183 results about "Confidence map" patented technology

The invention relates to a three-dimensional point cloud registration method and system based on a two-dimensional visual large model. A source point cloud and a target point cloud are decoupled into two-dimensional projection representation; constructing a structure consistency restoration domain, restoring geometric structure fracture and semantic deficiency in the projection image, and generating a dense and continuous restoration image; inputting the repaired image into a pre-trained visual geometry large model, carrying out cross-modal feature matching and pose resolving, and synchronously outputting a pixel-level matching confidence map; and inversely mapping a two-dimensional pose calculation result to a three-dimensional space to complete coarse registration, constructing a high-credibility anchor point set in the three-dimensional space by using a confidence map, and guiding a point cloud fine registration algorithm to converge to global optimum. According to the method, the registration large model for natural image training can be seamlessly migrated to the point cloud data, and retraining is not needed; in a weak texture and partially overlapped point cloud scene, a robust rough registration initial value can still be provided; the convergence speed and the anti-noise performance of fine registration are remarkably improved, and adaptive improvement from coarse to fine is achieved.

The invention relates to the technical field of detection, in particular to a complex shell detection method and system based on structured light scanning. The method comprises the following steps: calculating a wrapped phase value and a phase modulation amplitude of each pixel point under each frequency, calculating discrete probability distribution containing candidate stripe series and a corresponding probability for each pixel point based on a multi-frequency wrapped phase difference, generating an initial pixel confidence map, and constructing an energy function; the penalty weight of the smooth item is determined according to the phase modulation amplitude and the wrapped phase gradient local variance, obtaining an initial fringe order distribution diagram, updating discrete probability distribution by using an initial pixel confidence map, re-minimizing an energy function to obtain a corrected fringe order distribution diagram, and reconstructing a final three-dimensional shape. And iteratively optimizing a projector principal point and a radial distortion coefficient in system calibration parameters. According to the scheme, the geometric details of the surface can be reserved, and the processing effect and the measurement precision of the complex curved surface and the high-curvature area are improved.

The invention relates to the technical field of intelligent detection, and discloses a high-temperature alloy casting size on-line detection system based on machine vision, and the system comprises an imaging fusion module which collects image data of a high-temperature alloy casting, carries out the pixel-level registration of infrared image data and visible light image data, and generates a multispectral fusion image. The feature extraction module performs temperature distribution analysis and brightness feature extraction based on the multispectral fusion image, generates an edge confidence map and extracts a feature point set. And the calculation module performs weighted fitting on the feature points according to the feature point set to obtain thermal state size parameters. And the thermal compensation module performs thermal compensation and geometric correction on the thermal-state size parameters to obtain cold-state size parameters. And the judgment module carries out comparison to judge whether the casting is a qualified casting. According to the invention, stable imaging and accurate registration in high-temperature radiation and strong reflection environments are realized, and the detection efficiency and the size control level of the high-temperature alloy casting are improved.

The invention provides an electroencephalogram signal artifact removing method and device, equipment and a medium, and relates to the technical field of biological signal processing, collected electroencephalogram signal data is processed to obtain an optimal modal number and an optimal bandwidth parameter, and the optimal modal number and the optimal bandwidth parameter are used for conducting self-adaptive variational mode decomposition on the electroencephalogram signal data to obtain a plurality of modal components; then carrying out multi-dimensional feature analysis to obtain a plurality of feature indexes for judging an artifact suspicion mode and an effective mode component; performing short-time Fourier transform on the artifact suspicion mode, and constructing a time-frequency confidence map to guide weighted time-frequency independent component separation on the artifact suspicion mode to obtain an artifact component; suppressing the artifact component to obtain a suppressed independent component, and performing weighted reconstruction and multi-component fusion on the suppressed independent component and the effective modal component based on the time-frequency confidence map to obtain an artifact-removed electroencephalogram signal; and the fidelity, the robustness and the real-time performance of the electroencephalogram signal are improved.

