421 results about "Picture recognition" patented technology

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

The invention relates to the technical field of image recognition, in particular to a lightning arrester fault thermal imaging picture recognition method and system combined with gaze prediction, and the method comprises the steps: carrying out the down-sampling of an original thermal imaging image to a fixed size, sequentially passing through a multi-layer convolution and a Spatial Softmax layer, and outputting a predicted gaze point track sequence, extracting a key area and a non-key area according to the intensity of the fixation point track sequence; reconstructing the key area to obtain a compressed and recombined target area; inputting the non-key region into a variational auto-encoder to obtain a low-dimensional potential feature vector; and inputting the image-structure joint feature representation into an EffiCroprViT model, and finally obtaining a lightning arrester fault classification result. Local and global features are efficiently fused, the calculation complexity is reduced, background noise interference is effectively suppressed, real-time and accurate identification and early warning of the fault state of power equipment are realized, and the fault classification method has the advantages of high efficiency, high reliability and high reliability. Therefore, the safety and stability of power grid operation are ensured.

The invention discloses a method for improving the model picture recognition capability based on a multi-modal large model, and relates to the technical field of image recognition, and the method comprises the steps: building an infrastructure of the multi-modal large model, and training the multi-modal large model as a basic model through a large number of pictures; newly added images are collected, and corresponding standard questions and answers describing picture contents are prepared in combination with the images; and according to a picture recognition task requirement, designing a corresponding instruction, namely a cue word, and guiding the large model to carry out picture recognition. The multi-modal large model is trained by using massive pictures, the accuracy of a picture recognition technology can be optimized by fully utilizing a picture feature library of the multi-modal large model when the multi-modal large model is applied to the field of picture recognition, and the magnitude order of training pictures can be greatly reduced by using fine tuning training of the multi-modal large model, so that the accuracy of the picture recognition technology is improved. After composite fine tuning training is carried out, the multi-modal large model can have the recognition capability of generalizing trained pictures, and the number of pictures needing to be used for training can be greatly reduced.

The invention discloses a UI effect picture recognition method and system based on a multi-modal large model, and a medium. The method comprises the following steps: constructing a multi-modal pre-training model based on image coding, multi-modal projection and text coding; performing fine tuning training according to the historical image data and the multi-modal pre-training model to obtain a multi-modal large model; constructing a vector database based on the built-in component data structure; obtaining a to-be-recognized UI effect picture, and outputting first recognition data based on the multi-modal large model and the to-be-recognized UI effect picture; and searching a vector database for the first identification data by using a retrieval algorithm, and outputting generation information corresponding to the UI effect picture to be identified. The method has the beneficial effect that the generation consistency and efficiency of the UI effect picture are ensured.

The invention discloses an electric power knowledge picture content extraction method and system, and belongs to the technical field of electric power knowledge extraction, and the method comprises the steps: obtaining an electric power knowledge picture; recognizing the electric power knowledge picture into text information by adopting a pre-trained electric power field content extraction model; and performing quality judgment on the text information through a data quality discriminator, if the quality judgment is excellent, outputting corresponding formatted information, and otherwise, marking the text information as the corresponding formatted information through an electric power expert. According to the method, the electric power field content extraction model can quickly identify the character information, the quality of the character information is judged through the data quality discriminator, the accuracy is higher, and the processing efficiency is higher.

The invention provides an interface element positioning method based on picture recognition. According to the method, interface elements are recognized through an existing automatic user interface operation technology and numbered, and then screen pictures containing numbers are intercepted and transmitted to a large language model. And the big language model recognizes an operation target by utilizing the graph recognition capability and the natural language understanding capability of the big language model, obtains coordinate information of operation elements through UI automation attributes, and finally executes corresponding operation. According to the method, the recognition and positioning accuracy of the target operation element in the RPA system based on natural language interaction can be remarkably improved, and the error rate is reduced.

The invention relates to the technical field of picture recognition, and discloses a picture detection method and device, equipment and a medium, and the method comprises the steps: carrying out the portrait direction confirmation of a certificate picture, obtaining a certificate picture in a straightening state, obtaining a highlight region of the certificate picture in the straightening state, judging whether the highlight region is a light reflection region, and if not, carrying out the recognition of the certificate picture; if yes, sending the certificate picture in the straightening state to a server, otherwise, acquiring a non-reflective area picture of the certificate picture in the straightening state, identifying character area incomplete features and portrait area incomplete features in the non-reflective area picture, and identifying a complete character area according to the character area incomplete features by utilizing a character area boundary identification model; identifying a complete portrait area according to the incomplete features of the portrait area by utilizing a portrait area boundary identification model, segmenting a key information area of the certificate picture in the straightening state according to the complete character area and the complete portrait area to obtain a key information picture, and sending the key information picture to a server, and the accuracy and efficiency of picture detection are improved.

