146 results about "Computer vision algorithms" patented technology

The invention discloses an electric power operation risk early warning method and system based on knowledge enhancement and multi-modal fusion. The method comprises the steps that video monitoring data, sensor monitoring data and service system data are collected in real time through multi-source sensing equipment deployed on an electric power operation site; the method comprises the following steps of: extracting entities and relationships from unstructured texts such as regulation documents and job logs by utilizing a natural language processing technology based on deep learning, extracting behavior characteristics from video streams by adopting a computer vision algorithm, and constructing an electric power security knowledge graph with dynamic updating capability; designing a multi-modal feature fusion algorithm based on an attention mechanism, and effectively integrating visual features, text features and sensor data; a graph neural network is adopted to train a dynamic risk prediction model to carry out risk prediction, intelligent research and judgment of electric power operation risks are realized, accurate management and control of the risks are realized through a grading early warning mechanism, and closed-loop management from risk perception to early warning treatment is formed.

The invention relates to the field of photovoltaic module detection, and discloses a module hot spot defect infrared and visible light fusion detection method, which comprises the following steps: multi-mode acquisition: multi-mode data of a photovoltaic module are synchronously acquired, and the multi-mode data comprise infrared temperature original data, visible light polarization images and environmental parameters; data preprocessing: preprocessing the multi-modal data based on a radiation transmission model and a polarization optical principle to obtain preprocessed data including an infrared temperature field and a visible light image; and dynamic feature extraction: extracting a space-time gradient tensor and a Riemannian curvature tensor of the infrared temperature field from the preprocessed data based on differential geometry and a computer vision algorithm. By synchronously acquiring multi-modal data, based on a radiation transmission model and a polarization optical principle, interference of atmospheric attenuation and environment temperature and humidity on infrared temperature measurement and influence of uneven illumination and fog effect on a visible light image are eliminated, and dynamic characteristics of an infrared temperature field and structural characteristics of visible light are mined.

The invention provides an indoor human body positioning and track generation method and device, equipment and a medium, and the method comprises the steps: obtaining target Bluetooth data and position data with timestamps, achieving the data fusion through a data fusion algorithm, and obtaining a first moving track point set; based on a preset data acquisition period and timestamp information of each track point of the first moving track point set, when the track points are missing, detecting by using a computer vision algorithm and determining a corresponding target by using a tracking detection algorithm based on video data acquired by monitoring equipment, and obtaining an image sequence containing the target; determining an actual physical coordinate of a target in each image based on camera calibration and perspective transformation, obtaining an image positioning result and a second moving track point set, and aligning with a timestamp of the first moving track point set and realizing data fusion to obtain a third moving track point; and according to the map information, matching each track point of the third moving track point set to a grid map to form a target track map.

The invention relates to the field of computer vision algorithms, in particular to a defect detection data screening method based on a Pontryagin maximum principle, which comprises the following steps of: training a training data set and testing an evaluation data set to obtain a model reference index; based on a preset proxy data set, respectively calculating definition, labeling integrity and distribution deviation degree indexes to obtain an initial quality weight vector; iteratively updating the basic model parameters and reversely iteratively updating the target vector to obtain a sample quality score; a training scoring device scores and sorts full samples of the training data set; and dividing the sorted full samples into a plurality of candidate screening intervals, and screening high-quality data to train a final model. According to the method, the entropy weight method is adopted to weight the three dimensions to obtain the initial quality weight, so that the initial weight of the sample can reflect the own basic quality difference, the problem that the traditional uniform weight ignores the sample quality difference is avoided, and the accuracy of the sample quality score is further improved.

This disclosure describes an interactive augmented reality system for improving surgeon's view and context awareness during laparoscopic and video assisted surgeries. Instead of purely relying on computer vision algorithms for image registration between pre-operation (or intra-operation) images / models and later intra-operation scope images, the system can implement an interactive mechanism where surgeons may provide supervised information in initial calibration phase of the augmented reality function, thus achieving high accuracy in image registration. Besides the initialization phase before operation starts, interaction between surgeon and the system can also happens during the surgery. Specifically, patient tissue might move or deform during surgery, caused by for example cutting. The augmented reality system can re-calibrate during surgery when image registration accuracy deteriorates, by seeking additional supervised labeling from surgeons. The augmented reality system can improve surgeon's view during surgery, by utilizing surgeon's guidance sporadically to achieve high image registration accuracy.

