131 results about "Picture recognition" patented technology

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 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 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 provides a document processing method, a document question and answer method and a computer program product, and relates to the technical field of document question and answer. The document processing method comprises the steps of performing picture recognition and text processing on a to-be-processed document in a target format to obtain a plurality of text segments; wherein the to-be-processed document comprises a table and / or a picture; based on the plurality of text segments, generating multi-level abstracts and multi-level recommendation questions corresponding to the multi-level abstracts; and performing associative storage on the plurality of text segments, the multiple levels of abstracts and the multiple levels of recommendation questions to obtain a document database. The document question-answering method comprises the steps of obtaining an input question of a user; based on the input question, performing retrieval in a document database, and determining a recalled text segment and a recalled recommendation question; and generating an answer result in combination with the recalled text segment and the recalled recommendation question. According to the method and the device, characters, pictures, tables and the like existing in the document can be processed, and question and answer recall processing is carried out based on the extracted multi-level abstract and the generated recommendation question.

The application discloses a picture recognition model training and picture recognition method, system, device and medium. The method comprises the following steps: obtaining a target picture, performing feature extraction on the target picture based on a feature extraction network to obtain target features of the target picture; judging a picture type of the target picture based on a binary classification network and the target features; and determining a processing strategy for the target features according to the picture type obtained by the judgment. That is, after the feature extraction on the target picture, the target features obtained by the extraction are classified, different processing strategies are adopted for different types of picture types, the flexibility of the face recognition model training is improved, and the performance of the face recognition is improved.

The present disclosure relates to the technical field of face recognition, and provides a face recognition method and device in the presence of occlusion. The method comprises: acquiring a base data set, wherein the base data set contains multiple base pictures; generating an occlusion for each base picture to obtain an occluded picture corresponding to each base picture; when a face picture to be recognized is detected, extracting a first feature of the face picture, a second feature of each base picture, and a third feature of the occluded picture corresponding to each base picture by using a face recognition model; calculating the similarity of the first feature and the second feature of each base picture and the third feature of the occluded picture corresponding to the base picture respectively; determining a target base picture with the highest similarity to the face picture according to the similarity of the first feature and the second feature of each base picture and the third feature of the occluded picture corresponding to the base picture, and recognizing the face picture as a target object corresponding to the target base picture.

The present invention relates to an NFC tag-based application wake-up method and an NFC acceptance terminal. The NFC acceptance terminal comprises: an NFC tag module, used to provide an NFC tag supporting read and write forms; a camera, used to acquire a pose image of a mobile terminal approaching the NFC acceptance terminal; and a pose recognition module, used to recognize, on the basis of the pose image, an approaching pose of the mobile terminal approaching the NFC acceptance terminal, look up a correspondence table on the basis of the approaching pose to obtain application configuration information of an application corresponding to the approaching pose, and write the application configuration information to the NFC tag, the correspondence table storing one-to-one correspondences between a plurality of approaching poses of the mobile terminal approaching the NFC acceptance terminal and application configuration information of a plurality of applications. According to the present invention, a plurality of different applications can be supported on one NFC acceptance terminal without modifying the mobile terminal.

The application discloses an automatic test method and device based on picture comparison, equipment and a storage medium. The method comprises the following steps: importing original picture data into a picture library, defining a key field replacement area and a random replacement field of the original picture data; replacing the key field in the key field replacement area with the random replacement field to generate replaced picture data; calling an OCR test interface to test an OCR picture recognition system by using the replaced picture data, and obtaining actual return data of the OCR test interface, wherein the actual return data comprises corresponding fields of each key field in the replaced picture data and values corresponding to each key field; matching and comparing the replaced picture data with the actual return data to obtain a field matching success rate and a field recognition accuracy of the replaced picture data, and obtaining a test result of the OCR picture recognition system. The embodiment of the application greatly improves the test efficiency of the OCR picture recognition system.

