mask of the mud and sand flocs from the viewpoint of each of the image acquisition devices is projected back into three-dimensional space along the line of sight of the respective image acquisition device to obtain the view frustum corresponding to each viewpoint.

[0012] In this way, by using a binarized mask for each viewpoint and, based on the precise imaging geometric parameters (projection matrix) of the image acquisition device, inversely extending it into an infinitely extending prism (view frustum) in three-dimensional space, the two-dimensional image information is accurately transformed into three-dimensional spatial constraints. This step is a key geometric transformation that unifies and fuses multi-view two-dimensional observation data into the same three-dimensional coordinate system, laying the mathematical foundation for subsequently determining the spatial extent of the floc through intersection operations.

[0013] Further, the step of segmenting each of the multiple sets of two-dimensional images of sediment flocs to obtain a corresponding binarized mask of sediment flocs from the perspective of each image acquisition device is specifically as follows: Each frame of the two-dimensional image of each group of sediment flocs is grayscaled and Gaussian filtered for denoising to obtain the denoised image. The denoised image is subjected to adaptive threshold segmentation using the Otsu method to obtain an initial binary image; The initial binary image is processed by morphological opening to obtain the binarized mask of the mud and sand flocs.

[0014] By employing grayscale conversion and Gaussian filtering preprocessing, random noise during image acquisition is effectively suppressed, improving image quality. Otsu's method is used for adaptive threshold segmentation, which automatically adapts to the overall grayscale distribution of different images, separating the silt flocs from the background. Furthermore, morphological opening operations eliminate residual minute noise points in the segmented image and smooth the contour edges of the silt flocs, resulting in a clean, accurate, and coherent binary mask. This provides high-quality input data for subsequent 3D backprojection.

[0015] Further, acquiring the imaging geometric parameters of each of the image acquisition devices, determined through pre-joint calibration, includes: By setting a checkerboard calibration plate in the observation area and performing joint calibration based on Zhang Zhengyou's calibration method, the imaging geometric parameters of each image acquisition device are obtained; wherein, the imaging geometric parameters of each image acquisition device include the intrinsic parameter matrix and the extrinsic parameter matrix of each image acquisition device, the intrinsic parameter matrix includes at least the focal length and principal point coordinates, and the extrinsic parameter matrix includes the rotation matrix and the translation vector.

[0016] By using a standard checkerboard calibration board and the mature Zhang Zhengyou calibration method, the internal parameters (intrinsic parameters) of each image acquisition device and the relative position and attitude relationships (extrinsic parameters) between multiple image acquisition devices can be obtained with high precision and reliability. The obtained intrinsic and extrinsic parameter matrices together constitute an accurate imaging geometric model, which is the fundamental basis for subsequent inverse calculation of 2D image coordinates to 3D world coordinates, achieving accurate alignment and fusion of multi-view data, and ensuring the measurement accuracy of the entire 3D reconstruction process.

[0017] Furthermore, the projection matrix defined based on the imaging geometric parameters of each of the image acquisition devices is specifically as follows: The projection matrix is ​​obtained by performing matrix multiplication on the intrinsic parameter matrix and the extrinsic parameter matrix of the image acquisition device.

[0018] Thus, by defining the "projection matrix" as the matrix product of the intrinsic and extrinsic parameter matrices, a definite and computable mathematical foundation is provided for the 3D reconstruction process. The intrinsic and extrinsic parameters obtained from camera calibration are synthesized into projection operators for coordinate transformation through matrix operations, ensuring that every back-projected ray from the 2D image to 3D space has mathematical traceability, thus guaranteeing the geometric accuracy of spatial intersection of multi-view data in a unified 3D coordinate system.

[0019] Secondly, this application provides a sediment floc volume measurement system based on three-dimensional reconstruction, including: an image acquisition module, a three-dimensional morphology reconstruction module, and a result output module; The image acquisition module is used to simultaneously acquire multiple sets of two-dimensional images of sediment flocs within the observation area using at least two image acquisition devices that are aimed at the observation area from different perspectives; wherein each set of two-dimensional images of sediment flocs includes one frame image acquired by each of the image acquisition devices at the same time; and to obtain the imaging geometric parameters of each of the image acquisition devices determined by pre-joint calibration. The three-dimensional morphology reconstruction module is used to segment each of the multiple sets of two-dimensional images of sediment flocs to obtain a corresponding binary mask of sediment flocs from the perspective of each image acquisition device; based on the imaging geometric parameters of each image acquisition device, the binary mask of sediment flocs from the perspective of each image acquisition device is projected backwards into three-dimensional space to obtain multiple view frustums; the intersection operation is performed on all the view frustums to obtain the three-dimensional visual convex hull model of the sediment flocs; The result output module is used to calculate the structural parameters of the sediment flocs based on the three-dimensional visual convex hull model of the sediment flocs.