The invention discloses a three-dimensional scene reconstruction method and system based on monocular depth estimation, and belongs to the technical field of computer vision and three-dimensional reconstruction, and the method comprises the steps: carrying out the multi-scale feature coding of a monocular RGB image through a mixed attention depth coding module, and obtaining the hierarchical depth feature representation; carrying out autoregression depth decoding through a self-adaptive edge perception depth decoding module to generate an initial depth map; a depth confidence map is calculated through a geometric consistency constraint optimization module and is fed back to a coding module for iterative optimization, and a refined depth map is output; and three-dimensional Gaussian ellipsoid scene representation is constructed through the Gaussian ellipsoid scene reconstruction module. According to the invention, high-precision depth estimation and high-quality three-dimensional reconstruction are realized by constructing a depth-coupled closed-loop cooperative system.

The invention discloses a method for optimizing micro-crack segmentation based on deep learning and super-resolution reconstruction, and the method comprises the steps: cutting an image in real time, obtaining an image block which takes a component as a target main body, and synchronously recording a homography matrix for geometric mapping; selecting an amplification strategy to improve the resolution, and recording a scale mapping relation; inputting the enhanced image block into a double-flow network; adaptive fusion and reconstruction are carried out on the two branch features, and a high-resolution texture image is output; generating a geometrically corrected ortho-image, fusing the geometrically corrected ortho-image with original illumination information, and outputting a corrected image with a known pixel size; identifying cracks, spalling and honeycomb diseases in parallel; generating a unified defect confidence map; calculating real geometric parameters of the BIM in a BIM global coordinate system through coordinate back projection; and generating quantitative defect reports and maintenance suggestions. The method has the advantage that seamless connection between the detection result and the BIM global coordinates is realized.

The invention relates to a solid-state laser radar ranging method and system based on neural network processing, and belongs to the technical field of laser radar ranging, and the method comprises the steps: obtaining a laser echo signal when laser radar detection is carried out on a surrounding target object, and obtaining a photon counting histogram based on the laser echo signal; preprocessing the photon counting histogram, and inputting the preprocessed photon counting histogram into a pre-trained neural network model to obtain a peak confidence map containing an echo peak value and a peak time offset map; on the basis of the peak confidence map and the peak time offset map, obtaining one or more echo peak values of which the confidence exceeds a predetermined threshold and time position information of the one or more echo peak values of which the confidence exceeds the predetermined threshold; and based on the time position information, the distance information with one or more target objects is determined, so that the time position information measurement accuracy of one or more echo peak values is improved, and the laser radar ranging accuracy is improved.

The invention relates to a transient electromagnetic and seismic wave multi-mode joint inversion imaging method based on physical information Transform, and belongs to the crossing field of geophysical exploration and artificial intelligence. Comprising the following steps: carrying out anomaly detection, interpolation, filtering and normalization preprocessing on transient electromagnetic and seismic wave original data; extracting features representing electrical property, elasticity and cross physical significance; serializing the spatial data through a gridding and alternating fusion strategy, and constructing an enhanced code fusing absolute and relative positions and physical attributes; a physically constrained encoder-decoder architecture is designed to carry out multi-scale forward modeling-inversion; and quantizing the uncertainty of an inversion result by adopting a Bayesian Monte Carlo method, and generating a confidence map. According to the method, through physical rule driving and multi-modal depth complementary fusion, the fine recognition capability and interpretation reliability of hidden disaster-causing geologic bodies such as underground goaf and collapse columns are effectively improved while the physical consistency of data is kept.

