81 results about "Landslide detection" patented technology

The invention relates to a landslide detection method based on an improved YOLOv10n model, and belongs to the technical field of computer vision and geological disaster monitoring. The method comprises the following steps: firstly, collecting high-resolution remote sensing image data, and preprocessing by using a data enhancement technology; then, dividing the preprocessed remote sensing image data into a training set, a verification set and a test set, and carrying out real frame marking and category marking; then, an improved YOLOv10n model is constructed and trained on the basis of a Swin Transform backbone network, a depth separable convolution module and a context aggregation attention mechanism; and finally, outputting a landslide detection result by using the trained improved YOLOv10n model. According to the method, high efficiency and real-time performance are kept when high-resolution remote sensing images are processed, the method is particularly suitable for landslide real-time monitoring in a resource limited environment, the accuracy and efficiency of landslide detection are improved, the method can better adapt to complex terrains and background conditions, and efficient and reliable technical support is provided for landslide disaster monitoring.

The invention discloses a lightweight-based satellite landslide image intelligent detection method, device and equipment and a medium, and relates to the technical field of disaster detection, and the method comprises the steps: obtaining a whole-scene optical satellite image containing a landslide and a non-landslide region and landform auxiliary data; a dynamic segmentation strategy is adopted to carry out differential segmentation and standardized preprocessing on an image based on topographic data, and a standardized image is obtained. A target landslide image is screened through a double-layer machine learning model, the target image is input into an improved lightweight convolutional neural network for processing, an initial detection result is obtained, finally edge optimization and coordinate calibration are performed on the result, and an accurate landslide area detection result is output. The identification precision of the landslide image is effectively improved through dynamic segmentation and double-layer screening, the improved lightweight convolutional neural network realizes efficient detection in a low-resource environment, and the accuracy of a landslide detection result is further improved.

The invention provides a landslide detection method and device, a program product and electronic equipment, and relates to the technical field of computers. The method comprises the following steps: determining a to-be-detected remote sensing image, and then performing feature extraction on the to-be-detected remote sensing image through a backbone network of a landslide detection model to obtain a plurality of initial feature maps with different sizes; and through the neck network of the landslide detection model, carrying out fusion processing on the plurality of initial feature maps with different sizes to obtain a plurality of fusion feature maps with different sizes, and then through the receptive field attention sub-module of the landslide detection model, carrying out feature processing on the plurality of fusion feature maps with different sizes to obtain target receptive field features. Furthermore, the target receptive field features are detected and processed through the detection network of the landslide detection model to obtain a landslide detection frame and a landslide category label, so that the accuracy of detecting the landslide in the remote sensing image is improved to a certain extent.

The invention discloses a landslide disaster detection method based on an improved remote sensing landslide image segmentation model, and relates to the technical field of remote sensing image processing. Comprising the following steps: S1, constructing a multi-feature input data set based on gradient tensor data; s2, preprocessing the multi-feature input data set and generating an enhanced feature data set through a feature enhancement technology; s3, constructing a landslide detection model; s4, optimizing the landslide detection model by combining the training data set, the cross entropy loss and an Adam optimizer to generate an optimized landslide detection model; s5, correcting the optimized landslide detection model based on a segmentation evaluation result to generate a final landslide detection model; and S6, generating a current landslide disaster detection result based on the final landslide detection model and the acquired landslide image real-time data. Implicit feature information of remote sensing image data can be fully mined, semantic segmentation precision of a traditional model is effectively improved, and landslide extraction precision and accuracy are improved.

The invention discloses a method for detecting landslides on both sides of a reservoir based on global-local feature perception collaboration. The method comprises the following steps: acquiring landslide images by adopting air-water collaboration and constructing a landslide detection data set on both sides of the reservoir; extracting and fusing the overall landslide terrain trend, crack details and multi-scale edge textures step by step through a four-level layered structure of the constructed landslide feature extraction network, and outputting high-robustness landslide features; and finally, the detection head outputs a bounding box and a confidence coefficient by using the landslide features, and performs landslide detection and deformation judgment in combination with a multi-period data intersection-union ratio. For landslide scenes on both sides of a reservoir, the overall landslide terrain trend and crack details are cooperatively perceived, and global-local feature complementation is realized. The detection head locates a landslide area through a classification and regression network, quantifies the existence probability, completes landslide detection and deformation determination of two banks of the reservoir, achieves accurate monitoring of an existing landslide body in a complex reservoir area environment, improves the reliability and timeliness of deformation identification, and provides a guarantee for safe operation of the reservoir.