[0020] This application utilizes an image acquisition module to achieve standardized and synchronous data acquisition, providing a source guarantee for reconstruction. The 3D morphology reconstruction module integrates a complete algorithm flow from image segmentation and multi-view backprojection to spatial intersection, realizing automated and high-precision conversion from 2D image sequences to 3D models. The results output module directly outputs key parameters based on the 3D model, improving the overall efficiency and reliability of sediment floc morphology observation and analysis.

[0021] Thirdly, this application also provides a terminal device, characterized in that it includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein when the processor executes the computer program, it implements the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction as described in the first aspect above.

[0022] Fourthly, this application also provides a computer-readable storage medium, characterized in that it includes: a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to perform the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction as described in the first aspect above. Attached Figure Description

[0023] To more clearly illustrate the technical solution of this application, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating an embodiment of the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction provided in this application. Figure 2 This is a schematic diagram of the initial interface of a software interaction system according to an embodiment of this application; Figure 3This is a schematic diagram of the operation interface of a software interaction system according to an embodiment of this application after selecting a multi-view image file; Figure 4 This is a schematic diagram of the visualization of the structural parameters of silt flocs displayed by a software interactive system according to an embodiment of this application; Figure 5 This is a schematic diagram of the structure of a multi-view image acquisition device according to an embodiment of this application; Figure 6 This is a schematic diagram showing the comparison of a three-dimensional visual convex hull model before and after smoothing, according to an embodiment of this application. Figure 7 This is a schematic diagram of the view cone structure from three perspectives according to an embodiment provided in this application; Figure 8 This is a schematic diagram of an image segmentation processing flow according to an embodiment of this application; Figure 9 This is a schematic diagram illustrating the multi-view binarization masking effect of one embodiment provided in this application; Figure 10 This is a schematic diagram of an embodiment of the sediment floc structure parameter measurement system based on three-dimensional reconstruction provided in this application.

[0025] Labeling Explanation: 100, Image Acquisition Module; 200, 3D Morphology Reconstruction Module; 300, Result Output Module. Detailed Implementation

[0026] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings of the embodiments. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0027] In the description of the embodiments of this application, technical terms such as "first" and "second" are used only to distinguish different objects and should not be construed as indicating or implying relative importance or implicitly specifying the number, specific order, or primary and secondary relationship of the indicated technical features. In the description of the embodiments of this application, "multiple" means two or more, unless otherwise explicitly defined.

[0028] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of this application. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0029] In the description of the embodiments in this application, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, and B existing alone. Additionally, the character " / " in this document generally indicates that the preceding and following related objects have an "or" relationship.

[0030] In environmental monitoring, water treatment, and estuarine and coastal engineering research, cohesive fine particles (such as silt and clay) collide and agglomerate in water bodies to form loosely structured, highly irregularly shaped silt flocs. The true three-dimensional volume of these silt flocs is a key parameter for understanding flocculation mechanisms and predicting sedimentation flux. Traditional observation techniques (such as laser particle size analyzers or monocular cameras) can only acquire two-dimensional projections of the flocs and rely on the "equivalent sphere" assumption to infer the volume. For irregular silt flocs such as sheet-like or strip-shaped flocs, the measurement error of structural parameters is large, which cannot meet the needs of scientific research and engineering.

