559 results about "Morphological processing" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention relates to the technical field of water conservancy projects, in particular to a water conservancy project crack detection method and system based on intelligent visual identification, and the method comprises the following steps: obtaining a crack image, enhancing the brightness and contrast in different regions, extracting and optimizing the crack edge, executing morphological processing, denoising and smoothing, segmenting a crack region, and judging the connection condition. And analyzing the geometrical characteristic detection connectivity, and calibrating the crack position to obtain a positioning result. According to the method, through dynamic adjustment of image brightness and contrast, the crack is more prominent in a complex background, especially under non-uniform illumination, crack identification difficulty caused by illumination inconsistency is reduced, crack edges are optimized through morphological operation, the missing part of the crack can be filled, the crack contour can be smoothed, and noise can be eliminated. And in the crack segmentation stage, through self-adaptive threshold and geometric feature analysis, the fracture and connection conditions of the crack are accurately judged, and positioning of the crack end point and the continuous area of the crack end point is achieved.

The invention belongs to the technical field of ocean observation, and discloses an ocean wave field reconstruction method and device based on binocular vision. An ocean wave field reconstruction method based on binocular vision comprises the steps that internal parameters of a camera are calibrated through a Bouguet calibration algorithm, a binocular camera provided with an IMU sensor is used for collecting images and pose information, feature points are extracted through an SURF algorithm, feature matching is completed under a game theory framework, and abnormal points are removed through an RANSAC algorithm. Then, camera parameters are calculated based on an eight-point method, and the camera parameters and three-dimensional point coordinates are optimized through an SBA algorithm; thirdly, generating a disparity map by adopting epipolar correction and an SGM algorithm, and performing morphological processing on the disparity map; and the processed image is subjected to triangulation to generate a three-dimensional point cloud, and filtering is carried out. And finally, estimating a sea level position through an RANSAC algorithm, and optimizing a result through a least square method to complete three-dimensional reconstruction of ocean waves.

The invention discloses a machine vision-based intelligent detection method for steel galvanized surface defects, which comprises the following steps: S1, acquiring and preprocessing a steel galvanized surface image to obtain a standardized image; s2, constructing a specular reflection probability graph according to the brightness distribution and the gradient magnitude, and calculating a reflection intensity value; s3, calculating a structure tensor matrix, determining a main direction angle and an anisotropic consistency coefficient, and generating a direction feature matrix; s4, establishing a multi-scale direction adaptive phase kernel function, and performing phase modulation in a frequency domain by adopting an improved phase stretching transformation algorithm; s5, inverse Fourier transform is executed, and a phase response matrix is extracted; s6, performing weighted fusion to obtain a comprehensive phase response diagram; and S7, setting a threshold value according to the noise variance and the statistical characteristics, executing binarization and morphological processing, and outputting a defect region and boundary coordinates. According to the invention, high-precision identification and boundary positioning of steel galvanized surface defects are realized.

The invention relates to the technical field of electromagnetic spectrum monitoring, and particularly discloses a distance-based electromagnetic spectrum monitoring abnormal data detection method, which comprises the following steps of: performing short-time Fourier transform and normalization processing on an acquired original signal to generate an energy density distribution characteristic graph; constructing a multivariate Gaussian distribution model based on non-abnormal historical data; during real-time monitoring, the mahalanobis distance between the collected data and the mean vector of the historical model is calculated after the collected data is preprocessed. And comparing the distance metric value with a preset threshold value to preliminarily judge abnormity, and calculating a distance fluctuation variance through a sliding window mechanism to perform secondary verification. And finally, processing and positioning anomalies by using image morphology, and dividing the degree of anomalies according to the relative deviation between the energy density and the mean value of the historical model. According to the method, the mahalanobis distance and the multivariate Gaussian distribution are introduced, secondary verification and abnormal positioning are combined, the limitation of a traditional method is overcome, the detection accuracy and reliability are effectively improved, the misjudgment and missing judgment rate is reduced, and the method does not depend on a large amount of labeled data and is high in practicability.

