22results about How to "Improve annotation quality" 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 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 belongs to the technical field of data processing, and discloses an enterprise multi-type data labeling method and system based on feature engineering, and the method comprises the steps: collecting initial data of an enterprise internal information system, and dividing the initial data to obtain a text data set, a table data set and an image data set; feature vectors of different types of data are fused after features are extracted and screened, and a multi-dimensional feature space is constructed; constructing an annotation model according to the feature information of the feature space, constructing similar sample pairs as positive sample pairs and irrelevant sample pairs as negative sample pairs for unannotated new data, and comparing, learning and optimizing the similarity between the similar sample pairs; and carrying out label inference based on the feature vectors obtained by comparative learning, and generating a labeling result. According to the method, efficient and automatic labeling of various types of data can be achieved, the method is suitable for various heterogeneous data such as texts, tables and images, and high labeling precision and expansibility are achieved.

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 embodiment of the invention provides a small sample scene-oriented image and video marking system, which comprises a client and a server, and is characterized in that the client is used for requesting an image file, marking a target marking detection frame and a key point in a picture or marking an event in a video, and marking the target marking detection frame and the key point in the image or marking an event in the video after the marking is completed; uploading the labeled image file to a server side in a set labeling format; the server side is used for managing an image data set, a video data set and annotation data, monitoring and controlling annotation quality of the client side, calculating user scores and conducting data distribution according to rules, and annotation quality detection, a data distribution strategy, an annotation sampling inspection strategy and a data augmentation algorithm are included. The method is advantaged in that improvement of data labeling quality is facilitated, a neural network training overfitting problem caused by too little image data is solved, an effect of obtaining large-batch diversified image data from small-batch image data is realized, and related technical research of subsequent neural network target detection and event detection is supported.

The present invention discloses a method and device for image data processing, and relates to the field of computer technology. A specific implementation of the method includes: annotating a data set to be processed using an image annotation model trained with an initial training sample set to obtain an annotation result data set; determining the similarity between the annotation result data set and the initial training sample set; and updating the initial training sample set according to the similarity. This implementation can efficiently update and correct wrong labels in the initial training sample set, improve the annotation quality of the initial training sample set, and thereby improve the performance of the trained image annotation model.

The invention provides an annotation optimization method of gesture data and electronic equipment, which are used for improving the annotation quality of the gesture data. Comprising the steps that camera coordinates of all hand joint points in all hand images in any gesture sample are utilized to obtain all target hand images of all hand joint points; for any hand joint point, according to the image position coordinates of the hand joint point, obtaining the optimized world position coordinates of the hand joint point and the re-projection error of the hand joint point; updating an initial world coordinate of each hand joint point in the plurality of gesture samples by using the optimized world position coordinate of each hand joint point, and training a gesture estimation model by using a target gesture sample obtained by screening the plurality of gesture samples by using a re-projection error of each hand joint point and an initial training data set; and returning to use the camera coordinates of the hand articulation points in the hand images in any gesture sample until the training frequency is greater than the specified frequency, and determining the plurality of gesture samples as the optimized labeled gesture data.

The invention discloses an autofocus method based on pixel-level aberration detection, which belongs to the field of image processing technology. The method mainly uses Fraunhofer diffraction to calculate the pupil function and obtain the amplitude point spread function, then Fourier transforms the amplitude point spread function to obtain the amplitude transfer function of the coherent imaging system, and finally squares the amplitude transfer function to obtain the intensity point spread function of the incoherent imaging system. Drawing on the relationship between the pupil function and the point spread function in wavefront coding, a point spread function model at each pixel position in the space-variable system is constructed, thereby converting the estimation problem of the aberration in the autofocus into the estimation problem of the polynomial coefficients of the point spread function at each pixel position.

The application discloses an unmanned model training sample labeling method and device, equipment and a storage medium. The method comprises the following steps: obtaining a mind map, the mind map being provided with a sample labeling process, obtaining a sample image, determining an object to be labeled in the sample image, popping up a plurality of label options for a labeler to label according to the labeling process, selecting a target label from the plurality of label options to label the object in response to the labeling operation of the labeler, and popping up a plurality of label options for the labeler to label according to the labeling process in the mind map when the labeler labels, so that the labeler can select a suitable target label from the plurality of label options according to the content displayed in the sample image to label the object to be labeled, thereby improving labeling efficiency and labeling quality and saving labor costs.

The embodiment of the invention relates to a data annotation system with fast and slow systems cooperatively working. The system comprises a data preprocessing module, a slow system, a fast system and an annotation output module, the data preprocessing module is used for performing image / chart content and text content splitting on the scientific literature file, sending an image sequence and a literature text to the slow system, and sending the image sequence to the fast system; the slow system is used for performing annotation suggestion reasoning according to the image sequence and the literature text and outputting high-dimensional image-text fusion features to the fast system in the reasoning process; the fast system is used for carrying out labeling task processing according to the image sequence and the high-dimensional image-text fusion features output by the slow system; and the annotation output module is used for outputting a literature annotation report. According to the method and the device, the labeling efficiency can be ensured, and the labeling quality can be improved.