The invention discloses a multi-modal data retrieval enhancement generation method and system based on an RAG technology. The method comprises the following steps: performing character recognition on collected picture data by using a self-adaptive OCR (Optical Character Recognition) strategy; carrying out data loading on the collected document data and processing the collected document data into text data; performing text slicing and vectorization on the text data; using a vector database to store vectors and text blocks, and constructing an index; vectorizing the input text of the user, carrying out vector matching by using a matching algorithm, and querying a corresponding text; and generating and displaying a query result. According to the method, layout analysis is carried out on the picture, so that different OCR recognition strategies are adopted, and the accuracy of text recognition is improved; the multi-modal data is formed through the text identified by the picture and the text of the document, so that the retrieval and generation accuracy is improved.

The invention relates to the technical field of e-commerce, and discloses an e-commerce data processing system, which comprises a server, an input terminal, a reading module, a user terminal, a data extraction module and a behavior recognition module, the output end of the input terminal is connected with an input module, the output end of the input module is connected to the input end of the server, the output end of the server is connected to the input end of the reading module, the output end of the reading module is connected with a scanning module, and the output end of the scanning module is connected with a picture recognition module and a character recognition module. Through the arrangement of the AI analysis module, the data arrangement module, the parameter analysis module and the like, scanning analysis of propaganda drawings and characters can be carried out before all commodities are input, when a customer checks the commodities through the user terminal, the behavior of the customer can be analyzed to judge functions or parameters concerned by the customer, and the customer experience is improved. And then other commodities with the same or similar functions and parameters are screened out to be displayed preferentially, so that the required commodities are pushed to the clients more accurately.

A system that a system that enables a patron to sign up for a gaming establishment account independent of any direct interaction with gaming establishment personnel via utilizing one or more image capture devices to obtain images of a picture identification document presented by a user as well as images of the user presenting the picture identification document.

The invention provides a crop disease and insect pest judgment method and system based on image feature recognition, and relates to the technical field of picture recognition processing, and the method comprises the steps: carrying out the disease and insect pest recognition of a to-be-judged crop image through a pre-trained disease and insect pest analysis model, and obtaining a first disease recognition result; presetting similar pest and disease combination and corresponding combined spot features, judging whether the similar pest and disease combination exists in the first pest and disease recognition result, if not, outputting the first pest and disease recognition result, and if the first pest and disease combination exists, extracting a spot area of the to-be-judged crop image; traversing spot areas of the to-be-judged crop image, and if joint spot features appear in the spot areas, taking two diseases and pests in the similar disease and pest combination as output results; if the combined spot features do not exist, the pest and disease damage with the minimum confidence degree in the similar pest and disease damage combinations is removed; and updating the first pest identification result.

The invention discloses a reservoir dam safety monitoring method based on multi-source information, and the method comprises the steps: carrying out the high slope inspection through employing an unmanned aerial vehicle A and an unmanned aerial vehicle B, carrying out the high-definition camera and an infrared thermal imager, calculating the pitching angle between the unmanned aerial vehicles through GPS synchronization, image recognition and other technologies, and correcting the fusion image recognition potential safety hazards; path planning is carried out by adopting a cattle farming method, and the problems of unmanned aerial vehicle inspection path independence and task coverage are solved; the pixel value of the unmanned aerial vehicle B in the image A is compared with the normal horizontal state image, the pitching angle lambda is calculated and used for correcting the image A and the image B, and the potential hidden danger of the reservoir dam is fused and recognized; the method is low in cost and easy to maintain, a comprehensive inspection result is combined with a picture recognition deep learning algorithm by combining a manual inspection verification result, long-term tracking of a potential safety hazard part is established, and powerful support is provided for reservoir dam monitoring.

The invention provides a risk determination method and device, equipment, a storage medium and a program product. Relates to the technical field of information security. The method comprises the following steps: acquiring a target face recognition video, and acquiring at least one background picture in the video; identifying the illumination state corresponding to each background picture, and judging whether the illumination state meets the natural light state at the current time; if not, background features corresponding to the background pictures are extracted, whether the background picture with the most background features exists or not is judged, and if not, the background features corresponding to the background pictures are determined as a background feature set; and judging whether background features in a blacklist exist in the set, if not, inputting the set into a reinforcement learning model, thereby calculating a risk coefficient corresponding to the set according to a current dynamic calculation strategy, and determining the risk degree of the video based on the coefficient. And the accuracy of risk determination results is improved.