[0031] To address this, this application proposes a method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction. Please refer to [link / reference]. Figure 1 , Figure 1 This is a flowchart of an embodiment of the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction provided in this application. To address the problem of large measurement errors in irregular floc structural parameters caused by single-view observation and the assumption of sphericity in the prior art, an embodiment of this application provides a method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction, including steps S1 to S4, each step as follows: Step S1: Simultaneously acquire two-dimensional images of sediment flocs within the observation area using at least two image acquisition devices that are aimed at the observation area from different perspectives, resulting in multiple sets of two-dimensional images of sediment flocs; wherein each set of images of sediment flocs contains one frame image acquired by each image acquisition device at the same time; and obtain the imaging geometric parameters of each image acquisition device determined by pre-joint calibration. Step S2: Segment each of the multiple sets of two-dimensional images of sediment flocs to obtain the corresponding binarized mask of sediment flocs from the perspective of each image acquisition device. Step S3: Based on the imaging geometric parameters of each image acquisition device, the binarized mask of the mud and sand flocs from the perspective of each image acquisition device is back-projected into the three-dimensional space to obtain multiple view frustums; the intersection operation is performed on all view frustums to obtain the three-dimensional visual convex hull model of the mud and sand flocs. Step S4: Based on the three-dimensional visual convex hull model of the sediment flocs, output the structural parameters of the sediment flocs.

[0032] In some preferred embodiments, steps S1 to S4 described above can be executed automatically through an integrated software interaction system. Please refer to... Figure 2 , Figure 2 This is a schematic diagram of the initial interface of a software interaction system according to an embodiment of this application. The system provides a graphical user interface that efficiently guides users through data import, parameter setting, algorithm execution, and result output. Please refer to... Figure 3 , Figure 3 This is a schematic diagram of the operation interface of a software interaction system provided in this application, after selecting a multi-view image file. Users can import and configure the acquisition frame rate, physical scale, and image analysis parameters through the parameter setting module. The data preprocessing and management module allows users to select three different viewpoints of raw image sequences or video files. The system automatically decomposes the video frame by frame and establishes a standardized folder structure, in which the flocculent images from each camera viewpoint can be clearly viewed. Please refer to... Figure 4 , Figure 4 This is a schematic diagram of the visualization of floc structure parameters displayed by a software interactive system according to an embodiment of this application. After data import is completed, the system supports the initial calibration of the floc sedimentation trajectory through an interactive target tracking module. Subsequently, the user can trigger the "Reconstruction" function, and the system will automatically call the built-in algorithm to execute the aforementioned steps S2 to S4, generating a three-dimensional visual convex hull model of the floc and calculating its structural parameters. Through the result visualization and persistence module, the software can dynamically draw the three-dimensional motion trajectory of the floc within a time window and annotate the volume value of the floc in real time in the three-dimensional rendering view. Figure 4 This system provides a visual example of the trajectory and volume changes of a flocculent mass over 5 seconds. It automates the analysis of raw images and their 3D morphological parameters, reducing the barrier to entry for users and shortening data analysis time.

[0033] Among them, multiple sets of two-dimensional images of sediment flocs refer to a data set obtained by multiple image acquisition devices simultaneously. The "set" is the core concept, specifically referring to a data set consisting of images captured by all image acquisition devices (such as three cameras) at the same time.

[0034] Imaging geometry parameters are a key set of data describing the camera's imaging model, typically including the camera's intrinsic parameters (such as focal length, principal point coordinates, and distortion coefficients) and extrinsic parameters (i.e., the camera's rotation matrix and translation vector in the world coordinate system). These parameters are obtained through joint calibration and form the mathematical basis for the accurate conversion between two-dimensional image coordinates and three-dimensional world coordinates.

[0035] A binarization mask is an image obtained through image processing where pixel values ​​are only 0 (background) or 1 (foreground, i.e., clumps). The binarization mask clearly separates the clumps from the background and serves as the input for subsequent geometric projection.

[0036] A frustum is an infinitely extended three-dimensional volume formed by extending all foreground pixels in a binarized mask of an image acquisition device's viewpoint into three-dimensional space according to the imaging geometry of the image acquisition device. The frustum represents the entire spatial range that a floc may occupy from that viewpoint.

[0037] The structural parameters of sediment flocs refer to the volume, surface area, and three-dimensional fractal dimension calculated based on the three-dimensional visual convex hull model of fine-particle sediment flocs. The output structural parameters of sediment flocs based on the three-dimensional visual convex hull model include at least one of the above parameters. Through the three-dimensional visual convex hull model, the volume, surface area, and three-dimensional fractal dimension of sediment flocs can be calculated simultaneously. Volume reflects the spatial occupancy and mass carrying capacity of sediment flocs, and is a core physical quantity for calculating sedimentation flux. Surface area determines the size of the interface between sediment flocs and surrounding fluids, which is crucial for studying mass transport, adsorption reactions, and flocculation kinetics. The three-dimensional fractal dimension quantitatively characterizes the complexity and irregularity of the internal structure of sediment flocs, and is a key indicator for distinguishing different flocculation mechanisms (such as diffusion-limited aggregation or reaction-limited aggregation).

