741 results about "Image flow" patented technology

The invention relates to the technical field of PCB welding spot defect detection, and discloses a PCB welding spot defect detection system and method based on image recognition, and the system comprises an image preprocessing module which is used for obtaining an original image flow and dividing an interested detection area; the feature fusion module is used for extracting multi-modal features to form a fusion set; the defect judgment module is used for establishing a mapping index and obtaining a judgment result; the parameter calibration module is used for verifying the detection parameters and adjusting the mapping index; and the report output module is used for generating a defect detection report. The method comprises the steps of image preprocessing, feature fusion, defect discrimination, parameter calibration, report generation and the like. According to the system and the method, the PCB welding spot defects can be efficiently and accurately detected, the detection precision and stability are improved, a structured report is generated, and an effective solution is provided for PCB quality detection.

The invention relates to the technical field of electrical equipment defect detection, in particular to an electrical equipment defect detection system and method based on deep learning, which are characterized in that a three-dimensional model of a power grid region is constructed, and based on historical defect data, a neural network model is adopted to mark an importance score of an inspection object in the three-dimensional model; quantitative evaluation of the equipment fault risk level is completed, and the matching degree of inspection resources and defect risk distribution is improved. A power grid three-dimensional model and importance scores are combined, a reinforcement learning model is utilized to construct an unmanned aerial vehicle inspection route planning strategy, the inspection route comprehensively considers power equipment defect risks and space factors in the planning stage, task allocation is optimized, and the problem that a static route cannot adapt to equipment changes is solved. In the inspection execution process, the unmanned aerial vehicle is dispatched according to the planning strategy, and the image flow is synchronously acquired for defect detection, so that the linkage of the inspection action and the detection process is realized, and the response efficiency and the detection quality of potential defects are improved.

The invention discloses a water area surface flow velocity area monitoring method based on intelligent visual analysis, and relates to the technical field of intelligent visual analysis. According to the specific implementation scheme, continuous image collection is conducted on a target water area, dynamic images flowing on the surface of the water area are obtained, and image preprocessing is conducted on video frames of the dynamic images; directly comparing global feature similarities through feature extraction, feature matching and optical flow optimization by using an optical flow estimation algorithm of a convolutional neural network, and outputting optical flow field data of the water area surface flow velocity; after the optical flow field data are obtained, pixel displacement information of optical flow estimation is converted into a flow velocity field on an actual physical unit, and meanwhile regional distribution of the flow velocity on the surface of the water body is analyzed. According to the invention, the optical flow estimation algorithm of the convolutional neural network is utilized to directly compare global feature similarities and output water area surface flow velocity data.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.

The invention discloses a routing optimization method and system based on an attention mechanism and a time sequence diagram convolutional network, and belongs to the technical field of communication networks, the method classifies traffic in the network, when the network traffic is mouse traffic, a trained A3T-GCN model is adopted to predict the network state of each link, and when the network traffic is mouse traffic, the A3T-GCN model is adopted to predict the network state of each link; optimizing the link by adopting an improved butterfly optimization routing algorithm, calculating the time delay and the fluctuation of the link according to the network state, obtaining the link with the minimum time delay and the minimum time delay fluctuation through iterative optimization, and routing the mouse flow along the link corresponding to the optimal solution; when the network flow is an image flow, the A3T-GCN model is utilized to predict the average residual bandwidth of each link in the shortest hop count link set in the future w periods, and the link object flow with the maximum average residual bandwidth is adopted for routing; according to the method, distribution and utilization of network resources are optimized, so that the overall efficiency and throughput of the network are effectively improved.

The invention relates to the technical field of security and protection management, in particular to a campus security and protection management system based on deep learning, which improves the accuracy and robustness of identity recognition by acquiring access control card numbers, face images or fingerprint features and generating standardized identity authentication data. And on the basis of a comparison result of the identity authentication data and the campus database, a behavior chain initialization identifier is generated, and accurate identity binding of the school entering personnel is realized. Furthermore, by collecting multi-camera image stream data, pedestrian re-identification and similarity calculation are executed by using a deep feature matching network, and a cross-camera continuous trajectory data set is generated. And matching the behavior track data set with the conventional path template to generate a behavior offset feature vector. And carrying out joint modeling on the behavior offset characteristics and the identity information through a graph neural network model containing an attention mechanism, and outputting a behavior purpose label and a risk grade score. And a graded security response instruction is generated based on the risk score, so that the missing report rate and the false report rate are effectively reduced.

