900 results about "Feature detection" patented technology

The invention relates to a power construction monitoring method, system and device and a medium, and the method comprises the steps: carrying out the space-time calibration and anti-interference processing through collected construction images, temperature and dust data, and carrying out the fusion to generate a multi-source fusion data set containing environment information; performing feature extraction on the multi-source fusion data set based on a neural network model, synchronously identifying an action track of a constructor and equipment operation parameters, and constructing a feature map set associated with behaviors and equipment states; performing action and equipment anomaly conjoint analysis on the feature map set, and generating a construction risk event set by analyzing human body joints and temperature feature detection; performing spatial distance calculation and time monitoring on the risk event, and quantifying the coupling risk level; and based on the dynamic risk threshold, a grading early warning instruction is triggered, and precise response of field personnel warning, equipment power failure and evacuation guidance is realized. According to the method, the technical problems of low construction risk identification precision, equipment anomaly detection lagging and risk assessment deficiency in a complex environment are solved.

This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support enhanced sensor fusion techniques. In a first aspect, a method of includes receiving point cloud data for two or more frames from a radar device and generating scene flow parameter data based on the point cloud data. The method also includes generating voxel position adjustment data based on the scene flow parameter data, and generating feature concatenation information associated with two or more sensors based on the voxel position adjustment data and feature information associated with the two or more sensors. The method further includes performing feature detection and tracking based on the feature concatenation information to generate tracking information for one or more objects, and outputting the tracking information. Other aspects and features are also claimed and described.

The invention relates to the technical field of display screen driving, in particular to a low-power-consumption display screen driving method and system based on the OLED technology, and the method comprises the steps: S1, carrying out the image feature detection, collecting the inter-frame brightness change rate, the sub-pixel gray level distribution and the regional temperature gradient, and generating a dynamic driving signal parameter; s2, voltage compensation adjustment: dividing a power consumption area according to a brightness change rate, calculating a brightness compensation coefficient and correcting the voltage by combining gray level distribution and a temperature gradient, and realizing sub-pixel voltage dynamic adjustment; and S3, refresh frequency control: adjusting a local refresh rate according to the compensation coefficient and the correction voltage, executing a frequency reduction strategy on a high-power-consumption area, triggering ghosting suppression on a low-power-consumption area, and applying a temperature threshold to constrain a refresh upper limit. According to the invention, energy-saving driving, ghost suppression and brightness balance of the OLED display screen are realized, and the display performance and the system intelligence are effectively improved.

The invention discloses a quality detection system and method for a glue injection laser contour, and belongs to the technical field of quality detection, and the method comprises the steps: obtaining a reflection point cloud signal of a glue injection finished product, constructing a point cloud data stream, setting an image reconstruction method, constructing a glue layer three-dimensional model, synchronously generating a pseudo-color grey-scale map, and achieving the visual representation of the surface texture of a glue layer. A three-layer feature extractor is constructed, a quality primary judgment method is set, features of different scales are fused in a cross-layer mode, the problem of missing detection of complex defects in traditional single-feature detection is solved, basic defects are automatically recognized, and a quality primary judgment result is output; and performing defect judgment on the glue-injected finished product which is unqualified in primary judgment, setting a defect fine judgment method, performing voxel-level segmentation on the three-dimensional image, calculating defect characteristics, and judging whether the defects affect the use performance of the product or not so as to generate a fine judgment result, so that accurate positioning of complex defects is realized, and the defect type identification accuracy is improved.

The invention relates to the technical field of electronic component detection, in particular to an IC (integrated circuit) carrier plate detection method based on surface state image extraction, which comprises the following steps: acquiring a gray image, analyzing structural parameters, extracting gradient features, detecting boundary disturbance, integrating the image, calculating an abnormal score, identifying a defect position area, extracting features and outputting an identification result. According to the invention, by analyzing the structure parameters of the bonding pad in the gray level image, calculating the edge line segment, the center coordinate and the spacing, and constructing the two-dimensional coordinate system, the regional positioning reference is enabled to have geometric consistency, the coordinate mapping is combined with the gradient direction change frequency and the continuous aggregation point, the boundary disturbance identification precision is improved, and the image division is executed based on the disturbance region. According to the method, non-functional region mixing is effectively avoided, a clustering and probability model is introduced after region gray level statistics, a deviation scoring mechanism is constructed, gray level feature abnormity is accurately recognized, the discrimination capability of small-amplitude and low-contrast defects is improved, and the selectivity and target focusing performance of feature detection are enhanced.

