1864 results about "Microscopic image" patented technology

The invention relates to a microchip appearance defect detection method based on a convolutional neural network. The microchip appearance defect detection method comprises the following steps: acquiring a high-resolution microscopic image and carrying out adaptive brightness equalization processing; labeling defect features in the pre-processed image by adopting a layered labeling strategy, and dividing defects into a surface deformation class and a material anomaly class by coarse-grained classification labeling; a double-branch lightweight convolutional neural network model is constructed based on the annotation data, a first branch extracts global semantic features, and a second branch extracts local detail features. In the model training, a defect sensitivity loss function is adopted for optimization, and defect area attention, edge preservation and category balance loss items are included, so that the small-size defect detection capability and the boundary positioning precision are improved, and the problem of sample imbalance is solved. And finally, a detection result including defect types, position coordinates and confidence scores is generated by inputting a to-be-detected preprocessed microscopic image, and the defect detection performance with high precision and strong pertinence is achieved.

The invention relates to the technical field of data processing, in particular to a ceramic supply chain quality traceability data management system based on a block chain, which comprises a data acquisition and processing module, a dynamic identifier generation module, an identifier embedding control module, a cross-chain data synchronization module, a multi-modal verification module and a redundancy recovery module. The data acquisition and processing module integrates firing temperature, forming pressure parameters and microscopic image data through Internet of Things equipment and edge computing nodes to generate a structured data set, the system integrated resource scheduling module optimizes node computing resource allocation, and anti-quantum encryption and IPFS distributed storage guarantee data security. And the terminal equipment integration module simplifies the multi-modal data acquisition process, and finally collaborative optimization of the ceramic quality tracing reliability, the operation efficiency and the artistic integrity is realized.

A deep learning-based virtual HER2 IHC staining method uses a conditional generative adversarial network that is trained to rapidly transform autofluorescence microscopic images of unlabeled / label-free breast tissue sections into bright-field equivalent microscopic images, matching the standard HER2 IHC staining that is chemically performed on the same tissue sections. The efficacy of this staining framework was demonstrated by quantitative analysis of blindly graded HER2 scores of virtually stained and immunohistochemically stained HER2 whole slide images (WSIs). A second quantitative blinded study revealed that the virtually stained HER2 images exhibit a comparable staining quality in the level of nuclear detail, membrane clearness, and absence of staining artifacts with respect to their immunohistochemically stained counterparts. This virtual staining framework bypasses the costly, laborious, and time-consuming IHC staining procedures in laboratory, and can be extended to other types of biomarkers to accelerate the IHC tissue staining and biomedical workflow.

The invention discloses an improved convolution neural network model for leukocyte calculation, and relates to the field of artificial intelligence and medical image processing. Aiming at the problems of inaccurate feature extraction, strong background noise interference and insufficient multi-scale feature fusion in a traditional leukocyte calculation method, the model realizes fine processing and feature enhancement of a medical image through a multi-level modular design; the image preprocessing module is used for completing white blood cell positioning and image adaptive division and enhancement; multi-scale edge, texture and gray features are fused by means of an image feature extraction module, and a composite feature vector is constructed; through multi-scale convolution, a double-layer attention mechanism and convolution layer calculation of the improved convolution neural network model module, precise focusing of white blood cell contours and cell nucleus features, background noise suppression and calculation efficiency optimization are realized; and finally, a calculation result is output through a visual terminal, so that the accuracy and robustness of leukocyte counting are remarkably improved, and efficient technical support is provided for medical microscopic image analysis.

The invention provides a fine-grained phytoplankton microscopic image enhancement classification method and a model building method thereof, and belongs to the technical field of underwater image enhancement classification, and the method comprises the following steps: firstly, collecting phytoplankton samples from a plurality of sea areas, and obtaining image data through a microscopic imaging technology; screening, preprocessing and data enhancement are carried out on the images, and a high-quality training set and a high-quality test set are constructed; according to the method, a double-branch deep learning model HySwinFormer is developed, the double-branch deep learning model HySwinFormer comprises a multi-scale context capture branch based on hierarchical vision TransFormer and a local detail enhancement branch designed by adopting a parallel convolution kernel, and joint expression of features is realized through a designed weighted fusion strategy; and training and optimizing the model to obtain a final model. Experimental results show that the classification performance of the HySwinFormer model on multiple data sets is superior to that of an existing mainstream deep learning model, and the classification effect is remarkably improved.

