218 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 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 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 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 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 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 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 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 invention relates to a distributed energy storage site surveying and evaluating method and system based on multi-model collaboration. The method comprises the steps that picture description and a structure questionnaire form are collected through a mobile terminal; matching according to the picture description and the structure questionnaire form to generate a first cue word; calling a large model, identifying setting information and electrical characteristics in the picture description, and obtaining a picture identification result and a text analysis result; constructing a feature request by combining the form data to generate evaluation model parameters; the evaluation model parameters, the existing energy storage equipment configuration and the load prediction data are fused, and the station exploration evaluation result of the machine room is output through the evaluation model; matching and generating a second cue word according to the station exploration evaluation result; calling the large model based on the matched second cue word, and generating an evaluation report according to the station exploration evaluation result; by applying the mode and the method of the invention, the processing efficiency is greatly improved and the data accuracy is improved for the design of a distributed energy storage site survey evaluation scene with a large site number scale.

The present invention discloses a picture recognition method, a recognition model training method, a device and an electronic device. Among them, the method includes: obtaining a target 3D picture to be recognized; inputting the target 3D picture to be recognized into a first recognition model, where the first recognition model is used to recognize the target 3D picture to be recognized to obtain the picture type of the target 3D picture to be recognized, and the convolutional blocks of the first recognition model are the same as those of the second recognition model, and the second recognition model is a model obtained by training an original recognition model using target training samples, and the target training samples include cubes obtained by rotating and sorting N target cubes obtained from 3D sample pictures, and N is a natural number greater than 1; obtaining the first type of the target 3D picture to be recognized output by the first recognition model. The present invention solves the technical problem of low model training efficiency in the related art.

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.

The present invention discloses a method for judging on-street parking behavior, which includes identifying the outer berth reference line and the inner berth reference line in a picture and marking the marking points of each berth on the outer berth reference line and the inner berth reference line; detecting multiple vehicle frames in the picture and calculating the coordinates, width and height of the target vehicle frame; finding the reference berth corresponding to the target vehicle frame according to the coordinates, height of the target vehicle frame and the marking points of each berth; and judging whether the vehicle is parked on the reference berth according to the coordinates, width, height of the target vehicle frame and the coordinates of the reference berth. By analyzing the ratio relationship between the line segments defined by the vehicle frame and the berth reference line and the line segments defined by the reference berth and the berth reference line, it can accurately judge whether the vehicle is parked on the berth, and the judgment method is simple; in the case where the inclination angle of the berth recognized in the picture is too large, since the proportional relationship of the line segments does not change, it can still accurately identify whether the vehicle is parked on the berth, so the adaptability is strong.

The present application provides an image recognition method, apparatus, device and storage medium. First, a preset character recognition model is used to identify the human characters in the image to be identified to determine whether the human characters in the image to be identified include the preset human characters. If it is determined that the human characters in the image to be identified include the preset human characters, target feature data is obtained according to multiple target feature analysis models, and the target feature data includes the facial expression feature data of the preset human characters, the action feature data of the preset human characters and the text feature data in the image to be identified. Finally, it is determined whether the image to be identified is the target image based on the target decision model and the target feature data. This provides a multi-model effective recognition method for images to be identified that contain preset human characters, improves recognition efficiency and reduces the false recognition rate, and is conducive to maintaining network management specifications.

The application provides a face expression recognition model training method and a face expression recognition method, relates to the picture recognition technical field, and the training method flips the obtained face image, utilizes the natural symmetry feature of the face, calculates the consistency between the features of the face image and the features of the face flipped image, obtains a first loss value, and concentrates the model attention on the key features of the face; the face image is divided into a clean image subset and a noise image subset, the clean image is trained by using a supervised learning method to obtain a second loss value, and the noise image is trained by using a self-supervised learning method to obtain a third loss value; finally, the three loss values are fused, a total loss value and a preset loss value are combined, and the initial recognition model is adjusted to improve the efficiency of the recognition model training and enhance the robustness and accuracy of the face expression recognition model.

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 discloses a picture recognition management system based on video monitoring, and relates to the technical field of image management. The image processing unit is used for collecting image features of the basic data and carrying out gray processing on the image features, the image recognition management system based on video monitoring can enhance the image features by adjusting the foreground and background of a gray image, detail optimization can be carried out when character feature recognition blurring exists in a live broadcast image, and the image recognition efficiency is improved. The quality of the live broadcast picture is enhanced, and continuous adjustment is performed on the enhanced image features, so that the situation that the attention of the live broadcast room is not on a product and public opinions are caused due to sudden picture adjustment can be effectively avoided, a good live broadcast picture effect can be ensured, the attention of the live broadcast room can be reduced, and the user experience is improved. And the adjustment sequence of the plurality of image pictures is analyzed under the delimited rule, so that the pictures can be managed in a unified manner, and the value maximization of live broadcast picture optimization is ensured.

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.

To provide methods and devices for decoding video signals using inter prediction.SOLUTION: A method comprises constructing a reference picture list of a current picture in the video signal, and performing a prediction for a current picture by using the reference picture list. The step of constructing the reference picture list comprises, if a first entry of the reference picture list corresponds to an STRP entry, obtaining a POC difference value between a picture related to the first entry and another picture, and if a second entry of the reference picture list corresponds to an LTRP entry, obtaining a POC modulo value of a picture related to the second entry. A reference picture list for identifying a picture may be generated in a simple and effective manner, and thus, the compression performance may be increased and the computation time may be decreased.SELECTED DRAWING: Figure 2

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.