6923 results about "Performed Imaging" patented technology

The invention discloses a target identification tracking method and system based on multi-source fusion imaging, and the method comprises the steps: obtaining and preprocessing multi-modal environment monitoring data, carrying out the image enhancement based on the preprocessed multi-modal environment monitoring data, and obtaining the image-enhanced multi-modal environment monitoring data; performing multi-modal feature extraction on the image enhancement multi-modal environment monitoring data, and introducing a cross attention mechanism to fuse the extracted multi-modal features to construct cross-modal fusion features; constructing a cascade target recognition model, and inputting cross-modal fusion features to perform target recognition on the current monitoring scene; tracking a scene recognition target in the current monitoring scene to generate scene target tracking information, and storing the scene target tracking information in a preset track memory pool; and when a tracking target disconnection condition occurs during target tracking, obtaining a mismatched tracking trajectory, and performing tracking trajectory disconnection repair in combination with the trajectory memory pool. Therefore, the identification accuracy and tracking reliability of the target in the monitoring scene are improved.

The invention relates to the technical field of image recognition, in particular to a license plate recognition system and method based on an image technology and a medium. The method comprises the following steps: acquiring area sensing data and a camera image set, and performing deformation effect compensation to obtain an environment compensation image set; performing image diffusion reverse enhancement on the environment compensation image set to obtain a license plate area enhanced image set; performing character region high-dimensional topological mapping based on the license plate region enhanced image set to obtain a character segmentation matrix; extracting character morphological characteristics according to the character segmentation matrix, and performing character recognition on the character morphological characteristics to obtain a character recognition result; and carrying out cross-character semantic compensation on the character recognition result to obtain a semantic compensation license plate character vector, and carrying out multi-target cross verification on the semantic compensation license plate character vector to obtain a license plate recognition result. According to the invention, the accuracy and robustness of license plate recognition can be improved.

The invention discloses a bridge crack identification and automatic evaluation method based on image identification and AI modeling, and the method comprises the following steps: S1, obtaining an original image of a bridge structure surface, and carrying out the image preprocessing; s2, inputting the standardized image into an image recognition model, performing pixel-level segmentation on a crack region in the image, and outputting a crack mask graph; s3, performing feature extraction processing on the crack mask graph, extracting geometric feature parameters of the crack, and constructing a crack feature vector; s4, constructing an evaluation model based on a supervised learning method, and training the evaluation model; and S5, inputting the crack feature vector into an evaluation model, evaluating the structural risk level of the crack, and outputting a structural risk label. According to the method, image recognition and AI modeling are fused, automatic crack recognition and evaluation are achieved, and the method has the advantages of being high in precision, clear in boundary and intelligent in evaluation.

The invention relates to the technical field of multi-image fusion recognition, in particular to a data fusion method and system for CCD visual detection, and the method comprises the following steps: obtaining a horizontal pixel row calculation gradient construction trend sequence, repairing an edge fracture to generate an integrity index, extracting a gray value to detect feature mutation, and distributing fusion weights to establish a mapping relation. And executing image fusion and balancing the contrast to generate a fusion matrix result. According to the method, the fracture edge region is identified, interpolation compensation is executed, the structural similarity index of the local gray sequence in the image overlapping region and feature direction mutation detection are combined, accurate identification of the edge matching result is guided, and fusion weight factor mapping corresponding to signal-to-noise ratio distribution is introduced; according to the method, the distribution relation between the pixels in the region and the credible weight is effectively established, the edge transition among the multi-source images is more natural through Poisson constraint and contrast balance adjustment of the fusion region, and the structural fidelity and the judgment stability of the fusion image are remarkably enhanced.

The invention relates to an image processing and data processing technology, in particular to an intelligent coagulant dosing control method and system based on image recognition and multi-parameter modeling, the floc state is accurately quantified through image recognition and deep learning modeling, and a dosing prediction model with self-adaptive capacity is established in combination with raw water feed-forward information. And accurate control of coagulant addition is realized. The method comprises the following steps: collecting a floc image, carrying out image processing and floc feature extraction, and constructing a floc image description vector; time sequence input structure data fusing the floc image and the water quality data is constructed, and floc image sampling at each moment is defined as a time frame; performing enhancement and reconstruction processing on the training data of the dosing amount prediction model by adopting a data enhancement and sample equalization strategy to obtain continuously distributed synthetic samples; and constructing a hierarchical feature fusion enhanced dosing amount prediction model, fusing the previous water quality parameters, the current water quality parameters and the floc image joint feature vectors, and optimizing the dosing amount prediction precision layer by layer.

