220results about How to "Improve recognition efficiency" patented technology

The invention belongs to the technical field of oil and gas pipeline safety monitoring and unmanned aerial vehicle image analysis, and discloses a two-stage oil and gas pipeline occupation pressure monitoring method and system based on unmanned aerial vehicle space perception and deep learning fusion, and the method comprises the steps: obtaining image data and space data of a to-be-monitored region, combining the image data and the space data with a DEM, and obtaining a DEM image; realizing projection of geographic coordinates of the pipeline to image pixel coordinates to obtain a plane image of the pipeline; constructing and deploying a lightweight target detection network DRF-YOLOv8n, performing reasoning on a plane image of the pipeline based on the lightweight target detection network DRF-YOLOv8n, outputting a candidate bounding box, and performing screening and key frame judgment; a Promp-guide SAM pixel level segmentation module is used to realize pixel level segmentation of the key frame, and a candidate pixel mask is generated; and judging whether occupation occurs or not through the intersection of the pipeline projection line and the candidate pixel mask, and outputting alarm information with geographic coordinates.

This invention relates to the field of nuclear safety equipment testing technology. It provides a method for optimizing parameters in accelerated reliability testing of nuclear safety-grade valves. The method involves the following steps: establishing a correlation model between accelerated stress and life parameters; calculating the acceleration factor and formulating an initial test plan; deriving the theoretical sample size based on the success-failure test method combined with confidence requirements; constructing an extended coefficient table using the range-extended test method to optimize test parameters; generating multiple accelerated test plans through chi-square distribution verification; eliminating plans that do not meet the life characteristic threshold; determining the optimal sample size, number of failures, and test cycle through comprehensive test cost and risk analysis; and finally outputting a complete test plan including the action interval time. This method solves the problems of limited test samples, insufficient failure data, low confidence levels, and difficulty in adapting accelerated models in existing high-reliability valve technologies.

This invention designs a multi-scale star surface impact crater detection method, system, and medium. The method includes the following steps: S1: Acquire star surface images and segment them into image patches of different scales; S2: Construct multiple impact crater detection sub-models; S3: Train each sub-model using star surface image patches of different scales; S4: Input the segmented star surface image to be detected into each pre-trained sub-model to obtain the detection results; S5: Select the bounding boxes with high confidence and large area among all candidate bounding boxes of each impact crater as the final detection result for that impact crater. This invention can simultaneously identify impact craters of different scales in star surface images with high accuracy, especially improving the accuracy and efficiency of identifying small impact craters, providing more accurate and comprehensive data support for lunar science research, and promoting the in-depth development of lunar exploration and geological research.

The present application relates to the technical field of face recognition, and particularly relates to a recognition method and system based on a dynamic face database, a computing device and a storage medium, comprising a face tracking server configured to acquire face information obtained by a channel camera and a regional camera, and to merge the face information obtained by the channel camera and the regional camera; a central face server configured to store face information left by a user when registering remotely, and to respectively identify face information collected by the face tracking server from the channel camera and the regional camera, and to send the identification results to a channel face server and a regional face server; the present application reduces the number of face databases by using face tracking and face recognition technology, forms a face server with multiple face databases, improves the recognition efficiency by accurately simplifying the face databases, and reduces the false recognition rate of face recognition.

This application discloses an alarm method, device, storage medium, and electronic device, relating to the fields of video surveillance and artificial intelligence. The method includes: real-time detection of a target area to determine whether a target event exists within the target area, wherein the target event indicates a throwing event of a target object, and / or an event indicating the presence of the target object on the ground within the target area; upon detecting the presence of the target event within the target area, determining the falling position of the target object and / or the target position of the target object on the ground, and determining an alarm level corresponding to the target event based on the falling position and / or the target position; and issuing an alarm for the target event based on the alarm level. This application solves the problem of low recognition and alarm efficiency caused by relying on manual inspection for identifying and alarming cigarette butt throwing events in related technologies.

