21 results about "Image segmentation algorithm" patented technology

The present application provides a kind of geologic stratum lithology regional prediction method, system, terminal equipment and storage medium, it is related to computer system field.The system includes data management module, sample management module, model training module, model management module, lithology prediction module and the like.The present application utilizes geophysical, geochemical, remote sensing and other multi-source data, based on deep learning image segmentation algorithm, forms regional shallow surface stratum lithology identification model, carries out high-precision rapid stratum lithology prediction and regional segmentation to the bedrock exposed area and shallow cover area with lower exploration degree, supports geological mapping and mineral exploration work.

The application discloses a kind of modulation signal envelope extraction algorithm and electronic equipment, wherein algorithm includes the following steps: S1, according to modulation data, generate waveform image and obtain the image area corresponding to waveform image;S2, extract the original boundary of image area, obtain boundary pixel coordinate point set;S3, the coordinate point set after screening processing is handled to boundary pixel;S4, based on the coordinate point set after screening processing, complete envelope extraction, obtain the envelope set of modulation signal.The present application converts discrete data into waveform image, extracts target area using image segmentation algorithm, extracts area edge point coordinates using edge extraction algorithm of image processing, and then converts irregular area boundary at pixel level into an ordered coordinate set after screening, reduces data volume and improves the accuracy of subsequent envelope extraction, effectively solves the shortcomings in the existing commonly used algorithm;The present application has the characteristics of fast detection speed, safety and reliability, good practicability, high accuracy and the like.

The application discloses a wall crack visual detection method based on an adaptive image segmentation algorithm, and the method comprises the following steps: performing a gray-scale conversion on an original wall crack image, taking a complete gray value range as a search interval, performing a morphological operation and a filter pretreatment on the gray-scale image, traversing each threshold value in the interval to binarize the pretreated image, and extracting a morphological topology and a geometric feature sequence corresponding to each threshold value; determining an effective threshold value search space by taking a point with the largest number of connected domains as a lower limit, performing linear normalization on the morphological topology feature sequence, and performing logarithmic smoothing and normalization processing on the geometric feature sequence; constructing a coupled double-target fitness evaluation function to obtain an optimal segmentation threshold value; obtaining a binarization result by using the optimal threshold value, removing small-area noise points and connecting broken cracks through a morphological operation, and obtaining a final crack binarization image. The method has high recognition accuracy, can adapt to uneven illumination and a large noise environment, and has a good engineering application prospect.

The application discloses a ship lock concrete structure durability analysis method and system under the coupling action of multiple factors, which comprises the following steps: acquiring internal structure images of different erosion areas of a ship lock concrete test piece; identifying and quantitatively analyzing the internal structure images of different erosion areas by a deep learning image segmentation algorithm to generate an erosion structure factor set; calculating environmental characteristics corresponding to multiple environmental parameters by a multi-physical field coupling inversion algorithm; constructing a three-dimensional microstructure model of different erosion areas; dynamically simulating the three-dimensional microstructure model according to the environmental characteristics; performing spatio-temporal correlation analysis on simulation result data by a hypergraph neural network to generate a prediction result; and comparing an expert knowledge base and performing construction maintenance based on text information of the prediction result by an Euclidean distance similarity algorithm. The application introduces high-precision three-dimensional model multi-factor coupling simulation, and improves the ship lock concrete structure analysis precision through high-precision coupling prediction of the hypergraph neural network.

The present application belongs to the technical field of building material microstructure analysis, and in particular to a foam light soil microstructure analysis method based on image recognition; a foam light soil forming test piece is selected, and a flaky analysis sample is obtained after surface treatment; an imaging device is used to collect multi-view images of the sample, and two-dimensional images meeting the analysis accuracy are obtained; the collected images are subjected to denoising, segmentation and quality optimization processing, and the boundary features of the pores and matrix are highlighted; the pore area is extracted through an image segmentation algorithm, the pore profile is automatically identified and microstructure characteristic parameters are collected, and an analysis model is constructed based on the characteristic parameters; the present application includes sample preparation, multi-view image acquisition, optimization preprocessing, feature identification and analysis steps, and through multi-angle collection and algorithm optimization, the pore features are accurately quantified, the information loss and quantization errors of traditional methods are avoided, and the present application has the advantages of low cost, high efficiency and accurate analysis of foam light soil microstructure features.