The invention provides a small sample plankton image enhancement recognition method and a small sample plankton image model building method, and belongs to the technical field of underwater image enhancement. The method comprises the following steps: firstly, acquiring and sorting plankton microscopic image data, and constructing a normalized data set; secondly, designing a small sample plankton image enhancement recognition model based on a feature map reconstruction technology, performing feature extraction on the image by adopting a deep convolutional neural network, generating an intermediate layer feature map, and realizing category discrimination in combination with a feature map reconstruction mechanism; the model fuses multiple key designs oriented to cross-domain adaptation and structure perception, the distribution difference between different data domains is relieved, and the characterization capability of the model on the complex morphological structure of plankton is also remarkably improved; and finally, performing end-to-end training and optimization on the model under a meta-learning framework to obtain an optimal model. Experimental results show that the method shows excellent classification performance on a plurality of plankton data sets, and high-precision recognition can be achieved under the condition that the number of samples is limited.

The invention discloses an intelligent processing method and system for multi-modal biological medicine data. The method comprises the following steps: acquiring a tumor tissue microscopic image and clinical characteristics of a diagnosed patient object; performing semantic coding on the clinical features to obtain clinical feature semantic understanding feature vectors; performing feature extraction on the tumor tissue microscopic image through a tumor tissue morphological feature extractor based on a deep neural network model to obtain a tumor tissue microscopic feature map; performing target significance analysis on the tumor tissue microscopic feature map to obtain a sequence of target manifestation tumor tissue microscopic local feature vectors; performing class attention interaction fusion processing on the sequence of the clinical feature semantic understanding feature vector and the target explicit tumor tissue microscopic local feature vector to obtain a multi-modal medical semantic fusion feature; and determining a tumor type label based on the multi-modal medical semantic fusion features. Therefore, the tumor type can be detected and judged, and powerful support is provided for the aspects of early diagnosis, personalized treatment and the like of tumors.

The invention discloses a kit raw material quality detection method based on multi-modal data fusion, and relates to the technical field of automatic quality detection of chemical reagent raw materials. Comprising the following steps: S1, synchronously acquiring spectral data, microscopic image data and chemical detection data of a kit raw material through a spectral sensor, microscopic imaging equipment and a chemical sensor; s2, inputting the multi-modal data into a self-adaptive spatial-temporal feature calibration network, and analyzing local detail features and global time sequence features of the modal data through a multi-scale spatial-temporal feature extraction module; according to the kit raw material quality detection method based on multi-modal data fusion, by constructing a collaborative fusion system of an adaptive spatial-temporal characteristic calibration network and a layered information entropy screening mechanism, the core technical problems of misalignment of multi-modal data dynamic alignment and redundant interference accumulation are solved.

The invention discloses a microscopic image seamless splicing and enhanced reconstruction method based on deep learning, and the method comprises the following steps: S1, collecting a plurality of original images with overlapped regions, and recording the spatial position information and imaging parameters of the original images; s2, preprocessing the original image to generate a standardized image sequence; s3, inputting the standardized image into a structure perception feature extraction network, and extracting a feature map fusing textures and structures; s4, inputting the feature image and the original image into an image registration module; s5, inputting the registration image into the boundary attention splicing network; s6, inputting the seamless image into the residual hierarchy reconstruction network, and enhancing image details through hole convolution and multi-scale branches; s7, image quality evaluation is executed, and the structural similarity, the signal-to-noise ratio and the edge retention rate are calculated; and S8, constructing a training set and carrying out end-to-end training optimization based on a joint loss function. According to the method, a multi-module deep network is fused, and seamless splicing and high-quality enhanced reconstruction of microscopic images are realized.

The invention discloses a pathological feature recognition and negative elimination method based on microscopic imaging. The method comprises the following steps: S1, collecting a pathological section image and digitally generating original microscopic image data; s2, preprocessing the original microscopic image; s3, constructing a pathological image recognition network fusing converter coding and a gating dynamic receptive field mechanism, and outputting pathological feature vectors; s4, performing context modeling through an attention guidance and category perception decoder, and outputting an image classification result; s5, constructing a discriminant boundary separation model based on positive and negative sample embedding, and performing negative exclusion judgment; s6, performing confidence coefficient weighted evaluation in combination with the uncertainty and the boundary distance, setting a dynamic threshold value, and screening out low-credibility samples; and S7, coding the classification result and the negative label into structured data, and sending the structured data to a diagnosis auxiliary system. According to the method, multi-scale modeling and a negative screening mechanism are fused, and intelligent recognition and credible diagnosis output of the pathological image are realized.

