905 results about "Digital image processing" patented technology

The present invention pertains to the technical field of digital image processing. Disclosed are an unsupervised super-pixel segmentation method and system assisted by collaboration between an atrous pyramid and an attention mechanism. The method comprises: combining image RGB channel information with position information of a pixel point; constructing a channel attention module by using an attention mechanism; processing the result of the attention mechanism by using atrous spatial pyramid pooling; constructing a loss function, constructing a clustering loss term, and, by using a spatial smoothing loss term, constructing a reconstruction loss term; updating model parameters by using an Adam optimizer, and using for super-pixel generation an effective depth feature obtained from the last separation; and obtaining the maximum value of a channel dimension by using an argmax function, converting a processing result of the argmax function into a two-dimensional array, and, on the basis of a defining condition, completing adaptive super-pixel segmentation in a CPU. According to the present invention, the complexity is low, the adaptive and generalization capabilities are high, and effective support is provided for improving image processing efficiency and accuracy.

The invention provides an image restoration and super-resolution reconstruction system and method based on deep learning, and belongs to the technical field of digital image processing. The invention aims to solve the problems of high calculation complexity and resource consumption, limitation of long sequence processing, high training difficulty and texture scene deficiency when a multi-scale residual network based on a Transform architecture is used for image resolution conversion. The reconstruction system comprises: an image preprocessing module performing window division and video memory optimization on an input low-resolution image; the multi-layer fusion network dynamically adjusts the characteristics of the low-resolution image, captures channel information in different scenes, performs interactive fusion, performs comparison supervision, establishes an information communication channel, dynamically adjusts and optimizes parameters through negative feedback, and obtains a super-resolution image. And the loss function module maximizes the similarity of the super-resolution image and the high-resolution image in the segmentation feature space to obtain a final super-resolution image.

The invention discloses a document layout analysis method based on a hybrid method, and belongs to the technical field of digital image processing. Named body recognition and relation extraction are carried out on each text node. The invention provides a novel channel shuffling dynamic range convolution algorithm to ensure that global channel features can learn features of similar pixels rather than adjacent pixels, and an adapter is used to learn text features with less parameter quantity. And finally, taking the predicted text block category and relationship as a result. Finally, a challenging document data set is used as a verification index (FUNSD), and the result shows that the performance of the method can be obviously improved.

The invention discloses an image processing algorithm for pupil center real-time extraction and trajectory tracking in an eye closing state. According to the algorithm, from the angle of digital image processing, graying, contrast enhancement and smoothing processing are firstly carried out on an input eye video frame by frame, and then the boundary of a pupil area can be accurately extracted by adopting an adaptive threshold segmentation technology and combining morphological operation and a Canny edge detection operator. And then, accurate fitting of ellipse parameters is performed on the pupil contour by using a geometric distance-based least square method, so that center coordinates and area information of the pupil are obtained, and the accuracy and reliability of pupil monitoring in an eye closing state are remarkably improved. Through recording and analyzing pupil center position changes in a time sequence, real-time tracking of pupil movement tracks is realized. And finally, evaluating pupil light reflex (PLR) in an eye closing state before and after white light excitation by adopting baseline correction and smooth filtering processing. The method has the advantages of high robustness, adaptability to illumination variation, high real-time performance, high precision and the like.

The invention discloses a multi-light-spot centroid extraction method and device based on an FPGA, a medium and a product, and relates to the field of digital image processing, and the method comprises the steps: firstly, caching image line data through a first-stage FIFO and a second-stage FIFO, forming three continuous groups of image line data streams, effectively solving the problems of data synchronization and processing rate matching, and improving the data processing efficiency; then, effective pixels of the light spots are accurately determined by means of a pixel threshold value, and different light spot areas are accurately divided by means of line continuity judgment and boundary marking, so that repeated recognition and error recognition of the light spots are avoided, and the accuracy of light spot recognition is greatly improved. And finally, according to the weighted average positions of the gray values of the effective pixels of all the light spots, the target centroid coordinates of the target light spots are calculated, the internal gray difference of the light spots is fully considered, and compared with a conventional method, the light spot energy distribution center is more accurately reflected, so that the calculation precision of the light spot centroid is remarkably improved.