The present application provides an image processing method, device and storage medium, comprising: acquiring a to-be-tested image of each to-be-tested object in a plurality of same to-be-tested objects, acquiring a definition difference value between a first target region of a first to-be-tested image and a second target region of a second to-be-tested image, acquiring a target parameter according to the definition difference value, and if the target parameter is greater than a target threshold, determining that the first target region and / or the second target region is an image blur region. The present application compares the definition difference value between the target regions at the same position in different to-be-tested images to determine the image blur region, automatically determines the image blur region, improves the identification efficiency of the image blur region, avoids image blur judgment errors caused by background color difference, improves the accuracy of image blur region determination, and the identified image blur region can be subsequently adjusted smoothly to achieve clear detection.

The present disclosure relates to a terminal identification method and device, electronic equipment and storage medium. The method comprises periodically receiving a first beam signal transmitted by a second terminal and a first transmission angle corresponding to the first beam signal, the first beam signal being parallel to a horizontal plane, and the included angle between the first beam signal and a first plane being the first transmission angle, wherein the first plane is the projection plane of the current plane of the second terminal in the vertical direction; obtaining a first included angle between the received first beam signal and a second plane, the second plane being the projection plane of the current plane of the first terminal in the vertical direction; and determining whether the second terminal is a first target terminal according to the first transmission angle and the first included angle, so as to determine whether the second terminal is a target device to be identified without obtaining other identifiers of the second terminal, effectively simplifying the terminal identification process, improving the terminal identification efficiency, and thus effectively improving the user experience.

The invention discloses a wire tape production equipment remote monitoring and fault early warning system based on the Internet of Things, and relates to the technical field of equipment fault early warning, and the system comprises the steps: obtaining the operation state data of an industrial equipment part, the operation state data comprises vibration amplitude data and temperature change data, processing the running state data by adopting a time sequence analysis method so as to identify a performance degradation starting point and generate a performance degradation starting point identifier; acquiring sensor position information and determining a signal monitoring area through the performance degradation starting point identifier, and generating a coverage map identifier; according to the boundary of the signal monitoring area, extracting temperature change data and vibration amplitude data from the operation state data, and constructing a performance evolution law model by adopting clustering analysis and interpolation fitting; according to the invention, by constructing a spatial coverage modeling and performance evolution analysis mechanism based on multi-source state data, performance degradation identification, coverage area perception and fault risk early warning in the remote monitoring process of industrial equipment are realized.

This application discloses a method, apparatus, device, and computer-readable storage medium for image foreground segmentation, particularly relating to foreground segmentation of circular regions in images displaying circular regions of different colors. The method establishes a labeled information database based on the circular region image with labeled information, processes the information to obtain a threshold range corresponding to the target pixel region in the color space, and then accurately separates the monochrome circular particle foreground images from the user-provided image based on this threshold range. Each monochrome particle image is segmented separately to obtain monochrome particle foreground segmentation results. Finally, the multiple monochrome circular particle foreground segmentation results are fused to obtain the foreground segmentation result of the multi-dimensional circular region image. This method has higher recognition efficiency and better noise tolerance compared to the traditional watershed algorithm.

The invention discloses an oral cavity deformity recognition method based on multi-view oral cavity pictures and application thereof, and belongs to the technical field of image recognized.The method comprises the steps that after a multi-view 2D oral cavity picture set of a target maxillofacial surface is subjected to standardized preprocessing, the preprocessed multi-view 2D oral cavity picture set is input into an oral cavity deformity recognition model; probability distribution of a target maxillofacial surface under each malformation category is output through an oral cavity malformation recognition model, and the oral cavity malformation recognition model is a visual Transform model based on multi-modal fusion and comprises a modal conversion module, a 2D feature extraction module, a 3D feature extraction module, a feature fusion module and a multi-label linear classification head module. The oral cavity deformity recognition precision is effectively improved, various oral cavity deformity features can be captured, the high accuracy of multi-label classification is achieved, and the requirement for the oral cavity deformity recognition precision is met.