The invention provides an electron microscope automatic focusing method and system based on a deep learning model, and relates to the technical field of electron microscope automatic focusing, and the method comprises the steps: collecting a plurality of images at different focusing distances through an electron microscope, and recording the focusing distance of each image; calculating the characteristic value of each collected image, combining the focusing distance and the characteristic value of the image into a characteristic vector, and generating a characteristic sequence according to the characteristic vectors obtained under different focusing distances; training a pre-constructed neural network model by using the feature sequence to obtain a trained neural network model; and inputting a to-be-tested image into the trained neural network model, calculating a difference value between the predicted optimal focusing distance and the focusing distance of the collected image, and judging whether the difference value is within a set range or not. According to the invention, the microscopic image acquisition efficiency and quality are obviously improved.

The invention relates to the technical field of intelligent agricultural equipment, in particular to a tomato disease diagnosis method based on multi-modal data analysis, which comprises the following steps of: 1, synchronously acquiring and preprocessing multi-modal data, synchronously triggering a hyperspectral imaging device and a microscopic camera, respectively acquiring a plant canopy hyperspectral image and a stem microscopic image, and acquiring a plant canopy hyperspectral image and a stem microscopic image; meanwhile, temperature, conductivity and dissolved oxygen environment parameters are continuously collected in the root zone; 2, self-adaptive feature extraction and fusion in the growth stage are carried out, reflectivity correction and leaf segmentation are carried out on the hyperspectral image, and leaf surface spectrum curve features are extracted; step 3, hybrid model construction and space-time analysis: constructing a hybrid model comprising spectrum, microscopy and environment analysis networks, and dynamically adjusting each network weight through a gating network; and 4, generating a disease decision. The method can realize accurate, efficient and real-time tomato disease diagnosis, has high practical value, and can effectively improve the disease prevention and control capability in agricultural production.

The invention discloses a phytoplankton microscopic image classification model construction method, and belongs to the field of intelligent detection. For the problems of unstable microscopic image quality and small difference between phytoplankton classes, a double-branch ResNeXtViT model is provided: a ResNeXt branch enhances the texture feature extraction capability through a wavelet convolution attention module, and improves the adaptability to irregular shapes in combination with an adaptive convolution module; a Transform branch is used for capturing global context information; the feature fusion module integrates local details and a global structure. A combined data enhancement strategy including noise addition and color migration is adopted, and a joint loss function is designed to optimize intra-class compactness and local discrimination capability. Experiments show that the accuracy of the method on a Chaohu lake phytoplankton data set reaches 94.11%, which is superior to that of ResNet, ViT and other models, and the method can be deployed to an intelligent analyzer to realize rapid water quality monitoring.

The invention discloses a titanium alloy microstructure recognition method and system based on deep learning and a medium, and belongs to the technical field of image recognition, and the method comprises the steps: carrying out the serialization processing of a preprocessed microscopic image based on a trained image recognition model, inputting the serialized microscopic image into a Transformer encoder, and carrying out the recognition of a titanium alloy microstructure. And the features extracted by the Transform encoder are respectively output to a pixel-level phase classification branch and an image-level tissue classification branch, and phase classification and tissue classification are correspondingly carried out. The tissue type probability output by the image-level tissue classification branch is used as a gating signal, and the corresponding segmentation channel of the pixel-level phase classification branch is correspondingly enhanced or inhibited; based on an interactive attention mechanism, the classification semantics of the image-level organization classification branches are reversely projected to the middle layer of the segmentation branches of the pixel-level classification branches. According to the method, the microscopic structure analysis efficiency and the standardization degree are remarkably improved, and good practicability is achieved.

The invention relates to the technical field of hot galvanizing, and discloses an automobile hot galvanizing process parameter optimization method and system based on artificial intelligence, and the method comprises the steps: carrying out window division processing on initial cooling parameters, constructing a cooling rate matrix, and finely depicting heat exchange states of different cooling sections; the problem of insufficient cooling control precision under different cooling speed alternating conditions is solved, a microscopic image is combined to extract the grain main axis direction, calculate the mean value and range of included angles, generate a consistency index set and quantitatively describe the dispersion problem in the grain growth direction, the cooling speed and the consistency index are jointly matched, and a cooling speed structure offset matrix is constructed. The method comprises the following steps: identifying a space mutation region, accurately positioning abnormal coarse or abnormal arrangement positions of crystal grains, and finally screening target cooling parameters according to a cooling index value corresponding to an abnormal region to realize local adjustment of a cooling process, thereby reducing the abnormal growth rate of the crystal grains and improving the direction consistency of the crystal grains.

