350 results about "Real time vision" patented technology

The invention discloses a multi-modal fusion real-time environment monitoring visual robot system, and relates to the technical field of real-time vision. The system comprises a multi-mode sensing module, and is equipped with various sensors such as a binocular stereo camera and a laser radar to collect environment data. The heterogeneous data preprocessing unit cleans and downsamples data such as images and point clouds; the space-time alignment fusion module realizes multi-source data space-time registration and synchronization; the environment semantic understanding engine constructs an environment semantic graph through a multi-branch network in combination with an attention mechanism; the abnormal event detection unit identifies abnormity based on a historical data model; the path planning and decision-making module integrates multiple targets to generate an optimal path; the autonomous movement execution module controls the robot to move and operate; and the cloud cooperative control center supports model updating and remote intervention. According to the invention, through cooperative work of all the modules, full-process intelligentization of environment monitoring data acquisition, processing, analysis and decision making is realized.

The invention provides a robot navigation method and system based on visual identification, and relates to the technical field of computer vision, firstly, a continuous visual information set of the surrounding environment of a robot is obtained, the continuous visual information set comprises environment visual images of different directions of the advancing direction of the robot, and the object appearance features and the spatial arrangement relation are recorded; then establishing association mapping between the continuous visual information set and a preset navigation area to obtain a double-association mapping result; generating a semantic anchor point navigation path based on a double-correlation mapping result, wherein the semantic anchor point navigation path comprises a semantic anchor point sequence from the current position to the target position; real-time visual semantic features in the navigation process are obtained through a real-time visual collection module, and a semantic anchor navigation path is dynamically updated; finally, a final navigation execution path is output according to the updated path, the robot is driven to complete navigation operation, and the navigation capability and the intelligent level of the robot in a complex dynamic environment are improved.

The invention relates to the technical field of candid shooting detection, and discloses a monitoring screen area candid shooting detection method based on real-time visual identification, which comprises the following steps: determining a screen area, judging whether a suspected candid shooting behavior exists or not according to an overlapping result of a current image and a previous image of acquisition equipment, identifying a gesture action of the screen image, the method comprises the following steps: determining an object contour of a screen image according to an edge detection algorithm, searching an object contour value in a preset object contour table, obtaining a lens brightness value according to effective lens pixel points, outputting a contour weight factor of the object contour value and a brightness weight factor of the lens brightness value by adopting a candid shooting behavior model based on a gesture action, and comparing the candid photographing behavior value with a candid photographing behavior threshold value, and judging whether to send candid photographing early warning information to the signal shielding device or not according to a comparison result. The candid photographing behavior is comprehensively measured through the object contour value, the lens brightness value and the gesture action, and the candid photographing detection reliability is improved.

A system for real-time analysis of emotional feedback during motivational speeches, consisting of: a series of multimodal sensors, including at least one visual sensor configured to capture facial expressions of spectators, at least one directional microphone configured to capture the audio responses of the audience, and optionally one or more physiological sensors configured to capture biometric signals from spectators; an edge-based processing unit that is communicatively coupled to the arrangement of multimodal sensors, wherein the edge-based processing unit comprises the following: (a) a feature extraction module configured to extract visual features from captured facial images, acoustic features from voice responses, and physiological features from biometric signals; (b) an emotion inference machine configured to process the features using a deep learning-based emotion recognition model comprising a convolutional neural network (CNN) for classifying facial expressions, a recurrent neural network (RNN) for classifying voice emotions, and a multimodal late fusion layer configured to compute a composite emotion state vector representing the aggregated emotions of the audience; (c) a timestamp and speech alignment module configured to correlate the calculated composite emotion state vector with segmented portions of a live motivational speech based on real-time speech-to-text transcription and semantic analysis; and (d) a session-based storage unit configured to log time-indexed emotional state vectors and corresponding speech segments for post-event analysis; A speaker feedback interface comprising a portable display or a podium-mounted visualization panel, wherein the interface is configured to display visual indicators of emotional feedback in real time, the indicators being derived from the emotional state vector and including at least emotional trend graphs, threshold alerts, or engagement indices.

