36results about How to "Expand the dataset" patented technology

The embodiment of the invention provides an image processing method, device and system and a computer storage medium. The method comprises the step of generating a generative adversarial network, which comprises a generative model and a discrimination model, through the following way that: inputting an input image into the generative model to obtain an output image, wherein the input image and theoutput image have different styles; inputting the output image into the discrimination model to obtain an output value; according to a pre-defined loss function, training the generative model and thediscrimination mode until convergence is realized; and using the trained generative model to obtain a target image which has different styles with the input image of the trained generative model. Therefore, by use of the embodiment of the invention, the generative model can be obtained through training, on the basis of the generative model of the generative adversarial network, the target image which has different styles with the input image can be obtained, so that a dataset used for vehicle detection can be expanded, and therefore, a vehicle detection effect is guaranteed.

The invention belongs to the technical field of named entity recognition, and particularly relates to a Chinese named entity recognition method and device based on a RoBERTa-BiGRU-LAN model, and the method comprises the steps: converting a to-be-processed Chinese corpus into a word vector sequence; inputting the obtained word vector sequence into a first layer of BiGRU-LAN of a RoBERTa-BiGRU-LAN model, and obtaining a coding sequence fused with local information; inputting the obtained coding sequence into a second layer of BiGRU-LAN of the RoBERTa-BiGRU-LAN model, and obtaining attention distribution fused with global information; and obtaining a named entity identification result according to the obtained attention distribution. According to the improved word embedding method disclosed by the invention, Chinese representation is better carried out, and meanwhile, BiLSTM-CRF is improved into BiGRU-LAN, so that the parameters of the model are reduced, the complexity of the model is reduced, and the training time is saved.

The invention provides a new viewpoint image synthesis method based on a depth-aided full resolution network. The main content comprises the depth-aided full resolution network, the loss function andtraining. The process comprises the steps that the encoder part of the full resolution network firstly extracts important local features from the input image; then a depth predictor performs pre-training on the large image data set after detecting the global image information so as to estimate the depth map of the input image; then the local features and the depth are fed to the decoder and dual channel mapping of the target viewpoint position is indicated; and finally the decoder converts the combined features into the distortion field so as to synthesize the final target image based on flowdeformation. The full resolution network is designed and the local image features are extracted according to the same input resolution so that the blurred ghost in the finally synthesized image can beprevented, and the high-resolution and high-quality image can be acquired.

The invention discloses a depth estimation method based on a light field EPI image, and the method comprises the steps: 1, carrying out the refocusing of light field data, and obtaining the light field data under different focusing parameters; 2, extracting sub-aperture images of a horizontal view angle and a vertical view angle from the refocused light field data; 3, extracting light field EPI images in the horizontal and vertical directions from the sub-aperture images; 4, building a double-branch depth estimation model based on light field EPI image correlation reasoning, and using the extracted EPI images in the horizontal and vertical directions for training; and 5, carrying out depth estimation on the to-be-processed light field data by utilizing the trained depth estimation model. According to the method, the relevance between the central pixel and the neighborhood of the EPI image can be fully utilized, and the data enhancement can be realized by utilizing the light field refocusing principle, so that the accuracy of light field EPI image depth estimation can be effectively improved.

The invention discloses a motor imagery electroencephalogram signal classification method based on a parallel CNN-Transform neural network. The motor imagery electroencephalogram signal classification method comprises the steps of S1, preprocessing motor imagery electroencephalogram signals; S2, adding noise into the preprocessed motor imagery electroencephalogram signals to expand data; S3, performing time-frequency analysis on the motor imagery electroencephalogram signals processed in the step S1 and the step S2 to generate a two-dimensional feature map containing time features, frequency features and position information; S4, constructing a CNN model, setting network parameters, and extracting frequency features and position information in the two-dimensional feature map; S5, constructing a Transform model, setting network parameters, and extracting time features in the two-dimensional feature map; and S6, connecting the features extracted in the step S4 and the step S5 in series, and inputting the features into a classifier to obtain a motor imagery classification result. Through verification on a data set BCI complex IV dataset 2b, compared with a motor imagery classification method with good performance in recent years, an experimental result shows that the motor imagery electroencephalogram signal classification method has better classification performance.