The application discloses a cross-platform operation and maintenance replay scene intelligent identification method and a cross-platform operation and maintenance replay method. The identification method comprises the following steps: collecting operation and maintenance associated data from different platforms in real time, screening target associated feature data from the operation and maintenance associated data after preprocessing, determining time sequence features of the target associated feature data, and creating new features representing potential structures of the data; extracting key information from all the target associated feature data and the new features; extracting multi-scale features from the key information, performing feature fusion on the multi-scale features, and obtaining a plurality of composite features; screening a composite feature with the most independent features related to an identification target operation and maintenance scene from the plurality of composite features, and marking the composite feature as a target feature; and inputting the target feature into a target scene intelligent identification model to obtain a replay scene identification result. The method can automatically, efficiently and accurately identify an operation and maintenance operation scene that can be repeatedly executed between different operating systems and platforms.

The invention discloses a corn seed variety identification and contour segmentation method and device based on deep learning, and relates to the technical field of corn seed category identification. The problems that in the prior art, a trained corn variety recognition model is difficult to achieve integrated detection of variety recognition and seed segmentation, tiny differences between similar varieties and morphological changes between seeds of the same variety in a natural placement state are difficult to effectively process, the model parameter quantity is large, and the calculation cost is high are solved. The method comprises the steps that a training data set is constructed, each corn seed image comprises a plurality of corn seeds, an improved YOLOv11 model is constructed, on the basis of the YOLOv11 model, a backbone network is replaced with a ConvNeXt V2 network to optimize feature extraction efficiency and network weight reduction, an image-level variety classification head is newly added, a loss function adopts a double-task collaborative loss function, and the number of the corn seed images is smaller than the number of the corn seed images. Parallel output of image-level variety classification and seed contour segmentation is realized, and an improved YOLOv11 model is trained and applied.

This invention belongs to the field of power monitoring technology and discloses a brightness balancing and regional smoothing fusion algorithm for power monitoring images. The method includes: image denoising, using mean filtering and median filtering to smooth the image; image registration, performing optimal matching on two or more images obtained from different sensors, at different times, or from different angles; and image region fusion, smoothly fusing images with different angles and brightness contrasts. This method is applicable to panoramic video monitoring systems. Designed specifically for the complex lighting and high electromagnetic interference characteristics of power operation sites, this invention effectively eliminates on-site noise through adaptive preprocessing, and achieves a balance between global brightness uniformity and local detail preservation through layered brightness balancing, solving the problems of incomplete brightness balancing and detail loss in power scenarios encountered by general algorithms.

This application provides an open-vocabulary remote sensing image semantic segmentation method and system. The method acquires remote sensing images and learnable text, and obtains image-encoded features and text-encoded features through encoding operations. Low-frequency components are extracted from the image-encoded features, and structured noise is injected into these features to obtain low-frequency enhancement features. A similarity cost map is generated by calculating the similarity between the text-encoded features and the low-frequency enhancement features, and a semantic score is formed by aggregating these similarity cost maps. Detail-guided features are acquired, and the semantic score and detail-guided features are combined to output the segmentation result through image decoding. This method can analyze the low-frequency dominance of remote sensing images based on their frequency composition and enhance discriminative ability in the frequency domain through noise injection, thereby improving the segmentation performance of remote sensing images under open-vocabulary settings and increasing the recognition efficiency of remote sensing images.

The present application relates to a corner point recognition method and a computer readable storage medium and a system, one aspect of which is to build a corner point recognition model outputting corner point coordinates and confidence rates, take the confidence rate as a first evaluation index, judge whether the confidence rate of the corresponding corner point recognition model is greater than a first set threshold, if yes, retain the corner point coordinates output by the corner point recognition model, if not, discard the corner point coordinates output by the corner point recognition model, so as to screen the corner point coordinates output by the multi-corner point model in the combined model; another aspect is to determine the weight of each corner point recognition model through a neural network method, combine the screened corner point coordinates and the corresponding weight, and calculate the corner point coordinates of the image to be recognized. The whole is a multi-model fusion corner point recognition method, which can consider globally and analyze comprehensively to improve the accuracy, precision and recognition efficiency of corner point recognition.