The invention belongs to the technical field of digital image processing, and particularly discloses a low-illumination image enhancement method for simulating a foggy day degradation mechanism, and the method comprises the following steps: generating a virtual haze image; estimating atmospheric light searched by combining decreasing cutting with a genetic algorithm; coarse transmissivity calculation based on dark channel prior; refining the transmissivity graph; carrying out defogging and low-illumination enhancement on the virtual haze image; and brightness and contrast self-adaptive compensation. According to the method, the precision and robustness of atmospheric light value estimation are improved, a transmissivity graph which better conforms to a scene can be obtained, and the image restoration effect is improved; the method has remarkable advantages in the aspects of reducing artifacts, enhancing image details, denoising, keeping structural information and restoring details.

The invention provides a remote sensing image super-resolution reconstruction method and system based on residual hierarchical Transform, and relates to the technical field of digital image processing, and the method comprises the following steps: obtaining a low-resolution image; performing shallow feature extraction on the input low-resolution image through convolutional layer operation to obtain a shallow feature image; secondly, on the basis of the design concept that residual blocks are nested in residual groups, a multi-scale feature calibration mechanism, a Transform space-channel attention mechanism based on a hierarchical window and a space-gate feed-forward network are combined, and a depth feature image is obtained; and finally, performing fusion and up-sampling on the shallow feature image and the depth feature image through convolution and sub-pixel convolution operation to obtain a final remote sensing image super-resolution reconstruction result. By means of the scheme, the remote dependency relationship in the remote sensing image can be effectively modeled, the feature extraction capacity can be improved, and the problem that large-scale window self-attention calculation is high in complexity in a Transform mechanism can be solved.

The invention provides an intelligent digital image processing system for urinary surgery. The intelligent digital image processing system is suitable for accurate diagnosis and dynamic monitoring of diseases such as kidney tumors and bladder tumors. The system comprises a data acquisition module, a multi-modal image fusion module, an image segmentation module, a dynamic lesion tracking module, a three-dimensional modeling module, an intelligent diagnosis support module and a federal learning module. The data acquisition module receives CT, MRI and other image data; the multi-modal image fusion module generates a high-quality comprehensive image through a registration algorithm; the image segmentation module extracts a lesion area by using deep learning; the dynamic lesion tracking module calculates lesion volume, surface area and change trend thereof; the three-dimensional modeling module generates a three-dimensional visual model of the lesion area; and the intelligent diagnosis support module outputs a diagnosis report containing lesion features, development trends and treatment suggestions. According to the invention, through multi-modal fusion and dynamic monitoring, the accuracy of lesion area extraction is improved.

There is proposed a method for detecting a presence of a fluorescence pattern type on an organ segment via immunofluorescence microscopy and digital image processing. The steps comprise: provision of the organ segment, incubation of the organ segment with a liquid patient sample, incubation of the organ segment with secondary antibodies which have been labelled with a fluorescent dye, acquisition of a fluorescence image of the organ segment in a colour channel corresponding to the fluorescent dye, and provision of the fluorescence image to a neural network. What takes place by means of the neural network is simultaneous determination of segmentation information through segmentation of the fluorescence image and, furthermore, of a measure of confidence indicating an actual presence of the fluorescence pattern type. What further takes place is determination, on the basis of the previously determined segmentation information, of at least one sub-area of the fluorescence image that is relevant to formation of the fluorescence pattern type, determination, on the basis of the previously determined at least one sub-area, of validity information indicating a degree of a validity of the measure of confidence, and output of the measure of confidence depending on the validity information.

A system to capture a plurality of two dimensional digital source images of a scene by a user, including a memory device for storing an instruction, a processor in communication with the memory and configured to execute the instruction, a digital image capture device in communication with the processor configured to capture a digital image of the scene, the processor configured to execute an instruction to generate a plurality of two dimensional digital images of the scene from said first two dimensional digital image of the scene via a camera angle rotation of between 1-180 degrees of said first two dimensional digital image of the scene for each of said plurality of two dimensional digital image of the scene as a sequence, and a display in communication with the processor, the display configured to display a multidimensional digital image sequence.

The invention relates to the technical field of digital image processing and digital printing control, in particular to a digital printing color calibration method and system based on real-time image processing, and the method comprises the steps: constructing a fabric surface feature point coordinate set through a mixed feature tracking technology; calculating and predicting a target deformation field of the fabric at the printing position by applying a self-adaptive time domain prediction algorithm; and performing dynamic pixel interpolation on the original digital image by combining the target deformation field and the fabric texture feature map analyzed in real time to generate final printing image data. According to the invention, by combining high-robustness tracking, high-precision adaptive prediction and high-fidelity image generation technologies, the geometric precision and visual quality of printed products are significantly improved.

