207results about How to "Improve labeling efficiency" patented technology

The invention discloses a rolled metal surface defect automatic labeling method based on a multi-task self-adaptive model, which comprises the following steps: S1, collecting a rolled metal surface image, preprocessing and enhancing the rolled metal surface image, performing data labeling, and constructing a rolled metal surface defect data set; s2, constructing a multi-task adaptive model, and training the multi-task adaptive model by using the data set to obtain an automatic defect labeling model; and S3, inputting a to-be-labeled metal surface image into the defect automatic labeling model, and outputting a surface defect labeling result by executing the target detection task. According to the method, a multi-task learning framework is combined, joint training of target detection and instance segmentation tasks of metal surface defects in the rolling technology is achieved, the target detection task is focused in the reasoning stage, the labeling efficiency is improved, the labor cost is remarkably reduced, and the labeling efficiency is improved. The method can be widely applied to industrial defect detection and data automatic labeling scenes of rolling process metal, and is suitable for defect image automatic labeling tasks in a complex industrial environment.

The invention relates to the technical field of medical treatment, in particular to an AI-based medical image automatic segmentation and labeling system, which comprises an image data acquisition module, a data preprocessing module, a feature extraction module, an automatic segmentation module and an intelligent labeling module, the data preprocessing module improves image quality through noise suppression, artifact removal and modal alignment, the feature extraction module extracts a feature map of a potential lesion area, and the automatic segmentation module combines a full convolutional network, a bidirectional long-short-term memory network and an attention mechanism to realize accurate segmentation of an organ area and the lesion area. The intelligent labeling module is used for labeling anatomical names, lesion types and pathological features of the segmentation results by using a medical knowledge graph and a semantic embedding technology; the method has the advantages of being high in segmentation precision, comprehensive in marking and high in expansibility, and the automation and intelligence level of medical image analysis can be improved.

The invention relates to the technical field of image processing, and provides a training data set labeling method and device for AI visual identification, and the method comprises the steps: obtaining and processing original image data, extracting feature mapping, inputting a target detection model to obtain a preliminary labeling result, carrying out the analysis, and determining a clustering center and a candidate target set; the candidate target set is classified based on the clustering center, the confidence degree of the classification labeling result is calculated, labeling data with the confidence degree higher than a threshold value serve as high-confidence-degree labeling data, rechecking and consistency checking are conducted on data with the confidence degree lower than the threshold value, and consistency labeling data are generated; and the target detection model is optimized in combination with the high-confidence data, the low-confidence data is re-labeled by using the optimization model, and the high-confidence data and the re-labeled data are combined to generate a complete labeled training set to train the recognition model. Through the above scheme, the consistency and confidence of the data are ensured, and the problems of inconsistent labeling and missed labeling and false labeling in complex scenes and multi-target recognition are effectively improved.

The invention relates to a data annotation method and device, equipment and a readable storage medium. The data annotation method comprises the following steps: firstly, inputting to-be-annotated data and cue words into a first pre-trained large language model, generating a first prediction data annotation result, analyzing the confidence coefficient of the first prediction data annotation result, and generating a first data annotation result based on a result conforming to a first confidence coefficient threshold value; and inputting a result which does not accord with the first confidence threshold value into a second pre-trained large language model for re-labeling, analyzing the confidence, and generating a second data labeling result. And performing manual annotation on the result which does not accord with the second confidence threshold, and finally combining the first, second and manual annotation results to determine a final annotation result. According to the method, through automatic annotation and confidence analysis of a multi-round pre-trained large language model, and in combination with manual annotation, low-confidence tasks are processed, so that the annotation accuracy and efficiency are remarkably improved, and the dependence on manual intervention is reduced.

The invention discloses a semi-automatic labeling and semi-automatic auditing method based on an AI large model, and relates to the field of image processing, target detection and artificial intelligence application. Comprising the following steps: 1, obtaining a pre-labeling model by applying a large model and an unsupervised labeling technology, and obtaining a preliminary pre-selected labeling box; 2, training a labeling effect auditing network for screening out target frames which do not meet requirements; a third step of obtaining two network models of target frame pre-extraction and target frame confidence analysis based on the previous two steps, and manually performing further labeling on the screened pre-labeling labels to perfect the labels; and then, for the perfect label result, obtaining a target contour based on an SAM semantic segmentation large model, further screening a target frame according to the target contour, and auditing the labeling result by adopting a labeling frame detection model, and forming a final labeling result after the auditing is perfect.