The invention relates to the technical field of image data processing, in particular to a cable surface defect detection method and system based on machine vision. The method comprises the following steps: acquiring a surface image of a cable, and converting the surface image into a grayscale image; determining the local complexity of each pixel point; obtaining a plurality of areas of the grayscale image, performing complexity determination, and dividing each area to obtain a plurality of windows of each area; determining a contrast limit threshold value of each window; performing image enhancement by using a CLAHE algorithm to obtain an enhanced grayscale image; and carrying out cable surface defect detection on the enhanced grayscale image by using a defect detection algorithm. According to the method, the CLAHE parameters are adaptively adjusted based on local complexity, the window size and the contrast threshold are dynamically determined in combination with gray and gradient information, discontinuity is corrected and eliminated through boundary similarity, and the quality and reliability of a cable defect detection image are improved.

The invention discloses an SMT welding spot defect detection method based on image data. The method comprises the following steps: S1, acquiring an image of an SMT welding spot area; s2, carrying out image preprocessing; s3, carrying out welding spot region segmentation by adopting a self-adaptive region growing algorithm, and carrying out modeling on a welding spot region contour in combination with a boundary fitting optimization algorithm to generate a welding spot contour model; s4, multi-level comprehensive features are extracted from the welding spot area, and the multi-level comprehensive features are constructed into a feature vector set; and S5, inputting the feature vector set into a welding spot defect detection network, predicting a defect mode existing in the current batch of welding spots by adopting an improved ProtoNet model, and completing classification and identification of welding spot defects by adopting adaptive feature filtering. According to the method, the adaptive region growing algorithm and the improved ProtoNet model are adopted, accurate detection of the welding spot defects is achieved, and the method has the advantages of being high in detection precision, high in adaptability and good in robustness.

The invention relates to the technical field of photoetching machine calibration, and discloses a photoetching machine calibration method, device and equipment based on multi-view vision, and the method comprises the following steps: carrying out imaging and phase sensitive detection analysis on a mask plane and a wafer plane through a four-path optical beam splitting system to obtain four groups of mask-wafer initial alignment position information; performing scanning white light interference edge enhancement processing to obtain three-dimensional surface contour data; performing wavelet transform processing on the three-dimensional surface contour data, and performing dynamic registration on the four-path optical beam splitting system to obtain multi-view visual feature registration data; a comprehensive error model including mechanical errors, optical errors and environmental errors is established, real-time correction and error compensation are carried out on a six-degree-of-freedom motion platform of the photoetching machine, a multi-view vision calibration result of the photoetching machine is obtained, the influence of environmental vibration and thermal drift on calibration precision is effectively eliminated, and the calibration accuracy of the photoetching machine is improved. The optical system is ensured to be always in the optimal imaging state, and the calibration precision is improved.

To provide a sample analyzer capable of achieving shortening of a measurement time and improvement of efficiency, in an inspection using a reaction to a plurality of kinds of reaction substances whose measurement ranges are largely different.SOLUTION: A sample analyzer 1 includes: a measurement unit 23 as an imaging device for capturing an image including light reception information based on each reaction between a first reaction substance group comprising a plurality of kinds of reaction substances reacting to a component included in a sample and a second reaction substance which reacts to the component included in the sample and is a different type from a reaction substance included in the first reaction substance group; and a control unit 50 as a control device for analyzing, in the light reception information in the image, the light reception information on the basis of a first determination value corresponding to a first substance group and the light reception information on the basis of a second determination value corresponding to a second substance.SELECTED DRAWING: Figure 10

The invention discloses a foam concrete image processing method based on sub-pixel edge reconstruction, and particularly relates to the field of foam concrete image processing.The method comprises the steps that an image of foam concrete is obtained, and a boundary response feature set containing edge direction information, edge gradient change information and local intensity comparison information is extracted; performing edge structure classification on the image region based on boundary response feature set combined threshold judgment; and aiming at the image region which is judged to be the local direction convergence mutation region, identifying a cross mutation point set by fitting a direction tensor convergence trend, and executing directionally-guided corrosion and expansion image processing. By constructing a structure judgment variable based on image boundary response characteristics and guiding a corrosion expansion path and a vein backtracking path to execute sub-pixel-level aperture boundary separation, image structure processing and contour reconstruction of a porous adhesion area are realized.