The invention relates to the technical field of settlement observation, in particular to a non-contact monocular vision high-precision automatic settlement monitoring method, which comprises the following steps: firstly, performing target monitoring by using a YOLOv10s model, improving the real-time reasoning speed and recognition precision of target recognition, and solving the target recognition problem under a complex background; secondly, a digital image processing technology and an edge-based least square ellipse fitting algorithm are combined to realize accurate positioning of the target; thirdly, deriving based on a camera imaging principle, and introducing an improved world coordinate calculation method to solve world coordinates corresponding to the pixel coordinates so as to obtain three-dimensional world coordinates; and finally, calculating a settlement value of the monitoring area by taking the first frame image as a reference, predicting a future settlement value by using a deep learning model, and making an early warning for structure safety in advance. According to the invention, the installation demand and the calculation overhead are reduced, and the settlement monitoring precision is improved.

A deep learning-based binocular vision measurement method for cross-section fat thickness of pig middle square meat is characterized in that after left and right images are obtained through a binocular vision system, feature points in feature regions of the left and right images are matched and reconstructed to restore three-dimensional depth information of points to be measured, and more accurate cross-section fat thickness is obtained. According to the method, deep learning and digital image processing technologies are combined, section data information of the middle square meat is marked, an improved UNET network model is input, feature regions are segmented, feature point information is obtained, and then classification processing calculation is carried out on the middle square meat through a middle square meat triangular meat feature region judgment method. The method has the advantages of being non-contact, high in measurement precision, rich in feature information acquisition, high in speed and the like, and can effectively avoid the problems that in the field flow line production process, due to the fact that the placement position of the cut meat on the flow line is random, three-dimensional reconstruction information precision loss is caused, and then the precision of the measurement result of the section fat thickness is not high. And the automation degree in the industrial processing of the split pork is improved.

The invention discloses a rock mass fracture multi-field coupling two-phase flow analysis method and a test system, and relates to the technical field of rock fracture temperature-seepage-stress coupling action mechanism research. The analysis method comprises the following steps: saturating a crack sample with a first dyeing fluid under a target temperature and pressure condition, and obtaining a single-phase saturated flow image of the crack sample; on the basis of the single-phase saturated flow image, through optical iteration inversion calculation, obtaining fracture opening two-dimensional distribution characteristics under a target condition; under the condition of the same temperature and pressure, displacing the first dyeing fluid with the second dyeing fluid or co-flowing with the first dyeing fluid to obtain a two-phase flow image of the crack sample; performing digital image processing on the two-phase flow image and the single-phase saturated flow image, and extracting phase distribution information of the second dyeing fluid; and performing fusion analysis on the phase distribution information of the second dyeing fluid and the fracture opening two-dimensional distribution characteristics, and calculating a two-phase flow structure and saturation distribution characteristics. According to the method, rock mass fracture two-phase flow process quantitative analysis can be realized under different temperature and pressure conditions.

The invention discloses a multi-scale defect detection method based on a deep learning network, and belongs to the technical field of digital image processing. According to the method, a new defect detection MSE-YOLOv8 network is provided based on a YOLOv8 network, in a Backbone part, original Conv and C2f configuration is kept, and a local feature extraction layer and a global feature extraction layer are added; in the Neck part, an FPN + PAN layer is adopted, and a multi-scale feature fusion module is embedded; in the Head part, a semantic enhanced detection head is used, the design of a decoupling detection head continues to be used, and an improved space group enhancement module is adopted in a classification head to replace an original convolutional layer. Experimental results prove that compared with an existing detection method, the method has the advantages that the over-detection and missing detection conditions are optimized, and the detection precision is effectively improved.