The application relates to the technical field of artificial intelligence, and provides a picture recognition method and device based on artificial intelligence, electronic equipment and a medium, the method comprises the following steps: adopting OCR to perform target detection on a recognition picture to obtain a target detection result of each boundary box; if the recognition picture of the target detection result of each boundary box has an inclination angle, the recognition picture is corrected by being flipped to obtain a target picture; error correction processing is performed on the target detection result of each boundary box to obtain first text corresponding to each boundary box; a plurality of boundary boxes are sorted according to the first position information of each boundary box in the target picture, and a plurality of first texts of the sorted plurality of boundary boxes are merged to obtain second text; the second text is preprocessed to obtain third text; the third text is extracted, and a recognition result is returned. The recognition picture uploaded by a user at an arbitrary angle is flipped, and the picture recognition accuracy is improved.

Embodiments of the present application disclose a solid state disk internal code and external code matching method, device, equipment and storage medium, relating to the technical field of solid state disk. The method comprises: acquiring a to-be-tested picture; identifying barcode information and identification information contained in the to-be-tested picture, matching the barcode information with the identification information; determining a target solid state disk corresponding to the barcode information according to the identification information matched by the barcode information; and writing the barcode information into the target solid state disk, thereby completing writing of the internal code of the target solid state disk. The whole process is automatically completed by a terminal, avoiding repeated scanning or missed scanning of barcodes in manual code scanning input, and the problem of inconsistency between the internal code and the external code of the solid state disk.

The invention relates to the field of recognition of pictures with specific backgrounds, in particular to a method for recognizing pictures with specific backgrounds based on an AI large model. The method comprises the following steps: S1, receiving a living body detection picture; s2, performing character and background segmentation by adopting an SAM2-UNet model; s3, extracting a background embedding by using a SigLIP 2 image encoder; s4, L2 normalization is carried out on the extracted background embedding, and L2 normalization is carried out on the extracted background embedding; s5, performing supervised training by using a multi-layer perceptron (MLP) to realize prediction of a new sample; and S6, packaging a result of the classifier, and outputting the result to a downstream risk control system. According to the method, background features are extracted and normalized by combining an advanced image segmentation model and a multi-modal embedding model, and then recognition and risk scoring of the office background of the intermediary are completed through a trainable classifier, so that suspected intermediary groups are recognized and filtered.

1. Name of the product subject to this design: Intelligent Identification Graphical User Interface for Electronic Devices. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Purpose of the graphical user interface: It guides users to intelligently recognize the charging code. Through human-computer interaction, it enables users to take pictures, upload images, recognize the charging code, and jump to the fee confirmation page. The solid line represents the entire interface that requests partial appearance protection, while the dashed line shows the content screen that does not request protection. Clicking "Smart Solution" in the main view displays the changed state diagram 1. Clicking "I understand" in the changed state diagram 1 displays the changed state diagram 2. Clicking the circular "Shoot" button in the changed state diagram 2 displays the changed state diagram 3. Clicking "Confirm Upload" in the changed state diagram 3 displays the changed state diagrams 4 and 5 in sequence.

The invention belongs to the technical field of industrial detection and sorting technologies, and particularly relates to an aluminum profile surface defect online detection and sorting method which comprises the following steps: acquiring at least one framed picture when a target on a main conveying line is sorted for the first time; for each framed picture, processing the framed picture based on a picture recognition model to judge whether a defect exists on the surface of the target or not and determine the position of the defect; sorting the targets of which the defects are identified to a secondary sorting line; aiming at the position of the defect area, acquiring a three-dimensional point quantization parameter set of the defect; fine classification and cause tracing are carried out on the defects by using a multi-branch neural network; according to the method, accurate evaluation and flexible sorting of the surface defects can be realized by fusing two-dimensional visual identification, three-dimensional shape quantification and multi-stage intelligent decision, so that the differentiated quality requirements of customers are met to the maximum extent on the premise of ensuring the production feasibility, and the high-quality product rate and the resource utilization rate are improved.