Embodiments of the present application relate to a kind of processing method and system for lane line identification based on image, the method comprises: receiving first image;According to preset image size, the size of first image is adjusted to obtain corresponding second image;Second image is input into preset lane line prediction model to carry out lane line instance and attribute identification processing to obtain corresponding third image.Through the end-to-end lane line prediction model provided in the present application, the identification efficiency can be improved, and system algorithm power resource is saved.

The application discloses a component difference identification method and device in different version BIM models and a storage medium. The method comprises the following steps: selecting a component to be compared from different version BIM models; extracting contour lines of the component to be compared in each version BIM model; sampling the contour lines to obtain a key point set of the contour lines; sampling a surface of the component to be compared to obtain a description point set of the surface; calculating a logarithmic distance between each key point in the key point set and all points in the description point set; constructing a statistical histogram of the component to be compared according to the logarithmic distance; and identifying component differences according to the statistical histograms of the components to be compared in different version BIM models. The application can identify subtle differences of components, and improve identification efficiency and identification accuracy.

This invention provides an automatic identification method for sandstone and conglomerate based on imaging logging and combined logging data, comprising: Step 1, preprocessing imaging logging data; Step 2, constructing sandstone and conglomerate lithology sample data; Step 3, constructing an automatic identification network structure for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data; Step 4, training and validating the automatic identification network for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data; Step 5, conducting application tests of the automatic identification method for sandstone and conglomerate based on deep learning-based imaging logging and combined logging data. This automatic identification method for sandstone and conglomerate based on imaging logging and combined logging data takes one-dimensional logging data and two-dimensional imaging logging data as input, and utilizes the constructed combined identification network model to effectively fuse multi-dimensional logging data, achieving high-precision automatic identification of sandstone and conglomerate lithofacies and lithology.

The application relates to a method and device for identifying the identification content of an identification image, a computer device and a storage medium. The method comprises: selecting a positive sample image with a similarity to a target identification image satisfying a similarity condition from candidate identification images, and randomly selecting a negative sample image; performing feature fusion on a target feature of the target identification image, a positive relationship feature of the target identification image relative to the positive sample image, and a negative relationship feature of the target identification image relative to the negative sample image to obtain a fusion feature matched with the target identification image; screening a matching image with a matching degree of image features and the fusion feature satisfying a matching condition from the candidate identification images; and determining identification content contained in the matching image as identification content of the target identification image. The embodiments of the application can be applied to various scenes such as cloud technology, artificial intelligence, intelligent transportation and auxiliary driving, and can improve the identification efficiency when identifying the identification content of the identification image.

The application discloses a kind of impersonation user information identification method and system, it is related to information identification technical field.The method comprises the following steps: obtaining suspect user information, then pre-processing suspect user information, obtaining suspect user data, further matching all the user data to be protected in the preset protection library with suspect user data, descending order sorting is carried out to all the user data to be protected according to the matching result, and the user data to be protected sorting data is obtained, finally, based on the preset recall strategy and the user data to be protected sorting data, the user data to be protected is filtered, the user data to be protected that meets the preset requirement is recorded as filter user data, and the user information corresponding to the filter user data is output as impersonated user information, effectively solve the technical problem that the identification efficiency is low when the existing technology identifies suspect user information (impersonate nickname and / or avatar).