A deep learning-based digital / virtual staining method and system enables the creation of digitally / virtually-stained microscopic images from label or stain-free samples. In one embodiment, the method of generates digitally / virtually-stained microscope images of label-free or unstained samples using fluorescence lifetime (FLIM) image(s) of the sample(s) using a fluorescence microscope. In another embodiment, a digital / virtual autofocusing method is provided that uses machine learning to generate a microscope image with improved focus using a trained, deep neural network. In another embodiment, a trained deep neural network generates digitally / virtually stained microscopic images of a label-free or unstained sample obtained with a microscope having multiple different stains. The multiple stains in the output image or sub-regions thereof are substantially equivalent to the corresponding microscopic images or image sub-regions of the same sample that has been histochemically stained.

The invention relates to the field of intelligent detection, and provides a machined surface quality detection system and method based on microscopic image.The method comprises the steps that firstly, a multi-focal-plane image sequence of a target product sample is obtained, and a full-clear image of the target product sample is generated through super-depth-of-field image synthesis; then preprocessing and calibrating the full-clear image to obtain a standardized image of the sample, then analyzing and measuring the surface characteristics of the sample based on the standardized image to generate structured data of the product processing surface quality, and finally determining the quality of the product according to a preset quality standard. And processing the structured data to form a product processing surface quality evaluation report. In this way, the comprehensiveness and accuracy of quality detection of the machined surface can be effectively improved.

The invention discloses a molybdenum-rhenium alloy pipe fitting defect identification method and system based on a high-definition microscopic image. The method comprises the following operation steps that high-definition microscopic equipment is used for collecting the high-definition image of a molybdenum-rhenium alloy pipe fitting; preprocessing the high-definition image of the molybdenum-rhenium alloy pipe fitting to obtain a molybdenum-rhenium image to be detected; performing execution processing of a region growing algorithm on the molybdenum-rhenium image to be detected to obtain a defect region to be detected; performing defect identification on the to-be-detected defect area to obtain type defects; and judging the defect problem of the molybdenum-rhenium alloy pipe fitting according to the type defect.

The invention relates to the technical field of biomedical detection, and discloses a non-invasive sperm cell death and viability detection method, which utilizes a bright field image and a fluorescence image to train a fluorescence-assisted bright field attention network, introduces a fluorescence attention guidance module to provide guidance for training of a Transformer encoder, and realizes non-invasive sperm cell death and viability detection through priori knowledge of the fluorescence image. By means of the method, the model can be quickly focused on key features displayed in the fluorescent highlight area, the situation that training resources are consumed on a large amount of irrelevant information is avoided, convergence is accelerated, the model training time is shortened, meanwhile, the detection precision is improved, and then a more accurate and efficient sperm cell death and activity detection model is obtained. And during actual detection, the sperm cell death and viability detection can be realized only by collecting a microscopic image, pre-processing the microscopic image and inputting the preprocessed microscopic image into the model without dyeing a sperm sample, so that the damage of a traditional detection method to sperms is avoided, the non-invasive detection of the sperm cell death and viability is realized, and the method is more suitable for clinical detection.

The invention discloses an enhanced photoacoustic microscopic imaging method based on physical degradation learning, which comprises the following steps of: on the basis of a photoacoustic imaging principle, degrading an image of a real photoacoustic microscopic image to the ground into a low-quality photoacoustic microscopic image through physical model degradation learning with variability. A high-quality photoacoustic microscopic imaging image is used as a target of supervised training, deep learning is used as a basis, the high-quality photoacoustic microscopic imaging image is input into a self-designed convolutional neural network with an attention mechanism for network training, the quality of a reconstructed image is evaluated until the reconstruction quality reaches the standard, and otherwise, the above steps are executed again. And inputting a real low-quality photoacoustic image into the trained neural network to obtain a reconstructed high-resolution photoacoustic image. According to the method, the resolution ratio and the contrast ratio of the deep photoacoustic microscopic image can be effectively improved, meanwhile, the photoacoustic microscopic image can be denoised, the dependence on the data set can be effectively reduced due to the fact that the data set is obtained through the physical model, and the reconstruction effect under different environments can be improved.