The invention discloses an unmanned aerial vehicle electric power inspection image segmentation method and system, and relates to the technical field of image segmentation. By means of a labeled sample image set, positive and negative information is fully fused in a confidence map cooperation module to carry out confidence modeling, so that a cooperation confidence map can accurately reflect the difference between a target and a background; the negative sample distribution data effectively depicts the features of background interference, then high-discrimination positive and negative points are screened out in the point selection module, accurate prompt is provided for segmentation, iterative optimization is performed through the noise sensing and refining module, and therefore the segmentation efficiency is improved under the condition that only few labeled samples are needed. According to the method, high-precision, high-robustness and training-free segmentation of the target component in the electric power inspection image is realized, the influence of complex backgrounds and similar interferents is effectively resisted, the method has high robustness, the technical problems of low segmentation precision and insufficient robustness under the condition of few labeled samples in the prior art are fully solved, and the requirements of electric power inspection on automation and precision are met.

An autonomous warehouse robotics system integrates a multi-sensor platform, adaptive payload handling, dynamic task reallocation, advanced navigation, energy management, and comprehensive safety features into one mobile robot chassis. The system utilizes LiDAR, stereo vision, ultrasonic sensors, mmWave radar, thermal cameras, and event cameras to perform complete environmental sensing and obstacle detection. Sensor fusion combines adaptive weighting, multi-modal data integration, and statistical filtering to create high-confidence maps for reactive path planning and collision avoidance. The robot's payload system features machine vision for item recognition, telescopic lifts, variable-width grippers, and real-time toolhead verification to handle a variety of goods. Fleet management is achieved through dynamic task reallocation that considers robot location, battery level, and operational delays, all coordinated by a cross-platform middleware architecture that ensures standardized communication, remote monitoring, and over-the-air updates among diverse robot brands. Energy management optimizes power usage via predictive routing, autonomous return-to-charge, and auction-based scheduling. Safety is maintained through proximity detection, behavior-based intervention, and human-robot cohabitation protocols while advanced localization is enhanced by fusing ultra-wideband positioning with visual landmark alignment, inertial sensing, and machine learning to deliver high accuracy in non-line-of-sight conditions. An onboard edge AI module further refines navigation and task prioritization through neural network inference, ensuring robust, adaptive operation in dynamic, unstructured warehouse environments.

The invention discloses a skin disease diagnosis method based on image recognition, and the method comprises the following steps: obtaining a to-be-detected skin image, and recording an equipment identifier; carrying out white balance correction, reflection suppression, hair suppression and scale normalization to generate a standardized image; positioning the focus to generate a focus mask; generating an enhanced view set in the focus mask by the enhanced view set according to the enhanced times and the enhanced view set; calculating a color invariant graph, a sign response graph and an artifact confidence graph, and synthesizing a potential energy field set according to a fusion weight; calculating a local structure tensor and generating a Riemannian metric matrix; constructing an improved Morse-Smal complex under a Riemannian metric matrix, carrying out cross-complex clustering on the critical point set to obtain a stable score, carrying out complex simplification to generate a stable Morse-Smal complex, and calculating the confidence coefficient of the cell boundary; and extracting structured feature vectors, diagnosing, judging and outputting disease categories, grading results and re-checking and marking. According to the invention, the anti-artifact and cross-device stability is improved.

The embodiment of the invention provides a face forgery detection method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a pre-training detection model and a to-be-detected face image, and inputting the to-be-detected face image into the pre-training detection model, so that a face key point detector obtains a face key point graph according to the input to-be-detected face image; a semantic segmentation graph generator generates a semantic segmentation graph according to the face key point graph; the feature extractor performs basic feature extraction according to the to-be-detected face image to obtain basic features; the global feature extraction branch performs global feature extraction according to the basic features to obtain a global feature map; the feature learning branch obtains a region-level forgery confidence map according to the basic features and the semantic segmentation map; the feature fusion device carries out fusion processing according to the global feature map and the region-level forged confidence map to obtain classification features; and the classifier obtains a face forgery detection result according to the classification features. According to the invention, the accuracy of face forgery detection is improved.