The invention provides a landslide detection method based on Swin Transform and multi-scale feature fusion, and relates to the technical field of landslide image detection, and the method comprises the steps: employing a Swin Transform architecture as a backbone network of a detection system, and effectively capturing local and global context information in an input image through a hierarchical self-attention mechanism; a multi-scale feature fusion lateral connection module is introduced, so that cross-scale feature integration is realized, and the capture capability of the model on landslide feature details and the understanding of the model on wider context information are improved; and the local information aggregation module is adopted to enhance the processing precision of local information. Through the advantages of the Swin Transform architecture, the problems of insufficient local and global feature fusion, low multi-scale feature utilization efficiency, poor complex scene adaptability and the like in landslide detection are effectively solved, and high accuracy and high efficiency of landslide detection are realized; particularly, the method shows excellent performance in the aspects of long-distance global dependence mining and landslide and non-landslide area distinguishing, and shows high robustness to environment and topographic changes.

The invention discloses an ancient landslide detection method, and the method comprises the steps: training a detection model through a landslide image with a label, and obtaining a trained detection model for detecting an ancient landslide from the landslide image; acquiring a landslide image pair to be detected as an input image pair, and respectively inputting the input image pair to the semantic feature extraction sub-module and the local feature extraction sub-module; the semantic feature extraction sub-module extracts global context features of the ancient landslide of each image of the input image to obtain a semantic feature image pair; the local feature extraction sub-module extracts local features of terrain height change at the edge of the old landslide of each image of the input image through a mask reconstruction task and a contrast learning task to obtain a local feature image pair; a feature fusion module performs feature fusion on the semantic feature image pair and the local feature image pair to obtain a fused feature image pair; and the decoder performs resolution reduction on the fused feature image pair to obtain an ancient landslide detection result.

Disclosed are systems and methods that employ elastic fiber optic, time-of-flight sensors for long distance landslide monitoring with sub-mm precision. The time-of-flight (ToF) sensor is integrated in conjunction with a single-photon avalanche diode (SPAD). By coupling both the emission source and the detector with a stretchable optical fiber, our inventive systems and methods continuously monitor the length of the stretchable optical fiber by measuring a traveling time of an optical pulse traversing the stretchable optical fiber. A significant, detectable change in the length of the stretchable optical fiber – indicative of ground movement or deformation, triggers an alarm, providing an early warning for potential landslides. As such, systems and methods according to aspects of the present disclosure provide a reliable, sensitive, precise, cost-effective, real-time solution for landslide detection and monitoring – a problem that has plagued the art.

The invention discloses a landslide detection deep learning method fusing geometry, context and frequency domain multi-source features, and belongs to the technical field of geological disaster recognition and landslide remote sensing information processing. The problem that a traditional landslide detection method in the prior art is low in recognition precision is solved. The method comprises the following steps: constructing a landslide detection model based on a GCF-Net architecture, and inputting a to-be-detected image into the landslide detection model; capturing feature information of different scales in the to-be-detected image through a ghost pyramid feature fusion module; an output characteristic pattern is input to a decoder through a GCFmodule; and an EdgeECI module in the decoder is used to process the output feature map of the GCFI module, and a final output feature map after fusion is obtained. The landslide detection accuracy is effectively improved, and the method can be applied to geological disaster monitoring and early warning, mountain traffic and infrastructure safety, water conservancy and energy engineering operation monitoring, remote sensing big data processing platforms and other scenes.

The invention discloses a landslide detection method and device, equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: carrying out the real-time photographing and obtaining a current frame image of a side slope region of a road side; based on the current frame image, detecting through a preset target classification model to obtain a potential landslide target, realizing preliminary landslide target classification, and tracking the trajectory of the potential landslide target through a preset target tracking model to obtain the motion trajectory of the potential landslide target; and on the basis of the motion track, whether a landslide sign exists in the side slope area of the road side is judged, so that the landslide sign is found in time, and the problem of low reliability of landslide detection is solved.

