80 results about "Change detection" patented technology

One or more systems, devices, computer program products and / or computer-implemented methods of use provided herein relate to efficient change detection based on dynamic information retrieval by employing foundation models. For example, according to an embodiment, a system is provided. The system can comprise a memory that can store computer executable components. The system can further comprise a processor that can execute the computer executable components stored in the memory, where the computer executable components can comprise a data access component that can accesses multi-dimensional query data and features of interest (FOI) data related to the multi-dimensional query data. The computer executable components can further comprise an artificial intelligence (AI) component that can identify, based on the FOI data, one or more key frames within the multi-dimensional query data.

This invention discloses a method, apparatus, and medium for detecting abnormal changes on the surface of power equipment. The method includes acquiring a surface image of the power equipment at any given time as the image to be detected; acquiring a reference image of the power equipment; inputting the image to be detected and the reference image into a trained change detection network model to obtain a change detection result; wherein, the training of the change detection network model includes: acquiring surface images of the power equipment at different times as training images; labeling each training image with abnormal changes based on the reference image to generate a labeled image set; constructing a change detection network model based on a self-attention mechanism; and training the change detection network model using the labeled image set. This invention can effectively improve the detection accuracy of abnormal changes on the surface of power equipment, promptly detect suspected faults in power equipment, and ensure the safe and stable operation of power equipment.

This invention relates to the field of remote sensing image processing technology, specifically disclosing a building change detection method based on a dual-branch encoder and multi-feature fusion. By modeling the building body and edges separately, this invention elevates edge information to an equal level of importance with body information, providing a new design approach to solve the long-standing problems of boundary adhesion and ambiguity in building change detection, and facilitating the handling of irregular boundaries. A feature cross-fusion module effectively promotes the fusion of building integrity, ensuring the semantic consistency and accuracy of the results. In the decoder section, a hierarchical feature fusion noise removal module maximizes the identification and removal of abnormal image patches. This invention achieves efficient and high-precision building change detection by inputting the acquired dual-temporal remote sensing image (including the preceding and following temporal images) into the constructed building change detection model and outputting the building change detection results.

This invention discloses a device and method for detecting boundary changes in a video wall splicing mirror, relating to the field of optical inspection technology. The detection device includes a contact sensing structure, a non-contact optical structure, and a processor arranged relative to the video wall splicing mirror body. The detection of boundary changes in the video wall splicing mirror is achieved based on the contact sensing structure and the non-contact optical structure. Furthermore, when using the contact sensing structure, the edge sensor readings are corrected by introducing a mounting arm length parameter, eliminating Abbe errors caused by the misalignment of the mounting reference plane and the optical neutral plane, thus improving the accuracy and reliability of the contact detection. An event sensor is introduced into the non-contact optical structure; this event sensor can typically capture transient events of edge changes with microsecond-level time resolution, making it suitable for real-time detection in dynamic environments. In addition, the contact sensing structure and the non-contact optical structure can serve as backups for each other, improving the reliability of the detection device.

This invention discloses a gradient difference-enhanced remote sensing method and device for detecting changes in cultivated land. The method involves acquiring dual-temporal remote sensing images of the study area; obtaining a label map of actual cultivated land changes; constructing a CAGDE-CD model, which includes a twin encoder and decoder, and developing a loss function for the CAGDE-CD model; training the CAGDE-CD model; and inputting the predicted dual-temporal remote sensing images into the trained CAGDE-CD model to obtain the corresponding change detection result map. This invention addresses problems in cultivated land change detection such as false changes, missed detections, unclear boundaries of changed areas, and internal voids. More importantly, this method not only accurately detects the boundaries of changed areas but also enhances the completeness of the change detection results, significantly improving detection accuracy and exhibiting good robustness.

The application discloses a remote sensing change detection method based on a teacher-student framework and multi-space distillation, relates to the technical field of remote sensing image processing, and comprises the following steps: constructing a text prototype library and a visual prototype library; performing zero sample prediction on an image to be detected to generate text guided features; extracting multi-scale feature maps, global semantic features and spatial level features by a student network; calculating absolute differences, similarity differences and text guided differences to splice initial change features; respectively calculating channel attention and spatial attention of the initial change features to fuse multi-scale fusion features to splice spliced features; and inputting the decoder to obtain a binary change detection map. The method effectively improves the precision of remote sensing image change detection and provides important technical support for the application in the fields of city planning and environment monitoring.