The invention relates to the technical field of road defect detection, in particular to a road pit detection method and system, and the method comprises the following steps: S1, obtaining an original image flow of a road surface through a vehicle-mounted multispectral camera, and carrying out the motion artifact elimination to generate a vibration compensation image; s2, generating a shadow suppression image through asymmetric gamma correction; s3, adopting a dual-threshold connected domain analysis method to extract candidate pothole regions; s4, when the contrast difference value exceeds a texture mutation threshold value, determining that the area is a surface damaged area; s5, extracting a continuous pixel cluster statistical area proportion, and when the proportion is greater than 60%, determining that the feature is an effective pothole feature; and S6, calculating the minimum enclosing ellipse eccentricity rate and the ellipse area ratio, and generating a final pothole detection report. According to the method, through a multi-source information fusion and multi-stage feature extraction method, high-precision identification and standardized output of the pothole area in a complex road environment are realized, and the detection accuracy and the application reliability are improved.

The invention provides an industrial product defect automatic classification method and system, and the method comprises the steps: collecting original industrial product defect image data, and constructing a labeled image sample set and an unlabeled image sample set; constructing a training image sample set based on the labeled image sample set and the unlabeled image sample set in combination with a plurality of image synthesis strategies; based on the training image sample set, introducing a transfer learning strategy and fusing an attention mechanism, and constructing and optimizing an industrial product defect classification model; performing semi-supervised joint training and online learning based on the training image sample set and the real-time small-batch image sample set; and constructing an industrial product defect identification log based on the real-time image flow sample set, the classification model parameters and the corresponding classification prediction function. On the basis of multi-strategy image enhancement and semi-supervised training, transfer learning and a channel attention mechanism are fused, expansion of industrial product defect image samples and fine defect identification are achieved, and the method is suitable for an intelligent defect detection system in various industrial manufacturing fields.

The invention provides a humanoid machine control method and system based on model prediction, and the method comprises the steps: firstly obtaining joint sensing data (hip joint movement angle sequences and knee joint stress signals) and environment interaction data (depth image flow data and sole contact pressure data) of a humanoid machine, and generating a dynamic state feature set through feature correlation processing; the feature matrix comprises a joint cooperation feature vector and an environment constraint feature matrix; calling a pre-training model to carry out spatio-temporal joint prediction on the feature set, and outputting a future motion control sequence containing gait cycle features; then, a joint synchronous control model is constructed on the basis, and a driving control instruction set containing time coordination constraint is generated; and finally, the driving control instruction set is transmitted to the distributed execution unit, and joint state feedback data is collected for next round of processing, so that the motion control performance of the humanoid machine in a complex environment is improved.

The invention relates to the technical field of video decoding, in particular to a fast decoding transmission method and system for a video live broadcast image. The method comprises the following steps: acquiring a video live broadcast picture stream; performing image group division on the video live broadcast image flow to generate image group images; analyzing an inter-frame relationship of the picture grouping images, defining priorities of the picture grouping images according to the inter-frame relationship, and generating decoding priority mark data; entropy data fragmentation is carried out on the picture grouping images, and a picture basic entropy slice and a picture increment entropy slice are generated; stage picture transmission is carried out on the picture grouping images based on the picture basic entropy slice and the picture increment entropy slice, and an image streaming preloading strategy is generated; and performing terminal transmission feedback on the picture grouping images by using an image stream type preloading strategy to obtain terminal transmission feedback data. According to the invention, through optimization of video picture grouping, priority scheduling, bandwidth management, decoding acceleration and fault-tolerant reconstruction, the rapid decoding transmission performance of the video live broadcast image is improved.