This invention relates to the field of landslide control technology, and discloses a real-time landslide monitoring method and system based on multi-sensor fusion and intelligent control. The method includes placing at least one sensor inside the metal reinforcement structure and at key locations on the landslide body to monitor landslide data in real time; acquiring real-time water composition imaging data of the slope under rainfall conditions; deriving rainfall volume from the landslide water composition imaging data; automatically adjusting the valve openings of drainage and diversion channels based on the rainfall volume; calculating the stability parameter distribution from the rainfall volume; obtaining a safety factor based on the stability parameter distribution; obtaining the instability probability of the slope through multiple grid cell safety factors; and optimizing the structural design of the metal reinforcement piles and reinforcement frame through finite element analysis. This invention improves the accuracy of landslide detection and early warning capabilities through real-time monitoring, data analysis, and automatic control of multi-source data.

The invention relates to a landslide detection method based on a high-resolution optical image, and the method comprises the steps: constructing an edge feature extraction module through employing a Canny filter, constructing the Canny filter and a convolution attention module, introducing the Canny filter and the convolution attention module into a U-Net model, constructing a landslide data set based on an optical remote sensing image, and carrying out the detection of the landslide. According to the method, training data is used, a Canny filter and a CBAM convolution attention module are used for cooperative training, verification data are sent to a belt detection model for detection, ten-time cross verification is carried out on the verification data, then averaging is carried out, and the Canny filter and the CBAM convolution attention module are introduced into a classical U-Net structure, so that the detection precision of landslide in a remote sensing image is remarkably improved. According to the method, the edge information in the image can be effectively extracted, key features of the landslide area can be automatically concerned, and the detection problem of a traditional method in a complex terrain and a boundary fuzzy area is solved.

The invention discloses a photoelectric detection type landslide probe, and relates to the field of highway and railway slope landslide detection, and the probe comprises a probe cap which is internally provided with a communication module, a signal processing calculation module and a photoelectric detection module from top to bottom in sequence; the probe tube is of a cylindrical hollow structure; the probe end is connected to the bottom of the needle tube and corresponds to the probe cap up and down; the photoelectric detection module comprises a detection camera, a laser transmitter, a photoelectric target surface, a reflector and a detection camera laser transmitter. The probe tube and the probe end of the probe are arranged on the slope, whether the probe tube is deformed or not is analyzed according to the path change of light rays, so that whether landslide happens to the slope or not is judged, the information is sent to terminal equipment through the communication module for remote monitoring, landslide detection work can be completed through the single probe, the structure is simple, installation is convenient, and cost is low. The landslide detection precision is improved, and the cost investment is reduced.

The invention, which relates to the technical field of landslide detection, discloses a fully-polarized SAR image landslide detection method comprising the following steps: collecting a single fully-polarized SAR image of a landslide target area and carrying out data preprocessing; based on a data preprocessing result, taking the depolarization degree as a constraint, and combining double-sided angle scattering and surface scattering to construct a landslide identification detector; using a generalized gamma distribution model to represent statistical information of the landslide identification detector; and de-noising the binary image obtained by the represented landslide identification detector by using a morphological operator to obtain a landslide detection result, thereby completing the fully polarized SAR image landslide detection. According to the method, the technical bottleneck that the landslide position distribution is difficult to quickly and accurately obtain based on a single full-polarization SAR image in the prior art is solved.

This invention belongs to the field of fully polarimetric synthetic aperture radar (SAR) image processing technology, and specifically relates to a method and apparatus for identifying landslides in complex backgrounds by fusing polarization information. The method includes registering images with the same polarization combination as the pre-landslide single-polarimetric SAR image and the post-landslide PolSAR image to construct a dual-temporal SAR image pair; generating a coherence map through coherence change detection to extract the change region; extracting multiple polarization feature parameters from the preprocessed post-landslide PolSAR image; fully fusing these polarization feature parameters using weighted TOPSIS for landslide detection to obtain suspected landslide areas; and fusing the change region extracted from the coherence map and the suspected landslide area through a logical AND operation to obtain the final landslide area. This invention utilizes the superior-inferior solution distance method (TOPSIS) to fuse polarization information for landslide detection, combined with coherence change detection, which can effectively suppress false alarm rates and significantly improve the accuracy of landslide detection.