[0038] Specifically, in some embodiments, please refer to Figure 5 , Figure 5 This is a schematic diagram of a multi-view image acquisition device according to an embodiment of this application. In step S1, at least two image acquisition devices aligned with the observation area from different perspectives simultaneously acquire two-dimensional images of sediment flocs within the observation area, resulting in multiple sets of two-dimensional images of sediment flocs, specifically: Three image acquisition devices are set up so that the optical axes of the three image acquisition devices are distributed at a 120-degree angle on the horizontal plane and focus together on the observation area; The observation area was illuminated by a background light source, and three image acquisition devices were simultaneously triggered to acquire multiple sets of two-dimensional images of sediment flocs.

[0039] In one specific embodiment of this application, please refer to Figure 5This multi-view image acquisition device centers on a transparent hexahedral settling tank (D), with a central autonomous settling column (E) inside to accommodate the settling process of flocs. The bottom is a wooden base (I) covered by a glass base plate (G), with a circular slide rail (H) on the base. Three lifting platforms (F-1 to F-3) are arranged along the slide rail, each equipped with a camera support slide rail platform and metal optical axis clamps (C-1 to C-3) for fixing the camera and enabling multi-angle shooting. Light sources (B-1 to B-3) and their control boards (A-1 to A-3) are located at three corresponding positions above, providing uniform illumination for shooting.

[0040] Three cameras are mounted on a circular guide rail via adjustable brackets and can be freely moved and locked along the rail using sliders. Through precise adjustments, the optical centers of cameras A, B, and C are all located in the same horizontal plane, with the angle between the lines connecting any two cameras being 120 degrees, forming the three vertices of an equilateral triangle. By adjusting the pitch and rotation of the camera lenses, it is ensured that the optical axes of all three cameras point to and converge on the same observation area along the central axis of the hexagonal sedimentation column. This arrangement ensures simultaneous observation of the same sediment floc target from three completely different, uniformly distributed perspectives. To achieve high-quality backlit imaging, a large-area, uniformly emitting, adjustable-brightness LED panel is placed directly behind the observation area as a background light source, ensuring that the sediment floc is always presented in a high-contrast silhouette (i.e., dark foreground, bright background) in the image, simplifying subsequent image segmentation.

[0041] During data acquisition, a central synchronization controller sends a hardware trigger signal to three cameras. Upon receiving the same trigger pulse, the three cameras simultaneously begin exposure at the same microsecond interval, thereby acquiring a set of three synchronized two-dimensional images, which record the projection shape of the same floc at exactly the same time from three 120-degree angle directions.

[0042] The "three cameras evenly distributed at 120 degrees" layout in the specific embodiments of this application is a preferred solution of the present invention. Its advantages are: it achieves multi-view coverage of the observed target without redundancy or blind spots with the minimum number of hardware devices (three cameras); the symmetrical geometric relationship simplifies the coordinate calculation in subsequent 3D reconstruction; and through hardware synchronization and backlight design, it ensures the consistency of input image data in time and quality from the source, laying a reliable physical foundation for high-precision 3D volume measurement.

[0043] Specifically, in some embodiments, please refer to Figure 6 , Figure 6 This is a schematic diagram showing the comparison of a three-dimensional visual convex hull model before and after smoothing, provided in an embodiment of this application. After obtaining the three-dimensional visual convex hull model of the sediment flocs in step S3, the method further includes: The cross-sectional profile of the three-dimensional visual convex hull model is extracted, and the cross-sectional profile is fitted with B-spline curves to obtain a second three-dimensional visual convex hull model of smooth mud and sand flocs.

[0044] Please refer to Figure 6 (Left) The model obtained by intersecting the masks using only three viewpoints has a rough surface and obvious angular edges. This is mainly due to the limited number of viewpoints (sparseness) and the discretized back projection and intersection calculation. Although the initial 3D visual convex hull model defines the maximum outer boundary of the floc, its sharp surface differs from the streamlined and smooth physical morphology of the real floc. Directly calculating structural parameters such as volume based on this model may introduce systematic bias.