The invention discloses a face quality label generation method based on cooperation of a multi-modal large model and a computer vision algorithm, and relates to the technical field of artificial intelligence and computer vision crossing, in particular to a face quality label generation method based on collaborative decision making of the multi-modal large model and the computer vision algorithm. The method is especially suitable for a high-security-level identity verification system, an automatic identification photo auditing platform, a large-scale face registration pre-screening process, a living body detection front level and other scenes, and is used for carrying out fine-grained, interpretable and high-robustness quality evaluation and defect positioning on an input face image. The core problems of weak semantic understanding ability, unstable discrimination precision, rigid fusion strategy, poor scene adaptability and the like in the prior art are solved by constructing a four-level technical architecture of dimension decoupling, double-channel parallelism, dynamic confidence fusion and scene adaptive evaluation.

The invention discloses a metal internal characteristic selective extraction method, which comprises the following steps of: (1) scanning a block-shaped metal sample by adopting high-resolution X-ray CT (Computed Tomography), reconstructing characteristics including defect distribution, microstructure, holes and cracks in metal through a three-dimensional reconstruction algorithm, and generating a high-precision three-dimensional model; (2) selecting a required area, calculating voxel resolution according to the distance SDD between a radiation source and a detector and the distance SOD between a sample and the radiation source, displaying a reconstructed three-dimensional image in three-dimensional visual analysis software, and performing threshold segmentation through a computer vision algorithm, and automatically identifying and extracting required features for observation; the whole sample is placed in a square frame in an externally tangent form, and a Cartesian rectangular coordinate system is established. According to the invention, after the internal structure is observed by using the X-ray microscopic imaging technology, an interested area is selected for extraction by using linear cutting according to the three-dimensional structure information, and the internal defect of the sample can be efficiently and accurately positioned and extracted.

The invention discloses a calligraphy image recognition method and system based on a computer vision algorithm, and relates to the technical field of calligraphy image recognition, and the method comprises the steps: collecting dynamic trajectory data through a high-speed camera device and a pressure-sensitive pen, and generating a dynamic trajectory matrix; a multispectral imager is used for obtaining a material characteristic pattern of paper fiber deformation and ink mark penetration, and fusion data is generated through affine transformation alignment; inputting the fusion data into an LSTM-CNN network to extract spatio-temporal features, outputting a dynamic stroke segmentation map, and separating material codes, technique features and style latent variables from the fusion data through a three-channel decoupling network; carrying out preliminary classification, carrying out secondary classification on characters with similar shapes in combination with an attention mechanism, outputting a fine-grained classification result, carrying out weighted fusion on the classification result, carrying out style verification and track logicality, and outputting identification tags and style attributes; and misjudgment samples trigger incremental learning of the laminated classification model, and the weight of a secondary classifier is optimized.

The invention discloses a cognitive load assessment method based on fusion of behavior characteristics and heart rate variability in a classroom video. According to the method, face and behavior videos of students in a real classroom are collected, behavior characteristics such as eye movement tracks, sitting postures and facial expressions of the students are extracted through a computer vision algorithm, heart rate variability indexes are predicted in combination with a remote photoplethysmography technology, and a time sequence characteristic sequence is formed. And then, a time sequence deep learning model is adopted to carry out joint modeling on the multi-modal time sequence characteristics, and the cognitive load scale level is taken as a supervision signal to construct a classification model to realize cognitive load level prediction. The method has the advantages of non-contact, automation, high adaptability and the like, and can be applied to personalized teaching monitoring and intelligent teaching feedback.