The present application relates to a video event autonomous retrieval and intelligent decision system based on visual-linguistic alignment. Among them, the system comprises: a data acquisition unit, a data processing unit, a cross-modal alignment unit, an intelligent decision unit; the data acquisition unit is used for acquiring first video data and second language data; through corresponding processing, standardized video data is obtained; the obtained data is processed in corresponding format, and a cross-modal alignment data set is obtained; using a corresponding feature coding model, a third semantic vector and a fourth visual vector are obtained; the matching degree score is obtained by calculating the cross-modal similarity, the video segment of the corresponding visual feature is screened, and the visual-linguistic alignment processing is carried out, and the processing result is obtained; by obtaining a complete evidence chain video, picture recognition and labeling are carried out by using a frame detection mechanism, based on the labeled evidence chain video and the matching degree score, a retrieval result report is generated; the fusion processing and intelligent analysis of video and language data are realized.

The application discloses a specific picture recognition method based on multi-modal, relates to the field of specific picture recognition, and comprises two parts of feature information extraction of pictures and texts and a specific picture matching strategy of FH-LANet, adopts an image matching idea to extract features of pictures through a multi-modal feature extraction network FH-LANet model, selects a specific picture as a query library picture, the query feature is not a single modal picture feature, but a text feature, similarity calculation is carried out by using the features of the input picture and the query feature, threshold judgment is then carried out, the matched picture and filtered text are matched again, noise data is filtered out, the picture that does not match the query picture is secondarily matched with the query text, and finally, the recognition result of the picture is obtained, and the specific picture in the mass pictures can be accurately found out.

Embodiments of the present application disclose a picture classification method and device, electronic equipment and computer readable medium. The picture classification method comprises: obtaining a to-be-classified slice group; wherein the to-be-classified slice group comprises a plurality of to-be-classified slices, and the plurality of to-be-classified slices are obtained by splitting a to-be-classified picture; performing feature comparison on the plurality of to-be-classified slices and standard slices contained in a plurality of standard slice groups to obtain a first feature comparison value of each standard slice group for the plurality of to-be-classified slices; selecting a target picture from a preset overall picture set according to the plurality of first feature comparison values; wherein the preset overall picture set comprises standard pictures corresponding to the plurality of standard slice groups respectively; performing feature comparison on the to-be-classified picture and the target picture to obtain a second feature comparison value, and classifying the to-be-classified picture according to the second feature comparison value. Through the method, the picture recognition rate can be improved.

This invention relates to an autonomous decision-making method for unmanned ship systems. The method includes acquiring sensory data from the unmanned system, constructing a local expert knowledge base, building an autonomous decision-making thought chain, inputting image data into a small target detection model to obtain target category information and corresponding confidence levels, acquiring target recognition results, inputting the target recognition results and the thought chain into a multimodal large model, outputting a decision action, comparing and evaluating the output decision action with the corresponding decision actions in the expert knowledge base, obtaining the final output decision action, locating problems in erroneous decision actions, and iteratively correcting the problems. This decision-making method can improve the target recognition accuracy of various sensory data, streamline the reasoning process, improve the real-time performance of decision output, and ensure the reliability of decision actions. Simultaneously, it can automatically identify image recognition errors and decision output errors, perform iterative corrections, and enable the system to continuously improve recognition accuracy and decision rationality during operation.

The picture recognition system based on the pulse neural network with a self-attention mechanism comprises an encoding module, an attention module and a decoding module.The encoding module is composed of a convolutional encoding layer and position encoding, receives an input picture sequence and generates a pulse sequence containing feature information of the input picture sequence.The attention module is composed of multiple layers of self-attention blocks, receives the pulse sequence, is processed through time and space self-attention operations respectively, and outputs a pulse sequence with richer time and space features.The decoding module receives the pulse sequence, compresses and extracts a final feature vector through sequence pooling, and inputs the feature vector into a classification head to obtain a picture recognition result.The application constructs a novel architecture based on the pulse neural network for picture recognition, and achieves the most advanced effect in the pulse neural network in the picture recognition task.