Embodiments of the present disclosure provide techniques for performing virtual object placement in a video sequence using generative artificial intelligence models. An example method generally includes receiving an input prompt specifying an object to insert into a scene depicted in an input image stream; decoding, using a generative artificial intelligence model, perspective and lighting information for the input image stream; determining, based on the decoded perspective and lighting information, a location in the scene in which the object is to be inserted; and generating, using the generative artificial intelligence model, an output image stream including the object into the scene at the determined location, wherein visual effects for the object are based on the perspective and lighting information for the input image stream.

The invention relates to the technical field of intelligent medical teaching, and discloses a medical intelligent teaching model construction method based on an ultrasonic AI technology. According to the method, ultrasonic image sequence data and corresponding operation records of a target object are integrated to generate an original teaching data set; carrying out multi-dimensional teaching feature analysis on the dynamic teaching feature parameter set, and extracting a dynamic teaching feature parameter set; constructing an anatomical structure evolution feature tensor according to a time evolution rule of the parameter set, and calculating knowledge density distribution of historical typical cases; setting a teaching anomaly discrimination boundary and generating an anomaly feature index set; capturing ultrasonic image flow and operation behavior data in teaching operation in real time, mapping the ultrasonic image flow and operation behavior data to a multi-scale teaching knowledge space, and calculating spatial distribution similarity with the abnormal feature index set to obtain a real-time teaching deviation coefficient; and constructing a teaching risk prediction network model, and generating a teaching operation quality evaluation result and a teaching strategy adjustment scheme.

An unmanned aerial vehicle (UAV) is disclosed. The UAV comprises a body; a propulsion unit; a controller; and at least one adjustable camera unit. In some embodiments, each adjustable camera unit comprises, a camera; and a gimbal, mounting the camera, and configured to move the field of view (FOV) of the camera in at least two axes. In some embodiments, the controller is configured to: continuously receive a stream of images from the at least one camera; identify a tilted target in the stream of images; control the propulsion unit to approach the tilted target; and simultaneously control at least one gimble to rotate a corresponding camera such that the tilted target is continuously being identified in the stream of images.

The invention relates to the technical field of unmanned aerial vehicle intelligent inspection path planning, and discloses an unmanned aerial vehicle intelligent inspection path planning method and system based on visual inspection, and the method comprises the steps: collecting an image, and carrying out the differential superposition processing, and obtaining environment visual data; feature correction is extracted to determine a candidate area, and a template is matched to determine the type and severity of an abnormal event; performing local path planning according to an event generation mode switching instruction; according to the trajectory parameters meeting the minimum turning radius constraint, a safe fly-around path is obtained; the method comprises the following steps of: acquiring a real-time image stream, adjusting a speed parameter to obtain an optimized trajectory, processing the real-time image stream and inertial measurement data generated in the optimized trajectory through a visual inertial odometer technology to obtain environment three-dimensional point cloud data, and performing cyclic verification according to the environment three-dimensional point cloud data to obtain an event processing verification result. According to the invention, hierarchical response and dynamic local path re-planning of abnormal events can be realized.

The invention relates to the technical field of sports competition real-time penalty, and discloses a sports competition real-time penalty system and method based on multi-modal sensing and AI algorithms, and the system comprises a monitoring identification module, an instrument tracking module, a rule parameter construction module, a fusion penalty module, an output and snapshot module, and a wireless linkage and energy management module. The method comprises the following steps: acquiring image and instrument data, and constructing a multi-modal sensing structure; calling a rule base to generate a penalty vector and loading a structure constraint; fusing the image and the instrument features to generate a feature tensor; inputting an AI model to judge the legality of the action and outputting a penalty result; driving the snapshot logic to generate an image record and synchronizing the image record to the terminal; and adjusting the energy consumption strategy according to the task load and the equipment state. According to the invention, a multi-modal perception input structure fusing instrument motion data and a visual image flow is introduced, so that the system can synchronously acquire displacement, acceleration and attitude images in the action recognition process, and a penalty basis is checked in a cross manner from multiple dimensions.