The invention belongs to the technical field of pavement detection, and discloses a highway pavement roadbed detection method and system based on laser three-dimensional scanning. By establishing an analytic optical or radar scattering physical model, a distance measurement deviation initial value conforming to a multi-path reflection physical mechanism is provided for each laser measurement point, and then refined correction is performed on the initial value through a residual error regression network fusing local point cloud context information, so that advantage complementation of physical prior and data driving is realized, and the accuracy of distance measurement is improved. And finally, in combination with adaptive filtering based on a confidence map, ranging errors caused by a multi-path effect can be effectively distinguished and corrected in the presence of complex road surface conditions such as slippery road surface, greasy dirt or thin water film, and meanwhile, geometric details such as real road surface pits, textures and the like are completely reserved. Therefore, the reconstruction error and the disease false alarm rate of the pavement digital elevation model are obviously reduced, and the precision and the reliability of highway pavement roadbed detection are improved.

The invention relates to a pavement crack intelligent detection method, system and device based on image recognition and a medium. The method comprises the steps of obtaining an original pavement image, and performing preprocessing according to global brightness distribution of the original pavement image to obtain an image after illumination equalization; inputting the illumination equalization image into a pre-trained convolutional neural network to extract multi-scale texture features, and generating a multi-scale texture feature map; mapping the feature map into a crack confidence map through a segmentation network; performing threshold segmentation based on the confidence map to obtain a crack binary image; performing sub-pixel-level edge refinement on the binary image to obtain a sub-pixel-level crack edge set; and based on the edge set, crack width and length quantization data are calculated through geometric parameters, and a detection result is output. By adopting the method, local shielding interference can be effectively resisted, a scene with dramatic illumination change is adapted, sub-pixel-level precise positioning and quantification of the crack edge are realized, the recognition capability and detection continuity of the fine crack are improved, and the reliability and generalization capability in a complex road environment are enhanced.

The invention provides a multi-modal image fusion system and method for neurosurgery, and the method comprises the steps: carrying out the rigid transformation of a multi-modal magnetic resonance image and an ultrasonic image, and obtaining a preoperative magnetic resonance image and an intraoperative ultrasonic image after preliminary alignment; inputting the preoperative magnetic resonance image and the intraoperative ultrasonic image into a pre-trained lightweight deformation registration network for deformation modeling to obtain a voxel-level dense deformation field, and determining a confidence map of deformation field region registration based on the dense deformation field; according to the confidence map, multi-scale feature fusion based on confidence weighting is carried out on the deformed preoperative multi-modal labeling information and the corresponding features of the intra-operative ultrasonic image, and a three-dimensional anatomical structure chart for real-time navigation in the neurosurgery operation is obtained. Based on the scheme, brain tissue drift can be dynamically compensated in real time in a neurosurgery operation, and multi-modal image fusion is guided based on a compensation result.

The embodiment of the invention provides a special-shaped element pose estimation method fusing geometric prior and self-supervised learning and related equipment, and belongs to the technical field of computer vision and industrial automation. The method comprises the following steps: firstly, constructing an element geometric priori knowledge graph containing symmetry, boundary and key point topological information; collecting an RGB-D image, and detecting a depth failure region to generate a confidence map; rGB texture features and point cloud geometric features are extracted respectively, and adaptive fusion is carried out based on the confidence map; inputting the fusion features and geometric priori into a pose prediction network comprising a differentiable constraint module, and predicting an initial pose; training the network by adopting a two-stage strategy combining synthetic data supervised pre-training and real data self-supervised fine tuning; and finally, taking the initial pose as a starting point, fusing geometric priori to carry out iterative optimization, and outputting a refined 6D pose. According to the method, the pose estimation precision and robustness of the small-size, weak-texture and reflective special-shaped element are remarkably improved, and the dependence on labeled data is greatly reduced.