This invention discloses a method and system for detecting building changes based on multi-period UAV inspection videos. Specifically, it includes: acquiring multi-period UAV inspection videos and recording imaging attitude information to achieve unified management of cross-period inspection data; aligning cross-period time indexes and unifying spatial scale processing to construct a comparable video sequence; extracting building contour points, structural lines, and structural component regions to form the building's structural state; establishing a mapping relationship between structural states in adjacent inspection periods to form cross-period structural state pairs; constructing homotopy parameter intervals and setting target state nodes, performing multi-target structural deformation tracking and recording fracture and bifurcation information; generating free tracking results and constrained tracking results and calculating the differences; locating the cross-period interval where building changes occur and outputting the change detection results. This invention achieves precise location of building changes through cross-period homotopy tracking and counterfactual constraint comparison.

The application discloses a remote sensing image binary change detection network based on frequency domain feature interaction. The network is aimed at the pseudo change problem caused by light, season, sensor noise and complex background in high-resolution remote sensing images, and a binary change detection network composed of a hybrid encoder, a frequency domain feature interaction module, a difference feature enhancement module and a lightweight decoder is constructed. The hybrid encoder extracts multi-scale spatial detail features through a spatial encoder and extracts global semantic context through a semantic encoder, so as to give consideration to local boundary texture and advanced semantic information. The frequency domain feature interaction module converts the double-time-phase features into the frequency domain, generates a content-aware filter according to the semantic context, and applies asymmetric frequency domain filtering to the two time phases respectively, so as to suppress the pseudo change caused by the difference in imaging conditions. The difference feature enhancement module further extracts robust change difference features through multi-scale spatial interaction, frequency-aware double-gating and lightweight refinement, and finally outputs a binary change mask by the decoder. The application can improve the change region recognition accuracy, boundary positioning ability and robustness in complex remote sensing scenes.

The present invention relates to a system and method for automatically realigning low-ranking adaptive weights in response to the evolution or change of a base language model. The system of the present invention includes an atomic knowledge storage unit that stores user judgment cases, rules, and rationale information in a structured decision log format including a situation field, a judgment field, a rationale field, and a rejection field, independently of the specifications of the base language model; an adapter generation unit that generates a first low-ranking adaptive weight compatible with a first version of the base language model from the decision log; a model change detection unit that detects a change in the base language model; and an adapter realignment unit that generates a second low-ranking adaptive weight from the decision log of the atomic knowledge storage unit in response to the detected change, without using the parameter values ​​of the first low-ranking adaptive weight. According to the present invention, a closed-loop architecture is realized in which the judgment sense accumulated by the user is continuously preserved regardless of the generational change of the base language model and is automatically transferred to a new model, the user's intellectual assets are managed as vendor-independent assets not dependent on a specific vendor or model generation, and biases or errors of the previous adapter are not propagated to the new adapter.

This invention relates to the field of model update and switching technology, and particularly to a method for hot model update and intelligent switching under a cloud-edge collaborative architecture. The method includes: performing change detection on a stored list of model versions via a cloud platform, and generating an update instruction containing an identifier of the target updated model version based on the change detection results; retrieving the model update file from the model repository based on the update instruction; loading the model update file in a local cache area; switching the current model version to the new model version corresponding to the model file in the pending switch state while the current model version is running on the target edge node; monitoring the real-time running status of the new model version on the target edge node; and switching back to the current model version when the new model version's running status is abnormal. This invention enables dynamic hot updates and intelligent switching of model versions in a cloud-edge collaborative scenario, improving the efficiency of model deployment.