The present invention discloses a method for detecting landslides in full-polarization SAR images, which relates to the technical field of landslide detection and includes the following steps: collecting a single full-polarization SAR image of a landslide target area and performing data preprocessing; based on the data preprocessing results, using the depolarization degree as a constraint and combining double-sided angle scattering and surface scattering to construct a landslide identification detector; using the generalized gamma distribution model to characterize the statistical information of the landslide identification detector; using a morphological operator to denoise the binary image obtained from the characterized landslide identification detector to obtain the landslide detection result, thereby completing the landslide detection in full-polarization SAR images. The present invention solves the technical bottleneck in the prior art that it is difficult to quickly and accurately obtain the location distribution of landslides based on a single full-polarization SAR image.

The invention relates to the technical field of disaster detection, in particular to a mining area remote sensing landslide detection method based on an improved YOLOv8 model, and mainly solves the technical problems that an existing remote sensing landslide detection method is poor in detection precision, difficult in landslide feature extraction and low in detection speed. According to the method, an MSFE module is introduced behind an SPPF module of a backbone network of a traditional YOLOv8 model, so that the model can identify small target landslide and broken landslide areas more accurately, and the landslide extraction difficulty is reduced; an LSKNet module is introduced into the neck network, so that the landslide positioning capability can be improved, and the detection precision can be improved; in addition, the C2f module of the backbone network is replaced by the C2fDCNv4 module, and the CIoU is replaced by the EIoU, so that the calculation speed of the model can be greatly improved, the detection speed is improved, and early warning can be performed in time when the landslide occurs.

The invention relates to the technical field of landslide detection, and discloses a semantic segmentation method, system and device for landslide remote sensing and a medium. The method comprises the following steps: acquiring a remote sensing image of a landslide, and extracting a feature map of the remote sensing image; performing feature extraction on the feature map by adopting a first attention mechanism to obtain a first-stage feature map focused on the landslide main body texture; performing feature extraction on the first-stage feature map by adopting a second attention mechanism to obtain a second-stage feature map focused on landslide edge extension; performing feature extraction on the second-stage feature map by adopting a third attention mechanism to obtain a third-stage feature map focused in the landslide slope body direction; performing feature extraction on the third-stage feature map by adopting a fourth attention mechanism to obtain a fourth-stage feature map focused on the landslide stripping trace; and performing semantic segmentation on the remote sensing image by combining the first-stage feature map, the second-stage feature map, the third-stage feature map and the fourth-stage feature map.

The application provides a semantic segmentation method for landslide detection by using medium-resolution multi-source remote sensing data, mainly including five steps: data selection and download, data preprocessing, model construction, model training and result evaluation. The application faces medium-resolution multi-source remote sensing data, proposes a new double-encoder semantic segmentation network with a self-attention mechanism, realizes feature expression and hierarchical fusion of multi-modal data, and further improves the landslide detection precision, thereby providing a technical reference for landslide detection in a large spatial scale.

The present application relates to the technical field of geological detection, and particularly relates to a landslide detection device, which comprises a shell, a friction induction surface on the inner wall of the shell, an electro-induction turntable connected to the shell in a relative rotating manner, an electro-induction boss on the circumferential side of the electro-induction turntable, a driving assembly comprising a driving member arranged outside the shell and a transmission member connected with the driving member and extending into the shell for driving the electro-induction turntable to rotate, and a circuit system arranged in the shell and electrically connected with the friction induction surface, wherein the circuit system is configured to capture an electric signal generated by the friction between the electro-induction boss and the friction induction surface when the driving member drives the transmission member to drive the electro-induction turntable to rotate under an external force, and transmit the electric signal to an external monitoring device. The present application converts the ground movement during a landslide into the rotation of the electro-induction turntable and the friction between the electro-induction boss and the friction induction surface to generate an electric signal, and realizes the detection of the landslide by capturing the electric signal.