The utility model relates to a termite visualized picture recognition early warning monitoring device, which belongs to the termite monitoring technical field, and comprises a housing and a top cover buckled on the top of the housing, the inner top wall of the top cover is attached to and connected with a control cabin, two second bait cabins and a plurality of first bait cabins are vertically arranged in the housing, and the second bait cabins and the plurality of first bait cabins are arranged in the housing. A protection mechanism for termites to enter the shell is arranged on the outer side of the shell. According to the termite visual picture recognition early-warning monitoring device, during soil filling, an annular barrier strip can shield falling soil, when termites gnaw bait columns in a second bait bin to enable the height of the bait columns to be decreased, a Hall switch cannot sense the magnetic field of a magnetic block, a signal is sent out to remind a user, and the termites are prevented from being damaged. A GPS position sensor electrically connected with the cloud end is further placed in the control bin, the situation in the shell can be displayed and shot in real time through an image sensor, the visualization purpose is achieved, and the specific position of the device can be known through the GPS position sensor.

The invention discloses a driving mode judgment method and system based on computer vision, and relates to the technical field of driving mode judgment, and the method comprises the following steps: extracting a static image frame, and dynamically adjusting the frame extraction frequency; carrying out local contrast self-adaptive adjustment on the image frame to obtain an enhanced image; extracting and fusing a brightness gradient and texture distribution based on multi-level visual feature analysis to generate a unified feature vector; inputting the unified feature vector into an intelligent discrimination model for navigation picture recognition and outputting a result with confidence; and carrying out dynamic weighted updating on the confidence of the continuous frames based on time sequence attenuation and a sliding window mechanism. Through computer vision identification and time sequence regulation and control, intelligent identification and dynamic release of the navigation picture by the vehicle-mounted terminal are realized, interruption caused by error limit is avoided, navigation continuity and driving safety are guaranteed, and identification accuracy and response sensitivity are kept in stable and sudden change scenes through an adaptive adjustment mechanism, so that the navigation effect is improved. And the intelligence and user experience of the vehicle-mounted system are improved.

The application discloses a kind of based on OCR's scrap steel recycling pound single automatic identification system, including customer pound single configuration module for defining pound single pound single rule according to the pound single picture uploaded by customer;OCR training module is used to obtain the character in the pound single picture uploaded by customer is corrected and stored;OSS object storage module is used to store the pound single picture uploaded by customer;Pound single OCR image-text recognition module is used to obtain pound single picture and process, text information in pound single picture is identified based on OCR image recognition engine, and text information is converted into structured pound single data according to the pound single rule of customer pound single configuration module;Relational database storage module is used to store the pound single data obtained by pound single OCR image-text recognition module;Manual check module is used to provide pound single picture, pound single data to artificial and recheck.Pound single structured data information is identified automatically according to pound single picture, and rechecked by artificial, improve pound single recognition efficiency and accuracy.

The invention provides an optimization method and device for a multi-modal large model picture recognition cue word, and is applied to the technical field of data processing. The method comprises the following steps: firstly, extracting a preprocessed initial basic cue word and massive to-be-recognized pictures, and generating an initial cue word set and standardized picture data; identifying and screening a target picture by the model for the first time and generating scene matching json information, and separating a correct sample set from a misjudgment sample set through manual secondary verification; performing error analysis by utilizing the misjudgment sample set to generate a new cue word and difference degree data, and dynamically adjusting a scene cue word weight in combination with the number of samples; and the initial weight is newly established and configured for the scene with the too low weight, and the first 9 high-weight cues are screened for the next round of recognition, so that a cycle is formed to adapt to the spatial-temporal change, and the recognition accuracy is improved.

The application discloses a license plate recognition method, system and computer readable storage medium, and the license plate recognition method comprises the following steps: S1, extracting vehicle picture features to obtain a license plate feature map; S2, generating a license plate position mask according to the license plate feature map; S3, preprocessing the license plate position mask to obtain a first license plate picture; S4, reconstructing the first license plate picture to obtain a second license plate picture; and S5, recognizing the second license plate picture to output a license plate number. According to the application, the license plate feature map is obtained by extracting the vehicle picture features, the license plate position mask is further generated, the first license plate picture is obtained by preprocessing the license plate position mask, and the second license plate picture is obtained by reconstructing the first license plate picture, so that the influence of the angle deviation generally existing in the collected license plate image is reduced, the definition of the license plate picture is improved, and on this basis, the second license plate picture is finally recognized to output the license plate number, so that the accuracy and robustness of the license plate recognition are improved.