The embodiment of the invention discloses a privacy calculation method and system based on a block chain technology and trusted federal learning, and the method comprises the steps: constructing an alliance chain system, and enabling alliance chain roles on the alliance chain system to comprise a data provider, a data user, a data annotation party and a system platform management party; a system platform management party automatically distributes data downloading permissions of all nodes for an alliance chain role based on a data security rule, and submits a to-be-labeled data source and data labeling demand information corresponding to the to-be-labeled data source through a data provider node, and automatically downloading and decrypting the to-be-allocated data annotation task block through the data annotation party node, obtaining the data annotation demand information, confirming to accept the data annotation task, and automatically downloading and decrypting the available data resource block through the data user party node. The problems that in the prior art, data compliance, data authority, data credibility, data circulation, benefit distribution and the like cannot be solved are solved.

The present application provides a method, device, equipment and storage medium for labeling a production process knowledge graph, including: obtaining process parameters corresponding to each of multiple devices from a production process system, and obtaining text information corresponding to each of multiple devices from multiple production process files; inputting the process parameters corresponding to each of the multiple devices and the text information corresponding to each of the multiple devices into a trained neural network model to generate labeling information corresponding to each of the multiple devices, wherein the labeling information includes current process parameters and reference range information of process parameters; labeling multiple devices in the production process knowledge graph based on the labeling information corresponding to each of the multiple devices. The present application can improve the accuracy of data labeling.

The invention relates to a remote sensing image semi-automatic labeling method and system, and realizes high-efficiency and high-quality remote sensing image labeling. Preprocessing the to-be-processed remote sensing image, and constructing to obtain a remote sensing image slice sample set; screening the remote sensing image slice sample set by adopting an active learning strategy based on the pre-trained CNN model to obtain remote sensing image slices; a pre-trained CNN model is adopted to extract depth features of the remote sensing image slices, and multi-scale feature fusion is carried out to obtain a fusion feature map; reconstructing the fused feature map to obtain a pixel-feature matrix, predicting the pixel-feature matrix by adopting a pre-trained random forest model to obtain the category probability of pixels in the remote sensing image slices, and marking the remote sensing image slices according to the category probability of the pixels in the remote sensing image slices to obtain a pixel-level probability map; performing boundary refinement processing on the pixel-level probability graph by adopting a conditional random field mechanism to obtain a pre-labeled graph; and manually correcting the pre-annotated graph to obtain an accurate annotated graph.

The invention discloses an industrial labeling method and device, and belongs to the technical field of industrial detection. The industrial labeling method comprises the following steps: receiving a first input of a user on a target area of a to-be-labeled image; the first input comprises at least one of box selection input and click input; in response to the first input, labeling a feature image associated with the target area to obtain a labeled image; and outputting the annotated image, wherein the annotated image comprises the image features of the to-be-annotated image and annotation information. According to the industrial labeling method, labeling can be carried out according to features of different forms and types, the application scene is wide, the use threshold is reduced, great convenience is brought to labeling of workers, a large amount of manpower and time cost is saved, and labeling efficiency and labeling quality are improved.

The present application relates to the technical field of thunder and lightning early warning, in particular to a lightning early warning artificial intelligence data set labeling method, scientific and accurate data set is needed for artificial intelligence training, how to accurately standardize the position of thunderstorm cloud cluster and the intensity of lightning activity is crucial to the model training effect, the technology used in the present application is a lightning activity intensity labeling method. Through the lightning positioning system of networking observation, the probability and distribution range of lightning occurrence are quantified, and the problem of easy data representativeness of lightning original data in a short observation period is solved.

The embodiment of the invention provides a point cloud data labeling method and device, electronic equipment and a storage medium, and relates to the technical field of automatic driving. The method comprises the following steps: acquiring a to-be-processed point cloud sequence; inputting the to-be-processed point cloud sequence into a labeling model to obtain an initial labeling result of the to-be-processed point cloud sequence output by the labeling model; correcting the initial labeling result to obtain a target labeling result corresponding to the to-be-processed point cloud sequence; wherein the labeling model is trained based on the following mode: obtaining an object point cloud, a wind sand point cloud and a plurality of first sample point clouds; for each first sample point cloud, adding the object point cloud and / or the sandstorm point cloud into the first sample point cloud to obtain a second sample point cloud; and training the initial labeling model based on the plurality of second sample point clouds, and taking the trained initial labeling model as a labeling model. According to the embodiment of the invention, the processing capability of the labeling model for the wind and sand scene is improved, and the labeling quality is improved.