The invention relates to the technical field of reasoning models, and discloses a model complementation-based target recognition method, which comprises a picture preprocessing step, a target recognition model preliminary recognition step, a confidence coefficient filtering step, a large model scene complementation detection step and a multi-model result fusion step. According to the method, consistent image input is provided through standardization processing, feature extraction deviation caused by size and color differences is reduced, meanwhile, the target recognition model more systematically captures landslide features through grid division and feature extraction, and missed detection of features with tiny expressions is reduced; secondly, low-confidence misjudgment can be filtered through confidence threshold screening, and different feature requirements can be met; furthermore, secondary detection of the large model supplements the complex scene recognition capability, and missing detection and misjudgment of a single model under low confidence or rare geological scenes are reduced; in addition, the weight fusion mechanism integrates the advantages of the target recognition model and the large model, and the overall accuracy and robustness of landslide detection are improved.

The application discloses a SAR image earthquake landslide boundary detection method and system based on deep learning, and the method comprises the following steps: inputting two SAR images before and after a landslide to generate a normalized mean ratio image, inputting the normalized mean ratio image into a pre-trained landslide detection network model based on deep learning after upsampling, extracting a landslide, extracting a boundary, and obtaining a landslide edge detection result; the landslide detection network model comprises an encoder and a decoder, the encoder comprises a backbone network and a coordinate attention empty space pyramid module, and the decoder comprises an attention adjustment module and a multi-scale feature fusion resampling module.The application can realize rapid, high-precision and automatic detection of large-scale earthquake landslide boundaries, and can realize detection of different earthquake types and different sizes of earthquake landslides.

This invention belongs to the field of ground-to-air transient electromagnetic measurement technology using magnetic sources, and more particularly relates to a semi-aerial transient electromagnetic data real-time transmission method suitable for landslide detection. This method utilizes an unmanned aerial vehicle (UAV) platform for rapid flight, automatically forming and superimposing tracks in mid-air, and obtaining kilobyte-level measured data. This data is then transmitted back to a ground receiving station in real time via wireless communication, facilitating real-time data processing. This method can provide faster interpretation results than conventional methods during landslide geological disaster surveys, providing a basis for disaster management in emergency landslide situations.

The application relates to the technical field of landslide detection, and discloses a semantic segmentation method, system, device and medium for mountain landslide remote sensing. The method comprises the following steps: acquiring a remote sensing image of a mountain landslide, and extracting a feature map of the remote sensing image; adopting a first attention mechanism to perform feature extraction on the feature map, so as to obtain a first-stage feature map focusing on the main texture of the mountain landslide; adopting a second attention mechanism to perform feature extraction on the first-stage feature map, so as to obtain a second-stage feature map focusing on the edge extension of the mountain landslide; adopting a third attention mechanism to perform feature extraction on the second-stage feature map, so as to obtain a third-stage feature map focusing on the direction of the mountain landslide slope body; adopting a fourth attention mechanism to perform feature extraction on the third-stage feature map, so as to obtain a fourth-stage feature map focusing on the stripping trace of the mountain landslide; and combining the first-stage feature map, the second-stage feature map, the third-stage feature map and the fourth-stage feature map to perform semantic segmentation on the remote sensing image.

The invention discloses a landslide displacement measurement method and system, and relates to the technical field of landslide detection, and the method comprises the steps: burying a detection inductor on a landslide body, connecting the two ends of the detection inductor to the top of the landslide body through insulated wires, connecting a voltmeter, collecting monitoring data, and carrying out the preprocessing; measuring the magnetic field intensity and the magnetic induction line direction on the landslide mass, calculating the included angle between the magnetic induction line and the detection inductor, constructing a correction model, calculating correction parameters, and obtaining displacement parameters; the monitoring voltage of the voltmeter is recorded, the actual displacement of the detection inductor in the magnetic induction line is calculated, and the landslide displacement is calculated and judged by combining displacement parameters. According to the invention, the detection is accurate, the external environment interference is small, the applied detection equipment is less, the possibility of landslide slippage can be accurately analyzed, the detection of landslide displacement at different depths can be met, and the possibility of damage to the detection equipment is reduced.