The invention discloses a remote sensing image anomaly detection system and method fusing multi-source noise feature anomaly in the technical field of remote sensing image detection. The method comprises the following steps: acquiring optical satellite remote sensing image data to be processed; analyzing the optical satellite remote sensing image data to obtain remote sensing image detailed information; performing exception type labeling on the detailed information of the remote sensing image, and constructing an exception feature data set; based on the abnormal feature data set, performing multi-source noise feature detection through a pre-designed abnormal detection fusion framework; and outputting an abnormal region obtained by the multi-source noise feature detection and an abnormal detection result. Through a fusion detection mode, the problems of color anomaly, cloud shadow masking, pixel information anomaly and the like of the remote sensing image are found in time, the universality of a remote sensing image anomaly image detection algorithm is improved, and the remote sensing image anomaly detection efficiency is further improved.

The invention discloses an automobile part quality detection method and system based on artificial intelligence visual inspection, and belongs to the field of artificial intelligence machine visual inspection, and the method comprises the steps: firstly, carrying out the registration of a collected RGB image and a depth image, and extracting a part region through a saliency detection network; two-dimensional key points are extracted based on an RGB region image and are matched with key points of a three-dimensional model, an initial three-dimensional attitude is obtained by adopting a PnP algorithm, iterative registration is performed with the three-dimensional model in combination with a point cloud generated by a depth image, and a fine three-dimensional attitude is obtained. And calculating a geometric transformation matrix from the part to a standard front view attitude according to the attitude, and performing attitude correction on the RGB and depth region image. And then matching the corrected image with a standard template image by using a feature detection and matching network so as to correct the position of the detection window. And finally, the three-dimensional size of the part is calculated in the corrected detection window in combination with the depth value, and tolerance judgment is carried out. And the precision, the robustness and the automation level of online detection of the automobile parts can be obviously improved.

The invention provides an abnormal parking detection method and device in an expressway scene based on a large model, and relates to the technical field of artificial intelligence such as automatic driving, large language models and image feature detection. The method comprises the following steps: acquiring a road time sequence video shot for an expressway scene; extracting multi-scale features of each video frame forming the road time sequence video; determining image quality information and environment information of the corresponding video frame according to the multi-scale feature of each video frame; determining position information and category information of traffic elements existing in the corresponding video frames according to the image quality information, the environment information and the multi-scale features of each video frame; according to the corresponding position information, the category information and the time sequence information among the video frames, determining the motion trails of the same traffic elements of the traffic elements existing in the video frames; and determining an existing abnormal parking event according to the motion trail. By applying the method, the abnormal parking event in the expressway scene can be accurately detected.

The invention discloses a pavement disease image adaptive enhancement and restoration processing method, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a pavement disease image, and carrying out the frequency domain transformation; recognizing a pavement material type based on the frequency domain features and determining a material sensing parameter set; performing multi-scale frequency domain decomposition according to the material perception parameter set, and performing adaptive enhancement on a low-frequency illumination component and a high-frequency detail component; carrying out disease feature detection and generating a disease perception weight map, and carrying out key enhancement on a disease area; according to the method, the self-adaptive enhancement model for road surface material perception is established, and targeted image enhancement processing is realized.

In various examples, training sensor data generated by one or more sensors of autonomous machines may be localized to high definition (HD) map data to augment and / or generate ground truth data—e.g., automatically, in embodiments. The ground truth data may be associated with the training sensor data for training one or more deep neural networks (DNNs) to compute outputs corresponding to autonomous machine operations—such as object or feature detection, road feature detection and classification, wait condition identification and classification, etc. As a result, the HD map data may be leveraged during training such that the DNNs—in deployment—may aid autonomous machines in navigating environments safely without relying on HD map data to do so.

