191 results about "Color texture" patented technology

The invention relates to the technical field of image enhancement, in particular to a remote sensing image enhancement method and system based on visual analysis. The method comprises the following steps: firstly, acquiring a plurality of preliminary connected domains of a remote sensing image through a connected domain algorithm; then, according to the color, the texture condition, the area and the shortest distance among all edge pixel points in each preliminary connected domain, obtaining a plurality of connected domains of the remote sensing image, calculating local feature indexes of the connected domains, evaluating the merging possibility, clustering and dividing earth surface feature regions, determining the pixel point window side length and the neighborhood pixel weight, and finally, obtaining the remote sensing image. And performing enhancement processing on the remote sensing image according to the weight. According to the remote sensing image enhancement method provided by the invention, the quality of the remote sensing image is effectively enhanced, the earth surface feature recognition precision is improved, the noise is effectively removed while important details are kept, the filtering result can more accurately conform to the actual features of the image, key information is kept, and the processing efficiency and the visual effect are improved.

The invention belongs to the technical field of forest fire early warning, and discloses a forest fire prevention method based on smoke recognition, which comprises the following steps: acquiring heterogeneous image data from different monitoring devices, performing format unification and quality enhancement through a data preprocessing module, eliminating the difference of data from different sources, and obtaining preprocessed image data; a multi-scale feature extraction network based on deep learning is adopted, key features such as the color, texture and form of smoke are automatically learned and extracted from the preprocessed image data, and feature representation is obtained; according to the method, corresponding early warning threshold values are set according to different grades of fire judgment results, when the fire grade judgment results meet early warning conditions, early warning signals are triggered automatically, and early warning of the fire is achieved in the modes of message pushing, sound-light alarm and the like.

The invention provides a food freshness evaluation method and system based on image processing. The method comprises the following steps: acquiring a multi-view image of food, transmitting the image to a central processing center, setting personalized image acquisition parameters, and forming a food image database; based on the food image database, performing high-precision dynamic monitoring on color and texture changes of the food in different time periods, and generating a preliminary evaluation result; according to the preliminary evaluation result, combining food types, storage conditions and historical sales data, performing prediction modeling on the freshness of the food to obtain a final freshness evaluation report; and utilizing the freshness evaluation report to adjust an inventory strategy in real time, optimizing a food distribution plan, and generating an optimized management process. According to the technical scheme provided by the invention, the accuracy of food freshness evaluation is improved.

The invention discloses a multi-dimensional feature and three-plane representation enhanced three-dimensional grid reconstruction method and device, and belongs to the technical field of three-dimensional reconstruction, and the method comprises the steps: obtaining three-dimensional priori knowledge: after a multi-view image sequence is generated based on a single image, extracting the multi-dimensional features of the image sequence through a multi-dimensional feature extraction and fusion module, and obtaining the three-dimensional priori knowledge; performing feature fusion on the multi-dimensional features and the initial three-plane features by using a high-resolution three-plane representation construction module containing a double-flow Transform, a convolutional layer and a pixel rearrangement operation so as to organize the initial three-plane features into high-resolution three-plane representation; a three-dimensional grid construction module is used for generating a three-dimensional grid based on the three-plane representation, a color decoder is used for decoding and generating color textures of space points based on the three-plane representation, and the three-dimensional grid and the color textures are combined to obtain a high-precision three-dimensional grid with textures. The model effectively reduces the fuzzy and artifact phenomena of the grid surface and improves the accuracy of the geometric structure. The method can be widely applied to modeling processes of game and movie and television industries.

The invention provides an image generation method based on style feature injection, which can efficiently migrate the spatial layout and color texture of a style reference image on the basis of a text image. According to the method provided by the invention, on the basis of a Stable Diffusion text map, the spatial layout and the preliminary style of an image are learned by utilizing the capability of binding features of Dreambooth, and then attention injection is executed to strengthen color features and detail textures. Therefore, the method provided by the invention has high generalization in various styles, does not need tedious parameter adjustment for a single specific style, and is highly sensitive to context features such as composition, texture, color and the like. In addition, the model needs less data, only 3-5 training images are needed, only one attention reference image is needed in the reasoning process, additional training is not needed, and the robustness is very high. Therefore, the style image generation method provided by the invention can adapt to various different styles so as to have a higher degree of freedom in a personalized task.