This disclosure is directed to systems and techniques for generating actionable metrics data based on sensor data generated from one or more sensors within an environment. For instance, a brick-and-mortar retail environment or other materials handling facility may include one or more sensors, such as overhead cameras, which generate image data as users interact with items in the environment. Machine-learning models and / or computer-vision algorithms may then analyze the resulting image data to identify the interactions between the users and the items. The system may then generate metrics data indicating varying metrics associated with these interactions, which may be used to generate graphical user interfaces (GUI) for presenting the metrics data to vendors, brand owners, and the like.

The invention relates to the field of computer vision, in particular to an intelligent parking system based on a computer vision algorithm, which is provided with a data acquisition module, a resource integration module, an anomaly analysis module, a parking matching module and a parking guide module, determines a vehicle running value and an actual parking effective value, calculates a parking permission characterization value, and displays the parking permission characterization value. Determining whether a parking position for a target vehicle exists in the parking area; determining a target parking area; dividing the target parking area into a plurality of sub-areas; analyzing data information corresponding to the target parking area; and parking guidance is carried out according to the judgment result of the preset condition, and intelligent parking is completed. According to the invention, parking guidance is carried out by determining the target parking area, analyzing the corresponding abnormal data and comparing with the parking related area of the target vehicle, so that the smoothness of the parking path and the parking efficiency are improved.

The present invention relates to the field of semantic communication technologies, and discloses a lightweight image semantic communication method, system, and readable storage medium. The method includes: the sending end acquires a source image, extracts the texture semantics of the source image using a deep learning model, and extracts the color semantics of the source image using a traditional vision algorithm; the sending end sends the color semantics and the texture semantics to the receiving end; the receiving end fuses the color semantics and the texture semantics to reconstruct the source image. By splitting the semantic communication task and introducing simple traditional computer vision algorithms to replace some of the deep learning models used in the semantic communication process in the prior art, the present invention avoids the significant high latency that may be caused by over-reliance on deep models, and solves the problem that the semantic communication method in the prior art completely depends on a neural network model, resulting in excessive computing power required and difficulty in deploying and using on a real machine in the full scenario under the existing hardware conditions.

Disclosed are platforms, systems, methods, and media for performing networked video surveillance while preserving network bandwidth and performance by utilizing at least one video camera deployed on a quarantined sub-network, a local video recorder applying an edge computer vision algorithm to detect an area of an object above a threshold area, and one or more back-end applications applying one or more multi-stage hierarchical computer vision models to the uploaded recorded video to tag video with one or more identified classes of objects.

Eyewear devices that include two SoCs that share processing workload. Instead of using a single SoC located either on the left or right side of the eyewear devices, the two SoCs have different assigned responsibilities to operate different devices and perform different processes to balance workload. In one example, the eyewear device utilizes a first SoC to operate the OS, a first color camera, a second color camera, a first display, and a second display. A second SoC is configured to run computer vision (CV) algorithms, visual odometry (VIO), tracking hand gestures of the user, and providing depth from stereo. This configuration provides organized logistics to efficiently operate various features, and balanced power consumption.

The invention discloses a method for generating an intelligent autonomous navigation map of an agricultural robot, and relates to the technical field of agricultural robots, and the method comprises the steps: carrying out the image segmentation of a satellite map of a target farmland through an image segmentation model, so as to automatically recognize a farmland region, and rapidly extracting a farmland boundary contour through the edge curve fitting. Then a navigation path can be automatically generated based on the farmland boundary contour in combination with the basic attribute parameters of the target farmland, and an intelligent label is automatically added to obtain an intelligent autonomous navigation map for indicating the driving path and the working mode of the agricultural robot. The system realizes end-to-end automatic conversion from a satellite image to an intelligent autonomous navigation map, can adapt to diversified farmland environments with different topographic features, different crop types and different planting modes based on a deep learning computer vision algorithm, can meet the precision requirement of agricultural operation while ensuring the automation efficiency, and has a wide application prospect. And the method has strong generalization ability.