The invention provides a splicing method and a splicing device for a magnetic control capsule endoscope image of a digestive tract. The method comprises the following steps: screening out a candidate lesion area image sequence from an original alimentary canal image sequence collected by a magnetic control capsule endoscope; preprocessing the candidate lesion area image sequence to obtain a preprocessed candidate lesion area image sequence; performing feature extraction on the preprocessed candidate lesion area image sequence based on an OfficientLoFTR improved model, further performing feature matching on the preprocessed candidate lesion area image sequence to obtain matched feature point pairs, and performing image registration processing on each image of the preprocessed candidate lesion area image sequence according to the matched feature point pairs to obtain a pre-processed candidate lesion area image sequence; the improved model can improve the accuracy of feature detection and matching, a good foundation is laid for subsequent image registration and image fusion, image registration can be carried out by combining global homography transformation and local thin plate spline transformation, and the problem of non-rigid deformation is solved.

A method includes receiving image data captured by a traffic feature detection system. The image data is representative of a traffic feature within an environment. The method includes identifying a type of the traffic feature. The method includes isolating a portion of the image data containing the traffic feature. Based on the portion of the image data containing the traffic feature, the method includes determining a pixel color value for the traffic feature. Based on the type of the traffic feature, the method includes determining an expected pixel color value for the traffic feature. Based on a comparison of the expected pixel color value and the determined pixel color value for the traffic feature, the method includes determining a health status for the traffic feature.

The invention provides a remote nursing monitoring method and system based on multi-modal image fusion, and the method comprises the steps: obtaining multi-modal data, carrying out the multi-modal feature fusion processing of the multi-modal data, and generating cross-modal semantic information, the multi-modal data comprising at least two of a visible light image, an infrared thermal imaging and a point cloud; the cross-modal semantic information is subjected to dynamic optimization retrieval processing based on a query intention, a retrieval result set matched with the query intention is generated, and the query intention is input through a natural language or obtained through a preset template instruction; and performing abnormal feature detection processing on the retrieval result set to generate a real-time monitoring result. By adopting the method, the cross-modal semantic gap problem can be effectively solved, the precision and real-time performance of nursing monitoring are improved, and reliable technical support is provided for remote nursing monitoring.

The invention relates to the technical field of intelligent detection, in particular to an edge fatigue detection method and device based on adaptive threshold adjustment and a medium. The method comprises the following steps: acquiring facial image data and facial infrared data of a driver in a real-time state; preprocessing the face image data and the face infrared data; performing facial mark point positioning on the preprocessed facial image data and facial infrared data through a facial feature detection model to obtain an enhanced 3D mark point image; comparing the enhanced 3D mark point image with a physiological feature database to obtain physiological data; and calculating the physiological data through a dynamic threshold decision model and a multi-index fusion engine to obtain a fatigue decision. According to the method, the environmental adaptability can be improved, individualized precise adaptation is realized, precise adaptation is realized for individual differences, and the multi-working-condition robustness is enhanced.

In various examples, systems and methods are described that may be used to generate a mask of a geographic area and corresponding vector representations of one or more environmental features included in the area. In some embodiments, the method and system may generate or obtain a first tile image representing a portion of a path surface corresponding to a geographic area. One or more features may be extracted from the data where the extracted features may indicate environmental characteristics associated with the path surface—e.g., lane boundaries, medians, traffic signs, signals, etc. Additionally, the method or system may generate a mask corresponding to the tile image and vector representations of the portion of the geographic area. In some embodiments, the locations of the environmental features associated with the mask may be reconciled with one or more previously generated masks that include some of the same environmental features.

The invention discloses an intelligent ring with a physiological feature detection function and a physiological feature detection method. The smart ring comprises a pressure detector and an arithmetic processor. The pressure detector is used for detecting the value of pressure applied to the smart ring by the target object. The arithmetic processor is electrically connected with the pressure detector and used for comparing the pressure value with a preset condition and judging the behavior of the target object according to the comparison result of the pressure value and the preset condition so as to generate a corresponding operation instruction. The smart ring not only can utilize the pressure detector to correct the detection result of the light volume change tracing detector, but also can utilize the pressure detector to detect the degree of tightness, gestures or finger actions when a user wears the smart ring, and has diversified application changes.

