10results about How to "Improve recognition results" patented technology

The invention relates to a grassland human settlement space ecological security pattern construction method comprising the following steps: 1) identifying an ecological source land, including ecological source land preliminary identification and ecological source land screening; 2) based on grassland living space production and life characteristics, ecological resistance surface identification is carried out; and 3) combining an ecological source land evaluation result and an ecological resistance surface identification result, simulating an ecological corridor, an ecological key point and an ecological obstacle point by adopting a circuit theoretical model, and constructing an ecological safety pattern as an identification result of an ecological space in the grassland living space. The grassland human settlement space ecological safety pattern construction method developed by the invention can adapt to the particularity of the grassland pastoral area human settlement space, and provides a thought for grassland pastoral area territorial space layout optimization and management and control partition delimitation.

The invention relates to a vehicle driving area detection method, electronic equipment and a storage medium. The method comprises the following steps: acquiring image data and point cloud data in a driving area acquired by a target vehicle; lane line detection is carried out on the driving area based on the image data, and obstacle detection is carried out on the driving area based on the point cloud data; and when it is detected that a lane line exists in the driving area, obtaining a detection result of whether an obstacle exists in a target lane corresponding to the lane line. According to the embodiment of the invention, the image data and the point cloud data are combined, lane line detection is carried out by utilizing the image data, and obstacle detection is carried out by utilizing the point cloud data, so that whether an obstacle exists in the target lane or not can be quickly and accurately judged, the computing power can be saved to a great extent, and a better recognition result can be obtained.

The invention aims to provide a novel thunder-vision fusion method, so as to solve the problem that thunder-vision fusion is relatively tedious in a traditional mathematical method, and further improve the safety of an automatic driving system. The invention provides a front vehicle detection method based on deep learning, and the method employs the data fusion of a millimeter wave radar and a camera, and achieves the vehicle detection (Deep-ROI, DR) through ROI correlation. Pictures shot under different conditions are cut into images with the same size to create a dep-fusion data set, a new method for manufacturing a radar data set is provided, multi-dimensional information of an effective target is projected to pixel points, and matrix format data with the same size as the images are generated. A backbone network of the deep learning network comprises two pipelines which are used for processing radar data and image data respectively, and two parameter association credibility (R parameter) and environment characteristics (E parameter) are added on the basis of a Resnet model for fusion. And the constructed deep learning network module is trained by using multiple frames of images containing different light and weather conditions and radar data.

The present application relates to the field of digital medical treatment, and discloses a kind of identification method of image text in test sheet, comprising: obtaining the position information of different regions in test sheet image, convert the text information in different position regions in check sheet image into text feature vector, using the first network model trained by target detection algorithm, test sheet image is encoded into visual feature vector, each visual feature vector represents the visual feature in the preset image region in test sheet image, after text feature vector and visual feature vector are combined into multimodal sequence feature, linear network mapping is carried out to obtain the feature representing entity in test sheet image, using the feature representing entity in test sheet image, the text in test sheet image and the corresponding relationship of text are identified, and the text array contained in test sheet is obtained.The present application enriches text feature by the mode of multimodal feature, avoids introducing cascade error prediction, and improves the identification result of text in test sheet image.

The invention discloses a microscopic image enhancement and restoration method and system for tumor pathology, and relates to the technical field of medical imaging. Comprising the following steps: S1, acquiring a normalized value of a microscopic image, comparing the normalized value with a preset normalized value, and determining a final microscopic image; s2, determining an artifact area and an artifact type corresponding to the final microscopic image, and processing the artifact area; and S3, optimizing the microscopic image after the artifact region processing, and reconstructing a three-dimensional blood vessel structure in the microscopic image to obtain an optimized microscopic image. According to the invention, artifact identification is carried out on the preprocessed microscopic image through the artifact detection model, so that tiny noise existing in the microscopic image can be reduced, and the identification efficiency and the identification result can be improved, so that the artifact area in the microscopic image can be better identified, and the identification accuracy of the microscopic image is improved. And the recovery efficiency of the artifact region is further improved.