The invention provides a visual algorithm model development and reasoning method and device for urban governance. The method comprises the steps of defining services and problems; collecting related data according to the business and the problem, and labeling the collected related data through a data labeling platform to obtain a data set; training a visual algorithm model based on a YOLO framework by using the data set, performing pruning, algorithm chip adaptation, model evaluation and optimization iteration on the trained visual algorithm model, and deploying the visual algorithm model into a production environment; and inputting image data to be processed, preprocessing the image data to be processed by using the visual reasoning engine, inputting the preprocessed image data into the visual algorithm model for feature extraction, performing reasoning and decision making according to the extracted features, and outputting a final reasoning prediction result. The method has the advantages that the visual algorithm model can be improved in the aspects of model generalization ability, deployment complexity, model labeling efficiency, real-time performance, prediction accuracy and the like.

The invention provides a stone curtain wall millimeter wave image rapid defect marking, training and image screening method and system, and the system comprises an image display module which is used for displaying a collected stone curtain wall millimeter wave image; the automatic labeling module is used for carrying out preliminary defect labeling on the input millimeter wave image; the man-machine interaction module provides a user interface to allow a user to check, audit and correct the labeling result; the database result output module is used for outputting the final labeling result in a standard format and supporting multi-dimensional screening and exporting functions; the defect identification model is used for training and identifying defects in the stone curtain wall millimeter wave image based on a deep learning model; and the image screening module is used for evaluating the quality of the image and screening out a high-quality image according to a set threshold value based on a screening algorithm of image quality. Through automatic and intelligent technical means, the stone curtain wall detection efficiency and accuracy are improved, and the safety and durability of the building curtain wall are ensured.

The invention relates to the field of image processing, and discloses a violation image detection method and device, equipment and a storage medium. The violation image detection method comprises the following steps: acquiring pre-annotation data obtained by annotating a sample image based on a preset model auxiliary annotation system; training an image processing model of a preset decoupling framework based on the pre-annotated data to construct a violation image detection model; and performing violation detection on a target image based on the violation image detection model, the target image being obtained by preprocessing an original image based on a preset redundancy removal algorithm. By using the preset model auxiliary labeling system, the preset decoupling framework and the preset redundancy removal algorithm, the positioning and classification efficiency of labeling and the model training efficiency can be remarkably improved, the model deployment cost is reduced, the model processing efficiency is improved, efficient violation detection of the image is realized, and the detection accuracy is improved. The risk control scene of the vertical screen picture of the mobile phone can be effectively compatible.

The invention discloses a data annotation method based on a large model. The data annotation method comprises the following specific steps: acquiring data and preprocessing the data to obtain input data; performing model training based on the arbitrary-degree large model and the input data to obtain a pre-trained arbitrary-degree large model; performing interface packaging on the pre-trained random degree large model; performing fine tuning on the packaged pre-training arbitrary-degree large model to obtain a target arbitrary-degree large model; acquiring to-be-labeled data, inputting the to-be-labeled data into the target arbitrary-degree large model for labeling, and outputting to obtain labeled data; and monitoring, feeding back and recording the fine tuning process of the pre-trained arbitrary-degree large model and the labeling process of the target arbitrary-degree large model to obtain historical fine tuning data, historical data to be labeled and historical labeling data. According to the method, the problems of label imbalance, label inaccuracy and image label error are solved, the labeling quality and efficiency are improved, and the labeling cost is reduced.