The invention discloses an automobile production line gluing system based on visual inspection, and relates to the technical field of industrial automation and intelligent control, and the automobile production line gluing system comprises a gluing data acquisition module, a gluing feature extraction module, a compensation correction module, a gluing decision module and a closed-loop management and control module; the gluing data acquisition module preprocesses assembly line gluing track images and glue gun nozzle three-dimensional coordinate data, the gluing feature extraction module constructs a gluing feature sequence table, the compensation correction module outputs glue outlet opening degree compensation amount and nozzle track correction amount, and the gluing decision-making module outputs glue gun pressure adjusting amount and nozzle movement speed. And the closed-loop management and control module detects pressure abnormity and speed stability in real time, optimizes control parameters and generates a process quality map. Through real-time visual detection and dynamic closed-loop control, the glue line width precision is improved, the track compensation response is shortened, the glue gun speed fluctuation coefficient and glue waste are reduced, the gluing qualification rate is improved, the production cost is reduced, and the automobile manufacturing and production requirements are met.

Precise nerve detection during surgery is crucial for safe operational outcomes, which largely depend on the surgeon's skills. Therefore, automatic real-time identification of anatomical structures is of great importance. However, image processing times must be kept as low as possible to be a viable solution for real-time visual identification. By using a general purpose graphics processing unit (GPGPU) implementation for birefringence mapping, the process can achieve rates of 43 frames per second (FPS); a 118×gain from a CPU implementation. Furthermore, by including a deep learning network, the complete framework can automatically detect nerves close to 12 FPS. By leveraging GPU for the processing task, the framework can run on compact devices with NVIDIA modules, such as Jetson Xavier AGX, while still achieving reasonably fast nerve identification and visualization.

The invention provides a movable flexible docking platform, method and equipment based on visual guidance and a storage medium, and relates to the field of automatic auxiliary assembly. The docking platform comprises an AGV trolley, a six-degree-of-freedom platform erected in the middle of the upper portion of the AGV trolley, and a binocular vision system erected on the rear side of the upper portion of the AGV trolley. The binocular vision system provides real-time vision guidance in the advancing process of the AGV; a butt joint workpiece A is placed on a six-degree-of-freedom platform, and a butt joint workpiece B which has an assembly relation with the butt joint workpiece A is placed on a working table at a target position; and the AGV transports the butt joint workpiece A to the target position, and butt joint of the butt joint workpiece A and the butt joint workpiece B is completed. According to the invention, the six-degree-of-freedom parallel platform is arranged on the AGV trolley, so that the pose fine adjustment of the assembled workpiece is realized, the pose of the workpiece can be continuously adjusted in the transportation process to keep balance, and the workpiece transportation and high-precision butt joint can be realized.

The invention discloses a distribution line unmanned aerial vehicle edge end real-time visual identification and target detection method and system based on lightweight AI. The method is based on a YOLOv8 architecture, and constructs a complete lightweight detection framework comprising a feature extractor, an enhancement module and a simplified detection head by introducing a space structure maintaining assembly, a separated large kernel convolution and a weighted reconstruction feature pyramid. A cross-dimension semantic relation model is constructed by utilizing a combined attention structure, multi-level feature fusion is realized by reconstructing a feature pyramid network, model training is performed by adopting a hybrid optimization function and a dynamic sample adjustment mechanism, and the model is deployed on a mobile computing platform after being optimized by a hierarchical knowledge transfer method. According to the method, the detection speed is remarkably increased while high precision is kept, and real-time identification and anomaly analysis of the power line element are effectively realized.

The invention relates to the technical field of compartment remnant real-time detection systems, and discloses a real-time visual identification and detection system for remnant in a compartment of a subway last station. In the system, an image acquisition module acquires a monitoring video stream and generates a carriage image sequence set; the feature extraction module processes the image to obtain a time sequence association partition labeling set; the time sequence association module analyzes the time sequence stable section image frame and generates a feature matching overlay analysis result; the abnormity judgment module identifies abnormal points to form a remnant abnormal point set; and the risk output module is used for marking the point locations with the left risks and generating a real-time detection and risk early warning result. The system can adapt to the dynamic environment of subway carriages, the efficiency and accuracy of remnant detection are improved, and real-time identification and risk early warning are achieved.