The invention discloses an image analysis method and system for concrete apparent quality defect detection, and relates to the technical field of concrete apparent quality defect detection.The method comprises the steps that a camera device is used for collecting concrete surface images in a specified distance interval, and the optical axis of a camera lens is controlled to be perpendicular to the concrete surface; carrying out image preprocessing on the collected concrete surface image, and carrying out defect type image marking; carrying out key feature extraction on the preprocessed concrete surface image by adopting a convolutional neural network to obtain key feature information; constructing a multi-algorithm comparison verification framework, performing cross validation in combination with the key feature information to obtain defect detection results and evaluation results of a plurality of defect detection models, optimizing model parameters in combination with a confusion matrix, and determining a final concrete surface detection model; obtaining an apparent quality defect detection result based on the defect detection result and a preset quality defect grading threshold value; the efficiency of concrete apparent defect detection is improved.

The invention provides a distribution network power transmission line insulator damage detection method based on a YOLO algorithm, and the method comprises the steps: collecting a plurality of insulator defect images, and carrying out the data enhancement of the images; performing image detail enhancement on an insulator defect area in the image by using a super-resolution reconstruction algorithm; constructing a composite loss function to guide an image detail enhancement process; constructing an insulator defect detection model based on a YOLOv8 framework; constructing a multi-task joint loss function as an insulator defect detection model training target; and a real-time feedback mechanism is introduced, a weighted combination loss function is constructed, gradient updating optimization is executed, and the detection capability of the insulator defect detection model is improved. The method has high detection precision and robustness, can effectively improve the efficiency and accuracy of fault detection, significantly reduces the burden and cost of manual inspection, improves the safety and reliability of a power system, and provides powerful support for the operation and maintenance of power equipment.

The invention provides a weight metering automatic classification and calibration method and system based on image recognition, and relates to the technical field of weight metering, and the method comprises the steps: carrying out the imaging of the surface of a weight through dual-light-path high-speed camera shooting, carrying out the gamma correction, and extracting contour features and surface defect features; internal density distribution is obtained through X-ray imaging; performing multi-scale feature fusion on the surface features and the density distribution to construct a holographic feature model; analyzing the dynamic change trend of the characteristic parameters based on a deep variational Bayesian network, and performing evaluation and scoring in combination with a gradient boosting decision forest to obtain an initial classification; establishing a dynamic evaluation model by adopting a self-organizing competitive learning network, determining a weight grade and setting calibration parameters; selecting a corresponding reference weight to establish a grading calibration compensation model, and determining an adaptive weight coefficient for dynamic adjustment by combining defect distribution; and predicting a performance degradation trend by adopting a Shenchang differential equation network, and outputting calibration parameters and generating early warning information when the calibration precision meets a threshold value requirement.

Disclosed in the present invention are an edge-deployed semi-supervised anomaly detection method and system for a railway track foreign object. The method comprises the following steps: an edge device encoding and decoding a video stream captured by a camera to obtain an image frame sequence, and performing frame extraction; and using a semantic segmentation model to perform image segmentation on a certain image frame obtained by means of frame extraction, to obtain a railway track region segmentation image. The use of a single image as input may generate an expert model result having a high weight value; however, the determination based on a single image is not stable, multiple consecutive images of the task scene need to be inputted, the frequency of each expert model obtaining the highest weight is computed, and the expert model corresponding to the highest frequency is the final solution. The present invention supports scene-adaptive foreign object detection algorithm automatic selection, and a user can perform selection on the basis of prior knowledge, or selection may be performed by a scene-adaptive automatic algorithm selection method; the user only needs to provide a batch of image data of the current scene, and the optimal algorithm selection can be evaluated.