[0045] To further improve the physical realism of the 3D model and the accuracy of parameter calculations, this application introduces a surface smoothing post-processing method based on B-splines after obtaining the initial model described above. A specific example is provided below to illustrate this: First, the rough 3D visual convex hull model (such as...) is initially reconstructed. Figure 6 (As shown on the left) The image is sliced ​​at equal intervals along a direction perpendicular to its principal axis (usually vertical). For each slice plane, the polygonal contour formed by its intersection with the rough 3D visual convex hull model is extracted. This polygonal contour consists of a series of vertices.

[0046] Subsequently, for each layer of extracted polygonal contours, the midpoint of each edge is calculated, and these midpoints are used as control points. P i . use k B-order spline basis functions N i,k (u) Constructing smooth curves C ( u ): The curve C ( u Using these control points as guides, a smooth contour line with continuous curvature that passes through or approximates these midpoints will be generated. By sequentially performing the above B-spline curve fitting on the cross-sectional contours of all slices, and stacking all the fitted smooth contour lines along the slice direction, a smooth, more realistic 3D model of the sediment flocs is reconstructed, i.e., the second 3D visual convex hull model. Figure 6 As shown on the right.

[0047] By fitting and stacking B-splines on all cross-sections, a smoother, more realistic 3D model of sediment flocs can be reconstructed. Based on this model, the system can automatically calculate the true volume, surface area, and 3D fractal dimension of the sediment flocs. This B-spline-based surface smoothing method effectively eliminates artificial surface noise and sharp edges caused by limited viewpoints and discretized calculations, while maintaining the overall spatial footprint and external dimensions of the original visual convex hull model. Figure 6 As shown in the comparison, the smoothed and optimized 3D visual convex hull model is visually closer to the smooth hydrodynamic shape of real sediment flocs, thereby further improving the accuracy and physical reliability of structural parameters such as volume and surface area calculated based on the model.

[0048] Specifically, in some embodiments, please refer to Figure 7 , Figure 7 This is a schematic diagram of the view frustum structure from three perspectives according to an embodiment of this application. In step S3, based on the imaging geometric parameters of each image acquisition device, the binarized mask of the mud and sand flocs from the perspective of each image acquisition device is back-projected into three-dimensional space to obtain multiple view frustums, specifically: Based on the projection matrix defined by the imaging geometric parameters of each image acquisition device, the binarized mask of mud and sand flocs from the viewpoint of each image acquisition device is projected back into three-dimensional space along the line of sight of the corresponding image acquisition device to obtain the view frustum corresponding to each viewpoint.

[0049] Back projection achieves the crucial transformation from 2D image information to 3D spatial constraints. For each camera, its imaging relationship is described by a projection matrix. After obtaining a binary mask for a certain viewpoint, the system traverses all foreground pixels in the mask. For each pixel (u, v), using the mathematical inverse of the projection matrix corresponding to that camera, a 3D ray originating from the camera's optical center and passing through that pixel can be calculated. By collecting the rays corresponding to all foreground pixels in the same mask, a 3D prism extending infinitely in 3D space with a cross-sectional shape consistent with the mask's contour is defined; this is the view frustum for that viewpoint. Each camera performs this operation independently, thereby generating the corresponding view frustum for each viewpoint.

[0050] For details, please refer to Figure 7 , Figure 7This demonstrates three view frustums (A, B, and C) formed by backprojecting binarized masks from three different viewpoints. When the optical axes of the three image acquisition devices are distributed at a 120-degree angle on the horizontal plane, the backprojection operation of the binarized masks from the three viewpoints forms three infinitely extending prisms (i.e., view frustums) with different spatial orientations in three-dimensional space. Each view frustum represents the entire spatial region that the floc may occupy when viewed from the corresponding camera's perspective. This step successfully transforms the shape information (mask contours) on multiple two-dimensional planes into a series of strict geometric constraints (view frustums) in three-dimensional space, laying an indispensable mathematical and geometric foundation for the subsequent precise definition of the true three-dimensional spatial extent of the floc through Boolean intersection operations.