The invention provides a mountainous area tunnel crack intelligent identification and detection method based on deep learning, and relates to the technical field of deep learning, and the method comprises the steps: collecting multispectral image data, and carrying out the adaptive preprocessing to obtain an enhanced feature map; a double-flow network is combined with a space-channel cascade attention module to extract fusion features; constructing multi-scale feature representation through a feature association graph network; training the network by adopting a joint optimization target; and morphological processing and connectivity analysis are carried out to realize accurate identification and classification of the crack. According to the invention, the detection precision and the anti-interference capability of the tunnel crack in the complex environment are improved.

The invention relates to the technical field of road pavement disease recognition, in particular to an intelligent road pavement disease recognition method and system based on a multispectral image, and the method comprises the steps: obtaining road pavement image data collected by a multispectral camera carried by an unmanned aerial vehicle; generating a registered multispectral fusion image; performing enhancement processing on the multispectral fusion image based on a dark channel algorithm to generate an enhanced image; performing shallow feature extraction and deep feature extraction on the enhanced image according to a convolutional neural network to generate an initial disease extraction image; performing segmentation processing on the initial disease extraction image through adaptive threshold segmentation to generate a binary image; performing morphological processing on the binary image to generate a candidate disease image; extracting edge information of a disease area from the candidate disease image based on an instance segmentation technology, and generating a segmented image; the segmented image is subjected to crack refining processing, a crack refined image is generated, and more comprehensive disease information support is provided for collection and fusion of multispectral data.

The invention discloses a metal surface defect detection and classification method, system, equipment and medium, and the method comprises the steps: processing images of different defect types through gray normalization, adaptive histogram equalization, a Gabor filter, a maximum entropy algorithm, morphological processing and an improved local binary pattern; carrying out defect position and size calculation on the processed image, and carrying out GUI (Graphical User Interface) measurement page design; the system, the equipment and the medium are used for realizing the metal surface defect detection and classification method. According to the method, the image processing effect is remarkable, the image contrast is improved, the defect boundary identification is enhanced, the accurate segmentation of the image and the detailed optimization of the structure are realized, the image features are accurately extracted, and efficient classification training is supported; the method has the advantages of improving image quality, enhancing defect detection precision and optimizing feature extraction and analysis, and can be widely applied to the field of high-precision image analysis such as metal defect detection, industrial quality control and computer vision.

The invention discloses a vehicle paint surface scratch detection device fused with multi-modal imaging, and relates to the technical field of vehicle scratch detection, and the device is characterized in that multiple paths of annular light sources are configured, time-sharing exposure and continuous acquisition are carried out, and a multi-modal image is determined; setting a detection constraint condition; constructing a scratch detection module; identifying a multi-modal image, executing morphological processing and self-adaptive binarization processing of a grayscale image, performing multi-modal superposition fusion and scratch feature identification, and integrating and outputting a scratch detection list; and carrying out homologous induction marking, and carrying out visualization on a terminal display interface. The technical problems that existing vehicle paint surface scratch detection is limited to single-mode image acquisition and processing, tiny scratches and deep scratches on a vehicle paint surface are difficult to comprehensively capture, the detection efficiency is low, and the accuracy of a detection result is poor are solved, and the technical effect of improving the vehicle paint surface scratch detection efficiency and accuracy is achieved.