A subject analysis method and apparatus, and a computer device, a storage medium and a computer program product. The subject analysis method comprises: acquiring a target image sequence, clinical data and an image processing model, wherein the target image sequence is obtained by means of performing image sequence conversion on an image to be subjected to detection, and the image processing model includes a pre-trained feature extractor and a trained classification sub-model; performing feature extraction on the target image sequence by means of the feature extractor, and performing analysis processing on a feature extraction result by means of the classification sub-model, so as to obtain a target analysis result; and on the basis of a large-scale language analysis model, performing subject analysis on the target analysis result and the clinical data, so as to obtain a subject analysis result.

The invention provides a CT (Computed Tomography) image super-resolution reconstruction method based on a generative adversarial network. The method comprises the following steps of: 1, collecting high-resolution image data and corresponding low-resolution image data; 2, establishing a CycleGAN model comprising a first generator, a second generator, a first discriminator and a second discriminator; a third step of training a CycleGAN model by using the collected high-resolution image data and the corresponding low-resolution image data so as to optimize parameters of the CycleGAN model; and a fourth step of performing image conversion on the to-be-enhanced medical image which is input into the trained CycleGAN model.

The invention relates to the technical field of image segmentation, in particular to a plant salt tolerance response modeling prediction method and system based on a time sequence image. The method comprises the following steps: preprocessing acquired plant sample image data; performing image segmentation on the preprocessed image data by using a plant semantic segmentation model based on U-Net; a plant salt tolerance response prediction model based on TimeSform is constructed; and predicting the plant salt tolerance response grade by using the plant salt tolerance response prediction model. According to the time sequence image-based plant salt tolerance response prediction modeling method provided by the invention, a prediction process integrating image acquisition, preprocessing, dynamic feature extraction and depth time sequence modeling is constructed, so that the efficiency, precision and automation level of plant salt tolerance phenotype recognition are remarkably improved.

The invention discloses an SPR response region identification method based on image semantic segmentation and time sequence alignment, and the method comprises the following steps: collecting SPR image frame sequence data, and constructing an original image sequence; performing image preprocessing operation on the original image sequence, and outputting a standardized image sequence; constructing a time sequence window image set composed of multiple continuous frames; inputting the time sequence window image set into an improved SegFormer model, and generating a response region segmentation mask image corresponding to each frame; executing cross-frame time sequence alignment operation of the response area, and outputting time sequence consistency identification mapping of the response area; performing area statistics, intensity analysis and time positioning operation; and generating a structured response region recognition result. The spatial-temporal evolution process of the response area in the SPR image sequence can be effectively recognized, the accuracy and stability of response area recognition are improved, and the method is suitable for high-precision biological detection and real-time molecular analysis scenes.

The invention belongs to the technical field of space optical remote sensing, and relates to a high-resolution remote sensing image semantic segmentation method based on a diffusion model. The specific process is as follows: first-stage training: labeling the type of each pixel of a high-resolution remote sensing image to generate a label image, and training an auto-encoder comprising an encoder and a decoder by using the label image; in the second stage of training, on the basis of the auto-encoder, a conditional diffusion model is loaded and trained, the conditional diffusion model comprises a noise injection module, a conditional encoding module and a de-noising U-Net, and parameters of the auto-encoder are fixed during training; wherein the noise injection module adds noise to hidden variables output by the encoder, the conditional encoding module is used for performing multi-scale feature extraction on the high-resolution image, and the de-noised U-Net performs image component and noise component prediction and image reconstruction under the guidance of the multi-scale features; the decoder is used for decoding the reconstructed image; and image semantic segmentation: carrying out semantic segmentation on the high-resolution remote sensing image by using the trained network.

The invention relates to the technical field of panoramic image construction, in particular to a panoramic image reconstruction method and system based on multi-angle imaging. The method comprises the following steps: collecting a multi-angle original image based on a distributed multi-camera array, carrying out adaptive filtering denoising and adaptive panoramic imaging adjustment, and constructing a multi-angle imaging geometric constraint network; performing multi-view semantic information deviation elimination based on a multi-angle imaging geometric constraint network, and performing global semantic feature fusion to obtain a unified semantic space representation framework; identifying illumination feature information of different visual angles, performing multi-angle illumination corresponding compensation on the multi-angle original image, performing image semantic distortion correction based on a unified semantic space representation framework, and constructing a multi-angle illumination compensation image; and performing multi-scale texture structure analysis on the multi-angle illumination compensation image to generate a high-fidelity texture fusion image. According to the invention, a natural and seamless panoramic image is provided, a panoramic scene is perfectly presented, and the immersive visual experience of a user is improved.