[0051] Specifically, in some embodiments, please refer to Figure 8 and Figure 9 , Figure 8 This is a schematic diagram of an image segmentation processing flow according to an embodiment of this application. Figure 9 This is a schematic diagram illustrating the multi-view binarization mask effect of one embodiment provided in this application. In step S2, each set of two-dimensional images of sediment flocs is segmented to obtain a corresponding binarization mask for each image acquisition device's viewpoint. Specifically: Each frame of the two-dimensional image of each group of sediment flocs is grayscaled and Gaussian filtered for denoising to obtain the denoised image. The denoised image is then subjected to adaptive threshold segmentation using the Otsu method to obtain an initial binary image. Morphological opening operations are performed on the initial binary image to obtain a binarized mask of mud and sand flocs.

[0052] For each camera's original RGB image, grayscale processing is first performed to convert it into a single-channel grayscale image. Next, a Gaussian filter with a standard deviation of σ is used to convolve the grayscale image, effectively suppressing sensor noise and interference from tiny water bubbles, resulting in a denoised image. Then, the Otsu method is used for global thresholding of the denoised image. This method automatically calculates the optimal threshold, dividing the image into foreground (mud flocs) and background, generating an initial binary image. Finally, morphological opening operations are performed on this initial binary image, i.e., erosion followed by dilation. This step effectively eliminates residual isolated noise points and smooths the rough edges of the mud floc contours, ultimately outputting a clean and complete mud floc binary mask.

[0053] Specifically, such as Figure 8 As shown, it demonstrates the complete processing flow from "raw image" to "OTSU threshold segmentation" and then to "multi-window segmentation and merging" from top to bottom, intuitively presenting the process of gradually extracting and optimizing the target of mud and sand flocs. Figure 9 This demonstrates high-quality binarized masks (A, B, C) of sediment flocs generated simultaneously from three different perspectives (corresponding to three cameras). These masks have clear and continuous outlines, effectively suppress background noise, and accurately capture the projected morphology of the flocs from each perspective.

[0054] These masks have clear and continuous contours, and background noise is effectively suppressed, providing high-quality and reliable input data for subsequent steps to back-project them into 3D space and form the view frustum for intersection calculation. Through the combined processing flow of grayscale conversion, Gaussian filtering, Otsu's threshold segmentation, and morphological opening operation, this method can achieve highly robust floc target extraction in complex optical environments (such as in the presence of non-uniform illumination, pipe wall scratches, or micro-bubble interference), effectively ensuring the quality of input data in the 3D reconstruction process.

[0055] Specifically, in some embodiments, in step S1, obtaining the imaging geometric parameters of each image acquisition device determined through pre-joint calibration includes: By setting up a checkerboard calibration board in the observation area and performing joint calibration based on Zhang Zhengyou's calibration method, the imaging geometric parameters of each image acquisition device are obtained. The imaging geometric parameters of the image acquisition device include the intrinsic parameter matrix and the extrinsic parameter matrix of each image acquisition device. The intrinsic parameter matrix contains at least the focal length and principal point coordinates, and the extrinsic parameter matrix contains the rotation matrix and translation vector.

[0056] In some specific embodiments, camera calibration is required before starting the sediment floc observation experiment. A checkerboard calibration plate with known black and white square dimensions is placed within the observation area of ​​the sedimentation column. Multiple cameras are controlled to simultaneously capture multiple sets of images of the calibration plate from different angles. Subsequently, the Zhang Zhengyou calibration method is used to process the images. This algorithm detects the checkerboard corner points and utilizes multi-view geometric constraints to simultaneously calculate the intrinsic parameter matrix of each camera. K ) and extrinsic parameter matrix ([ R | t Internal parameter matrix K It includes intrinsic parameters such as focal length and principal point coordinates; the extrinsic parameter matrix describes the rotation of the camera coordinate system relative to a unified world coordinate system (via the rotation matrix). R (representation) and translation (via translation vector) t (Represented). These matrices together constitute the complete imaging geometry parameters for each camera.

[0057] In some other embodiments, the projection matrix defined based on the imaging geometric parameters of each image acquisition device in step S3 is specifically as follows: The projection matrix is ​​obtained by performing matrix multiplication on the intrinsic and extrinsic parameter matrices of the image acquisition device.