The invention relates to the technical field of image processing, and discloses an image data set construction method and system, and the method comprises the steps: employing a multi-mode large model CLIP to recognize the defects of an existing image data set, and carrying out the supplementation; annotating the image data set obtained after supplementation, and ensuring the annotation accuracy through multi-level quality control; expanding the marked image data set; and fusing the expanded image data set with other modal data to complete the construction of the final image data set. According to the method, the accuracy of data acquisition and the labeling efficiency are improved, the accuracy and the consistency of data labeling are improved, and human errors are reduced; according to the method, the data set can be dynamically expanded, so that the data set is more diversified, and the generalization ability of the model is improved; the method supports the fusion of the image and other modalities, and enhances the application scene and flexibility of an image data set by using the cross-modal alignment capability of a large model.

The invention discloses a method for detecting and classifying electroencephalogram events of electroencephalogram signals by using a target detection model DINO, and belongs to the technical field of deep learning. The method mainly comprises three stages of data set acquisition, data preprocessing, feature extraction and detection classification evaluation. According to the method, a complete electroencephalogram data file can be input, a tedious post-processing process is not needed, many defects of post-processing are overcome, an end-to-end model is constructed, original data are directly input, and an event detection result is output; meanwhile, many limitations existing in manual detection are overcome, a universal automatic electroencephalogram event detection framework is established by using a deep learning method, a doctor can be assisted to play a role in tasks such as epileptic seizure detection, sleep staging and artifact detection in various clinical application scenes, the electroencephalogram event labeling efficiency is improved, meanwhile, the burden of the clinician is reduced, and the workload of the clinician is reduced. And the pressure of medical resources is relieved.

The invention relates to the technical field of data annotation, and particularly provides a multi-modal data annotation method and device, a storage medium, equipment and a product, and the method can comprise the steps: mapping data in each modal in multi-modal data to a same feature space, and obtaining a feature vector in each modal, the multi-modal data comprises image data, text data and audio data; determining a to-be-fused feature vector in each mode by evaluating the correlation degree between the feature vectors in each mode; fusing the to-be-fused feature vectors in each mode to obtain a fused feature vector; the fusion feature vector is labeled, a labeling result is obtained, and the labeling result comprises a data type label and / or a data description text. According to some embodiments of the invention, accurate labeling of cross-modal data can be realized.

The invention relates to the technical field of artificial intelligence and the technical field of data annotation, and discloses an automatic annotation method, device and equipment and a storage medium, and the method comprises the steps: obtaining a current object image, and obtaining a candidate region outputted by a candidate box generation network based on the current object image; performing feature extraction on the region image corresponding to the candidate region to obtain a current object feature; generating category similarity corresponding to each preset object category; selecting a preset object category with the maximum category similarity as a current object category corresponding to the current object feature; the method comprises the steps of obtaining a polygonal envelope corresponding to an object in a current object image, combining information of the polygonal envelope with a current object category, generating annotation information of the current object image, adding the annotation information to the current object image, and generating an annotated current object image. According to the invention, the marking efficiency of the current object image is improved.

The embodiment of the invention provides a transaction data labeling method and a fraud classification model training method and device, and relates to the field of artificial intelligence. According to the method, after target transaction data and fraud description information corresponding to the target transaction data are obtained, a large language model is adopted to perform feature extraction on the fraud description information, and a fraud feature vector is obtained; and inputting the fraud feature vector into a fraud classification model to obtain a target fraud type identifier, and taking the target fraud type identifier as a label of the target transaction data to realize labeling of the transaction data. According to the scheme, the target transaction data is labeled by using the fraud description information and the fraud classification model, manual labeling is not needed, and the labeling efficiency is effectively improved.

The invention is suitable for the technical field of building structure recognition, and provides a building structure recognition method and device and electronic equipment, and the method comprises the steps: obtaining the information of a building structure, and the information of the building structure comprises the contour information of a wall body and / or a hole; wall body section contours with the same elevation are determined according to the information of the building structure; a DCEL structure is constructed according to the wall body section contours with the same elevation; determining all surfaces corresponding to the DCEL structure; the surfaces corresponding to the walls and / or the holes are removed from all the surfaces corresponding to the DCEL structure, the surfaces of the space in the DCEL structure are obtained, the surfaces corresponding to the walls are determined according to the contour information of the walls, and the surfaces corresponding to the holes are determined according to the contour information of the holes. Through the method, the marking efficiency and consistency can be improved.