Systems and methods for automatically adjusting the exposure settings (e.g., exposure time, analog gain, etc.) of imaging device(s) and / or camera(s) utilized in machine vision systems. According to embodiments of the present disclosure, one or more images or frames of a stream of images captured by an imaging device of a machine vision system can be processed and analyzed to determine exposure settings that may be applied to capture subsequent images by the imaging device. For example, a frame of the stream of images can be partitioned into a plurality of zones, and the pixels of each zone can be non-uniformly sorted into a histogram to represent the distribution of pixels in each zone. The histogram values for each zone can be aggregated to determine an aggregated exposure value associated with the frame, which can be compared against a target exposure value in view of a response function associated with the imaging device to determine the exposure value for capturing subsequent frames in the stream of images.

The invention discloses a power grid intelligent inspection method and system based on an unmanned aerial vehicle, and the method comprises the following steps: collecting image flow data of a target region, constructing a three-dimensional point cloud model, and generating an initial inspection path based on a fast marching tree method in combination with spatial position information; controlling the unmanned aerial vehicle to fly according to a path and collect image frames in real time, and executing an optical flow estimation algorithm through an edge calculation chip to obtain a pixel motion vector; associating the motion vector with a space coordinate corresponding to each inspection point in the inspection path, dividing an optical flow detection area and distributing an initial detection weight; recognizing a dynamic abnormal area according to the motion features, extracting image data and space coordinates, and driving an acousto-optic load assembly carried by the unmanned aerial vehicle to respond; and based on the space coordinates of the abnormal region, re-executing the fast marching tree method to generate a local update path, and adjusting the detection weight of the related region. According to the invention, dynamic sensing and path updating linkage in the unmanned aerial vehicle inspection process can be realized.

The invention provides an image acquisition control method, device and equipment based on machine vision. The method comprises the following steps: selecting a high-frame-rate image sensor acquisition mode according to environment state parameters; adjusting exposure parameters of the high-frame-rate image sensor according to the selected acquisition mode; fusing the optical flow speed and the brightness of the image flow into a weighting factor according to the exposure parameters, and injecting the weighting factor into an image space to generate a scene partition mask; and the scene partition mask is aligned with the exposure images with different exposure parameters, and the exposure images are fused into a target acquisition image. Through the implementation of the scheme of the invention, the environment state parameters are generated in real time, the corresponding acquisition mode is selected, the exposure parameters are optimized, the optical flow and the brightness data are fused to generate the weighting factor, the scene partition mask is created, the multi-exposure image is aligned through the scene partition mask, the optimal exposure part of each area is accurately fused, and the high-quality target image is obtained. Therefore, the image acquisition efficiency is improved.

The invention discloses a real-time pose updating method based on feature prediction and point cloud registration, and belongs to the technical field of image registration, and the method comprises the steps: extracting a bone surface point cloud which at least covers a feature region and an adjacent region from a three-dimensional model of a bone tissue of a patient, the point cloud is used as a target point cloud for registration with a real-time ultrasonic image; an ultrasonic image collected by the ultrasonic patch in real time is obtained, and a two-dimensional ultrasonic image with a spatial position is obtained; predicting the two-dimensional ultrasonic image by using a pre-trained artificial intelligence model to obtain bone-soft tissue interface features, and extracting a plurality of points on a bone-soft tissue interface from the bone-soft tissue interface to form a source point cloud for registration; and registering the source point cloud to the target point cloud to realize real-time pose updating. According to the invention, the pose monitoring in the operation can be realized through the real-time image flow under the non-invasive condition.

The invention provides a multi-head gun-type camera multi-angle intelligent tracking method and system, and the method comprises the steps: obtaining image flow data through a multi-head gun-type camera, building a D-NeRF model through combining with an improved multi-layer perception mechanism, outputting a virtual viewpoint image, a scene depth image, a shielding relation image and a motion vector field according to the D-NeRF model, obtaining a volume density field, and carrying out the multi-head gun-type camera multi-angle intelligent tracking. Analyzing the volume density field, acquiring a continuous aggregation area and performing cutting operation, generating a target instance, acquiring a bounding box and a centroid coordinate in combination with a scene depth map, acquiring appearance characteristics and morphological characteristics and performing matching, generating an identity recognition result, performing trajectory prediction through physical constraint, generating a probability space-time trajectory and performing evaluation, and performing identification on the probability space-time trajectory. According to the method, the uncertain area is obtained, the motion of the camera is subjected to joint optimization in combination with the probability space-time trajectory, the intelligent control instruction is generated, and multi-angle intelligent tracking is performed according to the intelligent control instruction, so that the intelligent level of multi-camera monitoring and tracking in a complex scene is improved.