Methods and techniques for generating a composite visual representation that integrates visual data of a pet with potential purchased items are provided. Visual data of a pet is received. Features of the pet are extracted by analyzing the visual data. A first mapping for the pet is generated based on the extracted features using one or more computer vision algorithms. Measurement data of a pet-related item selected by a user is collected. A second mapping for the pet-related item is generated based on the measurement data. A third mapping that corresponds to one or more dimensional differences between the pet in the first mapping and the pet-related item in the second mapping is generated. A composite mapping is created by combining the first, second and third mappings. The composite mapping is displayed in an augmented reality environment.

The present application belongs to the technical field of oil use peony phenotype parameter automatic extraction method, especially relates to a kind of oil use peony phenotype parameter automatic extraction method and system based on multi-organ image analysis, obtain the high-resolution original image of oil use peony multi-organ containing ColorChecker24 standard color card, the organ at least includes petal, blade, fruit pod;Color correction and geometric correction are executed to original image, 24 color blocks of color card are positioned using computer vision algorithm, color conversion matrix is constructed to map image from device-dependent RGB space to standard color space, while calculating pixel scale based on color block physical size;Four corner points of bearing tray are detected for fruit pod image, and perspective transformation matrix is constructed to complete perspective distortion correction.

The application provides a real-time conversion method for pixels and geographic latitude and longitude in low-altitude photogrammetry, which comprises the following three parts: unmanned aerial vehicle capability confirmation, real-time conversion service implementation deployment for pixels and geographic latitude and longitude in low-altitude photogrammetry, and GIS geographic information platform integration docking. Through real-time data collected by an unmanned aerial vehicle, combined with a computer vision algorithm and a spatial geometric transformation model, the specific pixel points in image data are associated with real geographic coordinates, so that accurate positioning and measurement of ground objects are realized. The application relates to the technical field of low-altitude photogrammetry, and through the cooperative work of real-time data collection by an unmanned aerial vehicle, real-time conversion service for pixels and geographic latitude and longitude in low-altitude photogrammetry, and a GIS geographic information platform, efficient mapping from a photographed image to a geographic coordinate is realized, and accurate and rapid technical support is provided for low-altitude photogrammetry.

The invention relates to a fault report analysis system and method based on image recognition, and the method comprises an image collection module which calls a camera API to obtain a fault report image; the image recognition and data extraction module is used for recognizing character information in the fault report image based on a deep learning OCR technology so as to extract a fault current value and distance measurement data; identifying and positioning a graph area based on a computer vision algorithm, and identifying data annotations in the graph in combination with template matching or a deep learning target detection model; the data processing and calculating module is used for arranging the fault current values, the distance measurement data and the data marks in the graph in an ascending order or a descending order according to timestamps; zero-sequence current is determined according to the fault current value, the ranging data and the data mark in the graph; and the report generation module is used for designing a fixed template and filling corresponding data in corresponding positions of the template to generate a final fault report. The method has the beneficial effects that the acquisition mode of the zero-sequence current data is optimized, and the intelligent level of fault report analysis and report is improved.

The invention relates to the technical field of air curtain machine control, in particular to an air curtain machine intelligent airflow control system based on computer vision, which comprises a sensing module, a data processing module, an execution module, an environment sensing module and a control module, the motion trail identification module is used for identifying a real-time motion trail of a motion target in the real-time video data through a computer vision algorithm and predicting a future motion trail of the motion target through a prediction algorithm; compared with the prior art that a passive control scheme adopting fixed wind speed or simple infrared triggering has the defects that energy consumption is high and airflow is stiff and abrupt, an active pre-control strategy based on computer vision prediction is adopted, the pedestrian trajectory is accurately predicted, and a matched airflow barrier is established in advance, so that the pedestrian trajectory is accurately predicted. Effective combination of non-inductive passing experience and on-demand energy supply is realized, and the system has the advantages of remarkably improving comfort and greatly reducing ineffective energy consumption.

