613 results about "Feature detection" patented technology

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 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 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.

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.

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 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 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 invention provides an AI text recognition method and device based on ensemble learning and advanced semantic statistical feature analysis, and the method comprises the steps: 1, respectively sending a to-be-recognized text into a Bert detector and a high-order natural language statistical feature detector for recognition, the high-order natural language statistical feature detector comprises a word logarithm probability detector, a word ranking logarithm detector, an Entropy detector and a confusion degree detector; and 2, performing election on detection results output by the Bert detector and the high-order natural language statistical feature detector by using an election module to obtain an AI text recognition result. According to the method, an integrated learning strategy is adopted, and a pre-training language model subjected to fine tuning is combined with high-order natural language statistical characteristics, so that when the model detects a large language model to generate a text, the strong expression ability of the pre-training language model can be fully utilized, and a deep rule of the text can be captured through the high-order statistical characteristics; and the detection accuracy is improved.

The invention discloses a radar weak target detection and positioning method based on four-polarization time-frequency feature fusion. The method comprises the following steps: constructing an initial four-polarization time-frequency feature detector; the initial four-polarization time-frequency characteristic detector is constructed by introducing four parallel Backbones into a YOLOv11 network; one parallel Backbone correspondingly processes one polarization channel; training the initial four-polarization time-frequency feature detector by using a pre-constructed training label set to obtain a four-polarization time-frequency feature detector; calculating detection statistic distribution based on the confidence corresponding to each pure clutter sample map, and determining a detection threshold according to the detection statistic distribution; based on a to-be-detected time-frequency diagram, a radar weak target detection and positioning method which is sufficient in feature utilization, high in detection capability and stable is provided by using a four-polarization time-frequency feature detector and a detection threshold.

The invention relates to the technical field of intelligent wearing, and discloses an intelligent interaction system and method based on AR glasses. According to the method, an environment scene is intelligently divided, a plurality of interaction areas are generated, then an initial interaction task instruction is received through a user interface, AR glasses are started to execute a task, initial environment data are collected in the execution process, and historical interaction records are obtained. Then, the interaction priority and the attention level of each interaction area are calculated by using the data, so that a customized navigation path is generated. When the AR glasses interact according to the path, position information of the AR glasses is collected in real time, interference source characteristics are obtained, whether the AR glasses enter an interference influence range is detected, if yes, an offset alarm is triggered, whether the AR glasses deviate from a preset path is judged based on the position information, and if not, a path correction command is generated. In addition, real-time visual data can be captured, an abnormal mode or a specific target is analyzed and recognized, and if the abnormal mode or the specific target is recognized, the position information and the corresponding visual data are stored.

The invention provides a fraud-related application detection method and device based on flow behavior analysis, a medium and a program product, and the method comprises the steps: carrying out the deep packet detection analysis of the current network downloading flow, comparing an application sample obtained through analysis with a preset white list and a black list, and screening out a missed to-be-detected sample; running a to-be-tested sample in a sandbox environment, collecting an interface image, extracting text features, and inputting a fraud-related classification model to judge whether the application program is a fraud-related application program or not; the method comprises the following steps: performing deep packet detection analysis on current network use traffic, extracting multi-dimensional behavior characteristics such as a terminal identifier, an application use frequency and an active time period, performing coding and scaling processing to form a fraud-related feature vector, and inputting a random forest detection model to judge whether a terminal has a fraud-related application use behavior or not. According to the method, through complementary fusion of content feature and behavior feature detection links, full-process identification of fraud-related applications in downloading and using stages is realized, and the coverage rate, the accuracy rate and the real-time performance of fraud-related detection are improved.

The embodiment of the invention provides an outdoor pipeline corrosion detection method based on visual identification of an unmanned aerial vehicle and a related device. The method comprises the following steps: acquiring an outdoor pipeline corrosion image; the outdoor pipeline corrosion image is input to a target detection model for corrosion detection, an outdoor pipeline corrosion detection result is obtained, the target detection model comprises a backbone network, a neck network and a detection head, the backbone network is used for carrying out feature extraction on the outdoor pipeline corrosion image to obtain multi-scale features, and the neck network is used for carrying out feature extraction on the multi-scale features; the neck network is used for carrying out feature fusion on the multi-scale features to obtain fusion features, the detection head is used for carrying out classification and rotating frame regression prediction on the fusion features to obtain an outdoor pipeline corrosion detection result, and the outdoor pipeline corrosion detection result comprises corrosion area positioning information and a detection frame with confidence; and generating a detection report according to the outdoor pipeline corrosion detection result, and sending the detection report to the user terminal. On the basis, the outdoor pipeline corrosion detection precision and efficiency can be improved.