The invention discloses a microscopic image super-resolution reconstruction method based on an improved diffusion model. The microscopic image super-resolution reconstruction method comprises the following steps: acquiring paired high-resolution images and corresponding low-resolution images as a data set; training an improved diffusion model by using the training set; a given low-resolution image is subjected to up-sampling by using an interpolation algorithm to obtain a high-resolution image, the low-resolution image and the corresponding high-resolution image are input into a trained improved diffusion model to obtain a corresponding super-resolution image, an efficient diffusion model is introduced, the reasoning efficiency is improved, and the reconstruction effect is good.

The invention discloses a floating algae flow type microimaging sheath fluid focusing micro-fluidic chip device and system, and belongs to the technical field of micro-fluidic control. According to the device, the three-channel sheath fluid flow chip design is adopted, a large-channel inlet structure is combined to avoid algae cell blockage, and a sheath flow focusing technology is utilized to compress a sample flow to the center of a channel and limit the sample flow within the field depth range of a microscope objective to obtain a clear image. The arc-shaped structure of the sample channel reduces laminar flow disturbance, and a four-side transparent observation area supports panoramic depth imaging. The system comprises a microscopic imaging module, a fluid driving module and a sheath liquid flow chip module, a flow rate ratio of sheath liquid flow to sample flow is accurately controlled through a dual-channel liquid flow driving injection pump, and stable imaging within the depth of field of an objective lens is realized by combining a manual focusing platform. Experimental verification shows that the system can effectively compress sample flow to 21m nuclear flow diameter, imaging of floating algae such as chlorella, scenedesmus quadricauda and the like is clear, and the contradiction between flux and imaging quality of a traditional micro-fluidic chip is solved.

The invention discloses a multi-scale digital rock core rapid reconstruction method based on an agent model, and belongs to the field of digital rock core rapid reconstruction. According to the method, through combination of multiple steps and the model, all-around support is provided for construction of a multi-scale digital core, key links of data acquisition, feature extraction, model construction and multi-scale information fusion are covered, a basis is provided for reservoir physical property evaluation through accurate prediction of permeability, and the reservoir physical property evaluation accuracy is improved through combination of microscopic images and macroscopic attributes. Effective connection of cross-scale information is realized, finally, a digital core with a real microstructure is generated, and a high-fidelity data model is provided for oil-gas exploration.

The invention provides an intelligent algae analysis method and system, and the method comprises the steps: S1, collecting a microscopic image in a microfluidic channel through a flow analyzer based on a target water body; s2, accurately marking the category, the position and the boundary characteristics of the microalgae in the microscopic image by using a marking tool supporting image segmentation and classification in combination with corresponding microalgae classification data; s3, based on the pre-training weight of the Ground SAM, performing fine tuning optimization on the model by using a transfer learning method so as to adapt to microalgae detection; s4, preprocessing the microscopic image acquired in the step S1; s5, for the preprocessed microscopic image, on the basis of the training model in the step S3, realizing target detection and segmentation of microalgae in the microscopic image by using Ground SAM; and S6, counting the types and volumes of the microalgae, identifying the types of the algae, and calculating the density of the algae. Under the support of the flow analyzer, each microalgae cell is accurately detected and counted, so that more detailed algae density data is obtained.

The invention relates to a super-resolution fluorescence lifetime microscopic imaging method and system based on pixel relocation. The method comprises the following steps: S1, carrying out multi-focus parallel scanning on a sample by using an acousto-optic deflector; s2, synchronously collecting a fluorescence intensity image and fluorescence lifetime data through an area array detector and a point detector; and S3, carrying out super-resolution reconstruction on the fluorescence intensity image based on a pixel relocation algorithm, and mapping fluorescence lifetime data to a reconstructed image to generate a super-resolution fluorescence lifetime microscopic image. According to the invention, an AOD is adopted to replace a scanning galvanometer in a traditional TCSPC-FLIM imaging system as a light beam scanner; addressing scanning of AOD, image acquisition of EMCCD and life data acquisition of PMT and TCSPC cards are closely linked by combining well-designed multiple paths of synchronizing signals, and fluorescence life data are distributed to each dot matrix through a later data processing method, so that organic combination of super-resolution and FLIM can be realized without complicated equipment, and SR-FLIM images of samples are obtained.