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 bearing defect detection method and system based on machine vision and ultrasonic detection, and particularly relates to the technical field of industrial automatic detection, and the method comprises the steps: S1, a synchronous collection module: carrying out pulse triggering synchronous collection, and generating a time-space reference table; s2, a feature extraction module: performing image noise reduction segmentation and ultrasonic frequency domain decomposition, and outputting a defect feature vector; s3, a fusion identification module: performing cross-modal feature alignment fusion to generate a defect classification conclusion; s4, a size measurement module: performing contour fitting to calculate inner and outer diameters, and outputting a size deviation value; and S5, a comprehensive judgment module: carrying out threshold comparison logic judgment, and generating a multi-modal detection report. According to the method, a space-time reference is established through an encoder, images are segmented in a self-adaptive mode, features are extracted through wavelet decomposition ultrasound, feature weights are re-calibrated through a parallel network and an attention mechanism, composite defects are recognized through cross-modal fusion, comprehensive judgment is conducted in combination with dimensional deviation, and a multi-dimensional quality evaluation system is achieved.

The invention relates to a welding track control method, device and equipment and a storage medium, and the method comprises the following steps: carrying out imaging scanning and welding contour extraction on the surface of a to-be-welded workpiece through a multispectral camera to obtain a welding contour feature sequence; based on the welding contour feature sequence, deep reconstruction is conducted on a welding area of a to-be-welded workpiece, welding three-dimensional point cloud data are obtained, and a welding track space curve is extracted from the welding three-dimensional point cloud data; track segmentation planning is conducted on a preset welding gun on the basis of the welding track space curve, and a segmented motion track sequence is obtained; based on the segmented motion trail sequence, pose mapping is conducted on an end effector of the welding gun, and a joint space motion sequence is obtained; and on the basis of the joint space motion sequence, the welding gun is controlled to conduct welding operation on the surface of the welding workpiece, and the technical problem of precise motion control of the robot welding gun under the complex space posture is solved.

The invention discloses a CLIP-based double-prompt optimized few-sample industrial anomaly detection method. The method comprises the following steps: respectively constructing a learnable normal text prompt template and an abnormal text prompt template by utilizing learnable normal word vectors and abnormal word vectors; performing image enhancement processing and image abnormal synthesis processing on the normal sample image for model training to generate an enhanced image and an abnormal synthesis image; inputting the learnable normal text prompt template and the learnable abnormal text prompt template into a text encoder in the CLIP model to obtain text prompt embedding; respectively inputting the normal sample image, the enhanced image and the abnormal synthetic image into an image encoder in the CLIP model to obtain visual embedding; and the similarity between text prompt embedding and visual embedding is measured to carry out optimization training on learnable text prompts, so that the trained CLIP model is utilized to carry out industrial anomaly detection.

The invention discloses a gestational diabetes auxiliary analysis system and method based on placenta ultrasonic textures, and the system comprises a multi-modal database which is used for storing placenta two-dimensional ultrasonic image data and clinical comprehensive data; the preprocessing module is used for screening the clinical comprehensive data and carrying out standardization processing and labeling on the image data; the image feature extraction module is used for constructing an image segmentation model based on a CNN network, performing image processing on the input placenta two-dimensional ultrasonic image through the image segmentation model, and obtaining an image feature data set; the risk prediction module is used for constructing a GDM risk prediction model; and the judgment module is used for carrying out risk classification on the GDM risk of the current pregnant woman by utilizing the image segmentation model and the GDM risk prediction model. By introducing a deep learning technology, intelligent analysis is performed on placenta ultrasonic images, clinical comprehensive data and other multi-modal data, and a reliable tool is provided for auxiliary analysis of gestational diabetes mellitus.