The invention provides a milling cutter wear volume in-situ calculation method and system based on three-dimensional reconstruction, and relates to the technical field of machine tool cutter state detection, and the method comprises the steps: obtaining a high-quality multi-view image, employing an improved SLIC superpixel segmentation algorithm to extract region characterization parameters of a wear region superpixel block based on the high-quality multi-view image, and calculating the wear volume of a milling cutter according to the region characterization parameters. Inputting the region characterization parameters into a multi-scale fusion feature extraction network, and extracting a two-dimensional binarization matrix of the wear region; wear region feature points of the two-dimensional binarization matrix are extracted through a fusion feature detection algorithm, and a high-precision sparse point cloud of a wear region is generated through optimization by adopting a dual-stage feature matching algorithm; reconstructing the high-precision sparse point cloud by adopting an improved PMVS dense optimization algorithm to obtain a high-precision wear area point cloud; and based on the high-precision wear area point cloud, carrying out precise quantitative calculation on the wear area by utilizing a double-model fusion algorithm, and outputting to obtain a wear volume value. According to the invention, in-place accurate measurement and calculation of the wear volume of the milling cutter are realized.

The invention discloses a multi-dimensional collaborative counterfeit feature detection method for digital content, which comprises the following steps of: S1, receiving the digital content to be analyzed, performing format analysis and image extraction on the digital content, and performing standardization processing on the extracted image data; and S2, carrying out multi-dimensional atomic forgery feature extraction on the standardized image data, detecting potential forgery traces of each dimension, and outputting a preliminary analysis result of each dimension. The invention provides a multi-dimensional collaborative counterfeited feature detection method for digital contents, which integrates counterfeited indication information from various sources through multi-dimensional feature extraction and innovative collaborative analysis and context sensing mechanisms, and performs association analysis on the information in an innovative manner, so that the counterfeited counterfeited information is obtained. Therefore, the detection capability of digital content tampering and the interpretability of the result are effectively improved, the accuracy, robustness and interpretability of complex forgery detection are improved, and the feasibility of implementation is considered at the same time.

The invention discloses a winter dual-polarization radar hydrogel phase state identification method and device and a medium, and relates to the technical field of radar meteorological detection, and the method comprises the steps: obtaining the radar data of a dual-polarization radar; performing space-time matching on the radar data and the sounding data to obtain matched sounding data; based on the matched sounding data, zero-degree layer height information is extracted, and the average melting layer height is obtained in real time in combination with the quasi-vertical profile; based on the height difference between the average melting layer height and the altitude of the radar station, adaptively switching to a summer two-dimensional melting layer feature detection algorithm or a winter three-dimensional melting layer feature detection algorithm, and obtaining fine melting layer space information; according to the method, all polarization parameters are obtained, hydrogel phase state identification is carried out on all the polarization parameters, an initial phase state identification result is obtained, the initial phase state identification result is constrained by utilizing fine melting layer space information, a final hydrogel phase state identification result is obtained, and the hydrogel phase state identification precision of the dual-polarization radar in winter is improved.

The invention provides a satellite denial high-altitude hovering positioning method based on visual perception and inertial measurement, which is applied to an unmanned aerial vehicle control system, and the unmanned aerial vehicle control system comprises a front-end processing module and a rear-end processing module, the front-end processing module executes the following steps that ORB feature detection and extraction are carried out on a ground environment image shot by the downward-looking binocular camera, and space coordinate information of feature points is obtained; constructing a key frame dictionary database based on a bag-of-words model, and completing loopback detection through similarity calculation of feature points; initial pose estimation is realized based on a PNP algorithm and inertial measurement unit IMU pre-integration, and an initial estimation pose sequence is generated; wherein the rear-end optimization module executes the following steps: performing real-time optimization on an initial estimation pose sequence by adopting a sliding window optimization algorithm; and solving the maximum posterior probability estimation through an L-M optimization algorithm, and determining the high-altitude hovering positioning pose of the target. And reliable guarantee is provided for stable operation of the unmanned aerial vehicle in a high-altitude complex environment.