The invention provides a spine centrum compression fracture recognition method and system based on artificial intelligence, and the method comprises the steps: receiving original spine CT sequence data, and outputting a preprocessed standardized image; inputting the preprocessed image into a three-dimensional U-Net + + network to obtain a centrum segmentation probability graph; outputting the segmented centrum set and the position code thereof; for each segmented centrum, extracting a corresponding area based on a centrum mask, and splicing all features to form a comprehensive feature vector of each centrum; inputting the three-dimensional image block of each vertebral body into an image flow network to extract visual semantic features; and automatically generating a structured PDF report. According to the spine centrum compression fracture recognition method and system based on artificial intelligence, the problem of image quality difference caused by different CT devices and scanning parameters is effectively solved through the multi-scale residual enhancement technology and self-adaptive window level adjustment, and standardized processing of images is achieved.

The invention discloses a wheeled mobile platform terrain detection method, comprising: acquiring a terrain classification data set, the data set comprising a tactile image stream corresponding to tactile sequence data collected in the traveling process of a wheeled mobile platform and pre-labeled terrain category information; training the initial classification model by using a tactile image stream in the terrain classification data set to obtain a terrain classification model; and inputting a to-be-detected tactile image flow into the terrain classification model to obtain a terrain detection result of the mobile platform. The terrain type of the wheel type mobile platform in the advancing process is effectively detected through the wheel type mobile platform terrain detection method, the trolley can avoid the terrain prone to sinking and bumping, damage is prevented, and the task success rate is increased.

The invention relates to the technical field of construction safety management, and discloses a construction safety penetration type management multistage linkage early warning system. The system comprises a management layer node, an execution layer node and a job layer node. The management layer generates a global security strategy, the execution layer is used for decomposing task issuing, and the operation layer is used for collecting field multi-mode security data including equipment vibration characteristics, environment image flow, personnel positioning tracks and security operation records. The system firstly analyzes equipment vibration characteristic changes, divides stable and abnormal working condition time periods, and screens high-risk areas according to difference quantities; in each high-risk area, calculating a safety state deviation degree of a job layer node in combination with the task identifier and the environment image flow; a responsibility tracing weight is generated according to the time-space relevance and the deviation degree of the personnel track and the operation record in the abnormal time period; and finally, screening root nodes, associating multi-modal data to construct a security risk propagation tree, and realizing accurate security management of multi-level linkage.

The invention relates to a multi-view three-dimensional human body posture estimation method and system based on double-flow space-view-time sequence modeling. The method comprises the following steps: inputting a multi-view multi-frame image sequence, and performing human body detection and cutting; performing two-dimensional human body posture estimation on the input image at each view angle and each frame, and extracting image features; constructing an image-attitude bimodal alignment expression; sequentially executing sequential modeling of intra-visual space interaction, cross-visual-angle interaction and intra-visual time interaction on the image flow representation and the attitude flow representation in the same layer to obtain attitude flow fusion features; performing regression on the attitude flow fusion features by using a three-dimensional regression head to obtain three-dimensional skeleton coordinates; training the three-dimensional skeleton by adopting supervised learning; and inputting a to-be-estimated image sequence into the trained model and the regression head to obtain a three-dimensional human body posture estimation result. According to the method, the precision, robustness and deployability of three-dimensional attitude reconstruction can be remarkably improved under the condition of not depending on camera calibration parameters and human body priori.