The invention relates to the technical field of laser radar meteorological detection, in particular to a wind field feature detection method and device based on a laser radar, and the method comprises the following steps: S1, controlling the laser radar to execute dynamic hierarchical scanning, and generating three-dimensional laser scanning data; s2, carrying out aerosol backscattering signal preprocessing on the three-dimensional laser scanning data; s3, extracting an aerosol motion vector of each range gate, and decomposing the aerosol motion vector into a horizontal wind field component and a vertical wind field component; s4, performing spatial gradient calculation to generate a spatial gradient distribution map; s5, respectively calculating a horizontal energy spectrum and a vertical energy spectrum; and S6, outputting wind field characteristic parameters. According to the invention, through fusion of the dynamic features of the laser radar point cloud, the wind shear space gradient and the energy spectrum analysis method, precise extraction of the turbulence intensity, the main energy scale and the three-dimensional vortex features is realized, and the accuracy and the spatial resolution of wind field structure identification are improved.

An apparatus and a method for processing an image are provided. The method includes obtaining one or more frames from at least one input visual media, for at least one frame of the one or more frames, detecting one or more features from the at least one frame based on feature detection model, determining at least one cropping window based on the one or more detected features and information regarding an aspect ratio of a display, obtaining one or more cropped frames based on the at least one cropping window, selecting one or more overlays based on one or more cropped out features, text, picture-in-picture display, and spaces left in the display, and generating one or more reframed frames by situating one or more selected overlays on the one or more cropped frame.

The invention discloses an electrical equipment part defect detection method and device, electronic equipment and a storage medium, and belongs to the field of defect detection.The method comprises the steps that visible light and infrared thermal images of all parts of electrical equipment are obtained; fusing the visible light and infrared image features of each part through a multi-modal model, and detecting whether defects exist or not and the types of the defects by using the fused features; the defective part is marked as a target part; extracting visible light apparent characteristics and infrared thermal characteristics of each target part; and generating a defect severity level based on a preset decision tree model by integrating the defect prediction result, the visible light appearance and the infrared thermal characteristics of each target part. Therefore, by implementing the method and the device, the problem of inaccurate or missing defect type identification caused by insufficient multi-source information comprehensive analysis capability in the prior art can be solved.

The invention discloses a method and system for positioning a panoramic camera in a point cloud map, and the method comprises the steps: carrying out the preprocessing of a panoramic image and three-dimensional point cloud map data, and obtaining two-dimensional line segment features and three-dimensional line segment features; based on the two-dimensional and three-dimensional line segment features, calculating two-dimensional and three-dimensional line segment main directions and comparing to obtain candidate rotation estimation; designing a two-dimensional distance function and a three-dimensional distance function based on candidate rotation estimation to evaluate the rough pose, designing a two-dimensional / three-dimensional line segment loss function to score the candidate pose, and screening out # imgabs0 candidate poses meeting requirements according to scores; and carrying out two-dimensional and three-dimensional key point extraction on the # imgabs1 # candidate pose, matching the two-dimensional key points with the three-dimensional key points, and carrying out further pose estimation on the candidate pose with the maximum matching quantity by adopting a PnP-RANSAC algorithm, so as to obtain final fine pose estimation. The method overcomes the defects that in the prior art, point feature detection is unstable and depends too much on an ideal static environment.

The invention provides a multi-modal large model-based content compliance detection method and system and a storage medium, and the method comprises the steps: inputting sample data into a multi-modal content compliance detection model for feature extraction and feature fusion, obtaining a sample heterogeneous graph, carrying out the feature weight adjustment of the sample heterogeneous graph according to the scene type of the sample data, and obtaining a multi-modal content compliance detection model; a feature heterogeneous graph is obtained; performing content prediction on the feature heterogeneous graph to obtain a sample prediction result, and determining model loss according to the sample prediction result; performing parameter updating on the multi-modal content compliance detection model according to the model loss until the multi-modal content compliance detection model converges; and inputting to-be-played data into the converged multi-modal content compliance detection model for content compliance detection to obtain a content compliance detection result. According to the embodiment of the invention, content compliance detection is carried out on the cross-modal to-be-played data by adopting a multi-modal feature detection mode, so that the accuracy of content compliance detection is effectively improved.