The invention relates to a steam foaming foam plastic particle uniformity judgment method. The method comprises the following steps: acquiring a particle image of to-be-produced plastic in a heating process through an image acquisition assembly; performing feature extraction on the particle image to obtain target features; the target feature comprises at least one of color, size and shape; performing image segmentation on the particle image based on the target features to obtain different sub-regions; and determining the particle uniformity of the to-be-produced plastic based on the deviation values of the target characteristics in the different sub-regions. Through real-time analysis of color, size, shape and other characteristics, particle aggregation, uneven melting or impurity mixing (such as carbonized particles) can be rapidly identified, the particle uniformity in the plastic production process can be monitored in real time, and hysteresis of a traditional detection method is reduced.

The invention provides a heading machine positioning method and system, and relates to the technical field of heading machines, and the method comprises the steps: pre-arranging a target on a heading machine, and building a heading machine gravity center coordinate system; capturing a plurality of frames of images with targets by adopting imaging equipment; performing image recognition on the multi-frame image by adopting a target detection model, extracting a feature point set of an area where the target is located, and generating multi-frame data of each feature point in the feature point set; performing multi-angle fusion on each feature point based on the multi-frame data of each feature point, and converting each feature point from a two-dimensional coordinate in an image coordinate system to a three-dimensional coordinate in a world coordinate system; based on the centroid coordinate system of the heading machine, converting the three-dimensional coordinates of the feature points in the world coordinate system into the three-dimensional coordinates in the centroid coordinate system of the heading machine, so as to generate position information of the heading machine; the problem that an existing heading machine is poor in pose positioning result accuracy is solved.

The invention provides a dynamic weight adjustment pest monitoring method and system based on a multi-modal remote sensing large model, and the method comprises the steps: obtaining data of a to-be-monitored region from a multi-source remote sensing platform, including a high-resolution optical image, a multispectral image and an SAR image, and carrying out the image preprocessing; extracting a multi-modal feature by using a feature extraction network, and constructing an FPN network structure of each modal for feature fusion to obtain a multi-modal fusion feature map; then multi-modal feature alignment is carried out, and multi-modal feature fusion is finally completed through a double attention module; feature enhancement is carried out by utilizing dynamic multi-granularity contrast learning, and features are extracted at different levels respectively; and through multi-granularity contrast learning, pixel-level, object-level and image-level contrast loss is calculated, and total loss is obtained through weighted summation and is used for model training. And deploying the trained model to an unmanned aerial vehicle or a satellite system, and collecting and processing remote sensing data in real time.

The invention provides a cotton field growth state evaluation and prediction system based on an unmanned aerial vehicle RGB image, and the system comprises a data collection module which collects initial image data; the data processing module is used for denoising, distortion correction, orthographic splicing and illumination correction to obtain standard image data; the image mining module is used for carrying out image segmentation and target detection and then extracting cotton profile features and growth indexes; the comprehensive evaluation module is used for constructing a growth state evaluation model to obtain a comprehensive growth condition score and an evaluation index; the growth prediction module introduces yield related factors to carry out yield prediction and growth abnormity early warning; and the real-time monitoring and decision support module visually displays the evaluation prediction result and automatically generates decision support. The cotton field growth vigor can be comprehensively analyzed, the comprehensiveness, the accuracy and the high efficiency of evaluation and prediction are improved, real-time monitoring and intelligent decision making can be carried out on the whole cotton growth process, and the efficiency and the precision of agricultural management are improved.

The invention relates to the technical field of red tide anomaly detection, in particular to a red tide anomaly detection method and system based on an improved multi-mode Transform. The method comprises the following steps: acquiring a remote sensing image and text data; respectively carrying out data preprocessing according to the obtained remote sensing image and text data; performing visual positioning and text selection based on the preprocessed data; performing cross-modal feature learning on the basis of a hierarchical Transform of a multi-modal capsule mechanism; guiding an attention mechanism based on a semantic path to carry out image-semantic feature alignment optimization; and carrying out multi-modal knowledge distillation on the optimized features. According to the method, an image and text preprocessing module, a visual positioning module, a keyword extraction module and other modules are combined, multi-angle accurate perception of a complex red tide scene is achieved, and the bottleneck that a red tide area is difficult to accurately recognize under the condition that data are single and information dimensions are limited in a traditional method is broken through.