The invention discloses a pipeline modeling method and system based on pipeline characteristics, and belongs to the technical field of image processing, and the method comprises the steps: screening line segment elements in a CAD drawing according to preset pipeline parallel characteristics and a circle center point in the CAD drawing, and obtaining a first center line set; based on the search range of the first center line, detecting the connection condition of the line segment elements at the end points and the line segment intersection condition, and expanding the first center line set according to the detection result to obtain a second center line set; and constructing a plurality of three-dimensional pipeline sections in combination with the pipeline attribute information extracted from the CAD drawing and the second center line set. Therefore, by implementing the method, the problem that in the prior art, it is difficult to accurately and rapidly extract pipeline segments from complex CAD drawings, and then accurate pipeline modeling is achieved can be solved.

The invention discloses a plastic product surface defect detection device based on machine vision, which relates to the technical field of plastic product detection, and comprises a support plate, a positioning table, a three-axis adaptive assembly arranged between the support plate and the positioning table, a compensation assembly arranged in the assembly, and a visual detection soft communication system. The three-axis adaptive assembly realizes three-axis movement of the visual camera, and the compensation assembly can finish micro-motion compensation of the camera and make up for a detection blind area of a large-size product; the visual detection soft communication system is in two-way signal connection with all execution and sensing components, automatic linkage control, image acquisition and processing and intelligent defect recognition and judgment are achieved, detection signals can be received, action parameters can be dynamically adjusted, and closed-loop detection is formed. The device solves the problems that traditional detection equipment is limited in detection range and low in recognition precision, improves the adaptability to products of different specifications, achieves defect global accurate detection, reduces defective product outflow, and is suitable for the production quality control link of plastic products.

The invention discloses a thread hole type identification method, device and system and machining equipment, and belongs to the field of machining. After the target complete circle on the to-be-detected part is obtained, the target bolt diameter corresponding to the preset bottom hole diameter is obtained based on the parameter table; constructing a theoretical cylinder at the position of the target complete circle; the theoretical cylinder and the to-be-detected part are differenced to obtain a target entity, and the type of the surface of the target entity is a cylindrical surface; if the target entity with the diameter being the diameter of the target bolt exists, the thread hole type of the position where the theoretical cylinder is located is determined to be the target type. According to the scheme, two parameters of the bottom hole diameter and the bolt diameter are actually adopted to identify the thread hole. Even if the thread hole is a non-standard geometry, the thread hole can be accurately identified and classified, and the identification efficiency and the automation level are greatly improved.

The invention provides a forestry pest recognition method and device based on Improved-YOLOv11 and a storage medium, and relates to the technical field of computer vision processing. According to the method, cross-geographic-area and multi-insect-state forestry pest images are collected and preprocessed, and a high-quality data set is constructed; on the basis of YOLOv11, a novel activation function, a dynamic loss function and a multi-scale feature fusion module are introduced, an Improved-YOLOv11 initial model is built, and the Improved-YOLOv11 initial model is used for recognition after being trained. According to the method, full-process automation is achieved, the problems that traditional model feature extraction is weak in pertinence, poor in multi-scale adaptation and low in regression precision are solved in a targeted mode, the types and the positions of the pests can be accurately output without manual intervention, the recognition efficiency is greatly improved, the cost is reduced, and rapid data support is provided for forestry pest control.

The application discloses a file processing system based on Internet of Things, and relates to the technical field of Internet of Things, which comprises a processing carrier and a plurality of terminals, the processing carrier is used for sending protocols to each terminal, and sharing data and files within a specified range; after accepting the protocols, the plurality of terminals mirror backup internal files and data, each terminal acquires a plurality of sets of marking information, each set of information represents the file and data classification of the terminal, and each terminal marks each working state of itself to acquire working information Z; each terminal comprises a processing module, a receiving module and a mutual inductance module. When working, each terminal in the application collects data or files generated during its own work, classifies and assigns weights to the data or files, and transmits the files and their attached information between the remaining terminals and the processing carrier, so that the working state change speed between different terminals can be improved.