The invention discloses a steel-wood combined beam-column joint damage identification method and system applying machine vision, and the method comprises the steps: S1, obtaining a visible light, near-infrared and thermal imaging multi-mode image sequence of a steel-wood combined beam-column joint, and carrying out the denoising processing through adaptive anisotropic diffusion filtering, and obtaining a denoised multi-mode image sequence; s2, performing Gaussian pyramid decomposition and Laplacian pyramid feature fusion on the de-noised multi-modal image sequence tensor to obtain a fusion feature map; s3, performing adaptive threshold segmentation and morphological processing on the fused feature map to obtain a final binary image; s4, training the deep learning network to obtain a trained deep learning network; and S5, performing damage identification by using the trained deep learning network. According to the method, the steel-wood combined beam column joint damage can be accurately identified, the segmentation probability graph is obtained, the segmentation result is marked, and the visualization of the damage area is realized.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention discloses a power equipment defect intelligent identification method, system and device and a medium, and the method comprises the steps: collecting an original image of power equipment, and screening the original image of the power equipment to obtain a channel image; carrying out enhancement processing on the channel image and then calculating a gray scale difference value to obtain a gray scale image; converting the gray level image into a frequency spectrum image by adopting fast Fourier transform, and constructing a Gaussian filtering function to carry out convolution and inverse transformation on the frequency spectrum image to obtain a spatial domain image; and performing adaptive threshold segmentation on the spatial domain image, dividing the image into a defect area and a non-defect area, obtaining a segmented image, and performing morphological processing on the segmented image to obtain an electrical equipment defect identification result. According to the method, the problem of aliasing in traditional spatial domain processing is solved, the defect area is accurately extracted, short-time interference and real defects can be effectively distinguished, and the segmentation accuracy is improved.

The invention discloses a method for identifying small target features in a ray detection image, and relates to the field of image processing, and the method comprises the steps: carrying out the size unification, pixel standardization and normalization preprocessing of an original ray image; constructing a training sample set through image region cutting, and introducing a dynamic sampling strategy to realize positive and negative sample proportion adaptive control; enhancing the number and diversity of small target samples in a training set by using point-shaped and linear artificial defect generation strategies; constructing an image segmentation model and introducing feature jump connection to fuse shallow space and deep semantic information; combining Dice loss and Focal loss to form a composite loss function, and guiding the model to pay attention to a target region with a small area and weak gray level; and finally, a segmentation result is optimized through morphological processing and connected domain analysis, and structured target detection information is output. According to the invention, the recognition accuracy and integrity of the tiny target in the ray image can be effectively improved, and the adaptive capacity of the detection method to the change of the imaging quality is enhanced.

The invention relates to the technical field of image processing, and discloses a morphology-based image texture feature extraction method, which comprises the following steps: an image preprocessing step: carrying out graying processing on an original image, and carrying out smoothing processing on a grayscale image by using a Gaussian filter; a morphological operation step: selecting structural elements suitable for image texture characteristics, performing expansion and erosion operation on the smoothed image, performing opening operation on the expanded image, and performing closing operation on the eroded image; and an adaptive feature selection step: carrying out local binary pattern feature extraction on the image after morphological processing, adaptively determining a threshold according to statistical distribution of LBP features, and segmenting the image into a foreground and a background. The image texture feature extraction method based on morphology aims at solving the problems that feature extraction is inaccurate, noise interference is likely to happen, and feature combination is complex in an existing image texture feature extraction method.

The invention discloses an IMU (Inertial Measurement Unit)-assisted moving target detection method under a dynamic condition, which comprises the following steps of: firstly, extracting feature points of a target image for matching to obtain feature point pairs; then, relative coordinate conversion is carried out on IMU sensor data, a corresponding basic matrix or homography matrix is solved, and then corresponding polar lines or mapping projection feature points are obtained; classifying the extracted feature points into two parts, namely static background feature points and dynamic target feature points; an affine transformation matrix is solved according to the corresponding relation of the static background feature points of the two adjacent frames of images and the motion parameter model, an inter-frame difference method is used for carrying out difference on the two frames of images and carrying out image binarization and morphological processing, and a corresponding motion target frame is obtained; and finally, traversing the moving target frame to carry out overlap ratio matching, and screening out a detection object of which the overlap ratio is greater than a set threshold value, namely an actual moving target in the current image. The method provided by the invention can effectively improve the precision and accuracy of moving target identification in a dynamic background.

The invention discloses a drilling rock core image automatic identification and recording method and system based on deep learning. The method comprises the following steps: preprocessing a collected to-be-detected drilling rock core image; the preprocessed image is input into a trained Transform semantic segmentation model, a pixel-level probability graph is output, and a binary mask image is generated through binarization processing; performing morphological processing, connected domain analysis and contour detection on the binary mask image to obtain edge coordinate information of the rock core frame, extracting a standardized rock core image from the original image according to the edge coordinate information, and performing result storage and visualization; inputting the standardized rock core image into a trained Transform multi-category rock core structure recognition model, outputting a pixel-level probability graph of multiple channels, and generating a binary mask image of each channel through binarization processing; and three-rate calculation and multi-structure statistical analysis of the drilled rock core are carried out, and data output and visualization are carried out.