The present application discloses a cloud recognition method, device, electronic device and readable storage medium for remote sensing images. The method includes updating the Fmask cloud recognition algorithm applicable to multispectral sensors in advance according to the data preprocessing method and band information of the hyperspectral sensor to obtain a hyperspectral cloud recognition method; in response to the data input mode selection instruction, obtaining the target data to be identified from the hyperspectral remote sensing data. Based on the hyperspectral cloud recognition method, the target data to be identified is image preprocessed to obtain the apparent reflectance information; based on the apparent reflectance information, the hyperspectral cloud recognition method is called to perform cloud recognition and cloud shadow recognition respectively to obtain cloud recognition results and cloud shadow recognition results; based on the cloud recognition results and cloud shadow recognition results, the cloud information of the hyperspectral remote sensing data is determined, thereby effectively improving the cloud recognition accuracy of the hyperspectral images of domestic hyperspectral satellites.

This invention relates to the field of computer image processing technology, and particularly to an optimized method for measuring the volume between intraocular implants and anterior segment structures. The invention utilizes anterior segment OCT grayscale images, preprocesses the OCT images using specific image processing methods, generates a high-resolution OCT grayscale image mask using a specific mask generation method, and combines mathematical methods to automatically identify, 3D reconstruct, and measure the volume between the posterior surface of the intraocular implant and the anterior surface of the lens in the image. This invention fills the gap in the fully automated measurement of the volume between intraocular implants and anterior segment structures based on anterior segment OCT grayscale image recognition, proposes a new image preprocessing workflow, optimizes the speed and accuracy of Mask RCNN for high-resolution anterior segment OCT grayscale image recognition, and simultaneously enables 3D reconstruction and automatic measurement of the spatial volume between the posterior surface of the intraocular implant and the anterior surface of the lens.

The application discloses a traffic sign category determination method and device, a vehicle and an electronic device, and relates to the technical field of vehicles. The method comprises the following steps: acquiring a to-be-tested image, wherein the to-be-tested image is an image comprising a traffic sign; performing gray scale processing on the to-be-tested image to obtain a position image, wherein the position image is used for representing the position of the traffic sign in the to-be-tested image; and determining a recognition result according to the to-be-tested image and the position image, wherein the recognition result is used for representing the category of the traffic sign. The application solves the technical problem that, in the prior art, the category of a traffic sign is identified by using a traditional computer graphics method, a machine learning method and other deep learning algorithms, so that the neural network model is complex, the recognition rate is relatively low, and the classification effect is not ideal.

Provided in the present application are a speech recognition method, apparatus and device, and a storage medium. The method comprises: acquiring a speech feature of target mixed speech and a speaker feature of a specified speaker; taking the direction of tending to a target speech feature as an extraction direction, and according to the speech feature of the target mixed speech and a speaker feature of a target speaker, extracting a speech feature of the target speaker from the speech feature of the target mixed speech, so as to obtain an extracted speech feature of the target speaker, wherein the target speech feature is a speech feature that is used to obtain a speech recognition result consistent with real speech content of the target speaker; and acquiring a speech recognition result of the specified speaker according to an extracted speech feature of the specified speaker. By means of the speech recognition method provided in the present application, speech content of a specified speaker can be recognized more accurately from mixed speech including speech of the specified speaker, thereby achieving a better user experience.

The embodiment of the invention discloses a training method of an integrity identification model, a traffic acquisition integrity identification method and device and electronic equipment, and the training method of the integrity identification model comprises the steps: randomly screening out a part of data from a plurality of pieces of acquired traffic data; according to whether the screened data is complete to generate a label, expanding the screened data and the label thereof by adopting a mask mode; converting the expanded data and labels into corresponding vector representation through a natural language processing model; performing feature extraction on the vector representation based on a self-attention mechanism to obtain low-dimensional features; and training a neural network model to identify whether the data represented by the low-dimensional features are complete or not by taking the low-dimensional features as input and a dichotomy result as output so as to obtain a completeness identification model, and the dichotomy result is used for identifying that the data are complete or incomplete.