Systems and methods are described for detecting changes at a location based on image data by a mobile robot. A system can instruct navigation of the mobile robot to a location. For example, the system can instruct navigation to the location as part of an inspection mission. The system can obtain input identifying a change detection. Based on the change detection and obtained image data associated with the location, the system can perform the change detection and detect a change associated with the location. For example, the system can perform the change detection based on one or more regions of interest of the obtained image data. Based on the detected change and a reference model, the system can determine presence of an anomaly condition in the obtained image data.

This document describes change detection criteria for updating a sensor-based map. Based on detecting an indication of a registered object in the vicinity of a vehicle, a processor determines a difference between a characteristic of the registered object and a characteristic of a sensor-based reference map. A machine learning model is trained using self-supervised learning to identify change detection from inputs. The model is executed to determine whether the difference satisfies change detection criteria for updating the sensor-based reference map. If the change detection criteria are satisfied, the processor causes the sensor-based reference map to be updated to reduce the difference, which enables the vehicle to safely operate in an autonomous mode using the updated reference map for navigating the vehicle in a vicinity of a coordinate location of the registered object. The map can be updated concurrently as changes occur in the environment and without impacting performance, enabling real-time perception to support control and improve driving safety.

The present application relates to a kind of fusion lightweight backbone network and remote sensing change detection method of convolution decoding, including using backbone network feature extraction module, for extracting multi-scale features with strong representation ability from input dual-phase remote sensing image;CNN-Decode feature alignment module, using convolutional coding structure is spatially aligned to dual-phase feature, avoid the feature confusion problem of traditional attention mechanism;And change detection module, based on the feature after alignment realizes pixel-level change identification.The present application is optimized by double-path cooperation, while enhancing the feature expression ability of model, it enhances spatial alignment accuracy, effectively improves the integrity and boundary accuracy of change detection under complex scene, under the premise of keeping low computational complexity, significantly improves detection performance, can be widely applied in urban planning, disaster assessment and environmental monitoring and other fields.

The application discloses a kind of based on optimization's guiding filter image fusion change detection method and device, method includes: by extreme minimum scale difference operator, pixel value difference value is to the picture of same scene different phase, obtains difference map;Using the way of guiding filter, difference map is fused on global scale, to obtain the optimal difference map;Mean filter is used to phase map I1 and I2, and the base layer difference map corresponding to each difference map is obtained, and the optimized brightness saliency map F1 and F2 are obtained by pca fusion as the image of generating saliency map, according to the principle of maximum saliency to determine weight map;Weight map is regularized for double-scale difference map reconstruction, and the final difference map is obtained;In clustering segmentation stage, by introducing soft threshold function, the final difference map is further processed, to suppress existing abnormal noise.The device includes: processor and memory.

This invention relates to an automatic IT asset discovery method based on multi-protocol fusion and incremental change detection. Compared with existing technologies, it solves the shortcomings of existing IT asset discovery methods, such as incomplete protocol coverage, low scanning efficiency, improper handling of multi-source data conflicts, and high update overhead, resulting in low asset discovery coverage, low accuracy, and synchronization delays. This invention includes the following steps: building a multi-protocol parallel scanning framework; intelligent scheduling of protocol priorities; multi-dimensional device fingerprint extraction; confidence-weighted fusion deduplication; incremental change detection; and intelligent identification of asset model classification. This invention introduces an intelligent asset classification mechanism based on a machine learning model to achieve automatic identification and intelligent management of asset types, realizing high coverage, high accuracy, low overhead, and low latency automatic discovery and management of IT assets.

This invention relates to the field of remote sensing image change detection technology, specifically to a method and system for remote sensing image change detection based on edge-aware multi-scale difference. The method includes: acquiring a pair of dual-temporal remote sensing images and a trained edge-aware multi-scale difference network; inputting the dual-temporal remote sensing images into an edge-aware central difference feature encoder; preserving high-frequency edge information and extracting multi-scale edge features through a three-level cascaded central difference convolution during the input stage; inputting the multi-scale edge features into a multi-scale spatiotemporal transform module to generate differential enhancement features; inputting the differential enhancement features into an adaptive region-aware progressive decoder; progressively fusing the features from coarse to fine through region-aware attention; and outputting the change detection result after multi-level supervision. This invention solves the problems of boundary blurring, false change detection, and missed detection of small targets in high-resolution remote sensing image change detection, improving the accuracy and robustness of change detection.