The invention discloses a method for laser-induced color texture pattern on titanium surface, comprising: step 1: grinding, polishing and cleaning the titanium alloy surface, drawing a pattern on the titanium alloy surface by using a laser, and continuously adjusting the parameters of the laser until a processing database in which the parameters of the laser and the color of the titanium alloy surface pattern are mutually correlated is obtained; step 2: using a colorimeter to measure and calibrate the color of the pre-drawn pattern with Lab values, calculating the color difference between the color of the pre-drawn pattern and each color in the processing database, and when the color difference is less than or equal to a predetermined value, selecting the parameters of the laser corresponding to the corresponding color for processing; the invention induces the generation of oxide film layers of different thicknesses and types on the titanium surface by a laser heat source with adjustable pulse width, makes the film layer present different colors through the interference, reflection and absorption of light, adjusts the spot size, controls the scanning speed, and is equipped with a high-speed galvanometer scanning system to realize the preparation of various fine patterns.

The invention relates to the technical field of defect detection, and particularly discloses a railway fastener defect detection method and system, and the method comprises the steps: controlling a high-speed industrial camera to scan a track region so as to collect a track image, carrying out the image preprocessing of the track image, extracting an ROI image block containing a potential railway fastener based on template matching, and then carrying out the detection of the potential railway fastener. The method further introduces a target detection algorithm to perform fastener accurate positioning and segmentation on the ROI image blocks to obtain finer fastener ROI image blocks, further extracts visual features of the fastener ROI image blocks, and performs feature space distribution saliency enhancement on the visual features to enhance expression effects of the visual features such as surface color, texture and shape of the fastener, so as to improve the image quality of the fastener. Therefore, intelligent identification of railway fastener defects is realized on the basis. According to the method, the limitation of a traditional detection method can be effectively overcome, the reliability of railway fastener defect detection in a complex environment is improved, and the method has relatively high universality and adaptability.

The invention provides a real-time monitoring method and system for milk powder stirring processing, and the method comprises the steps: collecting a current frame image of a milk powder material in a stirring container, and obtaining a reference frame image of the current frame image before a preset time interval; calculating a space texture feature set, a time sequence color texture feature set and a dynamic flow field feature set of the current frame image; cascading the space texture feature set, the time sequence color texture feature set and the dynamic flow field feature set to obtain a high-dimensional state vector; calculating a mahalanobis distance between the high-dimensional state vector and a target uniform state cluster core in a pre-constructed state space; when the mahalanobis distance is smaller than a first threshold value, it is judged that the current stirring state is uniform mixing; when the mahalanobis distance is greater than a second threshold value, determining an abnormal state; when the Mahalanobis distance is between the first threshold and the second threshold, it is determined that mixing is being performed.

The invention relates to the technical field of fruit and peach screening, in particular to a fruit and peach multi-stage screening and quality detection method which comprises the following steps: S1, fruit and peach variety judgment: collecting multi-angle images, extracting color, texture and contour features, and outputting variety labels and parameter sets in a classified manner; s2, growth feature mapping: extracting structure key points, matching the structure key points with a variety template, and generating a growth deviation vector; s3, area perception analysis: dividing fruit surface areas based on growth deviation, and independently identifying defects and maturity; s4, grade discrimination: fusing identification results, combining varieties and deviations, and dynamically evaluating a comprehensive quality label; and S5, screening instruction generation: converting the quality label into a control instruction, and driving the sorting device to complete output. According to the invention, integrated automatic processing of intelligent identification of fruit and peach varieties, accurate detection of regional defects and grade screening control is realized, and the accuracy and flexibility of fruit and peach screening are significantly improved.

The invention relates to a multi-model-based printed cloth pattern quality monitoring method, which comprises the following steps of S1, carrying out image acquisition by adopting a multispectral camera to obtain multispectral image data of printed cloth, and carrying out preprocessing; s2, introducing a generative adversarial network (GAN), and enhancing the image quality by using a generator in the GAN, including denoising and detail enhancement; s3, combining Swin Transform with dynamic convolution to construct an image segmentation model, and based on the image segmentation model, performing accurate segmentation of the printed cloth, and extracting each printed area; s4, according to the segmented image data, color, texture and pattern features of printing are obtained, and fusion features are constructed; and S5, constructing a printed cloth pattern detection model, identifying abnormal patterns which do not meet quality requirements based on the fusion features, and obtaining a quality score of the printed cloth according to a detection result of the printed cloth pattern detection model. According to the invention, accurate segmentation and feature extraction of the printed cloth are realized, and comprehensive quality detection is realized by fusing various feature information.

