6results about How to "Improve annotation quality" patented technology

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 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 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 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 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.