The utility model relates to the field of beverage bottle processing, and discloses an automatic beverage bottle labeling machine which comprises a processing table, a labeling roller rotatably installed on the processing table and a feeding conveyor belt in transmission on the processing table, a fixing frame is fixed on one side of the processing table, a connecting rod is lifted on the fixing frame, a rotating disc is rotatably arranged at the bottom end of the connecting rod, and the feeding conveyor belt is connected with the rotating disc. A lifting mechanism for driving the connecting rod to ascend and descend and a rotating mechanism for driving the rotating disc to rotate are installed on the fixing frame, the rotating disc is connected with an annular disc through a fixing rod, and cleaning brushes are evenly fixed to the annular disc in the circumferential direction of the inner ring. The beverage bottle labeling machine has the following advantages and effects that beverage bottle bodies can be automatically cleaned in all directions before the beverage bottles are labeled, and the labeling working efficiency is improved.

The invention discloses an automatic data labeling method and device and a storage medium, relates to the technical field of data processing, and is used for automatically labeling data and improving labeling efficiency and labeling quality at the same time. The method comprises the following steps: manually pre-labeling data of each task to obtain pre-labeled data; automatically deriving a plurality of different cue word schemes for each task; according to the cue word schemes, all tasks are imported into a multi-mode large model for independent question asking, and first output results corresponding to the cue word schemes are obtained; performing result extraction by adopting an unsupervised voting algorithm to generate a voting statistical result of each task; if the number of the positive correlation marks reaches or exceeds an election threshold value, judging that the task is a positive correlation sample; inputting the pre-annotated data and the positive correlation sample into a single-mode small model for training iteration; and automatically labeling the target data to be labeled in batches by using the single-mode small model, and outputting a labeling result.

The invention relates to a model training method and device, computer equipment and a storage medium. The method comprises the steps that a to-be-annotated image is processed based on prompt information and an image segmentation model, a target mask image is obtained, the prompt information is used for specifying a graphic code in the to-be-annotated image, and the target mask image is used for segmenting the graphic code from the to-be-annotated image; and based on the to-be-labeled image and the target mask image, performing hierarchical training on each network layer of an image decoding network of the image segmentation model to obtain the graphic code labeling model, the graphic code labeling model being used for labeling graphic codes in the image. By adopting the method and the device, the graphic code labeling efficiency can be improved based on a large model, and the data volume of the training data can be efficiently expanded.

The invention discloses an ice condition video image recognition method based on TS-MaskFormer. A rapid ACE defogging algorithm is adopted to enhance a night image, and an ice condition image detection and identification data set covering different stages of freezing, starting of ice melting, floating of floating ice and the like is constructed based on a semi-supervised labeling method of SAM; a TS-MaskFormer ice condition image recognition model is provided, a MaskFormer model and a fusion module of two different backbone networks are integrated, and accurate ice condition image detection is achieved by training, adjusting and optimizing model parameters. And identifying an ice condition image based on the optimized model, and calculating an ice surface coverage rate through an identification result. According to the method, more detail information can be reserved in the aspect of ice condition image recognition, and higher ice surface recognition precision and higher calculation speed are achieved.

The invention discloses a method and a system for standard labeling of a case report form, and the method comprises the following steps: S1, analyzing information in the case report form, and matching the information with information in a research design specification; s2, matching the research design specification with the standardized data set; s3, according to the matching relationship between the case report form and the research design specification and the matching relationship between the research design specification and the standardized data set, obtaining and recording the mapping relationship between the case report form and the standardized data set; and S4, generating a standardized label for the case report form. According to the scheme, the SDS file existing in each test item is used as the intermediate file between the case report form and the standardized data set to achieve automatic labeling, additional variable label adding actions are not needed, and a user only needs to upload the case report form needing to be labeled and the corresponding SDS file to a system to achieve automatic labeling. And the labeling accuracy is ensured while the labeling efficiency is improved.