The invention discloses a path planning method for a palletizing robot based on a multi-path convolutional neural network, and relates to the technical field of convolutional neural networks, and the method comprises the steps: employing the three-source data of a quality inspection report image, a damaged video and a microscopic image, extracting features through a parallel convolutional neural network, generating a dynamic maximum curvature constraint value after the fusion, and obtaining the path planning of the palletizing robot. And self-adaptive path planning of material characteristics is realized. And nodes conforming to curvature constraint are screened based on a probability route map (PRM) to generate a track, and the vibration spectrum energy of the mechanical arm is monitored in real time. When vibration exceeds a dynamic safety threshold value, subsequent path constraint is tightened in real time through a curvature penalty factor, and the damage risk is reduced by 90%; a closed loop of material analysis, path generation, vibration feedback and parameter correction is established, and balance of safety and efficiency is achieved in a high-speed stacking scene.

The invention provides a microscopic image representation method based on a dynamic pluggable mask self-supervision encoder. The microscopic image representation method comprises the following steps: step 1, establishing a network based on the dynamic pluggable mask self-supervision encoder and an MAE decoder; 2, constructing a loss function to train the network, and carrying out the combined optimization of image reconstruction and classification; step 3, using a pre-training dynamic pluggable mask self-supervision encoder to extract deep feature representation of the microscopic image; and further connecting a decoder of a downstream task, and carrying out mineral microscopic image super-resolution reconstruction, inclusion automatic analysis, intelligent diamond cleanliness rating and general cell segmentation. According to the method, the feature extraction quality of the field with the data volume disadvantage is improved through the pluggable module, and the problem that most categories of cross-field data sets are unbalanced is effectively solved. Meanwhile, compared with the addition of branches, the pluggable module reduces the extra calculation overhead brought by the addition of a structure by 50%, and endows the model with extremely strong domain mobility.

The utility model discloses a micro-fluidic chip imaging positioning device, and belongs to the technical field of micro-fluidic chip positioning. According to the technical scheme, the system comprises a microcosmic illumination module, a microcosmic imaging module, a chip moving and macroscopic illumination module, a macroscopic imaging module and an optical path multiplexing module; the microscopic illumination module comprises a fly's-eye lens assembly, the fly's-eye lens assembly comprises two fly's-eye lenses, the optical axes of the two fly's-eye lenses are parallel to each other, and the focus of each lens array element in one fly's-eye lens coincides with the center of the lens array element at the corresponding position of the other fly's-eye lens. The micro-fluidic chip clamp is applied to the aspect of micro-fluidic chip imaging positioning, overcomes the defects caused by an existing micro-fluidic chip clamp, solves the technical problems of universality, automation and high precision of micro-fluidic chip positioning, and has the characteristics of rapidness, automation, high precision and universality.

The present invention provides a method for identifying clue cells in a leucorrhea microscopic image, which belongs to the technical field of clue cell identification, and comprises: firstly, pre-processing the stained leucorrhea microscopic image, including graying, medical image enhancement and noise removal. Through spectral feature analysis, a feature space is constructed and adaptive threshold segmentation is adopted to extract the cell area, and then edge detection and morphological operations are performed to accurately extract the complete outline of the cell. The morphological feature parameters such as cell area, perimeter, roundness, grayscale mean and variance are innovatively calculated, and a feature discrimination equation group is established in combination with the spectral feature vector to realize accurate classification and identification of cells. The identification results are post-processed to remove noise interference and perform connected area analysis, the number and proportion of clue cells are counted, and annotated images are generated; and the iterative optimization of the data set verified by medical experts solves the technical problem that the consistency of the diagnosis results is poor due to the subjective experience differences of doctors.

The invention relates to the technical field of medical image processing and computer vision, in particular to a parasitic ovum microscopic image detection method and system based on polymorphic prior, and the method comprises the steps: obtaining a to-be-detected microscopic image, and carrying out the feature extraction of the to-be-detected microscopic image through a convolutional neural network, and obtaining an initial feature map; constructing a polymorphic convolution kernel library based on preset biological morphological characteristics of the parasitic ova; performing deep convolution and feature fusion operation on the initial feature map by using a polymorphic convolution kernel library to generate a space attention map; performing feature enhancement processing on the initial feature map by using the spatial attention map to obtain an enhanced feature map; and performing bounding box regression and category prediction on the enhanced feature map to obtain a parasitic ovum detection result. According to the method, morphological priori and attention mechanisms are introduced, so that the problems of egg form similarity, background interference and the like are solved, accurate and robust automatic detection is realized, and the clinical diagnosis efficiency is remarkably improved.