The invention discloses a code generation method and device based on a large model, a storage medium and computer equipment. The method comprises the steps that a UI prototype draft of a target service is acquired; performing element identification on the UI prototype draft through a picture identification large model, and determining element information in the UI prototype draft; generating a code generation cue word based on the element information and a preset cue word, inputting the code generation cue word into a code generation large model to obtain an intermediate code corresponding to the UI prototype draft, and arranging a target service code of the target service based on the intermediate code. The automatic process from UI design to code generation is achieved, the tedious process of manually writing UI components and debugging interaction logic in traditional development is avoided, repeated work and human errors are reduced, developers can put more energy into core service function development, then development efficiency is improved, code quality is guaranteed, and the development cost is reduced. And rapid and high-quality delivery of the project is promoted.

The embodiment of the invention provides a picture recognition method and system, and the method comprises the steps: inputting a to-be-recognized picture into a pre-trained picture recognition model, and enabling the picture recognition model to be obtained based on the training of a labeled picture sample and a non-labeled picture sample meeting a preset rule; and obtaining a recognition result of the picture recognition model on the to-be-recognized picture, and determining whether the to-be-recognized picture is a risk picture based on the recognition result. According to the method, whether the input to-be-recognized picture is the risk picture or not is recognized through the picture recognition model obtained through training based on the labeled picture sample and the unlabeled picture sample meeting the preset rule, so that the recognition efficiency of the risk picture is improved, and the difficulty of the picture verification process is reduced.

The invention provides a gastroscope image recognition and auxiliary diagnosis system and method based on deep learning and large language model fusion, and the system comprises a data collection and processing module which is used for obtaining and preprocessing various types of gastroscope images; the gastroscope picture recognition and classification module is used for inputting the preprocessed gastroscope pictures into a GastroHAF model for training and outputting recognition and classification results of the gastroscope pictures, and the GastroHAF model comprises a weak supervision anatomical mark positioning sub-module, a space co-occurrence context reasoning sub-module and an anatomical atlas guided hierarchical classification sub-module; and the auxiliary diagnosis module is used for outputting stomach diagnosis results and examination suggestions based on the fine-tuned Chinese medical big language model. The GastroHAF model provided by the invention can quickly, effectively and accurately judge the position of the gastroscope in combination with prior anatomical knowledge, and can effectively assist doctors in stomach examination in combination with a large language model.

The invention discloses a quantitative decomposition logic storage architecture for simplifying picture recognition, which relates to the technical field of computers, and comprises the following steps: step 1, collecting picture and video data; step 2, feature value extraction; 3, dividing by using a letter sequence in combination with the data; step 4, establishing an alphabet sorting form quantitative decomposition logic frame storage structure; step 5, matching the to-be-identified picture and video data; step 6, identifying and judging; according to the method, an independent logic storage space and independent identity letter sorting information are generated by constructing a letter sorting form quantitative decomposition logic storage architecture, the operation process is simplified, the address information transmission speed is increased, data processing can be efficiently operated under common hardware configuration, the recognition precision is greatly improved, and the recognition efficiency is improved. And the letter sorting form quantitative decomposition logic storage architecture has an encryption effect, so that the privacy leakage risk in the data distributed storage identification process can be effectively reduced, the data security is improved, and the management is more convenient.

The invention provides a vehicle attribute recognition method and device, electronic equipment and a medium, and the method comprises the steps: obtaining a vehicle image of a to-be-recognized vehicle and vehicle attribute inquiry information in response to an attribute recognition instruction sent by a user for the to-be-recognized vehicle, inputting the vehicle image and the vehicle attribute inquiry information into a multi-modal model for processing to obtain a first vehicle attribute, and inputting the vehicle image and the vehicle attribute inquiry information into the multi-modal model again for processing to obtain a second vehicle attribute; and inputting the vehicle image into a picture recognition model for processing to obtain at least one third vehicle attribute, further determining a target vehicle attribute, and displaying the vehicle image and the target vehicle attribute to a user as an attribute recognition result of the to-be-recognized vehicle. The accuracy and diversity of attribute recognition can be improved, the vehicle attributes are determined according to the user inquiry information, interactive output is achieved, and the user experience can be improved.