The invention discloses a power transmission channel forest fire risk early warning method and system based on spatial-temporal feature fusion, and relates to the technical field of risk early warning. The method comprises the following steps: constructing a power transmission channel risk grid and fusing historical multi-source data to generate a static flammability background map; the method comprises the following steps: collecting real-time visual and micrometeorological data, and after space-time alignment processing, respectively extracting visual risk features and environmental flammability trend features by using a double-branch space-time feature extraction network; inputting the static background and the dynamic characteristics into a space-time fusion module to obtain a comprehensive forest fire risk index; when the index exceeds a threshold, triggering a forest fire spreading deduction model corrected by combining a power transmission corridor effect, predicting fire spreading, evaluating a line tripping probability, and issuing a graded early warning and emergency strategy; according to the method, the defects of serious data islands and inaccurate early warning are overcome, the crossing from pure fire point monitoring to risk situation dynamic prediction is realized, and the accuracy of forest fire early warning and the intelligent level of power grid prevention and control are remarkably improved.

The invention discloses a robot visual identification decision control method based on deep learning, and relates to the technical field of control systems. Comprising the steps of constructing a thinking decision tree according to an input instruction and a real-time visual image, and obtaining a linear relationship between a middle-layer leaf node as a main body and a top-layer leaf node for realizing association between a current visual feature and a task intention. According to the method, deep fusion and unified representation of multi-source heterogeneous data are realized by constructing a multi-layer thinking decision tree structure and combining multi-modal feature fusion and a deep learning engine, and text instructions, visual images and other different modal information are effectively integrated by constructing a fusion search engine and a semantic alignment mechanism, so that the multi-source heterogeneous data fusion and unified representation are realized. Cooperative processing of accurate analysis of task intentions and environment perception is realized, and real-time response and decision accuracy of the service robot to complex tasks are improved.

The invention discloses an edge spraying compensation control method based on intelligent visual feedback. The method comprises the following steps: performing space scanning on a to-be-sprayed workpiece to obtain spraying image data, and establishing a space corresponding relation between a spray gun motion coordinate and a workpiece surface coordinate; registering the real-time spraying image by using the corresponding relation, identifying a geometric boundary line, brightness gradient change and a coating adhesion area of the edge of the workpiece, and generating edge feature data; according to the difference between the edge feature data and the target spraying image, the spraying coverage deviation and the boundary overlapping error are calculated, the coating thickness change trend, the optical density change rate and the boundary direction offset are extracted, and the spraying gun posture adjustment amount, the spraying distance correction amount, the spraying pressure correction amount and the path speed correction amount are generated; the angle, the spraying distance, the pressure and the movement speed of the spray gun are dynamically adjusted according to the parameters; according to the method, real-time visual feedback and self-adaptive compensation control in the spraying process are achieved, and the uniformity and consistency of the coating on the edge of the workpiece are effectively improved.

The invention relates to the technical field of data processing, and discloses a multi-point synchronous accurate hoisting positioning method and device for a bridge precast beam. The method comprises the following steps: optimizing three-dimensional scene data to obtain target environment data, performing path planning to obtain multi-point hoisting path data, and performing optimization processing on the path data to obtain an optimized hoisting path and attitude change data; performing real-time visual tracking processing on the precast beam to obtain real-time position and attitude data, and performing filtering processing on the real-time position and attitude data to obtain filtered pose data; and performing comparative analysis on the pose data, the optimized hoisting path data and the optimized pose change data to obtain control instruction data, and performing cooperative control on the multi-point hoisting system according to the control instruction data. The posture of the large precast beam can be accurately and efficiently controlled, the positioning precision is improved, and the requirement for rapid and efficient hoisting is met.

The invention provides a dynamic data traceability method and system oriented to network security, and relates to the technical field of network security and data governance crossing, and the method comprises the steps: carrying out the real-time visual monitoring of a user interaction interface of a data transaction platform, and capturing the visual behavior information related to data calling; extracting feature parameters based on the visual behavior information to obtain a visual feature data set, and performing structured conversion on the visual feature data set to obtain a structured call log record containing visual behavior associated information; based on the structured call log record, extracting a time sequence, a frequency and a spatial distribution feature of a call behavior as basic indexes; a plurality of feature sampling states are dynamically selected in a monitoring time window, and a dynamic feature evaluation set is constructed based on the feature sampling states. According to the invention, the accuracy and controllability of network security protection in a data transaction scene can be improved.