The application provides a kind of drawing feature and digital matching method, system, equipment and medium, comprising: obtaining three-dimensional model and automatically extracting all relevant feature information;Obtain two-dimensional drawing and automatically extract all label information;Overall projection is carried out to three-dimensional model, and the model transformation projection image and projection information corresponding to three-dimensional model are generated;The model transformation projection image and two-dimensional drawing are carried out hash calculation, and the section projection picture position of the three-dimensional model corresponding to two-dimensional drawing is found;According to feature information, label information, two-dimensional drawing and step S3, determine three-dimensional model projection image and projection information, determine which part of feature of three-dimensional model corresponding to label information, and correspond label information to three-dimensional feature list;Output labeled three-dimensional model and feature label data details.The application can improve the efficiency and accuracy of two-dimensional drawing information recognition in production and processing, solve the problem of feature label information that two-dimensional drawing cannot be automatically recognized in automobile production.

The application relates to a high-voltage direct-current bus of a vehicle, a noise scene recognition method of the vehicle and the vehicle, and relates to the technical field of vehicle noise control. A ferrite core is arranged in the high-voltage direct-current bus; wherein the ferrite core is used for eliminating target resonance effect in a target vibration scene, so as to weaken the electromagnetic interference of the ripple current of the high-voltage direct-current bus on a target vehicle body structure; the electromagnetic force generated by the electromagnetic interference is used for causing the target vehicle body structure to vibrate; the target vibration scene refers to a vibration scene in which the ripple current causes the target vehicle body structure to vibrate; the target resonance effect refers to an effect in which the ripple current is amplified due to the resonance of the high-voltage direct-current bus and a power switching device in the vehicle, and the target resonance effect can optimize the vehicle body structure noise caused by the ripple current of the high-voltage direct-current bus under the resonance effect.

This invention relates to the field of spatial planning technology, and in particular to a method, device, and electronic equipment for identifying potential urban and rural construction land reduction plots. By integrating multi-source data and utilizing spatial overlay analysis, it automatically generates a base map of potential reduction plots, replacing manual screening and improving identification efficiency and standardization. It assigns information values ​​from four dimensions: current population, implementing entity, construction intensity, and ecological importance, constructing a multi-dimensional comprehensive evaluation system that overcomes the limitations of traditional single-dimensional methods. Based on preset screening rules, it outputs the implementation priority level of each plot to achieve automatic sorting, thereby providing scientific and refined decision-making support for urban and rural construction land reduction and relocation.

The invention provides a welding defect real-time identification and positioning method, a welding defect real-time identification and positioning system, a welding defect real-time identification and positioning device and a computer readable storage medium. The real-time identifying and positioning method for the welding defects comprises the following steps: acquiring sensor data in a welding process, wherein the sensor data comprises process parameters, molten pool and electric arc data and weld bead contour data; in response to the fact that one or more of the sensor data exceed a preset parameter range, constructing a defect model according to the sensor data; and comparing and fitting the defect model with a reference defect model in the model association database so as to realize real-time identification and positioning of the defect. According to the real-time identification and positioning method for the welding defects, the defects can be found in time, the accuracy of real-time identification and judgment of the defects is improved, the identification efficiency of the defects is improved, and overall control over the welding quality is facilitated.

This invention relates to the field of noise testing technology, and particularly to a method and system for testing the noise of electric drive assemblies in new energy vehicles. The method includes: constructing a test platform for electric drive noise; delineating a test area; drawing a traffic network distribution map within the test area; marking all road segments; acquiring real-world images of each road segment; and generating environmental data, wherein the environmental data includes at least: road condition information, type, and traffic flow. Through the test platform, the method obtains data reading permissions for the test vehicle; selects the deployment location of the audio pickup device at the electric drive assembly; collects audio data; locates the real-time position of the test vehicle; and uses a preset timestamp. This invention, by generating a test report, can intuitively display the road segments corresponding to the noise and perform correlation analysis between noise data and road attributes, thereby quickly identifying concentrated areas of abnormal noise and improving the accuracy and readability of noise test results.