The invention discloses an inspection agent collaborative perception system based on semantic driving, and relates to the technical field of intelligent inspection, and the system comprises a prototype mapping module which collects inspection target category information and inspection target monitoring indexes, and generates an inspection task semantic prototype set and an inspection task semantic mapping table through semantic conversion; the semantic map module is used for collecting inspection image data and inspection video data through an inspection agent, performing pixel-level visual feature matching based on an inspection task semantic prototype set, and generating a semantic confidence map and a semantic request map; the coupling mutual sending module is used for executing sparse selection and directional mutual sending of semantic supply and demand coupling under the common constraint of the semantic confidence graph and the semantic request graph, and generating a multi-source sparse semantic feature queue; and the fusion remarking module is used for executing position-level semantic attention fusion and measurable sensitivity recalibration on the multi-source sparse semantic feature queue to generate a fusion probability graph and a fusion instance table.

The invention relates to a self-supervised multi-modal fusion and collaborative optimization method suitable for a curve and ramp scene. The method comprises the following steps: acquiring an original perception image; generating four paths of geometrically consistent recovery images; constructing a multi-modal fusion model, and generating a depth confidence map aligned with the restored image; uniform ground plane parameters are obtained; collecting all coordinate points to construct an initial 3D lane candidate point set; generating a high-density locally enhanced BEV lane representation; and outputting the fused global consistency 3D lane map. And outputting the structured 3D lane line marking data. Non-planar structures such as curve superelevation and longitudinal ramps can be accurately depicted, and geometric consistency in a complex road scene is greatly improved; the problems of single vehicle shielding, sensor noise and the like are effectively relieved, the precision, the integrity and the system-level reliability of 3D lane reconstruction are remarkably improved, meanwhile, dependence on a high-precision map or 3D true value marking is avoided, and the method has high engineering landing value.

The application discloses a CT image lung cancer recognition method based on reinforcement learning, generates a lung structure mask, outputs an initial lesion candidate region, an initial lesion mask, a morphological confidence map and a boundary response map, obtains a focused feature map, obtains an initial lesion fine mask, a boundary probability map and an uncertainty heat map, encodes a reinforcement learning state vector, establishes a DSAC-T refining intelligent agent, obtains an updated lesion fine mask, calculates a reward value, trains the DSAC-T refining intelligent agent by using the reward value, repeats the steps until a preset convergence condition is met or a maximum iteration number is reached, and outputs a final lesion fine mask; consistent lesion masks are generated, and comprehensive evaluation results for clinical diagnosis and follow-up are formed. The application improves the fine segmentation stability under a fuzzy boundary, high noise and extreme samples, and improves the detection rate of micro-lesions and clinical applicability.

The invention provides a single photon array image data processing method and system based on time correlation, and relates to the technical field of image processing.The method comprises the steps that a photon event sequence with a timestamp and position information is obtained through a detector array, a four-dimensional space-time tensor is constructed, and space-time data is obtained through normalization and noise estimation; performing time sequence analysis on each spatial position and neighborhood in the spatio-temporal data to extract time sequence features, separating target features from the time sequence features, removing noise features and generating a time confidence map; inputting the time confidence map and the spatio-temporal data into a variational optimization model for joint optimization, and dynamically updating a time weight field and a spatial support domain in optimization according to the confidence map to obtain an initial reconstructed image; and finally, inputting the initial reconstructed image and the time confidence map into a neural network, and outputting a final image through modeling and residual path restoration. According to the invention, the signal-to-noise ratio and dynamic scene adaptability of single photon imaging under extremely low illumination are improved.

This invention discloses a three-dimensional contour measurement method and apparatus based on multi-projection gratings, relating to the field of three-dimensional topography measurement technology. The method includes: obtaining intrinsic and extrinsic parameters through calibration; projecting a set of speckle patterns; preliminarily analyzing the surface topography gradient of the object to determine a non-uniform spatial frequency sequence; sequentially projecting and acquiring multiple sets of phase-shifting grating patterns, and calculating the wrapping phase map corresponding to each frequency; for the same pixel, based on its reliability assessment in wrapping phase maps at different frequencies, assigning confidence weights to the phase values ​​of each frequency, and calculating and optimizing the phase map and the corresponding fused confidence map; calculating gradient information and combining it with the fused confidence map to perform region division, and executing a region-guided path consistency verification unfolding algorithm to obtain the absolute phase; converting the absolute phase into three-dimensional point cloud coordinates of the object surface. This application aims to solve the problems of complex topography, discontinuities, and noise easily leading to unfolding path errors and order jumps during phase unfolding.