The invention relates to the technical field of biomedical engineering and digital image processing, in particular to a digital image enhancement method based on cytopathology. The method comprises the following steps: eliminating noise and artifacts caused by non-uniform dyeing, section folding and dust factors; the problem of dyeing difference caused by different scanning devices is solved; key areas including cell nucleuses and cell membranes are highlighted, so that the visual effect of a low-contrast image is improved; segmenting an area, including cell nucleuses and cytoplasm, of the cells, and extracting morphological characteristics; the detail resolution of a low-resolution image, especially the definition of a small cell structure, is improved; the brightness component Y is corrected to compensate for the difference of different dyeing methods and dyeing qualities in brightness response, then the chromatic value is adjusted according to the color correction factor to make the colors of the images more consistent, and finally, the images which are acquired by different dyeing methods and dyeing qualities and subjected to brightness and chroma correction are fused to make the color of the images more consistent. And a final corrected image is obtained.

The invention relates to the technical field of digital image processing, in particular to a digital image mixed watermarking method based on time domain and frequency domain synchronization. According to the method, a time domain and frequency domain synchronous watermark embedding algorithm and a multi-stage watermark extraction algorithm are innovatively combined, so that the concealment and robustness of the watermark are remarkably improved, and the problems of digital image copyright protection in the face of printing and scanning attacks and network attacks are effectively solved; the method ensures the security and reliability of digital image copyright protection, provides a more advanced and effective technical means for digital image copyright protection, has the advantages that a time domain and frequency domain synchronous watermark embedding algorithm enables watermarks to be fused into image details and is high in concealment, the watermarks are effectively protected in the face of various attacks, and the security and reliability of digital image copyright protection are improved. The multi-stage watermark extraction algorithm ensures accurate extraction when the image quality is reduced, and the watermark performance is integrally improved.

The invention relates to the field of digital image processing and information security, and discloses a document tampering detection method and system based on image data processing, and the method comprises the following steps: firstly, extracting the noise residual error and microscopic penetration characteristics of a document image, and constructing a physical potential energy field and a virtual viscous resistance field; performing dynamic evolution by using a Darcy law variant model to generate a virtual flow velocity vector field to simulate the slippage behavior of the fluid in the heterogeneous medium; and then a heterogeneous graph is constructed based on the flow field divergence singular points and the streamline trajectory, deep reasoning is carried out by using graph neural network aggregation node dynamic features, and finally a tampering localization mask is generated. According to the method, the fluid mechanics field theory is innovatively introduced, hidden static texture differences are converted into remarkable dynamic flow field anomalies, the problem that microscopic tampering traces are difficult to capture in the prior art is solved, and the detection precision and generalization ability in a complex document scene are remarkably improved.

The invention belongs to the technical field of digital image processing and computer vision, and particularly relates to a remote sensing image fusion method based on local-global spatial-spectral correlation prior. Comprising the following steps: performing feature mapping on a panchromatic image and a multispectral image by using an image mapping layer; a two-dimensional convolutional neural network and a spatial Transform network are used to carry out local and global spatial feature extraction on the panchromatic image; respectively carrying out local and global spectral feature extraction on the multispectral image by using a three-dimensional convolutional neural network and a channel Transform network; performing multi-level spatial-spectral feature fusion on the panchromatic image and the multispectral image by using adaptive weight; reconstructing the spatial-spectral fusion feature into a double-high remote sensing image by using an image reconstruction layer; and performing parameter optimization on the model under a supervised condition by using double loss constraints of content loss and style loss. According to the method, the sensitivity of the model to a dynamic scene is effectively reduced, and more accurate remote sensing image fusion is realized.

The invention relates to the technical field of digital image processing, in particular to an unmanned aerial vehicle image distortion correction and coordinate positioning compensation method, which comprises the following steps: collecting a large amount of unmanned aerial vehicle image data containing a damaged house, labeling the damaged house in the image, and generating a labeling file required by a YOLOv5 model; performing parameter calibration on the unmanned aerial vehicle lens to obtain a distortion coefficient of the lens, and constructing a nonlinear distortion correction model based on a lens parameter calibration result; establishing a dynamic affine transformation matrix in combination with the real-time flight attitude data of the unmanned aerial vehicle and the pixel coordinates of the detection frame; in a YOLOv5 detection stage, fusing feature maps with different resolutions, and fusing corrected coordinate data with a damaged house detection frame output by a YOLOv5 model; calculating coordinates of a center point of the damaged house according to the real-time latitude and longitude of the unmanned aerial vehicle; through dual optimization of image distortion correction and dynamic positioning compensation, the problem of coordinate misalignment caused by hardware limitation and unstable flight attitude in traditional unmanned aerial vehicle detection is solved.