The invention provides a point cloud labeling method and device and a storage medium. According to the main technical scheme, the method comprises the steps that information of an N-polygon marking frame of an image is obtained, N is a positive integer larger than 2, and the image is obtained by projecting point cloud to an image plane of a camera; determining N construction planes according to the position point of the camera and any two adjacent vertexes of the N-polygon marking frame; and determining a target point according to the directed distances from the points in the point cloud to the N construction planes. In this way, a user only needs to input the N-polygon labeling box on the image to determine the target point, point-by-point labeling is not needed, and the labeling speed and the labeling efficiency are greatly improved. And the target point is determined by using the directed distance from the point to the plane, so that the calculation complexity is greatly reduced.

The invention relates to the technical field related to data processing, in particular to an automatic labeling method, device and equipment based on a large model and a clustering algorithm. The method comprises the following steps: performing preliminary analysis on an original data set by using a natural language processing technology, and generating an initial label for each piece of data in the original data set; performing clustering analysis on the generated initial labels by applying a clustering algorithm, and grouping the initial labels based on similarity or relevance among the labels to obtain a plurality of initial label groups; selecting the most representative label from each initial label group to replace all labels in the initial label group so as to simplify a label system and reduce redundancy; judging the similarity between any two pieces of data in the original data set; and adjusting the label system based on the similarity.

The invention provides a calculation amount drawing-oriented large model training data labeling method, device and equipment, which are used for labeling key information of a construction engineering cost calculation amount drawing. The method comprises the following steps: displaying a to-be-labeled calculation amount drawing, determining a to-be-identified area, and identifying to obtain text content and position information of a text block; displaying a label list according to the keyword field; after a user selects the tag, activating the interaction state of the to-be-identified area, so that the text block is presented as an operable hot area; after the user selects the hot area, generating a key value pair by using the selected label and the hot area text; and collecting the key value pairs of the to-be-identified areas to generate answer content, and filling the answer content into an answer part of a preset question-answer template to obtain a labeling result. By adopting the method, the field and text matching can be quickly completed, the structured annotation data is generated, the annotation efficiency and consistency are improved, and the method can be conveniently and directly used for large model training.

The invention relates to the technical field of mechanical grippers, in particular to a bionic mechanical arm tube gripping device which comprises a base, a tray, a blood collection tube, a first sliding rail, a second sliding rail and a sliding block. A plurality of trays are installed on the base, a plurality of blood collection tubes are installed on the trays, first sliding rails are vertically installed on the two sides of the base respectively, a second sliding rail is horizontally installed between the two first sliding rails, and a sliding block is installed on the second sliding rail; the problems that when an existing bionics mechanical arm grabs a blood collection tube, the blood collection tube needs to be clamped and placed in a labeling device for labeling when the blood collection tube is clamped and labeled, the needed time is long, the occupied area of equipment is large, the labeling efficiency is low, and when the bionics mechanical arm grabs the blood collection tube, the labeling efficiency is high are solved. The blood collection tubes do not need to be placed in the labeling device for labeling, time is saved, meanwhile, the occupied area of equipment is reduced, cost is saved, and the labeling efficiency of the blood collection tubes is greatly improved.

The utility model discloses a label discharging structure for a labeling machine, and particularly relates to the field of labeling machines, the label discharging structure comprises a supporting plate, an adjusting mechanism of the labeling machine is installed on one side of the supporting plate, and the adjusting mechanism comprises an unwinding disc, a paper pasting belt, a first guide roller, a second guide roller, an abutting block, a labeling groove, a winding disc, a connecting block, a through groove and a threaded rod. By arranging the adjusting mechanism, the compactness of the labeling structure and a sticker tape is improved, the sticker tape in the unwinding disc is wound around the first guide roller and the second guide roller, and meanwhile, the length of the guide rollers is adjusted according to the width of the sticker tape, namely, the distance between the two connecting blocks is adjusted by rotating the threaded rod, so that the labeling structure is more compact. The overall length of the guide roller is adjusted, when the sticker tape is conveyed to the contact resisting block, a label on the surface of the sticker tape is tilted, so that commodities entering the labeling groove make contact, the commodities rotate to adhere the label, labeling is achieved, the sticker tape can continue to move to the winding disc to be wound after labeling is completed, and the labeling efficiency is improved.