The invention relates to the field of simultaneous localization and map creation, and discloses an image enhanced vision SLAM (Simultaneous Localization and Mapping) method and system suitable for a complex illumination environment, a medium and equipment, and the method comprises the steps: carrying out the preprocessing of an obtained original image of the complex illumination environment, and carrying out the classification; carrying out enhancement processing on different types of images by adopting different self-adaptive image enhancement strategies; the enhanced image is input into an improved SuperPoint neural network, an enhanced original feature point confidence map score map is obtained, and candidate feature points are determined; dynamically adjusting a confidence coefficient threshold value of the feature points according to matching feedback between image frames, and screening out final feature points from the candidate feature points; feature points of a current frame image and a previous frame image are matched, matching screening and geometric consistency verification are carried out, degradation caused by four-point near collinear or local aggregation is inhibited through quality sorting and scattered point selection, and the feature matching estimation precision is improved; and inputting the screened matching pairs into an SLAM system for front-end tracking and mapping.

The application discloses a multi-focus microscopic image fusion method, system and program based on depth map correction. The method comprises the following steps in sequence: image sequence acquisition, texture extraction, definition evaluation, depth map generation, confidence map generation, depth correction and color image reconstruction. According to the definition curve, the depth map and the confidence map are determined, the unstable points are determined by using the confidence map, the depth data of the non-texture and weak-texture areas are corrected by HSL color space guided filtering, and thus the phenomenon that the depth map is wrong in focus plane judgment due to the non-texture and weak-texture areas of the image source is avoided.

This invention discloses a training-free subjective-objective video object mapping method driven by spatial feature alignment. First, videos with target object annotations in both subjective and objective perspectives are designated as source perspective videos, while the other perspective video is designated as the target perspective video. A pre-trained dense feature matching model is used to process the source and target perspective videos, obtaining a bidirectional feature point mapping relationship and a matching confidence map. Based on the target object mask of the source perspective video, an effective feature point set is obtained. Based on this effective feature point set, keyframes and the target object masks of the corresponding frames in the target perspective video are selected and used as spatiotemporal cues. A pre-set cue-based video object segmentation model is then used for target segmentation, yielding the target object mask of the target perspective video frame, thus obtaining the subjective-objective video object mapping relationship. This invention requires no training and significantly improves the accuracy of subjective-objective video object mapping tasks on large-scale datasets by utilizing feature matching and cue-based video object segmentation.

A mechanism is described for image frame rendering. An apparatus of embodiments, as described herein, includes one or more processors to receive a plurality of past image frames including a plurality of pixels, receive a predicted optical flow, generate a predicted frame and a confidence map associated with the predicted frame based on the plurality of past image frames and the predicted optical flow, render a first set of the plurality of pixels in the predicted frame based on the confidence map and adding the rendered pixels to the predicted frame to generate a final frame.

The application discloses a skull local point cloud registration method based on adaptive geometry-appearance joint cost, relates to the field of point cloud registration, and comprises the following steps: performing three-dimensional reconstruction on preoperative CT slices to obtain a skull CT point cloud model; performing semantic segmentation on RGB-D data of an intraoperative anatomical structure to obtain a target region mask; performing spatial back projection on depth in the target region mask to generate surface point cloud with color attributes; performing guided completion and filtering on missing and weak areas of the surface point cloud with color attributes according to an edge confidence map extracted from color and depth, to obtain a completed point cloud retaining important boundaries; and constructing an adaptive geometry-appearance joint cost to perform rigid registration on the skull CT point cloud model and the completed point cloud, so that accurate alignment of a spatial position of a patient during operation and the skull CT point cloud model is realized without using body surface calibration points.