An apparatus and method for training and using a computing operation for digital image processing are provided. The apparatus and method may be used for 3-dimensional medical images. An exemplary method for digital image processing comprises: receiving an image displaying at least one detectable structure, determining the detectable structure; segmenting the image to obtain a segmentation mask that is associated with a geometric shape and comprises at least one quantifiable visual feature; generating a mesh based on the quantifiable visual feature; computing at least on quantifiable visual parameter based on the mesh; extracting quantifiable visual data from the image based on the quantifiable visual parameter; training the computing operation with the quantifiable visual data. The method for digital image processing further comprises: receiving another image; segmenting, generating a mesh, computing quantifiable visual parameters, and extracting quantifiable visual data; and classifying the extracted quantifiable visual data with the trained computing operation.

The invention belongs to the technical field of medical image digital image processing, and particularly relates to a prostate TRUS-MRI registration method based on multi-scale weighting and an adaptive window, and the method comprises the steps: firstly collecting an ultrasonic image and a magnetic resonance image of a prostate, dividing the ultrasonic image and the magnetic resonance image into a training set, a verification set and a test set, and sketching a mask for a prostate gland in each image; then the input image is preprocessed; then, UNet is adopted as a feature extractor, a deformation field is output in combination with a spatial transformation network, deformation is applied through an STN pair, and a registration image is obtained; the method comprises the following steps: firstly, acquiring three parts of an ultrasonic image, then constructing a network loss function, finally, acquiring a total loss function according to the three parts of the network loss function, performing staged training, finally, outputting the total network loss function, processing a subsequent ultrasonic image and a magnetic resonance image, and outputting a registration image. Compared with the prior art, the method has the comprehensive advantages of cross-modal adaptability, detail optimization capability, calculation efficiency and generalization performance, so that the method is remarkably improved.

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention relates to the technical field of digital image processing and computer vision, and discloses a remote multi-view video stitching and monitoring method and system based on digital twinning, and the method comprises the steps: constructing a digital twinning model containing geometric, material and semantic information, and calibrating and initially registering a multi-view camera; based on the model and camera parameters, collecting a preprocessed video stream, and refining the camera pose in combination with the model; predicting expected visual content by using the model and the refined pose, and performing non-rigid alignment on the preprocessed video frame; based on the aligned video frames, a model semantic optimization splicing suture line is utilized to generate a reconstructed video, and information loss is repaired in combination with a model context; and finally, performing integrated monitoring interaction on the repaired reconstructed video and the digital twin model, comparing and detecting abnormality, and updating the model by using the reconstructed video. According to the invention, the problems of traditional remote monitoring in the aspects of scene comprehensive perception, intelligent anomaly detection and synchronism of environment digital representation and physical reality are solved.

The invention relates to a computer vision and digital image processing technology, in particular to an image defogging method of a multi-scale convolutional neural network based on dark channel prior. Estimating the initial transmissivity and atmospheric light of the foggy image based on a dark channel prior method; the foggy image is converted into a gray level image, a gray level threshold value is selected to be used for distinguishing a sky area and other areas, and the initial transmissivity after screening is obtained; optimizing the initial transmissivity through a multi-scale convolutional neural network by adopting a coarse optimization stage and a fine optimization stage in sequence to obtain refined transmissivity; and reconstructing a fogless image according to the refined transmissivity by adopting an atmospheric scattering model. According to the method, the advantages of a physical model and deep learning are fused, the problems of supersaturation, edge artifacts, color distortion and the like in a sky region in a traditional method are solved, the PSNR (Peak Signal to Noise Ratio) of a defogged image is remarkably improved (up to 23.30), the SSIM (Subscriber Identity Module) (up to 0.978) and the like, and a robust visual enhancement scheme is provided for scenes such as automatic driving and traffic monitoring.