The invention relates to an electrofluid repair printing device and an ink-jet printing system.The electrofluid repair printing device comprises a mounting frame and at least one repair assembly, the repair assemblies are mounted on the mounting frame, and each repair assembly comprises a three-axis movement mechanism mounted on the mounting frame; the electrofluid spray head is vertically arranged, and the three-axis movement mechanism drives the electrofluid spray head to move up and down, left and right and front and back; and the imaging direction of the real-time visual imaging piece is inclined to the horizontal plane, and the real-time visual imaging piece is aligned to the drop point position of the liquid drops sprayed by the electrofluid spray head. The electrofluid spray head is vertically arranged, the electric field force is consistent with the spraying direction of the functional liquid, the liquid drops sprayed by the electrofluid spray head fall onto the bonding pad in a linear path, and the liquid drops are not prone to being subjected to the force in the horizontal direction, so that the possibility that the liquid drops move in the horizontal direction in the falling process is reduced, the precision of the dropping point of the liquid drops is improved, and the production efficiency is improved. The liquid drops are ensured to be printed on the corresponding bonding pads, and the repairing effect is ensured.

The invention relates to a robot action generation method and system combining a universal model and a special model, and the method comprises the steps: constructing the universal model and the special model, carrying out the pre-training of the universal model, carrying out the parameter fine tuning of the pre-trained universal model, and training the special model based on the fine-tuned universal model; obtaining a task instruction and real-time visual information, inputting the task instruction and the real-time visual information into the fine-tuned universal model, and outputting an action sequence and task potential features; and acquiring real-time point cloud sensing data, inputting the real-time point cloud sensing data, the action sequence and the task potential features into the trained special model, and outputting continuous actions of the robot. Compared with the prior art, the robot action generation speed is increased, and the generalization of robot action generation is enhanced.

The invention provides an intelligent driving control method and system for an unmanned ship with a body based on a world model, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting a real-time visual image, navigation state data and control motion data of the unmanned ship, carrying out the preprocessing and synchronous alignment of the data, and obtaining a real-time visual image; standardized vision, state and action input and a standardized data packet are obtained; the standardized vision, state and action input is mapped into latent representation, autoregression calculation is carried out by predicting a backbone network, and a multi-step latent state and latent action sequence is predicted; and finally obtaining a future state sequence and a future action sequence based on the predicted potential state and potential action sequence. According to the invention, interpretable autonomous intelligent driving is realized.

The invention relates to the technical field of medical body robots, in particular to a visual navigation method and device for a medical body robot and a medium, and the method comprises the following steps: S1, obtaining a unified feature vector; s2, constructing an initial model; s3, optimizing the initial model; and S4, completing visual navigation. The invention mainly solves the technical problems of real-time visual navigation and multi-task adaptability of the medical intelligent robot in a complex medical scene, especially fusion and processing of multi-modal data in a resource-limited environment. By optimizing the neural architecture and adopting delay control and multi-modal data alignment technologies, the real-time performance and accuracy of the robot in an operation environment are improved, and it is ensured that the robot can process different tasks and adapt to environment changes in a dynamic scene.

The invention provides a real-time visual positioning correction method and device, electronic equipment and a storage medium. The method comprises the following steps: performing feature registration on an initial feature value of an initial image acquired by an image acquisition device and a real-time image feature value to generate a calibration matrix; calibrating the current pixel coordinate of the target identified in the real-time image based on the calibration matrix, and determining the calibrated pixel coordinate of the target; and performing coordinate conversion on the calibrated pixel coordinates based on calibration information of the image acquisition device, and determining real-time positioning information of the target. According to the real-time visual positioning correction method disclosed by the embodiment of the invention, the target information is sensed in real time through deep learning under the condition that only the image acquisition device is used and no additional equipment (a gyroscope, an inertial sensor and the like) is added, and the real-time visual positioning correction method is obtained according to the calibration data obtained in the initialization stage and the image pixel information obtained by target detection. And converting the target to world coordinates to realize real-time positioning.