The invention provides an otoscope image processing system for establishing database model analysis based on deep learning, and relates to the technical field of image processing, the otoscope image processing system comprises an acquisition layer, a processing layer, an extraction layer, a modeling layer and an analysis layer, the processing layer adopts an AI image expansion algorithm to carry out fusion splicing and interference elimination processing on acquired images, and the extraction layer adopts a mixed feature extraction method to extract color, texture and shape features of the images processed by the processing layer and introduce MPEG-7 descriptors to express the features; according to the method, otoscope images of a patient are collected, features and feature values of the images are extracted, a focus global shared image library is constructed through deep learning modeling, the focus global shared image library is compared with the otoscope images of the patient for training, and corresponding focuses are determined, so that different ear diseases can be automatically identified and diagnosed by learning and analyzing a large amount of ear image data, and the diagnosis accuracy is improved. And more accurate and timely diagnosis information is provided for doctors.

The invention relates to the technical field of computer vision, and discloses a jade defect intelligent detection method and system based on machine vision and deep learning, and the method comprises the following steps: S1, based on a high-resolution industrial camera and a laser three-dimensional scanner, adopting a multi-mode synchronous collection strategy, and rotating a jade sample through a precise motion control system, a jade surface high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are respectively obtained, and a jade multi-mode original data set is generated. A high-resolution two-dimensional color image and high-precision three-dimensional point cloud data are integrated through a multi-modal synchronous acquisition strategy, and multi-dimensional feature expression under unified coordinates is constructed, so that the limitation of a single data source is effectively overcome; an image registration algorithm and a feature pyramid network are combined with a point cloud network to perform multi-modal feature fusion, complementarity of color texture and geometric morphology information is enhanced, and image quality is optimized based on adaptive histogram equalization and non-local mean filtering.

The invention relates to the technical field of image data processing, and discloses a three-dimensional scene construction method and device based on point cloud, electronic equipment, a computer readable storage medium and a computer program product. And the generated three-dimensional model is easy to have a large gap and a poor display effect. The method comprises the following steps: acquiring original three-dimensional point cloud data, and segmenting and projecting the three-dimensional point cloud data to obtain at least one point cloud plane; obtaining color attribute information of each point in the point cloud plane and performing color texture fitting to obtain a color texture image corresponding to each point cloud plane; filling each point cloud plane based on the color texture image; and constructing a three-dimensional model based on the filled point cloud plane. According to the method, the display effect of the three-dimensional model constructed based on the three-dimensional point cloud data is improved, and meanwhile, the data volume needing to be stored is reduced.

The invention relates to the technical field of computer graphics and deep learning, in particular to a simulation bait automatic coloring method and system based on a 3D model. The method comprises the following steps: acquiring an uncolored 3D model and a reference image, and generating standard data through analysis verification, curvature grid division and image compliance detection; performing color conversion, texture enhancement and multi-scale downsampling on the compliant image to construct an image pyramid; performing multi-level feature extraction and adversarial training optimization based on a pre-trained convolutional neural network and a generative adversarial network, and generating an enhanced color texture map; performing UV expansion, color mapping and normal mapping fusion in combination with the model topology, and constructing an intermediate model with physical rendering attributes; and batch color consistency verification is realized through color histogram comparison, adaptive threshold segmentation and iteration parameter adjustment, and a standard model group is generated. According to the invention, efficient and highly realistic automatic coloring of the simulated bait is realized, and color consistency and rendering quality in batch production are guaranteed.

The invention provides a 3D target detection tracking method based on visual image and radar tensor sparse proposal fusion, which comprises the following steps: firstly, carrying out generalization extraction on color texture information of a visual image, and establishing multi-scale semantic high-dimensional features; secondly, extracting multi-scale space high-dimensional features of a radar tensor by using SCAN, respectively mapping a learnable sensing probe to radar and visual high-dimensional feature spaces, and performing generalization sparseness on different modal features by means of multi-head deformable attention to form radar and visual proposal features; sparse proposal fusion of radar and visual proposal features is carried out, and 3D target detection is completed; and finally, carrying out mixed multi-feature cascade matching and batch track management on a detection result, and feeding back generated track time sequence information to a front-end learnable sensing probe to realize an active target detection and tracking integrated circulating progressive network based on time sequence information guidance. According to the scheme of the invention, an integrated active sensing framework of single-frame target detection and time sequence target tracking is established, and the reliability of environment target sensing by a multi-source sensor in automatic driving is enhanced.