The invention discloses power distribution terminal equipment based on edge calculation and multi-mode perception, a power distribution network inspection method, equipment and a medium, and relates to the technical field of automatic power distribution. Therefore, active early warning can be carried out through the edge computing capability at the initial stage of equipment abnormity, the real-time performance and reliability are good, and the state monitoring of the power equipment is ensured to be supported. The power distribution terminal equipment comprises a regulation and control module and an Internet of Things module; the regulation and control module acquires telemetry data of the power distribution network regularly and transmits the telemetry data to the Internet of Things module through a preset private protocol between the regulation and control module and the Internet of Things module; and the Internet of Things module receives and stores the telemetry data transmitted by the regulation and control assembly, receives a real-time image of the power distribution network acquired and uploaded by an externally accessed camera assembly in real time, carries out research and judgment processing on the real-time image, generates a patrol inspection result of the power distribution network in combination with the telemetry data, and transmits the patrol inspection result to an information management area for processing.

This disclosure presents a system and method for automating the monitoring, reporting, and verification (MRV) of sustainability projects. The system includes a mobile application for users to submit data and images, which are transmitted to a cloud-based storage system. Metadata from the images is extracted and validated against predefined coordinates. A computer vision algorithm evaluates the images, and results are stored in a cloud-based database. The system further includes a secondary verification module using remotely sensed imagery. This automated and distributed approach reduces the cost, complexity, and time of MRV processes, enhancing transparency, reliability, and accuracy. The method supports scalability across diverse sustainability projects, including regenerative agriculture, biodiversity, and greenhouse gas reduction, by leveraging integrated data collection, automated analysis, and distributed network validation.

The application discloses a vehicle identification and tracking method based on deep learning, which is characterized by the following steps: enabling a deep learning computer vision algorithm for a monitoring camera or a camera of a unmanned aerial vehicle, analyzing a real-time video stream, detecting and tracking a vehicle appearing in the video stream, filtering a specific vehicle by input information, capturing the specific vehicle, uploading a captured image to a background system for recording, and fully utilizing an existing hardware camera with high identification precision.

The application discloses a kind of lightweight multi-modal tea garden picking robot control systems, and the application relates to robot control system technical field.The lightweight multi-modal tea garden picking robot control system, including parameter loading module, tea leaf identification judging unit, tea leaf movement prediction module, picking direction planning module, robot motion control module;Tea leaf identification judging unit comprehensively uses image acquisition, pretreatment and computer vision algorithm, accurately identifies tea leaf and judges tender bud maturity.Tea leaf movement prediction module, when wind starts, uses relevant algorithm to predict the stable position of tender bud, to ensure that the cutting basis is reliable.Picking direction planning module under the condition of wind, through sensor monitoring branch sway, calculates relevant parameters and plans cutting path and direction, improves cutting accuracy.Robot motion control module according to the planning result, combined with kinematic model generates joint trajectory and speed curve, can also adjust the angle of scissors, realize smooth and accurate picking and collection.

The invention discloses an assessment test and decision-making method for regression use of a domestic plastic package layered device based on layering characteristics, and relates to the field of reliability assessment and test of plastic package layered devices. The process comprises the following steps: obtaining a screened plastic package layered device, and determining an initial state and basic parameters of a to-be-tested device; extracting hierarchical feature data; classifying the devices based on the hierarchical features; designing a multi-stress assessment test, and carrying out the multi-stress assessment test on different layered devices to obtain reliability layered characteristic changes and test data of the devices; and judging the regression use qualification of the device. The hierarchical feature data is obtained through an acoustic scanning microscope and a computer vision algorithm based on OpenCV; the design method of the multi-stress assessment test is an iterative test framework comprising configurable environmental stress (such as high temperature and high humidity, temperature cycle) and synchronous electrical stress (such as dynamic / static signals); the regression use decision-making method is a comprehensive decision in combination with the category of the device, the electrical parameter state after examination, the layering proportion change trend and whether reliability improvement measures such as coating and the like are added or not; according to the method, the screened layered devices can be scientifically evaluated and recycled, and the accuracy and economical efficiency of screening decisions are remarkably improved.