The invention discloses a rock and ore microscopic image splicing method and system based on deep learning, and relates to the field of image processing and the technical field of microscopes, and the method comprises the steps: obtaining a local rock and ore microscopic image of a rock and ore slice, carrying out the preprocessing of the local rock and ore microscopic image, and carrying out the overlapping region coarse registration of the preprocessed local rock and ore microscopic image through a phase correlation method; based on an improved image feature detection model, basic features and description features in the local rock and ore microscopic image after coarse registration are extracted, and local image features of the local rock and ore microscopic image are obtained; performing feature matching on the local image features of the two groups of local rock and mineral microscopic images by using an image feature matching model to obtain a matching corresponding relation of the local image features; and based on an image fusion algorithm of a homography matrix and a partial differential equation, splicing and optimizing the local rock and ore microscopic images in combination with a matching corresponding relation of local image features, and generating a large-view-field rock and ore microscopic image. According to the method, the complex transformation between the images can be better processed.

The invention relates to the technical field of easy-to-open cap production control, and discloses a beverage easy-to-open cap production line control system and method. A sensing monitoring module of the system collects a cover deformation sensing data flow and an assembly pressure sensing data flow in real time; the quality feature detection module performs geometric feature analysis on the basis of a preset cover deformation constraint condition in combination with the cover deformation sensing data stream to generate cover mass distribution feature information; the process decision-making module generates a primary process decision-making scheme based on an embedded process decision-making space and a process fitness constraint condition in combination with cover body mass distribution characteristic information and an assembly pressure sensing data flow; a trend prediction module performs production trend prediction on the two types of data to obtain predicted quality distribution characteristic information and predicted assembly pressure data; the process compensation module compensates and optimizes the primary process decision scheme based on the prediction data to generate an optimized process decision scheme; and the execution control module controls the punch forming mechanism based on the optimization scheme.

The present invention discloses a method for simulating the flow pattern of gas-liquid two-phase flow in a high-temperature and high-pressure wellbore and calculating the liquid holdup, which includes the following steps: establishing a wellbore geometric model and a gas-liquid two-phase flow field in the wellbore according to the basic wellbore parameters; constructing a local feature detection surface in the wellbore and detecting the instantaneous gas holdup of the wellbore cross-section; determining the probability density distribution characteristics of the gas holdup of specific flow patterns and the influence of temperature and pressure on the transition between different flow patterns; determining the critical gas holdup for different flow pattern transitions and the boundary equations for different flow pattern transitions; establishing the boundary equations for the flow pattern transition of gas-liquid two-phase flow in a high-temperature and high-pressure wellbore and determining the calculation method for the liquid holdup of different flow patterns. The present invention overcomes the drawback that the existing experimental conditions cannot conduct experimental research on high-temperature and high-pressure gas-liquid two-phase flow.

A low-light environment image enhancement and adaptive feature fusion target detection method relates to the technical field of image detection, and comprises the following steps: original image information is collected and input into an improved YOLO-MFL network, the improved YOLO-MFL network is composed of an MA-Retinexformer low-light enhancement layer, a trunk of YOLOv11, an FL-ASFF small target detection layer and a detection head of YOLOv11, and the detection head of the MA-Retinexformer low-light enhancement layer and the trunk of the YOLOv11 are connected with the FL-ASFF small target detection layer; carrying out dark light enhancement on the original image by utilizing an MA-Retinexformer low-illumination enhancement layer in the improved YOLO-MFL network, and continuously processing the image subjected to dark light enhancement through the improved YOLO-MFL network to obtain a detection result; according to the invention, illumination estimation and damage recovery are carried out on the low-illumination image through an MA-Retinexformer framework, so that the quality of the low-illumination image is improved, and the small target detection precision of the model when the processing illumination condition is poor is effectively improved. And meanwhile, an additional feature detection level is added on the basis of a conventional ASFF algorithm, so that the coverage range of the target scale is widened, the recognition capability of the model on the multi-scale target is enhanced, and the overall detection precision is improved.