The embodiment of the application provides a kind of optical remote sensing image slice level change detection method and device, the method comprises: obtaining the image slice pair of two time phase optical remote sensing image;Feature extraction is carried out to image slice pair using twin encoding, and two time phase multilevel feature map is obtained, and twin encoding is obtained by processing based on sensitivity network pruning method;Two time phase global feature vector is obtained by using multilevel feature compression module to compress processing two time phase multilevel feature map;According to two time phase global feature vector, difference feature vector is obtained, and difference feature vector is input into decision network to carry out change detection, and the change detection result of image slice pair is obtained.The application is based on multilevel feature compression and network pruning technology to realize optical remote sensing image slice level change detection, while guaranteeing detection precision, maximum degree compression model complexity and improve reasoning speed.

This invention discloses a remote sensing change detection method based on edge-enhanced cross-attention and multidimensional loss. The method includes: inputting dual-temporal images into a trained Siamese backbone network to extract multi-scale backbone features from the dual-temporal images; inputting the multi-scale backbone features into a trained unsharpened mask cross-attention fusion model to extract spatial and semantic information from the dual-temporal images through cross-attention operations, and extracting edge information from the dual-temporal images through sharpening convolution kernels to obtain multi-level differential features at different scales; inputting the differential features into a trained multi-scale hierarchical connection decoder to obtain preliminary multi-scale prediction results for the changed region; and performing weighted fusion of the preliminary multi-scale prediction results to obtain the change prediction result. This invention can fully utilize the multi-scale information of the Siamese backbone network and improve the ability to extract edge information from dual-temporal images, thereby improving the accuracy of change detection.

The application relates to the technical field of remote sensing image processing, and discloses an image building change detection method and system fusing a Transformer and an HRNet, a terminal and a storage medium. The method comprises the following steps: performing preprocessing on double-phase high-resolution remote sensing images of a region to be detected to obtain standardized image blocks; inputting the standardized image blocks into a change detection subnetwork to extract global semantic features of the two-phase images and perform feature fusion, and generating a change map; inputting the standardized image blocks and the change map into a fine segmentation subnetwork to output a ground object category probability map; determining a building change type according to the change map and the ground object category probability map, and generating a building change detection result. The application realizes fine segmentation of a building edge and accurate differentiation of easily confused ground objects in a complex scene, automatically outputs a structured result containing an accurate boundary, a ground object category and a specific change type, and improves the robustness and precision of building change detection.

The application provides a remote sensing image change detection method based on a modular learning network in the field of change detection, which decouples the change detection method, and regards the change detection as the change of the object related to the detection task. Specifically, in the framework, two special components are designed: a task-centered learner and a difference detector. The task-centered learner is based on an encoder-decoder structure and is committed to identifying the object related to the task. The difference detector combines a difference module and a feature pyramid network and is used for identifying the change between targets. The method can greatly improve the prediction accuracy of the network.

This disclosure relates to a method, apparatus, and storage medium for detecting regional changes in unsupervised ground-based micro-change monitoring radar images. The method includes: acquiring a time series of images of a target area; obtaining the correlation between the time series images at equal intervals; obtaining an initial difference map based on the correlation images; and clustering the processed initial difference map to obtain change detection results. Through the various embodiments of this disclosure, the difficulties of ground-based micro-change monitoring radar, such as high noise levels, difficulty in distinguishing images before and after changes, and difficulty in obtaining difference maps, are overcome.

The application relates to a SAR image water body submergence range change detection method and device for a flood scene. The method comprises the following steps: acquiring a training sample set containing pre-disaster and post-disaster SAR sample images and water body mask data, performing coherent speckle noise suppression and contrast enhancement preprocessing on the sample images, inputting the preprocessed images into a backbone double-branch twin network, extracting and interacting multi-scale features to generate double-time multi-scale feature maps, performing upsampling, splicing and channel space attention enhancement on the multi-scale feature fusion unit to obtain an optimized fusion feature map, generating a prediction result by a detection head, generating an intermediate heat map by a deep supervision unit, training a model to convergence by combining mask data to calculate a loss, inputting a to-be-detected image into the model after preprocessing to obtain a water body submergence range change detection result. The method can accurately extract the water body submergence range of a complex flood scene SAR image, effectively suppress noise, strengthen feature fusion, and improve the detection precision of the model to adapt to the real-time demand of flood emergency monitoring.