The invention provides a graph model-based instance relationship judgment method and device, equipment and a product. The method comprises the following steps: acquiring an annotation data set of target instances in a target scene, wherein the annotation data set is obtained by performing feature annotation on each target instance in the target scene according to a pre-training model; according to the annotation data set of each target instance, constructing a graph structure representing an association relationship between the instances; based on the graph neural network model, performing relation analysis on the graph structure to obtain an initial instance relation judgment result; and according to the spatial constraint rule, calibration processing is performed on the initial instance relationship judgment result to obtain a target instance relationship judgment result, so that the detection precision and the labeling efficiency of the target instance in the complex shielding scene are improved, and the instance relationship judgment efficiency is improved.

This invention discloses a method and system for labeling prompt words in a large-scale communication domain model based on knowledge graphs. Belonging to the fields of communication technology and artificial intelligence, it addresses the following technical problems: low efficiency in labeling specialized terms in communication domain prompt word annotation, low accuracy and efficiency of automatic recognition, limited prompt word dimensions, and lagging domain knowledge. The method includes: sequence labeling and entity extraction using a trained combined model to output entities; generating relation triples as candidate relations using a trained generator, and scoring and ranking the selected relation triples using a trained discriminator; semantic parsing of user input to extract key entities and intents; multi-hop reasoning from a dynamic knowledge graph to retrieve relevant entities and relations; eliminating illusory content using a trained GAN network; generating entity prompt words from three levels: basic, extended, and application dimensions; and introducing a triple quality verification mechanism.

The utility model is suitable for product labeling technical field provides a kind of multi-opening continuous labeling device, containing labeling roller and winding roller, still include labeling station, the labeling station is equipped with conveying assembly, for moving to label article, labeling assembly, setting in labeling station, through the setting of labeling assembly, to realize the quick disassembly or installation of labeling roller and winding roller, to further improve the efficiency of the article labeling, and according to the size of article flexible adjustment label height, make its applicability to a variety of article labeling is strong, satisfy the demand of a variety of article labeling;Through the setting of stable component, to realize the labeling of article, can be according to the size shape of article, adjust the distance of article clamping and the clamping force, so that the device, clamp and labeling mold do not need to be replaced, greatly reduce the cost of labeling instrument use, make the flexibility of the device high.

The invention relates to the technical field of image processing, and provides an image labeling method, device and equipment, a medium and a computer program product.The method comprises the steps that based on the position relation between a virtual capsule endoscope and a gastral cavity model, space pose information of the virtual capsule endoscope is determined; the gastral cavity model is created based on historical gastral cavity data; establishing an association relationship between the virtual gastral cavity image and the spatial pose information; comparing a real gastral cavity image with the virtual gastral cavity image, and determining spatial multi-dimensional data of the real gastral cavity image based on a comparison result and the association relationship; and marking the real gastral cavity image based on the spatial multi-dimensional data to obtain multi-dimensional image marking information. According to the method, by labeling the multi-dimensional spatial data for the gastral cavity image, the workload of personnel is reduced, the data labeling efficiency of the gastral cavity image is improved, more comprehensive and accurate data are labeled for the gastral cavity image, and accurate construction of gastral cavity analysis related algorithms is facilitated.

The utility model relates to the technical field of beverage preparation, in particular to a clamping device and labeling equipment. The clamping device comprises a clamping connecting frame, a clamping driving assembly and two clamping mechanisms. The two sets of clamping mechanisms are connected to the clamping driving assembly, the two sets of clamping mechanisms are arranged in a spaced mode so as to form a containing space used for containing a cup body, and the clamping driving assembly is used for driving the two sets of clamping mechanisms to move close to or away from each other so as to clamp or release the cup body; wherein at least one clamping mechanism comprises a rotating assembly, and the rotating assembly is used for driving the cup body to rotate to a preset position. According to the clamping device, the clamping driving assembly is arranged to be matched with the clamping mechanism, when the cup body is placed in the containing space, the rotating assembly can clamp and drive the cup body to rotate to the preset position, the labeling device can label the cup body conveniently, the structure is simple, and the adjusting efficiency is high.