[0058] The imaging geometry parameters for each camera include an intrinsic parameter matrix. K and extrinsic parameter matrix [ R | t By performing matrix multiplication on these two matrices, the image acquisition device for each image acquisition device can be obtained. i projection matrix P i = K i [ Ri | t i This mathematical relationship ensures that the transformation from two-dimensional pixel coordinates (u, v) to three-dimensional ray directions or points is deterministic and reversible, providing a precise mathematical tool for the entire 3D reconstruction process. In software implementation, the calibration program outputs... K and[ R | t After that, the system will automatically perform this matrix multiplication to generate and store the projection matrix for each camera, which can be called during real-time image processing.

[0059] Please refer to Figure 10 , Figure 10 This is a schematic diagram of a sediment floc volume measurement system based on three-dimensional reconstruction in some embodiments of this application. The system includes: an image acquisition module 100, a three-dimensional morphology reconstruction module 200, and a result output module 300. The image acquisition module 100 is used to simultaneously acquire two-dimensional images of sediment flocs within the observation area using at least two image acquisition devices that are aligned with the observation area from different perspectives, resulting in multiple sets of two-dimensional images of sediment flocs; wherein each set of images of the multiple sets of two-dimensional images of sediment flocs contains one frame of image acquired by each image acquisition device at the same time; and to acquire the imaging geometric parameters of each image acquisition device determined by pre-joint calibration. The 3D morphology reconstruction module 200 is used to segment each of the multiple sets of 2D images of sediment flocs to obtain the corresponding binarized mask of sediment flocs from the perspective of each image acquisition device. Based on the imaging geometric parameters of each image acquisition device, the binarized mask of sediment flocs from the perspective of each image acquisition device is back-projected into 3D space to obtain multiple view frustums. The intersection operation is performed on all view frustums to obtain the 3D visual convex hull model of the sediment flocs. The output module 300 is used to output the volume of the sediment flocs based on the three-dimensional visual convex hull model.

[0060] It is understood that the above system embodiments correspond to the method embodiments of this application, and can implement the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction provided by any of the above method embodiments of this application.

[0061] It should be noted that the system embodiments described above are merely illustrative, and some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the system embodiments provided in this application, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0062] For ease of description and brevity, the system embodiments of this application include all the implementation methods described in the above embodiments of the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction, and will not be repeated here.

[0063] Based on the above embodiments of the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction, another embodiment of this application provides a terminal device, which includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor. When the processor executes the computer program, it implements the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction of any embodiment of this application.

[0064] For example, in this embodiment, the computer program can be divided into one or more modules, which are stored in the memory and executed by the processor to complete this application. The one or more module units may be a series of computer program instruction segments capable of performing a specific function, which describe the execution process of the computer program in the terminal device.

[0065] Based on the above-described method embodiments, another embodiment of this application provides a computer-readable storage medium including a stored computer program, wherein, when the computer program is executed, it controls the device where the computer-readable storage medium is located to execute the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction as described in any of the above-described method embodiments of this application.

[0066] The modules / units integrated in the device / terminal equipment, if implemented as software functional units and sold or used as independent products, can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, recording media, USB flash drives, portable hard drives, magnetic disks, optical disks, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signals, telecommunication signals, and software distribution media, etc.

[0067] The above description is the preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications are also considered to be within the scope of protection of this application.

Claims

1. A method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction, characterized in that, include: Multiple sets of two-dimensional images of sediment flocs are obtained by simultaneously acquiring two images of sediment flocs within the observation area from at least two image acquisition devices that are aimed at the observation area from different perspectives; wherein each set of images of sediment flocs contains one frame of image acquired by each of the image acquisition devices at the same time; and the imaging geometric parameters of each of the image acquisition devices are obtained by pre-determined joint calibration. Each of the multiple sets of two-dimensional images of sediment flocs is segmented to obtain a corresponding binarized mask of sediment flocs from the perspective of each image acquisition device. Based on the imaging geometric parameters of each image acquisition device, the binarized mask of the mud and sand flocs from the perspective of each image acquisition device is projected backwards into three-dimensional space to obtain multiple view frustums; the intersection operation of all view frustums is performed to obtain the three-dimensional visual convex hull model of the mud and sand flocs. Based on the three-dimensional visual convex hull model of the sediment flocs, the structural parameters of the sediment flocs are output.