The application discloses a kind of super-light twin multiscale network remote sensing image change detection methods, comprising: obtaining two remote sensing images of same area different time phases;Two images are input into feature extraction network based on twin architecture, the network includes two weight-sharing encoder branches, and obtains multi-level two-phase feature map by multi-level extraction;Difference is generated in each level of encoder to two-phase feature map, and generates multi-scale difference feature map;Decoder is constructed, and the difference map of the corresponding level of encoder is cascaded decoding by feature fusion module to last-stage decoding feature, and the resolution is recovered to obtain decoding feature gradually;Change probability map is generated based on decoding feature and output result.The application enhances feature expression by multi-scale hollow convolution and spatial-spectral feature correlation mechanism, realizes super-light by combining channel scaling factor, reduces parameter quantity and amount of calculation while ensuring high detection accuracy, and is suitable for edge device.

A system, method and non-transitory computer readable medium for generating validated remote sensing change images that includes a user input device for selecting high-resolution satellite images, and processing circuitry to generate a depth map and a semantic map from a static image. A change simulator determines candidate areas for change simulation and generates a change depth map and change mask focusing on objects removed from the static image. An image diffusion neural network applies a control network and stable diffusion to generate pre-change and post-change image tiles. Validation processing circuitry iterates through a validation process to validate the pair of change tiles to obtain a validated pair of change tiles and a validated change mask.

This invention discloses an automated detection method and system for heterogeneous remote sensing images (optical and SAR). First, optical and SAR images from different time phases are preprocessed. Then, a heterogeneous change detection model based on a pseudo-twin channel feature extraction encoder is constructed, and multi-level features of the optical and SAR images are encoded in parallel. A semantically knowledge-guided multi-path feature enhancement module strengthens both shallow and deep features. Subsequently, a progressive fusion decoder is used to fuse the multi-scale features output from the encoding stage step by step. A difference map refinement module fully mines the complementary information and differences between the optical and SAR images, accurately locating and identifying change areas, and generating the final change detection result. This invention effectively improves the accuracy, recall, and overall accuracy of heterogeneous remote sensing image change detection, and exhibits good robustness and scalability.

The application discloses a land use change detection system based on remote sensing cloud computing. The system comprises a remote sensing data acquisition module, a spectral shift compensation module, a response field construction module, a confidence evaluation module, a multi-scale segmentation optimization module and a sub-pixel boundary optimization module. The application firstly realizes accurate spectral alignment by using invariant ground feature spectral reference, then establishes a spatial confidence evaluation model through change direction consistency and amplitude dispersion analysis to filter core change pixels; and based on boundary stability and internal homogeneity, the optimal segmentation scale is automatically determined, and the change area boundary is accurately corrected through sub-pixel spectral decomposition technology, which significantly improves the reliability and accuracy of land use change detection, greatly reduces the false change detection rate, effectively enhances the boundary positioning accuracy, adapts to complex change scenes and improves the processing efficiency.

Provided is a condition change detection system capable of accurately detecting a change in the condition of a subject even when progression rates of symptoms are different. A condition change detection system 100 comprises a biological information measurement device 101 that measures vital signs of a subject P, a motion detection sensor 110 that detects motion of the subject P around a bed, and a host device 200 that manages a condition change of each subject P. The host device 200 mainly comprises a storage unit 201, an input unit 202, a control unit 203, an output unit 204, and a communication unit 205. In this case, the control unit 203 is provided with an index calculation unit 203a that calculates a biological index for evaluating fluctuations in vital signs of the subject P over time, a change detection unit 203b that detects a change in the condition of the subject P on the basis of the calculated biological index, and a condition notification unit 203c that notifies of the change in the condition. FIG.１