The invention discloses an intelligent fish tank monitoring method and device based on the Internet of Things, electronic equipment and a medium, and the method comprises the steps that water quality parameters of a fish tank are collected through a sensor group, and the water quality parameters comprise at least one of the temperature, the PH value, the dissolved oxygen concentration, the ammonia nitrogen content and the nitrite concentration; video data of a fish tank are collected through a camera, biological behavior characteristics of fishes are extracted through a computer vision algorithm, and the biological behavior characteristics comprise a swimming activeness index, a food taxis index, a respiratory rate index and posture anomaly detection; performing multi-modal data fusion on the water quality parameters and the biological behavior characteristics to generate a comprehensive evaluation result of the health state of the fish tank; and generating a control instruction according to the fish tank health state comprehensive evaluation result so as to control fish tank equipment to execute corresponding operation. According to the method, the water quality parameters and the fish biological behavior characteristics are synchronously obtained, the comprehensiveness of fish tank monitoring is improved, and more comprehensive and more accurate basic data support is provided for subsequent health assessment.

The present invention discloses an intelligent monitoring and data analysis management platform for municipal pipe networks. The system includes multiple monitoring areas, an environmental sensor group, an environmental data processing unit, a corrosion risk assessment module, an intelligent patrol unit, a data transmission unit and a cloud platform. The environmental sensor group collects environmental data in the monitoring area in real time, and the environmental data processing unit analyzes the data in real time through edge computing technology and generates historical data. The corrosion risk assessment module evaluates the corrosion risk based on real-time data and historical data, determines high-risk areas and generates detection tasks. The intelligent patrol unit automatically navigates to the target area for spot projection detection, and the deformation analysis module analyzes the image data through computer vision algorithms to identify the corrosion area and its information. The data transmission unit uploads the detection information to the cloud platform. The present invention improves the efficiency and accuracy of corrosion detection through automated patrol and spot projection, ensuring the safe operation of pipe corridor equipment.

The present invention discloses a method and system for constructing a classification model for birds and drones based on centroid motion characteristics. The method comprises the following steps: shooting video data of birds and drones flying; pre-processing the video data and cutting it into several fixed-duration trajectories to form a video data set; marking the centroid motion trajectory using a computer vision algorithm and performing wavelet denoising pre-processing; calculating the speed and acceleration value motion characteristics of the trajectory segments in the centroid motion trajectory, and extracting the maximum fluctuation amplitude of the trajectory segments in different frequency intervals; using machine learning to establish a speed classifier, an acceleration classifier, and a trajectory fluctuation classifier respectively for the speed, acceleration, and trajectory fluctuation characteristics of birds and drones; weightedly integrating the prediction results of the three classifiers, obtaining the optimal weight using a five-fold cross-validation method, and establishing a classification model for birds and drones; the classification model for birds and drones provided by the present invention is fast and accurate, does not require manual judgment, and reduces labor costs.

Techniques are provided for measuring interpupillary distance (IPD) using a mobile device application that integrates with augmented reality (AR) eyewear. The mobile application guides users through a measurement process using the device's camera and sensors to capture facial data. Computer vision algorithms analyze the data to determine IPD measurements while considering facial geometry and depth perception. The system provides real-time positioning feedback and allows manual entry of known IPD values. Measurements are stored in user profiles rather than on AR devices, enabling cross-device synchronization. The integration of IPD measurement into the initial device setup process, combined with flexible measurement options and profile-based storage, provides an accessible solution that maintains accuracy while eliminating the need for specialized measurement hardware.

This invention discloses a navigation system based on an intelligent bicycle, belonging to the field of navigation technology. It addresses the problems of path planning and safety control under dynamic road conditions. The system first constructs a digital situational awareness model of the vehicle by synchronously acquiring data from a multi-dimensional motion sensor group and wheel speed pulse sequences to calculate real-time attitude angles, speed decay, and lateral sway amplitude. A road condition analysis module uses computer vision algorithms to reconstruct a three-dimensional digital model of the road surface, identifying height abrupt changes and determining obstacle crossing costs. A cost fusion module uses a multi-modal data fusion algorithm to map vehicle condition parameters into energy factors and generates a navigation cost chain that has undergone local smoothing. A path generation module performs path search based on this cost chain and outputs the optimal steering command. This invention achieves a deep mapping between the physical driving environment and the digital cost space, significantly improving navigation accuracy under complex dynamic road conditions.