The invention discloses an automatic mural protection and repair area identification method and system based on image analysis, and the method comprises the steps: generating degradation states of a mural at different time nodes through an image simulation algorithm, converting a two-dimensional damage image into a three-dimensional space model through the combination of a three-dimensional mapping technology, and precisely calibrating a high-risk damage point. The clustering analysis algorithm further integrates and disperses damage points, the property and spatial features of a damage group are extracted, and the curve feature detection algorithm automatically identifies damage boundaries by analyzing texture continuity and line trend. Dynamic sequence analysis tracks inter-frame differential change, a degradation extension track is disclosed, damage information is efficiently compressed and stored by a coding technology, and a matched degradation mode is mined and a potential damage area is updated by historical comparison analysis. According to the method, three-dimensional space modeling is taken as a core, multi-dimensional analysis and efficient storage are fused, accurate positioning, dynamic monitoring and preventive protection of mural damage are realized, and the scientificity and efficiency of cultural relic protection are remarkably improved.

The present invention relates to a motion amplification method, device, and storage medium based on an event camera. The method comprises: providing an optical splitter, an event camera, and an RGB camera, with the output of the optical splitter connected to the event camera and the RGB camera, respectively, to form a dual-camera imaging system; acquiring event signals via the event camera and image signals via the RGB camera; processing the acquired event and image signals through an encoder, an amplifier, and a decoder, respectively. The encoder is configured to model the features of the event and image signals and is equipped with a second-order recursive propagation module and a channel attention fusion module; the amplifier is configured to amplify the motion amplitude detected by the features output by the encoder; and the decoder is configured to model the features of the amplifier back into the image space to obtain a final motion-amplified image. Compared with existing technologies, the present invention can economically perform motion amplification and significantly improve the effectiveness of high-frequency motion amplification.

The invention belongs to the technical field of computer security, and relates to a dynamic detection architecture, strategy, system and method for attack variants, and the multi-layer risk detection architecture is composed of a behavior change detection layer, a code structure detection layer and a data flow detection layer. The dynamic adjustment detection strategy is formed by respectively introducing a behavior change detection layer, a code structure detection layer and a data flow detection layer in a multi-layer risk detection architecture into a self-learning model; the dynamic detection system comprises a system interface module, a risk preliminary screening module and a variation detection module; the dynamic detection method comprises the following steps: primarily screening data input through the system interface module by using the risk primary screening module; inputting the primarily screened data into a variation detection module for detection and analysis, and configuring the access authority of the input data according to an output result; according to the method, the comprehensiveness of variation feature detection is improved, and the detection performance of the model is improved so as to adapt to continuously changing attack features.

The invention discloses a satellite angular rate identification method and related equipment, and relates to the technical field of rocket launching, and the method comprises the steps: obtaining a rocket telemetry video frame sequence after satellite-rocket separation; for each frame of image of the rocket telemetry video frame sequence, determining a dynamic image area of the target satellite based on the static area mask; based on a feature detection algorithm, binary descriptors are extracted from dynamic image areas of two adjacent frames of images; based on the binary descriptor, matching corresponding feature point pairs between two adjacent frames of images; estimating a rotation matrix between two adjacent frames of images through an iterative optimization algorithm based on the corresponding feature point pairs; and determining the angular rate of the target satellite according to the rotation matrix and the time interval between the two adjacent frames of images. According to the method, rapid and high-precision identification of the satellite angular rate is realized, the problem that the traditional technology depends on indirect data and is high in delay is solved, task success or failure can be judged in real time, and the launching task evaluation efficiency is improved.

The invention discloses an automatic PCB (Printed Circuit Board) detection system based on machine vision, which belongs to the technical field of electronic manufacturing and comprises an image information acquisition module used for acquiring image information of a normal welding spot of a PCB and marking the image information as reference image information; the reference image information comprises shape features, position features, texture features, color features and connectivity features of the welding spots; the image processing module responds to the reference image information and is used for preprocessing the reference image information, and preprocessing comprises graying, binaryzation and filtering denoising of the reference image information; and the image acquisition module is used for acquiring a welding spot image on the PCB to be detected and comparing the welding spot image with the reference image information. Through multi-feature analysis, dynamic reference value adjustment and misjudgment correction mechanisms, the quality of the welding spot can be comprehensively evaluated, misjudgment caused by single feature detection is avoided, and the accuracy and reliability of a detection result are ensured.