The invention discloses an automobile part picture recognition method, which comprises the following steps: acquiring an automobile part picture, and performing data cleaning processing on the automobile part picture to obtain a plurality of picture subsets; performing data annotation processing on the plurality of picture subsets to obtain picture training data; inputting the picture training data into a model constructed based on a ResNet network for training to obtain an accessory picture recognition model; performing global feature extraction on each picture in the picture training data through a preset global feature extraction model to obtain a global feature vector set; obtaining a to-be-recognized picture, and predicting the to-be-recognized picture through the accessory picture recognition model to obtain a recognition result; and when the identification result is judged to be a commodity picture, taking the identified accessory commodity picture as a return result, otherwise, by calculating the Euclidean distance between the global feature vector corresponding to the picture to be identified and each vector in the global feature vector set, determining an image retrieval result according to the Euclidean distance, and returning the image retrieval result to the user.

The invention discloses an aviation intelligent test platform, and relates to the technical field of aviation test. According to the method, a locally deployed domestic large model and a knowledge base are introduced, a large model fine tuning technology is integrated to realize automatic generation from software requirements to test cases, then on the basis of graph or video identification of a multi-modal large model, characteristic graphs of avionics display control are identified through pictures, a test result is obtained, and the test efficiency is improved. Therefore, automatic execution of the whole testing process is achieved, many problems caused by manual testing are avoided, high efficiency is achieved by reducing labor input, the accuracy of a testing result is improved through an image characteristic recognition technology, and testing requirements of different industry fields can be met by building an exclusive knowledge base and a directional fine tuning model.

The invention relates to the technical field of data analysis, in particular to a security detection method and system based on artificial intelligence, and the method comprises the steps: S1, constructing a security check model, and training the constructed security check model; s2, a security check picture is obtained, and a security check model is adopted to carry out hidden danger auxiliary decision making; s3, performing real-time security check picture recognition by adopting the security check model; wherein S2 comprises the steps of obtaining a security check picture, performing hidden danger prediction through a security check model, and generating a prediction result; wherein the prediction result comprises a security inspection object and a security inspection hidden danger type; the prediction result is verified, and if the prediction result is matched with the hidden danger identified manually, the prediction result is transferred into a marketing system; and if the prediction result is not matched with the artificially identified hidden danger, feeding back that the prediction result has a problem, and optimizing the security check model. According to the scheme, safety inspection can be assisted, and the quality and efficiency of safety inspection are improved.

The invention provides a zero-code report management method and system. The method comprises the following steps: 1) identifying a shot paper table picture to obtain a table title and table items; processing the spreadsheet to obtain a sheet title and a sheet item; 2) processing the obtained table titles and table items to obtain database names and field names, and establishing a database table and in-table fields; outputting the processed database name and field name; 3) dynamically generating report management data according to the input database name and field name so as to realize the functions including adding, deleting, modifying and querying of the report data; 4, the needed UI template is selected according to the input database name and the input field name, and a report management interface and an editing interface are dynamically generated according to the input item and the selected UI template style.The requirement for zero-code report management is met by photographing and recognizing a traditional paper report, importing an electronic report and generating the needed report through one key.

The invention discloses a picture resource identification method and a related device, and relates to the technical field of picture identification, and the method comprises the following steps: responding to a picture resource identification trigger instruction, and identifying a picture resource from a resource file name under a directory structure of an iOS project; screening out all first picture resource file names meeting a preset picture resource file extension name requirement to obtain a first array, determining second picture resource file names having a reference relationship with the code files from the code files under the directory structure of the iOS project to obtain a second array, and comparing the first array with the second array to obtain a first picture resource file name and a second picture resource file name; and searching the target picture resource file names which exist in the first array and do not exist in the second array, and determining the picture resources corresponding to all the target picture resource file names as the identified useless picture resources. According to the method and the device, the useless picture resources in the iOS project are automatically identified, the consumed time is short, the efficiency is high, and the useless picture resources can be accurately identified.

The present invention provides a crop pest and disease identification method and system based on image feature recognition, which relates to the technical field of image recognition and processing. The present invention uses a pre-trained pest and disease analysis model to identify pests and diseases in a crop image to be identified, and obtains a first pest and disease identification result; presets similar pest and disease combinations and corresponding joint spot features, and determines whether there is a similar pest and disease combination in the first pest and disease identification result; if there is no similar pest and disease combination, outputs the first pest and disease identification result; if there is the first pest and disease combination, extracts a spot area of ​​the crop image to be identified; traverses the spot area of ​​the crop image to be identified, and if a joint spot feature appears in the spot area, both pests and diseases in the similar pest and disease combination are used as output results; if there is no joint spot feature, eliminates the pest and disease with the lowest confidence in the similar pest and disease combination; and updates the first pest and disease identification result.