The invention provides a wearable sight distance measurement method, and belongs to the field of visual depth extraction, the wearable sight distance measurement method comprises a pupil tracking algorithm and a sight distance measurement algorithm, the pupil tracking algorithm is used for tracking the pupil position state of a subject in real time, and performing preprocessing and Hough circle fitting on an eye image obtained by wearable eye movement video acquisition hardware; therefore, the pupil contour and the pupil center point of the subject are calibrated in real time; the sight distance measurement algorithm is based on data of a pupil tracking algorithm, the position of an eyeball center point is fitted through a center calibration algorithm, established data of an image sensor and eye parameters are combined to be imported into a visual depth model, so that sight distance measurement key information is obtained, and then the real-time visual depth of a subject is calculated through a distance measurement equation. According to the wearable sight distance measurement method, a wearable binocular sight distance detection algorithm system for multiple scenes is constructed, and accurate real-time data is provided for application scenes needing binocular sight distance.

The embodiment of the invention provides a ceramic diaphragm automatic positioning system based on high-precision visual feedback and closed-loop control, and the system comprises a parameter reference setting module which is used for setting a theoretical reference point and a visual detection parameter; the real-time visual detection module is used for collecting an image of the to-be-stacked film and outputting an image coordinate of the mark point; the core operation module is used for converting the image coordinates of the mark points into actual coordinates based on the visual detection parameters, reading a theoretical reference point of the current to-be-stacked diaphragm and calculating a position error vector between the actual coordinates and the theoretical reference point, and the position error vector comprises a translation compensation amount and a rotation compensation amount; and the motion control and closed loop feedback module is used for receiving the position error vector and generating a control instruction to drive the workbench to move. According to the system, through parameterized reference setting, real-time visual detection, intelligent error calculation and real-time motion compensation, the technical limitation is effectively overcome, and the lamination alignment precision and efficiency are remarkably improved.

The invention provides a self-adaptive contour extraction and target recognition system and method for hand-eye calibration, and the system is characterized in that a hand-eye calibration system which integrates intelligent sampling planning, self-adaptive visual perception, online quality evaluation and closed-loop feedback control is constructed; the core defects of the prior art are overcome. According to the system, through deep fusion of real-time visual perception and motion control in a traditional calibration process, stable, accurate and adaptive extraction and processing of target contour features are realized. The method has the beneficial effects that the calibration robustness and precision in unstructured environments such as illumination variation, shielding and complex textures are remarkably improved, meanwhile, the safety and efficiency of the sampling process are ensured, and the method is suitable for high-dynamic and multi-interference industrial field application.

The invention discloses an unmanned aerial vehicle high-altitude cleaning path precise planning method and system based on visual navigation, and the method comprises the following steps: constructing an initial three-dimensional grid model of a to-be-cleaned building, dividing cleaning regions according to the initial three-dimensional grid model, and setting priorities; the unmanned aerial vehicle collects visual data of a cleaning area through a multi-mode visual sensor, wherein the visual data comprise surface texture, depth information and a stain distribution thermodynamic diagram; performing local correction on the initial three-dimensional grid model based on the real-time visual data to generate a high-precision dynamic three-dimensional grid model; generating a cleaning path by adopting an adaptive grid traversal algorithm according to the priority of the cleaning area and the coverage range of the unmanned aerial vehicle cleaning head; obstacles in the path are detected in real time through a visual feature matching algorithm, and when the obstacles are detected, path re-planning is carried out to avoid the obstacles until all the areas are cleaned. According to the method, the modeling precision and the dynamic adaptability are improved, and the intelligence and the pertinence of path planning are realized.

This disclosure describes systems and methods for a graphical interface including a graphical representation of medical data. The graphical interface platform may receive medical data and provide medical safety reporting capabilities including reporting of history data and real-time visual monitoring data. The graphical interface platform may be configured to identify potential problems and corrections to medical devices in operation while a reporting cycle is underway through visual representation of performance metrics.