The invention belongs to the technical field of medical image processing, and particularly relates to a semi-supervised pericardial fat segmentation method based on multi-model cross teaching. The method comprises the following steps: firstly, carrying out ROI extraction on pericardial adipose tissue by using a lightweight SAM model to obtain a rough ROI; the lightweight SAM model comprises an image encoder and a visual Transform decoder, and an adapter layer is embedded in each Transform block of the image encoder; performing morphological processing on the rough ROI to obtain a fine ROI; mapping the fine ROI to an input image and cutting the fine ROI to obtain a fine ROI image; then, a segmentation model composed of an encoder, a bottleneck processing module and a decoder is built based on TransUNet, and the bottleneck processing module comprises an ASPP module and a plurality of WTS blocks; and finally, performing semi-supervised training on the segmentation model, the UNeXt model and the Mamb-UNet model based on multi-model cross teaching, and applying the trained segmentation model to pericardial fat segmentation. According to the method, the edge and detail information of pericardial fat can be better captured, and the segmentation precision is improved.

The invention discloses a flexible touch screen surface defect automatic detection method and system, and the method comprises the following steps: S1, compensating the illumination of a surface image of a flexible touch screen through a Retinex method, and converting the illumination to an HSV color space, and obtaining a converted image; s2, calculating a directional feature map based on the converted image; s3, constructing a Gaussian mixture model based on the directional feature map, and clustering the features by using an expectation maximization algorithm to obtain an initial defect region; s4, optimizing the initial defect area by utilizing morphological processing to obtain an optimized defect area; and S5, splicing the converted image and the optimized defect area to obtain an input image, and performing defect detection on the input image based on a deep learning-based multi-scale feature fusion network. According to the invention, rapid and accurate detection of the surface defects of the flexible touch screen can be realized.

The invention belongs to the field of road maintenance detection, and discloses an asphalt pavement crack detection method and system based on image analysis, electronic equipment and a storage medium, and the method comprises the following steps: collecting an image of an asphalt pavement, and preprocessing the collected image to obtain a first image; segmenting the preprocessed image by using a full convolutional neural network, and performing morphological processing on the segmented image to obtain a second image; calculating a gray level co-occurrence matrix of the second image and extracting texture features, detecting edge information of the second image by using an edge detection algorithm, and combining the texture features with the edge information to obtain a crack candidate area; constructing a convolutional neural network model, and training the convolutional neural network model through the historical crack image and the historical non-crack image to obtain a crack recognition model; and inputting the crack candidate region into a crack identification model for crack identification to obtain a crack identification result.

The invention discloses a laser beam positioning method based on a deep neural network. The method comprises the following steps: step 1, acquiring a light spot image of a laser beam on a reflector through image acquisition equipment; step 2, a deep neural network DeepLabV3 + model is utilized to perform pixel-by-pixel classification on the light spot image, light spot pixels and non-light-spot pixels are distinguished, a light spot mask is generated, the foreground pixel value of the light spot mask is set to be 1, and the background pixel value of the light spot mask is set to be 0; and step 3, performing morphological processing on the generated light spot mask, and calculating a centroid coordinate of the light spot by using a centroid method. The method has high noise immunity and generalization ability, can accurately and reliably extract the position and area of the light spot, and can give an accurate recognition result under the condition that the laser light spot is abnormal.