The invention belongs to the technical field of fan blade detection, and provides a fan blade surface small-size feature detection method and system based on deep learning, and the system comprises an MS-FEM module, a high-resolution feature pyramid module, and a small target optimization prediction head. The method comprises the following steps: preprocessing an input image, inputting the image into a YOLOv11 network, and generating a feature map; the MS-FEM module enhances the feature map, the high-resolution feature pyramid enhances the fusion feature of the spatial information flow, and a high-resolution feature map is generated; and the small target optimization prediction head predicts and outputs the high-resolution feature map. According to the method, through multi-scale feature fusion and global context modeling, the problem that tiny target information in a shallow feature map is lost is effectively solved; a high-resolution feature pyramid is introduced to reserve more small target space information, a small target optimization prediction head is improved to improve small target detection sensitivity, and a small target sensitive loss function is improved to strengthen small target gradient return.

The invention discloses a wind turbine generator full-blade load monitoring method based on tower wall vision measurement, and the method comprises the steps: S1, arranging an observation platform, and installing a camera; s2, arranging a blade mark group; s3, synchronizing image acquisition and time; s4, camera calibration and distortion correction; s5, feature detection and tracking; s6, performing three-dimensional reconstruction; s7, centrifugal force and gravity compensation calculation; s8, blade net displacement calculation; and S9, carrying out aerodynamic force load inversion and result output. According to the invention, monitoring of the load in the edge direction of the whole blade of the wind turbine generator can be effectively realized, and reliable technical support is provided for safe operation, optimal design and economical efficiency improvement of the blade of the large-scale wind turbine generator.

The invention provides a large model dynamic adaptation and collaborative extraction method based on a unified framework. The method comprises the steps of preprocessing to-be-processed multi-modal data; dynamically identifying a modal type and carrying out adaptive coding to obtain a basic feature; the semantic anchor points are combined to generate fusion features through a parallel Transform encoder, feature distortion is synchronously monitored, and error correction is carried out; realizing cross-modal information interaction based on a cross-attention mechanism of an anchor point weight enhancement matrix, and generating a cross-modal feature vector; determining a differential fusion weight according to the type of the relationship between the modes, and obtaining a global fusion feature; semantic consistency is detected, and the weight is dynamically adjusted; semantic information is converted, fine-grained data is supplemented, and a preliminary result is formed; verifying and complementing attributes based on the knowledge graph; and generating a multi-granularity extraction result containing the global result and the fine-granularity grounding information. According to the method, efficient adaptation, accurate fusion and high-quality information extraction of multi-modal data can be realized.

The invention discloses a panoramic image splicing method, system and device, and relates to the technical field of image processing, and the method comprises the steps: collecting images around a vehicle at different visual angles; using the MASK masks to generate overlapping region masks of the different view angle images, and performing local feature detection on the overlapping region masks to generate a transformation matrix; aligning the images of different visual angles according to the transformation matrix; a Canny edge detection operator is fused in splicing seam energy detection, the energy weight of the image edge area of the aligned image is enhanced, and a splicing seam is guided to actively avoid an object with remarkable structural features; b spline interpolation is introduced to optimize an initial splicing seam path; constructing a Laplacian pyramid along Gaussian blur of a splicing seam, and sequentially carrying out gradual fusion to generate a seamless panoramic image; according to the method, the basic function of eliminating the visual blind area is realized, a clear and visual panoramic view can be provided for a driver, and the driving safety is effectively improved.

The invention relates to the technical field of defect detection, in particular to a defect detection method and device, a storage medium and a computer program product. The method comprises the following steps: acquiring a target detection image, and inputting a target detection model into a lightweight detection model; performing feature extraction processing on the target detection image by using a feature extraction network, and obtaining a target feature map according to a feature map output by a target layer backbone network; performing feature fusion processing on the target feature maps of different levels by using a feature fusion network in combination with fusion space weight parameters corresponding to the target feature maps of various scales obtained by adaptive learning to obtain fusion feature information; and detecting the fused feature information by using each detection head of a feature detection network to obtain a defect detection result. According to the scheme, the problems of large parameter quantity, low reasoning speed and the like of the existing defect detection network model can be solved on the premise of ensuring the detection precision.

The invention relates to the technical field of fire detection, and provides a flame monitoring and positioning method and system based on improved YOLOv11. According to the method, in a backbone network of a flame monitoring model, feature information during flame feature extraction is enhanced through a plurality of SPD convolutional layers, and part of obtained flame feature maps with different scales and different dimensions are input into a neck network; in a neck network of a flame monitoring model, part of flame feature maps with different scales and different dimensions input by a backbone network are respectively input into a plurality of bidirectional feature pyramid network modules after convolution, and the flame feature maps with the same scale and channel number after up-sampling are fused in the bidirectional feature pyramid network modules. Obtaining a multi-scale feature enhanced fusion feature map; and finally, flame feature detection is performed on the multi-scale feature enhanced fusion feature map output by the neck network through a plurality of detection heads of the head network of the flame monitoring model, and flame targets of different scales are output.