The invention provides a landslide area extraction method and device, electronic equipment and a storage medium, and belongs to the technical field of image data processing. The method comprises the following steps: respectively inputting a first remote sensing image of a time phase before occurrence of a landslide, a second remote sensing image of a time phase after occurrence of the landslide and a slope map into a first feature extraction network, a second feature extraction network and a third feature extraction network of a landslide extraction depth model, a first feature map, a second feature map and a third feature map which are correspondingly output are obtained; inputting the first feature map, the second feature map and the third feature map into a feature fusion module of a landslide extraction depth model to obtain a fused feature map output by the feature fusion module; and inputting the fused feature map into a decoder of the landslide extraction depth model to obtain a landslide extraction result output by the decoder. According to the method, the accuracy, robustness and efficiency of landslide automatic detection and mapping are improved, the slope map is introduced as a supplement of topographic information, and the identification capability of the model for the potential landslide area is enhanced.

The invention discloses a landslide mass detection method based on an improved distributed scatterer InSAR, and aims to accurately evaluate the movement speed of a landslide. According to the method, a regularization means is integrated in a covariance matrix estimation link, so that the phase error is effectively reduced, and the signal-to-noise ratio of a displacement time sequence is enhanced. Whether a synthetic data test or actual application data verification is passed, the technology shows that the precision and efficiency of landslide speed detection are remarkably improved while the coverage range of reliable monitoring points is expanded. The method is of great significance to accurate monitoring of landslide activities around the power line, and can effectively prevent potential threats of landslide to power transmission facilities.

The invention discloses a self-attention landslide segmentation method based on point cloud neighborhood structure perception, and relates to the technical field of landslide detection. Acquiring laser point cloud data of the landslide area; using the point cloud data of each point in the laser point cloud data as the input of a pre-trained landslide segmentation model for operation to obtain a landslide classification identification result of each point in the laser point cloud data; the landslide segmentation model comprises a plurality of neighborhood perception and feature coding layers, a plurality of neighborhood perception and feature decoding layers and an output layer. The plurality of neighborhood sensing and feature coding layers are used for performing sampling and feature extraction of different scales on the point cloud data, determining neighborhood sensing coefficients of neighborhood sampling points and updating sampling point features; and the plurality of neighborhood sensing and feature decoding layers are used for decoding and splicing the updated features so as to serve as the input of the output layer to obtain a landslide classification and recognition result. The self-attention landslide segmentation method based on point cloud neighborhood structure perception disclosed by the invention can improve the accuracy of landslide detection.

The present application relates to the technical field of remote sensing image, and particularly relates to a highland mountainous area landslide detection method based on an improved YOLOv11 deep learning model, comprising: obtaining remote sensing images of a target area and performing pretreatment; constructing a YOLOv11 target detection model, and introducing a spatial adaptive feature modulation module and a convolution channel mixer into the model, the former is used for adaptively recalibrating the spatial dimension of a feature map to enhance the landslide area feature representation, and the latter is used for extracting local context information; using an improved point distance-based intersection over union loss function as a positioning loss, and training the model in combination with a multi-scale training strategy; inputting the image to be detected into the trained model, and outputting a landslide detection result. The cascade design of long-range feature modeling and local feature extraction enhances the feature extraction capability of the model for landslide targets, the loss function improves the boundary box regression accuracy, and effectively improves the accuracy and robustness of landslide detection in the complex background of highland mountainous areas.

This invention discloses a method and device for geological disaster prevention and control of power transmission and transformation tower foundations, belonging to the field of landslide detection technology. The method involves: real-time acquisition of optical remote sensing images, radar remote sensing images, microwave remote sensing images, and thermal infrared remote sensing images; overlaying optical and radar remote sensing images to generate slope topographic spatial distribution data; analyzing vegetation growth status in the optical remote sensing images to obtain vegetation spatial distribution data; overlaying the slope topographic spatial distribution data and vegetation spatial distribution data to obtain topographic-vegetation coupled spatial distribution data, thereby determining the landslide sensitivity level of each preset grid unit and generating landslide sensitivity level spatial distribution data; and retrieving water storage spatial distribution data based on microwave and thermal infrared remote sensing images to assess the stability of the tower foundation area where the power transmission and transformation equipment is located, obtaining landslide level distribution data to determine control measures. This invention solves the problem of low accuracy in landslide detection in existing technologies.