The application relates to an insulator image segmentation method based on an unmanned aerial vehicle infrared enhanced image, and relates to the technical field of image processing, and comprises the following steps: acquiring an infrared image sequence through an infrared enhanced camera to obtain a standard infrared image sequence, performing state recognition by using an image perception prior engine, and outputting a predicted insulator attention heat map and a predicted boundary confidence map; optimizing and adjusting an initial threshold segmentation algorithm and an initial region growing algorithm to obtain an adaptive threshold segmentation algorithm and an adaptive region growing algorithm; performing image segmentation on the standard infrared image sequence to output an insulator image sequence, and performing early warning judgment on the insulator image sequence based on an adaptive early warning mechanism. The application solves the problem that the traditional insulator image segmentation method cannot effectively deal with the problems of low temperature contrast, large noise interference and complex background of the infrared image, so that the insulator segmentation is prone to false segmentation and missed segmentation, and the high-precision requirement of fault detection cannot be met.

The invention provides an unmanned aerial vehicle routing inspection method and system based on semantic guidance and an asset value cost graph, and the method comprises the steps: reasoning a possible distribution region of a target when the target is not directly observed through analyzing the structure law of semantic information, such as an equipment number, a function partition identifier and the like, in a transformer substation; generating a confidence map to guide the unmanned aerial vehicle to efficiently inspect; constructing a semantic cost graph fusing the static asset value and the dynamic operation risk, and quantifying potential loss of different regions when collision or falling occurs; according to the real-time health state of the unmanned aerial vehicle, semantic guidance is emphasized during normal inspection through a dual-guidance function fusion and switching mechanism, and the unmanned aerial vehicle is automatically switched to asset value cost minimization guidance in an abnormal or emergency state to realize safe disposal; the system adopts a framework of semantic reasoning, cost evaluation and flight control module decoupling, and guidance intelligence and control reliability are ensured. According to the invention, the inspection blindness of the unmanned aerial vehicle is obviously reduced, and the abnormality handling risk is reduced.

The invention discloses an unsupervised rice mapping method based on priori knowledge, and the method comprises the steps: constructing a PSS-Unet deep learning framework, and automatically generating an initial pseudo tag through RiceTcolor-CIE chromaticity transformation and an NDWI water mask by employing a Sentinel-2 transplanting period image; the pseudo labels are fused with time sequence optics, time sequence radar and SATELD features respectively, and a training set composed of multi-source heterogeneous feature data is constructed; training a U-Net model based on a BCE loss function with an ignoring mechanism; a dynamic pseudo label optimization mechanism is introduced, a confidence map generated by model prediction is utilized, and progressive improvement of pseudo label quality is realized by combining confidence screening and an iterative re-labeling strategy so as to realize rice planting area identification; a pseudo label generated by priori knowledge is used as an initial supervision signal to drive U-Net network learning, and a dynamic optimization mechanism based on model confidence is designed to realize collaborative iterative evolution of the pseudo label and a model, so that the label reliability and the model discrimination capability are gradually improved.

The invention belongs to the technical field of image processing, and particularly relates to a circuit graph feature matching method based on dense matching, which comprises the following steps of: firstly, respectively carrying out multi-scale feature fusion on an image to be detected and a template image to obtain a low-resolution fusion image, and then carrying out convolution on the multi-scale features of the two images to obtain key point heating power; the low-resolution fusion image passes through a linear layer to obtain a confidence map, finally, in a feature matching stage, key points with high confidence scores form feature descriptors, the feature descriptors are adjusted to obtain image descriptors, the similarity between the image descriptors is calculated to carry out nearest neighbor matching, and rough key point matching pairs are obtained through calculation; the matching result obtained from the coarse-grained matching stage is used for further fine alignment, and the positions of the corresponding key points in the image are adjusted more accurately, so that more accurate matching is obtained. According to the method, the positions of the matched key points are more accurate, and more accurate and efficient image matching is realized.