Devices, systems, and methods for sharing an image sensor data stream with an active alignment subsystem are provided. An example device includes an image sensor coupled to a first processor. The first processor is coupled to a multiplexer and the image sensor. The first processor is configured to receive an image stream from the image sensor, transmit the image stream to a second processor via the multiplexer operating in a first state, and transmit the image stream to at least one test port of the data capture device via the multiplexer operating in a second state. The multiplexer is configured to connect the second processor to the first processor when operating in the first state, and connect the at least one test port to the first processor when operating in the second state. Responsive to receiving an enable signal, the multiplexer operates in the second state, else the first state.

A method for real-time surgical navigation, including: capturing an intraoperative image stream from a distal portion of an endoscopic instrument inserted into a patient's body; processing the intraoperative image stream with a machine learning-based segmentation algorithm configured to identify anatomical structures; matching segmented images to a patient-specific three-dimensional (3D) model of the anatomy; and outputting navigational data indicating the position of the endoscopic instrument relative to the anatomical structures.

The invention relates to a method for use in assessing eye contact of a subject participant in a social interaction scenario between participants. The method involves using camera apparatus to capture video images of the subject participant as a recorded image stream, processing the captured video images to provide data representing spatial and temporal features of the recorded image stream, and using a computer processing module to apply an action recognition algorithm to the data to predict occurrences of eye contact of the subject participant. The invention has particular application in evaluation of children, to assess development of social skills in a simulated, real-life interaction scenario such as a free play task guided by a facilitator in a way that prompts eye contact by the subject participant child.

The invention provides an image flow cytometer based on an optical fiber micro-fluidic chip, which comprises an optical fiber micro-fluidic chip, the optical fiber micro-fluidic chip comprises a plurality of excitation areas at different positions and corresponding optical fiber sockets, the optical fiber sockets are used for inserting corresponding optical fibers, the optical fibers are connected with corresponding optical fiber lasers, and the optical fiber lasers are connected with the optical fiber micro-fluidic chip. The fiber laser is used for transmitting corresponding excitation light to the excitation area through optical fiber conduction; when the cell samples sequentially flow through the excitation areas, the cell samples are sequentially excited by the corresponding excitation light to generate independent fluorescence signals of different spectrum bands, and when the bright field light source irradiates the imaging detection areas where the excitation areas are located, bright field signals are generated; independent fluorescence signals and bright field signals of different spectrum bands are synchronously collected through a fluorescence bright field synchronous collection light path, and independent multi-spectrum-band fluorescence images and bright field images are obtained. According to the invention, independent multispectral fluorescence images and bright field images can be obtained at the same time without using a high-cost laser and matched laser calibration, focusing optical elements and optical light splitting elements, and the complexity and the cost are relatively low.

The present teachings provide a method and device of virtually sharing physical contents across multiple sessions. The teachings provide retrieving, from a data store, virtual whiteboard content derived from a first whiteboard session. The virtual whiteboard content includes first physical data from a physical surface, first virtual data from a virtual whiteboard, first metadata corresponding to one or both of the first virtual data or the first physical data. The virtual content is categorized based on the first metadata. The categorized virtual content is transmitted as an image stream according to the retrieved virtual whiteboard content to a client device in a location of the physical surface to cause the image stream to be presented on the physical surface or a second physical surface during a second whiteboard session. The data store is updated with second content elements obtained from the physical surface during the second whiteboard session.

The invention belongs to the technical field of optical communication data encryption transmission, and particularly relates to a photoelectric detector with a multi-channel encryption function and application thereof. According to the method, array structure photoelectric detector units based on bias voltage selective configuration are adopted, and for different incident light signals, bias voltages of all the photoelectric detector units in a silent state are calibrated to serve as secret keys for encryption; the method breaks through the channel number limitation of a dual-channel technology, has flexible expansion capability (the channel number n is greater than or equal to 3), and can be successfully applied to high-security transmission of static image and dynamic image streams. The physical encryption strategy not only improves the image security, but also has very strong universality and expandability. And by increasing the channel number n and the array scale m, the encryption level is further improved, and the information transmission security is ensured. The method is of great significance to development of a large-scale data communication technology and can be used for encryption and authentication of large-scale image data.