The application relates to the technical field of medical treatment and artificial intelligence, in particular to an intelligent tongue diagnosis system and method, which comprises a camera transmission module, a server, a tongue image detection module, a tongue image segmentation module and a tongue image and constitution recognition module, and a client application; the camera transmission module is used for collecting original tongue images and transmitting the original tongue images to the server; the tongue image detection module is used for extracting tongue region images based on a deep learning target detection algorithm; the tongue image segmentation module is used for segmenting the tongue and the background based on a deep learning image segmentation algorithm to obtain tongue image images; the tongue image and constitution recognition module is used for performing feature extraction and analysis based on a deep convolutional neural network and outputting tongue image feature recognition results and traditional Chinese medicine constitution classification results; and the client application is used for displaying the results; thus, through separation of image collection, server-side intelligent analysis and client-side display, professional and efficient image analysis is realized while the portability of equipment is ensured.

The application discloses a kind of multi-feature supported spillway remote sensing identification method and system, and the object level target is obtained by image segmentation algorithm;Spectrum, shape, geometric stereo feature of object level target is extracted, and preliminary extraction of spillway object is realized by multi-feature joint decision;The spatial direction and distance relationship between objects are comprehensively considered, and the fine spot around the preliminary identification result is identified and integrated again, contour modification is carried out simultaneously, and finally the relatively neat spillway boundary vector is output.This method specifically designs the geometric stereo feature parameters of spillway to highlight the unique features of spillway in three-dimensional space, effectively overcoming the difficulty of distinguishing spillway from general buildings.At the same time, the method also provides an automatic remote sensing spillway identification process to improve the accuracy of spillway identification, which has important practical application value in the fields of spillway remote sensing monitoring and water conservancy construction.

The application discloses an enhanced method for defect detection of an ultrahigh-voltage transmission line based on one-way LSTM online learning. The method comprises collecting an ultrahigh-voltage transmission line image and pre-processing the image; using an image segmentation algorithm to segment and extract a core area of the transmission line from the pre-processed ultrahigh-voltage transmission line image; using a feature extraction model to extract multi-scale features in the segmented image and fuse the features to obtain an initial defect map; inputting the initial defect map into a defect recognition model for defect detection to obtain a final defect map and performing threshold segmentation to obtain a defect area position, and combining a transmission line defect type feature to obtain a defect type and a confidence level. The application is aimed at the repetitive and linearly extended defects of the ultrahigh-voltage transmission line, uses time sequence dependence and an attention mechanism to improve precision and robustness, and through online self-adaption, light weight and explainable design, realizes dynamic optimization, low-power deployment and traceability, and balances performance, flexibility and practicability.

Embodiments of the present application disclose a method for judging airway obstruction and related devices, which are used to improve the accuracy of judgment. The method comprises: acquiring a to-be-processed image of a diseased part; performing image segmentation processing on the to-be-processed image according to pixel features of pixels in the to-be-processed image based on an image segmentation algorithm to obtain a segmentation mask image including an obstruction region and an airway region; extracting shape regularity of the obstruction region, adhesion degree between the obstruction region and the airway region, gray mean value difference between the obstruction region and the airway region, and gray contrast of the obstruction region from the segmentation mask image; acquiring a trained judgment model, wherein the judgment model includes a foreign matter judgment module; inputting the shape regularity, the adhesion degree, the gray mean value difference and the gray contrast into the foreign matter judgment module to obtain a corresponding foreign matter judgment result, and the foreign matter judgment result is foreign matter obstruction or pathological obstruction.

The application discloses a tobacco safety foreign matter identification method based on an image segmentation algorithm, comprising the following steps: S1, collecting an original image of tobacco to be detected; S2, preprocessing the original image to generate a standardized image; S3, extracting multi-scale features by using a ConvNeXt model to generate a feature image set; S4, inputting the feature image set into an improved Mask2Former model, outputting an instance segmentation mask through a scale perception query and a local texture attention mechanism; S5, calculating foreign matter categories and coordinates according to the mask; and S6, packing to generate a visual identification result data packet. Through the fusion of the powerful feature extraction capability of ConvNeXt and the accurate segmentation mechanism of the improved Mask2Former, the identification precision and robustness of small, low-contrast and texture-similar foreign matters are improved, the missing detection and false alarm problems of traditional methods in a complex industrial background are solved, and a high-precision detection scheme is provided for tobacco storage safety.