The invention provides a new crown epidemic situation comprehensive evaluation method based on a classification regression model. The method specifically comprises the following steps: P1, obtaining feature information influencing epidemic situation factors; p2, constructing a classifier to classify the influence factors; p3, fitting a regression model between the influence factors and the severity of the epidemic situation; and P4, calculating factor values of all levels, weight coefficients and epidemic situation risk coefficients, and analyzing important factors influencing the epidemic situation. The computer technology of artificial intelligence and big data analysis is comprehensively utilized, the classification regression model of machine learning is dynamically established, through multiple times of adjustment of the fitting function and the floating module, evaluation of various levels of influence factors on epidemic situation risks is optimized, main and key aspects influencing epidemic situations in a target area can be analyzed, effective warning and guidance are provided for prevention and control of epidemic situations, effective and positive influences are providedfor life, economy and related aspects of people in a target area from optimization of macroscopic regulation and refinement of microcosmic prevention and control, and the effect of epidemic situationprevention and control work is greatly improved.

The present disclosure relates to systems and methods for processing real-time video and detecting objects in the video. In one implementation, a system is provided that includes an input port for receiving real-time video obtained from a medical image device, a first bus for transferring the received real-time video, and at least one processor configured to receive the real-time video from the first bus, perform object detection by applying a trained neural network on frames of the received real-time video, and overlay a border indicating a location of at least one detected object in the frames. The system also includes a second bus for receiving the video with the overlaid border, an output port for outputting the video with the overlaid border from the second bus to an external display,and a third bus for directly transmitting the received real-time video to the output port.

The invention relates to the field of natural language processing, in particular to a reading understanding choice question answering method based on data enhancement. The method comprises the following steps of: cutting a background material of a reading understanding choice question by utilizing a sliding window method; standardizing background materials, question stems and option data formats of reading understanding choice questions; using a TF-IDF method to extract answer candidate sentences from a word frequency perspective to obtain an answer material sentence set X; extracting answer candidate sentences from the angle of the high-dimensional sentence vector by utilizing Bi-Attention to obtain an answer material sentence set X; combining the sentence sets X and Y obtained in the steps S3 and S4 to obtain a candidate sentence set Z; expanding the candidate sentence set Z by utilizing an EDA strategy adaptive to reading understanding choice questions to obtain a final data enhancement candidate sentence set; and inputting the final data enhancement candidate sentence set into a BERT model for reading understanding choice question answer prediction.

The invention discloses a real-time detection and counting method for solanaceous vegetables in a plant factory. The system comprises an image and video acquisition unit, a cloud computing platform unit, a computer terminal unit, a handheld intelligent terminal unit, a data acquisition module, a data annotation module, a data enhancement module, a data set conversion module, a data set preprocessing module, a detection model training module, a detection model evaluation module and a target detection and counting module. The method comprises the following steps of: labeling image data containing a target by using a modified Labelimg and an automatic labeling and artificial auxiliary supplementary correction method, and constructing a special data set; training the optimized deep learning model in combination with an improved real-time detection method to obtain a target detection model and a parameter file; and the computer and the intelligent terminal are imported for real-time detection and counting of vegetables and fruits in the plant factory, so that the detection precision and speed are improved, a large amount of manual labor is saved, and the production efficiency is improved.

The invention provides a three-dimensional velocity geologic modeling method with a randomly arranged structure and wave velocity, which comprises the following steps: determining a base point in a three-dimensional space, establishing an equation according to the base point to determine a planar layered model, complicating an inclined layer of the planar layered model, and constructing a wrinklelayer model of a curved surface in the three-dimensional space; establishing a three-dimensional fault wrinkle model based on the three-dimensional curved surface wrinkle layer model in combination with the fault surface of the random reference point and the displacement of each point in the global coordinate system; based on the three-dimensional fault wrinkle model, constructing a speed model containing the salt dune, and simulating upward invasion of the salt dune in a geologic body with a certain depth; and carrying out random wave velocity amplitude according to the set stratified category, the set velocity range and the wave velocity difference range between the geology of layers, and achieving three-dimensional velocity modeling. According to the invention, the model data volume ofthe deep learning method used for geophysical inversion is improved, and the inversion effect of the deep learning method is improved.

A technique for simplifying structure data for representing an environment. Polyhedrons can make up structure data used in an application such as modeling, visualization, and navigation. Consequently, the operations that are performed on the data often involve determining, for each polyhedron that defines a structure such as a building, whether the polyhedron obstructs a line-of-sight line between a first point in space being considered in the application and a second point. In order to determine whether a polyhedron obstructs a line-of-sight line, a data-processing system operating on the structure data must determine whether any walls of the polyhedron intersect the line. Thus, the more polyhedrons there are or the more vertices that are in each polyhedron, the more walls there are, and the more intersection checks are required, thereby adding to the computations. The disclosed technique reduces the number of walls by simplifying objects that make up the structure data.