The invention provides an unmanned aerial vehicle image ship detection method and system based on FAST features and an HSO algorithm, and relates to the technical field of infrared image processing. The problems that a traditional method cannot efficiently position the ROI and is poor in threshold segmentation effect are solved. According to the technical key points, the method comprises the following steps: carrying out FAST feature detection on a target unmanned aerial vehicle image, and determining an interest region image according to a feature detection result; determining a candidate detection threshold value set for the interest area image by adopting an HSO algorithm, performing iterative optimization processing on the candidate detection threshold value set until a termination condition is met, and outputting a detection threshold value when the termination condition is met; and carrying out image segmentation on the region of interest image by adopting the detection threshold, and carrying out ship detection on the segmented image to obtain a ship detection result in the target unmanned aerial vehicle image. According to the method, the processing range can be greatly reduced, the target detection segmentation threshold search efficiency is improved, the noise immunity is enhanced, and an image target extraction technical system adaptive to multiple complex scenes is formed.

The invention provides a three-dimensional modeling method and system based on multi-source heterogeneous data, and relates to the technical field of three-dimensional reconstruction. Multi-view point clouds, images and environment data in the same time state are obtained, and point cloud alignment precision is enhanced through feature detection and descriptor matching; an iterative nearest point algorithm is utilized to complete initial point cloud fusion to generate a three-dimensional model, position calibration is realized in combination with geometric features of environmental data, triangular meshes are divided by adopting a triangulation algorithm, surface defects of the model are repaired by adopting a hole filling algorithm, camera calibration is performed on image data, the image data are projected to the corresponding triangular meshes, and the three-dimensional model is obtained. And setting a pixel boundary threshold value to control a projection position, realizing accurate texture mapping, and defining a degradation coefficient to evaluate a local deformation condition of the three-dimensional model. According to the method, by fusing multi-source heterogeneous data and combining feature matching, position calibration and degradation coefficient evaluation, high-precision and complete three-dimensional model construction is achieved.

The invention discloses a remote merchant inspection method, which belongs to the technical field of mobile internet, and comprises the following steps: obtaining and analyzing an inspection task package to be audited, splitting data, then carrying out relevance verification, and screening out space-time abnormal data; identifying an image scene and calling a corresponding exclusive model, and triggering cascade calling when multiple scene features exist; extracting image feature detection quality and compliance, and fusing scores to generate a result; manual auditing is pushed according to the priority, and difference information is recorded; integrating the multi-dimensional score, outputting a result, and synchronizing the result to a risk control system; collecting samples every day, and updating the model regularly; traditional manual in-shop inspection is replaced by remote data analysis and relevance verification, the manpower and travel cost in a cross-regional and high-frequency scene is greatly reduced, time-consuming links such as shift arrangement and road are omitted, the inspection efficiency is remarkably improved, and by means of multi-dimensional data relevance verification, scene AI quality inspection and double-layer priority auditing, the inspection efficiency is greatly improved. And the problems of on-site picture counterfeiting, missing shooting and the like are effectively avoided.

A leak of a cold fluid (e.g., a chilled fluid, or a fluid that is initially pressurized and becomes cold on leakage) is detected using a sequence of Infrared (IR) and Visual (VI) images. Using neural nets, in each of VI and IR image-level features are extracted from images and compared with image-level features from images of different times to obtain motion-enhanced features. The motion enhanced features from VI and IR are then compared to obtain fused features from which the leak is detected. The image-level features may be extracted using a neural net with multiple stages. The motion-enhanced and fused features may be obtained in parallel using the image-level features from the multiple stages, and the leak detection based on the stage-specific fused features.

The application is suitable for the technical field of industrial detection, and provides a variable data printing defect detection method and system based on a YOLOv5s model, which comprises the following steps: obtaining a to-be-detected image of a to-be-detected printed matter based on a preset camera; inputting the to-be-detected image into a pre-trained improved YOLOv5s model to generate a feature detection image; and determining whether the to-be-detected printed matter has a printing defect according to the feature detection image. The application can efficiently and accurately determine possible printing defects in the to-be-detected printed matter, and has a detection efficiency much higher than manual detection, can adapt to detection of different types of printed matters and detection in different backgrounds, has better robustness and generalization, can timely find products with problems in printing quality, avoid inflow into the market to cause an impact on the enterprise image, protect the rights and interests of consumers, and improve the experience of consumers in purchasing or using products.