The invention provides a historic building heritage colored drawing layer AR visual analysis method and system. The method comprises the steps that correlation analysis is conducted through a multi-dimensional mapping method according to feature vectors of colored drawing colors, texture details and historical information obtained through separation, and a fusion relation matrix between attributes is obtained; according to the fusion relation matrix, a virtual reconstruction algorithm is utilized to carry out spatial recombination on colored drawing colors, texture details and historical information, and a three-dimensional virtual colored drawing model is generated; for the generated three-dimensional virtual colored drawing model, performing change simulation on color and texture details in a time dimension through a dynamic rendering technology to obtain a dynamic presentation effect; according to the dynamic presentation effect, a user control interface is embedded in the interaction environment, a switching function is set for different historical information nodes, and an interactive virtual display framework is determined; according to the corrected virtual model, dynamic rendering and interaction environment configuration are executed again, and a final visual visualization result is obtained.

The invention discloses a fruit and vegetable maturity inspection and classification method based on fruit and vegetable identification, and the method comprises the steps: collecting a fruit and vegetable RGB image and a near-infrared image through a high-definition camera and a near-infrared sensor, carrying out the preprocessing of denoising, enhancement, segmentation and the like, and extracting a fruit and vegetable region; extracting features such as colors, textures and shapes by using a pre-trained convolutional neural network, inputting the features into a support vector machine classification model, judging the maturity of the fruits and vegetables, and classifying the fruits and vegetables into immature fruits, mature fruits and over-mature fruits and vegetables. According to the invention, the method has a self-learning capability, can optimize the classification model through quality inspection feedback, improves the classification accuracy, and provides effective technical support for the quality control and management of fruits and vegetables.

Image rendering techniques for use with virtual worlds and interactive media are described herein. Techniques may include: acquiring a first scene depth texture map and a first scene color texture map of a first image frame; acquiring, based on the first scene depth texture map, a first spatial position of a vertex of a target triangle face in a first clipping space; mapping, based on a first camera parameter and a second camera parameter, the first spatial position to a second spatial position in a second clipping space; generating a second scene depth texture map and a second scene color texture map based on the second spatial position and the first scene color texture map; and displaying a second image frame based on the second scene depth texture map and the second scene color texture map. Image display frame rates for a virtual scene may be improved, thereby improving visual effects.

The invention discloses a multi-modal three-dimensional point cloud semantic segmentation method based on adaptive noise filtering, and belongs to the technical field of automatic driving environment perception. The method comprises the following steps: firstly, acquiring data by using a laser radar and a monocular camera, and constructing a multi-modal panoramic feature tensor containing a geometric structure and color textures through projection and mapping; extracting shallow geometric distribution features through a residual context module, and extracting multi-scale environment features through an expanded residual encoder; performing global context aggregation by using a self-attention mechanism of a Transform architecture, and establishing a full-image pixel dependency relationship to make up for a convolution locality defect; in the decoding stage, a channel cross fusion attention module (CCA) is adopted to process deep semantic features and shallow jump connection features, and a channel weight mask is dynamically generated to adaptively screen effective features and suppress high-frequency noise; and finally, outputting a two-dimensional semantic segmentation result and back-projecting the result to a three-dimensional space to obtain a semantic point cloud. According to the method, through global semantic integration and local detail screening, the problems of terrain misjudgment and noise interference of severe weather (such as rain, snow and dust) in a cross-country scene are effectively solved, and the robustness and precision of automatic driving perception are remarkably improved.

The invention provides a visual and auditory cross-modal object material retrieval method and system, and relates to the technical field of artificial intelligence. The visual and auditory cross-modal object material retrieval system comprises a data acquisition unit for acquiring visual images containing objects made of various materials and corresponding audio data; feature extraction: feature extraction is performed on the visual and auditory data, visual features include but are not limited to colors, textures and shapes, and auditory features include but are not limited to spectrum features and time domain features; feature fusion: designing a fusion algorithm, and effectively fusing visual and auditory features to form cross-modal feature representation; and retrieving a model, wherein the model can learn a mapping relationship between the cross-modal features and the material of the object. By combining visual and auditory information, the system can more comprehensively understand the characteristics of the object, the visual information can provide the appearance characteristics of the object, the auditory information can provide the dynamic characteristics about the material of the object, and the fusion is beneficial to improving the accuracy of material identification.