The invention relates to the technical field of perovskite thin film detection, in particular to a perovskite blade coating thin film defect detection device and method based on real-time visual monitoring, and the device comprises a support, an image acquisition unit, an illumination unit and a self-adaptive adjustment system which are arranged in a glove box and located at a coating machine, a flash evaporation box and a heating stage respectively; the camera and the lens are installed on the adjusting frame, and the first driving piece achieves height adjustment. The light source is installed on the supporting frame and controlled by the second driving piece to ascend and descend. The image analysis unit evaluates the definition, contrast and illumination uniformity of the acquired image, and the control unit automatically adjusts the height and angle of the light source and the position of the camera according to the result so as to keep the image quality stable; according to the scheme, real-time monitoring is carried out in the working procedures of coating, flash evaporation, heat treatment and the like, the comprehensiveness, real-time performance and accuracy of thin film detection are effectively improved, influences caused by environmental fluctuation are avoided, and the accuracy and consistency of defect recognition are guaranteed.

The disclosed invention introduces an advanced Visual Alarm and Guidance System designed to significantly enhance safety in industrial environments. This innovative system integrates state-of-the-art computer vision and light projection technologies, along with trajectory prediction algorithms, to dynamically identify potential hazards and guide workers in real-time out of potential collision pathways. It marks a considerable advancement over traditional safety methods by not only detecting imminent dangers but also providing clear, visual navigation aids. This system is adaptable to various high-risk settings, seamlessly integrates with existing infrastructures, and offers a novel approach to real-time, visual safety management in industrial settings.

The invention provides a defect detection system and method for bracket processing, and relates to the technical field of defect visual inspection. A visual inspection device is used for collecting a to-be-detected image in real time; performing pixel registration on the to-be-detected image to obtain defect edge uniformity in a defect area on the surface of the bracket, and determining a line form boundary on a defect position on the surface of the bracket according to the defect edge uniformity and an adjacent identification value between adjacent pixel points in defect pixel points; performing texture screening on the defect extension information to obtain gray scale transaction deviations on defect extension abrupt change points in a defect area on the surface of the support, and determining a neighborhood span index in an area to be detected on the surface of the support according to all the gray scale transaction deviations; and determining a pixel incongruous trend according to the grain form boundary and the neighborhood span index, and carrying out visual defect detection on the bracket processing surface. According to the method, accurate real-time visual detection can be carried out on the bracket processing surface defects in a bracket processing dynamic detection scene, so that the accuracy of bracket defect detection is improved.

A universal system and method for dynamically evaluating and visualizing the performance of any predictive model, including machine learning models. The system and method compute performance metrics based on test set data and display visual representations in real-time, allowing users to interactively explore model performance by adjusting parameters that reflect model-deployment scenarios. Key features include model-agnostic design, support for both technical and business metrics, and the ability to compare multiple models. The system and method's extensible architecture enables custom metrics and visualizations, making them scalable across various modeling use cases and industries. By providing intuitive, real-time visual feedback, embodiments of the invention empower both technical and non-technical stakeholders to gain deeper insights into model behavior, leading to more informed decisions about deployment and optimization.

The invention provides an AI-powered speech, language therapy, and language learning system that utilizes Natural Language Processing (NLP), Convolutional Neural Networks (CNNs), and Generative Adversarial Networks (GANs) to deliver personalized, real-time therapy and learning for individuals with speech disorders or those seeking to improve language proficiency. The system analyzes user speech, language comprehension, and facial expressions, providing immediate feedback on pronunciation, fluency, articulation, and sentence structure. A GAN-generated avatar interacts with the user, mimicking human expressions and offering dynamic, engaging sessions. The platform adapts exercises based on user performance using personalized algorithms to ensure continuous progress. Additionally, it securely stores user data in compliance with privacy regulations, making it accessible through web and mobile platforms. This invention improves upon existing speech therapy and language learning solutions by integrating real-time visual and auditory feedback with AI-driven personalization, offering a more immersive and effective experience.

The invention relates to the technical field of medical instruments, and discloses a tracheotomy anatomical structure image recognition method and system, and the method comprises the steps: obtaining real-time visual image data and spatial positioning data of an endoscope, carrying out the time synchronization, carrying out the mode recognition of the spatial positioning data, and locking the remarkable anatomical features in the real-time visual image data, and according to the characteristics and the spatial positioning data, calculating a geometric transformation relationship between an endoscope visual coordinate system and a spatial positioning coordinate system, and finally fusing the data and displaying anatomical structure information and a surgical tool position in real time. The method can effectively solve the problem that in the prior art, an endoscope is irregularly deformed in the narrow trachea with physiological bending of a patient.