The invention provides an exosome automatic identification and analysis method based on microscopic imaging, and belongs to the technical field of exosome identification and analysis, and the method comprises the steps: collecting an exosome fluorescence microscopic image, carrying out the preprocessing, and obtaining an exosome region through adaptive threshold segmentation and morphological processing. A GPU is adopted to process and extract morphological, texture and statistical feature matrixes in parallel, and meanwhile, the CPU recognizes an abnormal region. And combining the features to form a region feature set, inputting the region feature set into the preliminary screening model for scoring and screening high-credibility regions. Deep feature extraction is carried out on the high-credibility region, and multiple features are fused by using an attention fusion mechanism to construct vectors; and inputting a depth classifier to obtain a classification probability matrix and a screening result. And finally outputting the number, position, classification and credibility score of the exosomes. The problems that an existing exosome analysis method often depends on manual recognition, and automatic and high-throughput analysis is difficult to achieve are solved.

The invention relates to the technical field of sewage treatment, in particular to an accurate aeration operation method for a sewage treatment plant, which comprises the following steps: acquiring a bubble image in an aeration process through a camera, and performing morphological processing and feature extraction on the bubble image to obtain features such as area, area number, deformation, trajectory curvature and average speed of bubbles; a sensor is used for collecting the DO content, and a correlation model of bubble characteristics and water quality is established and used for predicting the future DO content after aeration response; establishing a power consumption model by calculating power consumption of an air blower and a water pump in the aeration process, and generating total power consumption in the aeration process; an aeration reward value is calculated in combination with the total power consumption and the future DO content, and an optimized aeration strategy is generated by using a DQN model according to the aeration reward value. According to the invention, the accuracy of aeration operation of the sewage treatment plant can be improved, and efficient and energy-saving aeration operation is realized.

The invention provides a new energy battery coating thickness control method, which comprises the following steps: presetting a chromatic value range of a coating color, segmenting a stable image into a coating area and a non-coating area, carrying out binarization processing on the segmented coating area to obtain a coating binarization image, according to the segmented coating binarization image, carrying out closed operation on a coating area, filling small holes in the coating area, and removing noisy points outside the coating area through open operation to obtain a coating area image after morphological processing; a motion model of color change is established according to historical focusing data, the color change trend of a next frame of image is predicted, a predicted value is used as a feedforward control signal of a focusing system, and oscillation of the focusing system is suppressed.

The invention relates to a hyperspectral remote sensing image radiation normalization method, in particular to a hyperspectral remote sensing image relative radiation normalization method based on pseudo-invariant region pairs, and solves the technical problems that an existing pixel-based relative radiation normalization method is low in space matching precision and has spectral feature deviation. According to the method, a pseudo-invariant region is screened by adopting super-pixel segmentation, morphological processing and super-pixel guiding methods, then relative radiation normalization is performed on a to-be-corrected highlight remote sensing spectrum image through adaptive learning by adopting a self-correction model based on spatial spectrum constraint, and a quantitative method is adopted to correct the relative radiation of the to-be-corrected highlight remote sensing spectrum image. Not only can the spatial spectrum characteristics of the hyperspectral remote sensing image be fully utilized, but also the radiation intensity of the hyperspectral remote sensing image under different time phases can be adaptively corrected, and the consistency between time sequences and the quantitative inversion precision are improved, so that the accuracy and consistency of relative radiation normalization are improved, and the accuracy and the reliability of the relative radiation normalization are improved. The method is suitable for radiation normalization of hyperspectral remote sensing images with high spatial resolution and complex ground object scenes.

The invention provides a slide scanning bar code identification optimization method and device and a readable storage medium thereof. The method comprises the following steps: carrying out image preprocessing downsampling and carrying out illumination compensation through sub-region mean value estimation; a bar code area is positioned through morphological processing, and accurate angle correction is achieved by combining a minimum enclosing rectangle and Hough straight line clustering; quickly calculating a local mean value and a standard deviation based on the integrogram, and carrying out adaptive binaryzation through a dynamic threshold formula; and after morphological operation is carried out on the binary image, according to a 39-code rule that 10 units / group contains 3 wide strips, counting the black-and-white interval width and reconstructing the bar codes in groups. According to the scheme, the problems of uneven illumination, angle deviation and bar code adhesion loss are solved, the recognition accuracy and robustness are improved, and the method is suitable for rigid carrier bar code recognition.