The invention provides a dynamic feature contribution degree-based color textile fabric image retrieval method. Innovation and improvement are carried out aiming at single feature limitation and fixed weight fusion defects existing in a traditional retrieval technology. Color, texture and shape features are separated through a multi-modal feature decoupling technology, and coupling interference between the features is eliminated; a dynamic contribution degree quantification mechanism is innovatively constructed, gradient back propagation is used for calculating the contribution proportion of features to retrieval results in real time, and weight self-adaptive adjustment is achieved. According to the method, the problem of retrieval deviation caused by complex characteristics such as color gradient and blending structure difference of the colored spun yarn fabric is solved in a breakthrough mode, weight distribution can be intelligently matched according to inquired image characteristics (for example, the color gradient fabric focuses on color characteristics, and the jacquard fabric focuses on shape edges), and the retrieval result better fits human visual perception. According to the technical scheme, the image retrieval precision of complex fabrics in the textile industry is effectively improved, and an intelligent solution is provided for production process data association.

A simulation scene image generation method, an electronic device and a storage medium are provided. The method includes: acquiring semantic segmentation information and instance segmentation information of a white blank 3D environment model; receiving instance text information of the white blank 3D environment model, the instance text information being editable information and used for describing an instance attribute; and generating a simulation scene image based on the semantic segmentation information, the instance segmentation information, the instance text information and a pre-trained generative adversarial network. In the method, only the establishment of the white blank 3D environment model is required, so that the simulation scene image can be generated based on the semantic segmentation information and the instance segmentation information of the white blank 3D environment model, and attributes such as color, texture and illumination do not need to be refined during establishment of the scene, thereby improving a generation efficiency.

The invention discloses an image-text retrieval method and system based on big data, relates to the technical field of data processing, and solves the technical problems that the capability of dynamically adjusting screening conditions according to data characteristics and user requirements is lacked, the internal relation between images and texts cannot be fully mined, and the user requirements are difficult to accurately match. According to the method, multi-dimensional features such as colors, textures and shapes are extracted from image information, features are extracted from text information through a bag-of-word model, deep mining of image-text information is achieved, essential features of images and texts can be represented more comprehensively, the retrieval accuracy is improved, single matching, correlation calculation and screening and secondary matching are carried out, and the retrieval efficiency is improved. The text feature and image feature matching results are further combined and screened, finally, the combined retrieval results are matched and screened again, irrelevant information can be effectively eliminated through a multi-level screening mechanism, images and texts meeting requirements are accurately positioned from mass data, and the quality of the retrieval results is greatly improved.

The invention relates to a coal quality detection method, system and device and a medium, and the method comprises the steps: obtaining color texture characteristic parameters according to a pre-obtained standard coal image, and obtaining Raman spectrum characteristic parameters according to a pre-obtained standard coal Raman spectrum chart. And on the basis of the Raman spectrum characteristic parameters, by increasing the weights of the color texture characteristic parameters, constructing a prediction model of the ash content in the coal. And on the basis of the color texture characteristic parameters, by increasing the weight of the Raman spectrum characteristic parameters, constructing a prediction model of the water content and the volatile component content in the coal. And coupling the prediction model of the ash content in the coal and the prediction model of the moisture and volatile content in the coal to obtain a target coal quality prediction model. Color texture characteristic parameters and Raman spectrum characteristic parameters of a to-be-detected coal sample are obtained, and the component content of the to-be-detected coal sample is obtained based on the target coal quality prediction model. The problem of low accuracy of coal quality detection in related technologies is solved.

The invention discloses a dual dynamic three-dimensional code based on random color textures and a generation method, a coding method, a recognition method, a decoding method and a generation device thereof, a unique three-dimensional code is generated by adopting random combination and arrangement of specified color textures on the basis of a two-dimensional code, the three-dimensional code has two-dimensional and three-dimensional layers, and the two-dimensional and three-dimensional functions are integrated. Meanwhile, part of information in the encoding process is recorded and encrypted and converted into a decoding core for decoding, the dynamic decoding core provides dynamic three-dimensional hierarchical information under the condition that the corresponding three-dimensional code is not changed, and the content of the dynamic decoding core can point to a specific crowd and is dynamically updated. A two-dimensional level is generated by adopting a standard specification, so that a three-dimensional code is downwards compatible, and the public can directly obtain open information through mobile equipment. By designing a three-dimensional code template, using a multiple three-dimensional code information error correction technology in the generation and decoding process and using a double-flow image processing technology in the image recognition process, the self-adaptive repair capability, the recognition accuracy and the recognition speed of the three-dimensional code are improved, and the equipment requirement is reduced.