The invention discloses an on-chip light quantum bit feature enhancement extraction method, which is suitable for portable field detection in the field of biomedical detection. According to the method, by combining multiple image preprocessing and feature extraction technologies, rapid and high-sensitivity recognition of the light quantum bit features is realized. The method mainly comprises the following steps: 1) image preprocessing: automatically analyzing brightness characteristics in an original image, and identifying and classifying a brightness extreme value image; 2) image enhancement: adopting wavelet transform denoising and non-local mean denoising technologies to improve the image quality, and optimizing the image contrast through histogram equalization; and 3) feature extraction and enhancement: performing difference analysis and superposition processing on the processed image, enhancing target features and reducing background noise interference at the same time. According to the invention, the accuracy and repeatability of detection are remarkably improved while high efficiency and portability are ensured, and the method is suitable for rapid biomedical detection in an environment with limited resources.

The disclosed embodiments are about a data processing method and device, an electronic device, and a storage medium, and relate to the field of artificial intelligence technology. The data processing method includes: dividing the data to be processed into labeled data and unlabeled data, labeling the unlabeled data according to the current model trained with the labeled data, and determining the selection strategy corresponding to the autonomous learning; selecting a target number of sample data from the unlabeled data using the selection strategy corresponding to the autonomous learning, and labeling the sample data to obtain labeled sample data to obtain updated labeled data; labeling the unlabeled data except the sample data according to the next model trained with the updated labeled data, until the termination condition is met to obtain the target model; predicting and labeling the remaining unlabeled data according to the target model to obtain the labeling result. The technical solution disclosed in the present invention can improve the accuracy and efficiency of data processing.

The application discloses a kind of on-chip light quantum bit feature enhancement extraction methods, suitable for portable field detection in biomedical detection field.The method is realized by combining various image preprocessing and feature extraction techniques, and the rapid and high sensitivity recognition of light quantum bit feature is realized.The method mainly includes the following steps:1) image preprocessing: automatically analyze the brightness characteristics in the original image, identify and classify the brightness extreme image;2) image enhancement: wavelet transform denoising and non-local mean denoising technology are used to improve the image quality, and the image contrast is optimized by histogram equalization;3) feature extraction and enhancement: difference analysis and superposition processing are performed on the processed image, and the target feature is enhanced while the background noise interference is reduced.The application guarantees the efficiency and portability, significantly improves the accuracy and repeatability of detection, and is suitable for rapid biomedical detection in resource-limited environment.

The present invention discloses a method for automatic labeling of rolled metal surface defects based on a multi-task adaptive model, including: S1, collecting rolled metal surface images, performing data labeling after preprocessing and enhancement, and constructing a rolled metal surface defect data set; S2, constructing a multi-task adaptive model, and training it using the data set to obtain a defect automatic labeling model; S3, inputting the metal surface image to be labeled into the defect automatic labeling model, and outputting the surface defect labeling result by executing the target detection task. The method combines a multi-task learning framework to realize the joint training of target detection and instance segmentation tasks for metal surface defects in the rolling process, and focuses on the target detection task in the reasoning stage to improve the labeling efficiency. The labeling method of the present invention significantly reduces labor costs, can be widely used in industrial defect detection and data automatic labeling scenarios of rolled metal, and is suitable for defect image automatic labeling tasks in complex industrial environments.

The application belongs to the technical field of natural language processing, and relates to a Tibetan-Chinese bilingual corpus collaborative labeling and versioned publishing system and method. Through the system architecture formed by the front-end interaction module, the business processing module, the data storage module and the basic management and control module, relying on the Tibetan-Chinese bilingual labeling, intelligent collaborative management and control, corpus version management and standardized publishing core units integrated by the business processing module, cooperating with the distributed correlation index storage mechanism of the data storage module, the fine-grained permission and operation log management and control function of the basic management and control module, the core technical problems of the Tibetan language characteristics adaptation deficiency, the low efficiency and frequent conflicts of multi-person collaborative labeling, the lack of version tracing and quality grading control of the corpus, the non-standard publishing process and the disconnection of the corpus and the downstream model in the prior art are solved. The accuracy of Tibetan-Chinese bilingual corpus labeling and storage is ensured through exclusive Tibetan adaptation processing, and the orderly promotion of multi-person labeling is realized through modular collaborative management and control.

The application provides a two-dimensional icon labeling method, system and device based on three-dimensional model feature recognition, which obtains model parameter information; extracts three-dimensional features of a target three-dimensional model in the model parameter information, determines the correlation of the three-dimensional features under multiple views based on a multiple view search method; selects an optimal projection direction according to the correlation to project the three-dimensional features to a virtual space plane, and determines shape information and size information of two-dimensional features; determines feature naming according to the matching relationship between the two-dimensional features and the counterpart information, and matches the two-dimensional features according to a preset industry rule to determine tolerance information; encapsulates the view position information, feature space position, tolerance information and feature naming corresponding to the two-dimensional features to generate a labeling data package; and optimizes the layout formed by the labeling data package to generate a two-dimensional icon. The application does not require manual intervention throughout, greatly reduces the labor cost and human error, and improves the labeling efficiency and quality.