The application discloses a blood vessel positioning method based on a multi-parameter fusion and self-learning model, and relates to the technical field of blood vessel positioning. The method comprises the following steps: collecting three-dimensional medical image data, and pre-processing the image; segmenting the aorta and branch blood vessels by using an image segmentation algorithm, determining a region where a target blood vessel is located, continuously collecting infrared blood vessel images in the region where the target blood vessel is located, and naming the images as a first image and a second image respectively; determining the position of the target blood vessel in the second image by using an optical flow tracking method according to the position of the target blood vessel in the first image, and realizing blood vessel positioning. The application fuses multi-scale, multi-direction and multi-source heterogeneous information, and introduces a self-learning mechanism to optimize the segmentation and tracking process, so that real-time positioning of the target blood vessel is realized.

This application provides a blockchain-based intelligent processing method, system, electronic device, and storage medium for cross-border e-commerce orders, relating to the field of blockchain technology. The method includes: receiving order information, material spectral data, and genuine product feature data to construct a multi-dimensional knowledge graph; triggering hyperspectral scanning based on product identification to generate material composition data, and performing spectral matching with the knowledge graph to obtain a matching degree; simultaneously extracting key region feature sets from the hyperspectral image using the U-Net image segmentation algorithm, and comparing them with an anti-counterfeiting feature library to obtain spatial similarity; when both indicators meet customs thresholds, activating the target smart contract based on an emergency identifier and generating an execution record; finally, encapsulating the verification data into a verification package, generating a hash value, and writing it to the blockchain, establishing a bidirectional index with the order through the smart contract to achieve state synchronization. This application improves the automation level and data reliability of cross-border e-commerce product customs clearance verification.

The application provides a root system distribution feature and slope erosion correlation analysis method, comprising: acquiring root system image data, extracting root system distribution features from soil samples through a scanning device, separating root system structures by using an image segmentation algorithm, and obtaining a root system density distribution map; calculating a congestion degree index according to the root system density distribution map, grouping root system areas by using a clustering algorithm, and determining the numerical change of the congestion degree at different growth stages; for the stage division result, acquiring slope erosion simulation data, generating an erosion intensity distribution by using a water flow simulation software, and determining a corresponding matrix of the erosion intensity and the congestion degree; analyzing a non-linear corresponding relationship from the corresponding matrix, fitting a function relationship between the congestion degree and the erosion intensity by using a regression analysis model, and obtaining a fitting parameter set; inputting real-time root system data into an optimized corresponding relationship model to generate an erosion control effect prediction, and determining an optimal erosion prevention intervention point.

The application provides a method and device for automatic identification, positioning and following grabbing of a dynamic surface-mounted device, and realizes grabbing of the surface-mounted device. The method comprises the following steps: receiving an image collected by an industrial camera, recording an image collection time, and processing the image to obtain a standardized image; inputting the standardized image into a target detection model to obtain all surface-mounted devices in the standardized image, and position and type information of each surface-mounted device, and then determining a target device to be grabbed; extracting an edge contour of the target device by using an image segmentation algorithm to obtain a center coordinate of the target device and a minimum circumscribed rectangle of the target device, and then obtaining an inclination angle of the target device; based on the above information, a mapping relationship between an image coordinate system and a global coordinate system of a robot is used to obtain a space-time coordinate of the target device; the above steps are repeated until the space-time coordinates of the target device at N time points are obtained, so as to fit a space-time motion model of the target device, and the robot can grab the target device.

This invention relates to an online ore particle size analysis method and system based on visual segmentation. The system includes an image acquisition module, an image preprocessing module, an algorithm processing module, a data processing module, a data communication module, a display module, and a control module. The analysis method establishes a pre-training dataset by collecting ore images from different locations and time periods at different project sites, training a pre-trained model with strong generalization ability, and then using a small number of images from the target site to establish a fine-tuning dataset. Through fine-tuning training, a final fine-tuned model with high accuracy is obtained. After processing the ore images using the final fine-tuned model, the preliminary edge segmentation map of the ore is refined using a traditional image segmentation algorithm to obtain the final ore region segmentation map and perform statistical calculations. This invention can improve the accuracy of ore image recognition, thereby obtaining accurate segmentation maps, and ensuring the statistical accuracy of ore particle size without manual screening.

The application discloses a method, system and device for monitoring and early warning of remaining feed, and a storage medium, and belongs to the technical field of intelligent livestock breeding. The method comprises the following steps: acquiring a to-be-identified feeding trough image; performing image segmentation on the to-be-identified feeding trough image based on an image segmentation algorithm to obtain a binary mask image, wherein the binary mask image contains a remaining feed area; extracting multi-dimensional quantitative feature parameters based on the binary mask image, wherein the multi-dimensional quantitative feature parameters are used to represent the state of the remaining feed in the feeding trough; and comparing and analyzing the multi-dimensional quantitative feature parameters with preset early warning rules, and generating early warning information according to the comparison and analysis result.