The invention belongs to the technical field of image processing, and provides an image processing method and an image processing terminal, and the method comprises the following steps: obtaining an original binary image from an image source, and carrying out the preprocessing of the original binary image; performing run-length coding on the preprocessed binary image to obtain an image data result after run-length coding; performing morphological processing on the image in the row direction by using the structural element matrix; re-integrating each row of data processed in the row direction into a complete image representation, and performing morphological processing in the column direction; taking a processing result in the column direction as a binary morphological calculation result of the original binary image; according to the method, the image is independently processed in the row direction and the column direction, specific shapes and structures can be more flexibly extracted and enhanced, proper structural elements are adopted according to specific application requirements, and the processing effect can be optimized; by using the run-length coding to represent the image, the overhead of data storage and transmission can be significantly reduced, and the processing speed is improved.

The invention discloses a citrus defect detection method based on pedicel influence, which comprises the following steps of: calibrating through a binocular horizontal moving camera, acquiring accurate geometric parameters of the camera, processing a binocular image by adopting an image registration and fusion technology, and extracting a pedicel contour through means such as color space conversion, graying and morphological processing. Depth information is obtained by using a three-dimensional reconstruction technology, and an accurate pedicel image is obtained by applying Otus binarization filtering and morphological closed operation; according to the method, a self-adaptive Gamma correction algorithm based on orange multi-scale texture fusion and asymmetric brightness diffusion characteristics is improved, a defect and pedicel mixed region is corrected, a pedicel image is removed through a mask method, a defect region is highlighted, finally, an active contour model and a Green formula are utilized, the orange area and the defect area are calculated respectively, and finally, the defect area is calculated. And comparing with a set value to realize accurate citrus defect detection.

The invention proposes a dynamic target detection method based on deep learning, and the method comprises the steps: extracting a dynamic target through unsupervised background modeling and morphological processing, combining a deep feature network with a lightweight visibility network, and outputting a target structured state; constructing a trajectory velocity modeling network through a differential equation according to the bounding box and the semantic features, generating a target motion trajectory, and performing numerical integration on the differential equation to obtain a predicted spatial position; fusing the semantic feature vector with the predicted spatial position to generate future appearance features of the target; when a frame image is input, constructing a candidate region based on the predicted spatial position, and calculating a matching confidence coefficient between the candidate region and a future appearance feature of the target to confirm a target position and a target feature; and if the matching confidence coefficient is lower than a preset threshold value, the previous frame state is left, and updating is not carried out, so that the robustness in a shielding or fuzzy state is improved.

The present invention discloses a method and system for automatically detecting surface defects of a flexible touch screen, comprising the following steps: S1: using the Retinex method to compensate for the illumination of the surface image of the flexible touch screen, and converting it to the HSV color space to obtain the converted image; S2: calculating the directional feature map based on the converted image; S3: constructing a Gaussian mixture model based on the directional feature map, and clustering the features using the expectation maximization algorithm to obtain the initial defect area; S4: optimizing the initial defect area using morphological processing to obtain the optimized defect area; S5: splicing the converted image and the optimized defect area to obtain an input image, and performing defect detection on the input image based on a multi-scale feature fusion network of deep learning. The present invention can realize fast and accurate detection of surface defects of a flexible touch screen.

The invention provides a method and system for interpreting expression of ER and PR in breast cancer by using artificial intelligence, and relates to the technical field of artificial intelligence, the method comprises the following steps: collecting labeled breast cancer tissue pathological section images, and performing data enhancement to obtain a sample image set; performing chromatic aberration balance on the image data in the sample image set to obtain a preprocessed data set; training a preset initial segmentation network by using the preprocessed data set; inputting the breast cancer tissue pathological section image into the trained cell nucleus segmentation model to obtain a cell nucleus region image; performing morphological processing on the cell nucleus region image to obtain a processed target region; and extracting color features and morphological features in the target area, and inputting the color features and the morphological features into the classification model to obtain an interpretation result. According to the method, cell nucleus segmentation and feature extraction are carried out through the deep learning model, subjectivity and errors of manual interpretation are reduced, and the interpretation accuracy is improved.