The invention discloses a multi-scale Retinex low-illumination image enhancement method based on modified bilateral filtering, and belongs to the field of digital image processing. Aiming at the problems of insufficient image brightness, detail loss, noise interference and color distortion in a low-illumination environment, the invention provides an improved image enhancement algorithm. First, the RGB image is converted into an HSV color space, and the luminance component (V) is separated for subsequent processing to avoid color distortion caused by direct processing of the RGB channel. And then, a corrected bilateral filtering function is adopted as a center surrounding function of a multi-scale Retinex method, a brightness component is enhanced, a halo phenomenon is suppressed, and meanwhile, detail enhancement of a dark region and excessive suppression of a bright region are realized in combination with an improved Gamma transformation function. And for the reflection component, non-local mean filtering denoising is utilized, and image edge details are enhanced in combination with a Laplace operator. In addition, the color of the enhanced image is ensured to be natural and consistent by adaptively adjusting the saturation component. Experimental results show that the brightness and definition of low-illumination images can be remarkably improved, detail information and visual impressions are improved, and the method is suitable for various scenes such as night monitoring, medical image processing and agricultural monitoring. The method has the advantages of being excellent in enhancement effect, high in halo suppression and small in color distortion.

The invention belongs to the technical field of digital image processing, and discloses a pneumatic thermal radiation effect image sequence brightness homogenization method and system, and the method comprises the steps: constructing a weight function of a bias field fitting coefficient vector based on the pneumatic thermal radiation effect bias field fitting coefficient vector of each frame of degraded image in a degraded image sequence; constructing a bias field sparse vector of each frame of degraded image by using the weight function; fitting a bias field of each frame of degraded image; and subtracting the corresponding fitted bias field from each frame of degraded image to obtain each frame of corrected image so as to realize brightness homogenization of the degraded image sequence. Furthermore, a fitting function based on image sequence gray level distribution of each frame of corrected image is constructed, the fitting function is used for replacing the mean value of the corresponding corrected image, an updated corrected image is obtained, and further uniformization of the degraded image sequence brightness is achieved. According to the invention, the brightness of the pneumatic thermal radiation effect image sequence can be homogenized.

The invention discloses an event-image dual-mode fusion video turbulence correction method, medium and system, and belongs to the field of digital image processing, and the method comprises the steps: synchronously obtaining an event voxel of a target and a to-be-corrected image sequence; inputting the event voxels into a pre-selected coding and decoding structure to obtain features, projecting the features along the optical flow direction, and performing three-dimensional reconstruction to obtain target motion features; extracting background scene representation; fusing the target motion feature and the background scene representation in a channel dimension to obtain an edge guide feature; inputting the image sequence and the image sequence into a pre-trained video restoration network to obtain a turbulence-corrected video; the training loss of the coding and decoding structure comprises an optical flow field estimated according to an image sequence without turbulence disturbance and a target motion field obtained by projecting the feature along the optical flow direction. The method can accelerate the recovery process and improve the recovery quality.

The invention discloses a discharge channel extraction method and system based on edge detection and texture feature fusion, and belongs to the technical field of power equipment fault diagnosis and digital image processing, and the method comprises the steps: obtaining an ultraviolet weak light image of power equipment, and carrying out the preprocessing; multi-scale edge detection is executed based on the preprocessed image, edge information is fused through a multi-scale voting mechanism, and a candidate edge graph is generated by combining direction consistency connection fracture edges; extracting texture features of the pre-processed image, wherein the texture features comprise a rotation invariant local binary pattern uniformity feature and a multi-direction gray level co-occurrence matrix feature; according to a dynamic distribution weight coefficient of a discharge type, carrying out weighted fusion on the edge intensity of the candidate edge graph and the texture features, and generating a discharge channel confidence score graph; and carrying out binarization segmentation and contour optimization processing on the confidence score graph of the discharge channel, and extracting morphology quantization parameters of the discharge channel.

The invention provides a quantitative detection method and a quantitative detection system for mesoscopic defects of a soil-rock aggregate based on CT image multi-threshold segmentation, and belongs to the technical field of nondestructive testing and digital image processing of geotechnical engineering materials. The method comprises the following steps: firstly, carrying out multi-threshold segmentation on a CT image by adopting a fuzzy C-means clustering algorithm, and analyzing the image into four types of regions including a background region, a block stone region, a matrix soil body region and a mesoscopic defect region according to gray features; then, based on the heterogeneity characteristic of the material, the mesoscopic defects are divided into matrix defects and interface defects according to defect space distribution; the boundary contour of the block stone is extracted through morphological operation, and intelligent judgment of defect types is realized by combining spatial topology analysis of a defect binary image; and finally quantifying the volume ratio parameters of the two types of defects based on pixel statistics. According to the method, the limitation of a traditional homogeneous material defect analysis method is broken through, and a new technical means is provided for research on damage evolution of the earth-rock aggregate multiphase structure.