2. The method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction according to claim 1, characterized in that, The process involves simultaneously acquiring two-dimensional images of sediment flocs within the observation area using at least two image acquisition devices positioned at different angles, resulting in multiple sets of two-dimensional images of sediment flocs. Specifically: Three image acquisition devices are set up such that the optical axes of the three image acquisition devices are distributed at a 120-degree angle on the horizontal plane and focus together on the observation area; The observation area is illuminated using a background light source, and the three image acquisition devices are simultaneously triggered to acquire multiple sets of two-dimensional images of sediment flocs.

3. The method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction according to claim 1, characterized in that, After obtaining the three-dimensional visual convex hull model of the sediment flocs, the method further includes: The cross-sectional profile of the three-dimensional visual convex hull model is extracted, and the cross-sectional profile is fitted using a B-spline curve to obtain a second three-dimensional visual convex hull model of the mud and sand flocs with a smooth surface.

4. The method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction according to claim 1, characterized in that, Based on the imaging geometric parameters of each of the image acquisition devices, the binarized mask of the mud and sand flocs from the perspective of each image acquisition device is projected backwards into three-dimensional space to obtain multiple view frustums, specifically as follows: Based on the projection matrix defined by the imaging geometric parameters of each of the image acquisition devices, the binarized mask of the mud and sand flocs from the viewpoint of each of the image acquisition devices is projected back into three-dimensional space along the line of sight of the respective image acquisition device to obtain the view frustum corresponding to each viewpoint.

5. The method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction according to claim 1, characterized in that, The step of segmenting each of the multiple sets of two-dimensional images of sediment flocs to obtain a corresponding binarized mask for the sediment flocs from the perspective of each image acquisition device is specifically as follows: Each frame of the two-dimensional image of each group of sediment flocs is grayscaled and Gaussian filtered for denoising to obtain the denoised image. The denoised image is subjected to adaptive threshold segmentation using the Otsu method to obtain an initial binary image; The initial binary image is processed by morphological opening to obtain the binarized mask of the mud and sand flocs.

6. The method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction according to claim 1, characterized in that, The acquisition of imaging geometric parameters of each of the image acquisition devices, determined through pre-joint calibration, includes: By setting a checkerboard calibration plate in the observation area and performing joint calibration based on Zhang Zhengyou's calibration method, the imaging geometric parameters of each image acquisition device are obtained; wherein, the imaging geometric parameters of each image acquisition device include the intrinsic parameter matrix and the extrinsic parameter matrix of each image acquisition device, the intrinsic parameter matrix includes at least the focal length and principal point coordinates, and the extrinsic parameter matrix includes the rotation matrix and the translation vector.

7. The method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction according to claim 4, characterized in that, The projection matrix defined based on the imaging geometric parameters of each of the image acquisition devices is specifically as follows: The projection matrix is ​​obtained by performing matrix multiplication on the intrinsic and extrinsic parameter matrices of the image acquisition device.

8. A system for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction, characterized in that, include: Image acquisition module, 3D morphology reconstruction module, and result output module; The image acquisition module is used to simultaneously acquire two-dimensional images of sediment flocs within the observation area using at least two image acquisition devices that are aligned with the observation area from different perspectives, resulting in multiple sets of two-dimensional images of sediment flocs; wherein each set of images of the multiple sets of two-dimensional images of sediment flocs includes one frame image acquired by each of the image acquisition devices at the same time; and to obtain the imaging geometric parameters of each of the image acquisition devices determined by pre-joint calibration. The three-dimensional morphology reconstruction module is used to segment each of the multiple sets of two-dimensional images of sediment flocs to obtain a corresponding binary mask of sediment flocs from the perspective of each image acquisition device; based on the imaging geometric parameters of each image acquisition device, the binary mask of sediment flocs from the perspective of each image acquisition device is projected backwards into three-dimensional space to obtain multiple view frustums; the intersection operation is performed on all the view frustums to obtain the three-dimensional visual convex hull model of the sediment flocs; The result output module is used to output the structural parameters of the sediment flocs based on the three-dimensional visual convex hull model of the sediment flocs.

9. A terminal device, characterized in that, The method includes a processor, a memory, and a computer program stored in the memory and configured to be executed by the processor, wherein, when the processor executes the computer program, it implements the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that, include: A stored computer program, wherein, when the computer program is executed, it controls the device containing the computer-readable storage medium to perform the method for measuring the structural parameters of sediment flocs based on three-dimensional reconstruction as described in any one of claims 1-7.

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