The present application relates to the technical field of agricultural management, and particularly relates to a farmland soil analysis method, device, equipment and storage medium, the method comprising: in the process that a unmanned aerial vehicle flies at a relatively low height from farmland soil, capturing a farmland soil image through the unmanned aerial vehicle; analyzing and identifying the farmland soil image, when it is identified that the farmland soil image contains an insect image, using an image segmentation algorithm to separate the insect image from the background in the farmland soil image; based on a filling or repairing algorithm, filling the area in the farmland soil image after the insect image and the background are separated, which is blocked by the insect; based on a visual neural network, analyzing the filled farmland soil image, thereby judging the fertility and water content of the farmland soil, so that in the case that the farmland soil image is captured by the unmanned aerial vehicle with insect interference, the precise analysis and evaluation of the farmland soil are realized, and more effective farmland management suggestions are provided for farmers.

The application discloses a kind of fusion local topology and coordinate decoupling honeycomb cell tracking and lineage reconstruction method, belong to biological image processing and cell lineage tracking technical field.It includes: obtaining time sequence microscopic image and extracting cell segmentation mask data based on image segmentation algorithm;Adjacent frame mask is globally registered;Local topological neighborhood graph of surrounding ordering is constructed, and the coordinate decoupled feature matrix consisting of vector angle, distance ratio and area ratio is extracted;Fusion morphology and topological feature construct comprehensive cost matrix and globally optimize solution obtain tracking correlation;Based on skeleton and topological graph network extraction vectorized polygon contour;By reverse fusion and boundary morphology evolution generates virtual mother cell to establish division relationship;With neighborhood voting, conflict adjudication is carried out and lineage fault is repaired by division priority.The application realizes high robustness of automated cell tracking and lineage reconstruction in non-aligned long-interval microscopic image.

The application relates to the technical field of image data processing, in particular to a numerical control machining quality detection method and system based on image processing, which comprises the following steps: acquiring a numerical control machining surface image to be detected; performing enhancement processing on the numerical control machining surface image to be detected by using an improved edge detection algorithm based on fractional differential to obtain an enhanced numerical control machining surface image to be detected; performing segmentation processing on the enhanced numerical control machining surface image to be detected by using an image segmentation algorithm to extract a defect contour, and applying a connected domain marking method to acquire geometric attributes of the defect contour; and if the geometric attributes are not within an allowable error range of standard geometric attributes, it is determined that the numerical control machining surface image to be detected has quality abnormalities. The application solves the problem of low quality detection precision.

This invention relates to the field of defogging technology for new energy vehicles, and particularly to an intelligent vehicle defogging method and system based on the synergy of front-facing vision and temperature difference dew point. By collecting and preprocessing vehicle dynamic data, front-facing image data, and environmental data (including vehicle speed and wiper operation frequency), a fog detection window area is extracted from the preprocessed image of the inside of the windshield. An image segmentation algorithm is used to divide the fog detection window area into fog feature regions and interference regions. The average gray value of the fog feature regions and the standard deviation of the gray values ​​of the interference regions are calculated, and the visual signal-to-noise ratio (SNR) is calculated based on the average gray value and the standard deviation. This invention uses image segmentation and edge detection to calculate the visual SNR and fog edge blurriness, and combines this with a visually reliable fog index to effectively distinguish fog from raindrop reflections and other interference, overcoming the shortcomings of single sensors that are prone to false triggering, and significantly improving the accuracy of fog recognition.

The application provides a kind of anti-collision warning method and system based on preceding vehicle reversing lamp identification, belong to vehicle active safety technical field, the method includes: obtaining the real-time image sequence in front of the vehicle, obtains the vehicle state data;When judging to meet the warning starting condition according to the vehicle state data, all preceding vehicle images in the real-time image sequence are identified and positioned using a target detection algorithm based on deep learning to the real-time image sequence, and the region of interest of the tail lamp group structure of each preceding vehicle is identified and positioned by image segmentation algorithm to all preceding vehicle images;When at least one reversing lamp of the region of interest of the tail lamp group structure of each preceding vehicle is identified to be continuously lighted, output warning information.The application advances the risk identification node from "vehicle has moved" to "intention has been expressed", realizes the real "nip in the bud".