The invention relates to the technical field of picture recognition methods, in particular to an abnormal picture automatic recognition method, which comprises the steps of reading monitoring video files from object-oriented storage in batches, and performing distributed frame extraction on the video files; stratified sampling is carried out on the picture after frame extraction, and a training set which is all positive samples is formed to train a PatchCore model; determining an abnormal score threshold, and reasoning all the pictures subjected to frame extraction to obtain an abnormal score and an abnormal thermodynamic diagram of each picture; judging whether each picture is an abnormal picture, and inputting the abnormal picture and the abnormal thermodynamic diagram into a multi-modal large model; the abnormal thermodynamic diagram is divided into nine areas according to nine grids, abnormal areas in the nine areas are input into a multi-modal large model, abnormal description of an abnormal picture and a specific coordinate frame of the abnormal areas are recognized, and result information is stored in object-oriented storage; according to the method, the recognition accuracy of the abnormal data can be improved under the condition of lack of abnormal samples.

The invention discloses a GIS-BIM-based in-service road disease section marking cooperation platform and method, and the method comprises the steps: constructing an EBS structure tree model, importing the EBS structure tree model into a GIS system to form a GIS-BIM platform, and transmitting a to-be-inspected line and a stake number coordinate table to an unmanned plane; the unmanned aerial vehicle inspects and shoots a long picture and matches a stake number label, and the server cuts the picture and identifies disease features; and the GIS-BIM platform maps the disease pictures to the corresponding BIM model segments, and deletes the maps if no disease pictures are inspected again. The stake mark coordinate table is generated according to stake mark progressive increase, disease recognition adopts a convolutional neural network model, and color gradient distinguishing is performed according to severity. The platform integrates EBS and WBS dual-core structure trees, realizes full-period collaboration through stake mark interval dynamic mapping, improves the disease marking efficiency and precision, and realizes full-process digital operation and full-period automatic collaboration.

The invention relates to the technical field of intelligent recreation facility management, and discloses a recreation facility management and maintenance system based on big data. According to the system, high-definition pictures of a large recreation facility and a small recreation facility are shot through a high-definition camera in a picture acquisition module, a picture processing module carries out brightness adjustment, contrast adjustment, denoising, zooming and normalization operation on the acquired pictures, and a feature extraction module carries out feature extraction on the processed picture data. The intelligent image recognition module takes the images subjected to image processing and feature extraction as a sample set and inputs the sample set into a convolutional neural network training image recognition model, and the big data prompt module applies the trained image recognition model to carry out image recognition. And the supervision requirement, the use management requirement, the maintenance requirement and the taking precautions of taking the large-scale recreation facility and the small-scale recreation facility are prompted and displayed through a visual interface.

The application relates to an intelligent clipping application method through picture recognition, which comprises the following steps: acquiring medical image video data to be clipped; performing grouped image recognition on the medical image video data to be clipped to obtain a key frame index; and performing clipping on the medical image video data to be clipped based on the key frame index. The application does not require a clipping personnel with medical knowledge to perform clipping, and the clipping personnel can complete the video clipping operation without watching the whole content of the video or repeatedly watching the video for multiple times, so that the efficiency of the video clipping is improved. The application also relates to an intelligent clipping application device through picture recognition, a storage medium and equipment.

The invention discloses an elevator monitoring video static picture recognition system and method based on an SSIM algorithm, and the system comprises a video collection module which collects monitoring video data in an elevator, and transmits the monitoring video data to a preprocessing module; the preprocessing module is used for preprocessing the monitoring video data to obtain target video data; the video processing module is used for processing the target video data through an SSIM algorithm to obtain inter-frame similarity of the target video data; the static picture recognition module is used for setting a corresponding threshold value, comparing the threshold value with the inter-frame similarity and judging to obtain a static picture; and the record output module is used for recording static picture information corresponding to the static picture and outputting the static picture information to the display equipment. According to the method, the dynamic and static images in the elevator monitoring video can be stored at the same time, the integrity of the monitoring images is guaranteed, the accuracy of video monitoring is improved, and different from an existing AI algorithm, the SSIM algorithm used in the method is small in calculation amount and low in cost.