The invention belongs to the technical field of material identification management, and particularly relates to a molybdenum concentrate dryness evaluation method and system based on a material image, and the method comprises the steps: extracting depth features capable of representing colors, textures and luster from a material image sequence through a convolutional neural network; constructing a multi-scale feature pyramid, and performing motion alignment and differential operation in combination with an optical flow method to obtain a feature change rate field sequence representing instantaneous change of the material; generating a smooth rate diagram through scale fusion and time window filtering, and calculating a statistical moment of the smooth rate diagram to obtain a dynamic feature vector representing a drying macroscopic trend and spatial uniformity; and combining the dynamic characteristics with the instantaneous static evaluation value, and constructing a normalized comprehensive decision cost function to obtain a final comprehensive evaluation result. According to the invention, dynamic, comprehensive and accurate evaluation of the drying process can be realized.

The present invention discloses a distributed intelligent recognition and processing system for collaborative aerial images of a cluster of unmanned aerial vehicles (UAVs), which belongs to the technical field of UAV image processing. The system specifically comprises: UAVs collect aerial images according to a geographical division scheme and store them in a local edge cache, and then split the aerial image data into two-layer tasks according to color and texture features and upload them to a regional cache; each node obtains data from a three-level cache system, completes target recognition using an intelligent collaborative recognition algorithm after preliminary processing, and stores the results in a global cache; the central node monitors the load weight of each node in real time, and allocates new tasks using a weighted minimum connection number algorithm; the central node aggregates the recognition results in the global cache according to the grid ID, eliminates splicing dislocations through a consistent hashing algorithm, and generates an aerial image recognition report output; the present invention effectively solves the problems of low processing efficiency and large collaborative errors in a centralized architecture, and improves the efficiency and accuracy of aerial image processing of UAV clusters.

The invention relates to the technical field of film and television infringement evaluation, in particular to a film and television infringement evaluation method and system based on an image matching technology. The method comprises the following steps: collecting to-be-evaluated film and television image data; through constructing an adaptive multi-scale convolution denoising network and an image enhancement algorithm, film and television image data are subjected to denoising, enhancement and cutting processing so that image quality is improved and sizes are unified. Extracting image color, texture and shape features, constructing a multi-modal convolution feature extraction network, and improving the matching accuracy by using an adaptive weighted image matching algorithm; after image matching, similarity is calculated, and infringement probability evaluation is carried out in combination with legal standards; an evaluation result is displayed through a report, and copyright party processing suggestions are provided; in addition, a template image matching method is introduced, and first-order and second-order matching codes are used for carrying out rapid preliminary matching on the images, so that the high efficiency and accuracy of evaluation are ensured. The method comprehensively considers image quality, feature matching and legal standards, and has high practicability and flexibility.

The invention discloses an unmanned ship navigation scene complexity integrated classification evaluation method and system based on color texture visual field joint features. The method comprises the following steps: dividing a water area data set into a training set and a test set according to a preset proportion; classifying and marking the water area data set according to scene complexity; performing color, texture and view feature extraction on the data set, and fusing feature extraction results to obtain a feature matrix; training a navigation scene complexity classification model based on a feature matrix corresponding to the training set; and performing model performance evaluation on the trained navigation scene complexity classification model based on the feature matrixes corresponding to the training set and the test set. Based on the data processing flow, the limitation of a single type of features can be overcome by fusing color, texture and visual field features, meanwhile, a multi-classification model integrating multiple machine learning algorithms is constructed, and quantitative decision is made for visual complexity perception of a navigation scene.

The invention relates to the technical field of sewage treatment, and discloses a sewage biochemical treatment process abnormity intelligent early warning method and system based on image recognition, and the method comprises the steps: periodically collecting a sludge image and water quality sensing data; extracting floc form, bubble distribution and color texture features of the image, and performing space-time alignment fusion with the sensing data to construct a multi-source fusion feature matrix; inputting the matrix into a pre-trained digital twin evaluation model, calculating a dynamic deviation degree between a current state and a normal working condition, and identifying an abnormal mode; and generating a graded early warning signal according to the deviation degree and the abnormal mode and pushing the graded early warning signal. The system comprises an online image acquisition unit and a water quality sensor array which are used for data acquisition, and a feature processing module used for feature processing and fusion. According to the scheme, early-stage, accurate and intelligent early warning of abnormality in the sewage biochemical treatment process is realized, the false report and missing report rate is reduced, and the accuracy and the reliability of the system are improved. And the operation stability and the regulation and control efficiency of the system are improved.