The invention provides a picture understanding method and device based on artificial intelligence, equipment and a storage medium, and belongs to the field of artificial intelligence, and the method comprises the steps: obtaining a to-be-understood target picture and a picture understanding model; performing region identification on the target picture through the region detection layer to obtain a data type of each picture region; according to the data type of each picture region, selecting a matched recognition network from the content understanding layer to perform picture recognition, and obtaining picture description information of each picture region; and carrying out description information splicing based on the position of each piece of picture description information through an information splicing layer to obtain target picture description information of the target picture. According to the picture identification method and device, the corresponding identification network can be selected for the data area of each data type in the picture to carry out picture identification, so that the obtained picture description information is more accurate. The method can be applied to the technical fields of medical treatment and finance, and can improve the efficiency and accuracy of picture understanding in financial services and medical services.

The application relates to the technical field of financial technology, and discloses a fund holding picture recognition method and device, computer equipment and a storage medium, the method comprises the following steps: acquiring a fund holding picture; performing image recognition on the fund holding picture, and extracting text information in the fund holding picture; performing semantic analysis on the text information, and extracting initial fund holding information in the text information; and after data verification and error correction are performed on the initial fund holding information based on a query matching rule and a pre-constructed data index, final fund holding information is obtained. The application improves the accuracy of character recognition, effectively integrates data, improves the integration capability of fund holding information, and solves the problems of inaccurate fund holding picture recognition and poor data integration capability.

The invention provides an image matting method and device for multi-mode image matting image recognition, equipment and a medium. The method comprises the steps of receiving a to-be-processed image; calling a multi-mode identification service to analyze the preset standardized image data; displaying a selectable label corresponding to the target category in a graphical user interface; when a selection operation of a user on the selectable label is detected, creating an image layer corresponding to the selected label; rendering the category masks to corresponding image layers, and displaying the image layers in an overlapping manner according to a preset hierarchy sequence; when cancel operation of the user on the selected label is detected, positioning and deleting the corresponding image layer according to the unique identifier; and in response to a confirmation instruction of a user, collecting mask data of all the user-defined category layers, merging the mask data to generate a final mask, applying the final mask to the to-be-processed image to generate a matting result, identifying that a region label possibly exists in the image, and seamlessly linking with the matting capability.

The invention discloses a tea garden tender shoot picking decision-making system based on lightweight YOLOv8. The tea garden tender shoot picking decision-making system comprises a picture recognition module, a camera real-time recognition module, a user interface interaction module, a statistics and export module, a model loading module and a parameter adjustment module. The invention also discloses a tea garden tender shoot picking decision-making method based on the lightweight YOLOv8, and the method comprises the steps: collecting a plurality of pictures in a tea garden to form a tea tender shoot recognition data set, marking each picture in the data set, carrying out the normalization processing, then dividing the pictures into a training set and a verification set, and constructing a tea tender shoot recognition model based on the YOLOv8; and training and verifying the tea tender shoot recognition model by using the training set and the verification set, and inputting a tea garden picture to be recognized into the verified tea tender shoot recognition model to obtain a tea garden tender shoot recognition result. The problems that an existing tea garden tender shoot picking manual identification method is low in efficiency and difficult to be used for large-scale and standardized production of tea leaves are solved.

The invention provides a method and system for quickly generating game levels, equipment and a storage medium. According to the method, when a real object is used for completing the design of a game level, the level design is photographed; all game elements are automatically recognized through automatic picture recognition and processing procedures, and in the recognition process, recognition and attribute extraction of the building block type game elements are efficiently achieved by using a cold start rapid image recognition method. After game elements are recognized, the sizes of the game elements are counted to reversely deduce a basic unit of a grid, and the basic unit is utilized to construct a grid structure to cover all the elements, so that the size of the whole level is finally obtained. And finally, generating a loadable level file of the game according to the size of the level and the coordinates of the game elements in the level. Through the embodiment provided by the invention, the efficiency bottleneck of a traditional editor or pure manual input is broken through, the conversion time of a single level from a design drawing to a playable level is greatly shortened, and tedious and error-prone manual identification and input work is also reduced.