915 results about "Aerial image" patented technology

The invention relates to an unmanned aerial vehicle aerial image small target detection method and a computer readable storage medium. The method comprises the steps of obtaining unmanned aerial vehicle aerial image data, and dividing the data into a training set and a verification set after processing; yOLOv8n is used as a basic model, traditional convolution structures of a shallow layer and a middle layer are replaced by full-dimensional dynamic convolution in a backbone network of the YOLOv8n model, an enhanced space attention mechanism based on routing is introduced into the backbone network of the YOLOv8n model, and an original SPPF structure of the backbone network is replaced by a multi-scale context modulation module. Performing collaborative design of a multi-scale structure and a detection head in a neck network and a head network of the YOLOv8n model to obtain an improved YOLOv8n model; training and verifying the improved YOLOv8n model by using the training set and the verification set to obtain a trained unmanned aerial vehicle aerial image small target detection model; and inputting a to-be-detected unmanned aerial vehicle aerial image into the trained unmanned aerial vehicle aerial image small target detection model to obtain a detection result. According to the invention, the small target detection precision and speed are improved.

The invention discloses an unmanned aerial vehicle aerial image target detection method based on PSO-DETR, and belongs to the technical field of unmanned aerial vehicle aerial image target detection. Firstly, a parallel patch perception attention feature extraction module is constructed, and an efficient multi-branch backbone network C3KCSPnet is designed by fusing a CSPDarknet53 structure; according to the network, gradient flow is improved through deep optimization, and the capturing capacity of high-level semantic information is enhanced. And secondly, an enhanced channel offset hybrid operator is provided, the dependency relationship between channels is enhanced through a channel shuffling mechanism, and cross-channel interaction of local space information is realized in combination with channel offset operation, so that the feature recovery quality and fusion efficiency in an up-sampling stage are improved, and the problem of missed detection of a shielded target is further relieved. And finally, a re-parameterization hierarchical aggregation network is designed, effective integration of shallow details and deep semantics is realized through an efficient hierarchical fusion mechanism on the premise of ensuring controllable calculation complexity, and the detection performance of the small target is further enhanced.

The invention belongs to the technical field of computer vision and artificial intelligence, belongs to the cross technical field of target detection, deep learning and image processing, and particularly relates to a YOLOv8 algorithm improvement method based on an unmanned aerial vehicle aerial image small target detection model, which comprises the following steps of: introducing a user-defined feature enhancement module into a YOLOv8 backbone network, a neck part and a detection head part; the self-defined feature enhancement module comprises a context guide self-adaptive fusion module introduced into a backbone network so as to replace part of traditional convolution operation; a space edge sensing feature up-sampling module and a space sensing enhanced convolution module are adopted in the neck fusion network; a fine-grained dynamic pruning detection head is introduced into a detection head detection network. According to the method, the performance of the model in a small target detection scene is effectively enhanced, and the accuracy, robustness and real-time response capability of a detection system are remarkably improved.

The invention relates to an aerial photography target detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring an aerial image acquired by an image sensor; inputting the aerial image into a pre-trained target detection model to obtain a target detection result; the target detection model comprises a backbone network, a neck network and a head network; wherein the backbone network is used for performing feature extraction on an aerial image to obtain image extraction features; the neck network is used for performing multi-scale fusion on the image extraction features to obtain a plurality of fusion features of different scales; the neck network comprises a bidirectional information transfer network, the bidirectional information transfer network comprises a separation and attention enhancement module, and the separation and attention enhancement module is used for generating fusion features corresponding to tiny targets; and the head network comprises a detection head corresponding to the tiny target and is used for obtaining a target detection result corresponding to the fusion feature based on the fusion feature. By adopting the method, the detection accuracy under the complex aerial image can be improved.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention relates to the field of urban road modeling, in particular to an aerial photography data processing method and system for urban road modeling. The invention discloses an aerial photography data processing system for urban road modeling. The aerial photography data processing system comprises an aerial photography video acquisition module, a road modeling module, a photogrammetry module, a spatial registration module and a rendering synthesis module. According to the invention, on the basis of a spatial calibration mechanism of the original aerial image and the BIM model, redundant processes such as point cloud acquisition, data fusion and manual correction are eliminated, and linear processing from data acquisition to dynamic result output is realized; according to the architecture, the hardware resource dependency and the operation complexity are greatly reduced, and designers are endowed with the capability of completing large-scale scene visualization processing in a conventional engineering environment; according to the method, a transmission chain from original data to decision support is remarkably shortened, and important engineering scheme deduction and dynamic review have unprecedented timeliness.

The invention relates to the technical field of computer vision and deep learning, in particular to an aerial image target detection method based on frequency domain decoupling multi-scale feature fusion, which comprises the following steps of: acquiring an aerial image of an unmanned aerial vehicle, establishing a data set, and performing preprocessing and data division; an aerial image target detection network is constructed, and the aerial image target detection network receives an input image and outputs a target category and a bounding box position; loss functions are determined, wherein the loss functions comprise classification loss representing matching quality, coordinate loss representing prediction coordinate relevancy and bounding box regression loss representing bounding box positioning accuracy; training the aerial image target detection network based on the data set and the loss function; inputting a to-be-detected aerial image into the trained aerial image target detection network to obtain a to-be-detected target category and a bounding box position; the method can improve the feature fusion degree, retains high-frequency details, and enhances the small target recognition rate.

The invention discloses a target detection method based on a YOLO model, electronic equipment and a storage medium, and relates to the technical field of target detection. Comprising the following steps: inputting an aerial image of an unmanned aerial vehicle into a trained target YOLO model; the target YOLO model comprises a backbone network, a neck network and a head network, and a feature extraction module in the backbone network performs multi-scale feature extraction by adopting a double-branch architecture attention mechanism; performing multi-scale feature extraction on the aerial image by adopting a dual-branch architecture attention mechanism through a feature extraction module in the backbone network, and constructing to obtain a plurality of layers of first comprehensive image features of the aerial image; performing feature fusion on the first comprehensive image features of different levels through a neck network to obtain second comprehensive image features of multiple levels; and inputting the multiple levels of second comprehensive image features into a head network to obtain a target detection result of the aerial image. According to the invention, the accuracy of small target detection can be improved.

The invention discloses a laser point cloud shielded vehicle completion method and device based on a thunder-vision fusion deep learning framework in the technical field of automatic driving environment perception. The method comprises the following steps: acquiring original laser point cloud data and an aerial image; constructing a fan-shaped shielding area based on the original laser point cloud data; identifying two-dimensional bounding boxes, orientations and category labels of all vehicle targets in the image; inputting the fan-shaped occlusion area and the two-dimensional bounding boxes, orientation and category labels of all the vehicle targets into a pre-trained double-branch deep learning network, and predicting whether an occluded vehicle exists or not and the position, orientation and category information of the occluded vehicle; and selecting a vehicle point cloud template based on the category information of the shielded vehicle, and then generating a scene point cloud after vehicle point cloud completion as a final result of vehicle completion in the shielded region. According to the invention, an image-point cloud space mapping mechanism based on the Leiyu fusion deep learning framework is introduced, so that the capability of automatically identifying and positioning the shielded vehicle is remarkably improved.

The invention relates to the technical field of flight path planning, in particular to an autonomous flight path planning method and system for a road slope inspection unmanned aerial vehicle, and the method comprises the following steps: controlling the unmanned aerial vehicle to fly in a road slope region according to a preset flight path, collecting the aerial image data of the unmanned aerial vehicle, analyzing the aerial inspection adaptive feature value of the unmanned aerial vehicle, and determining the flight path of the unmanned aerial vehicle. Judging whether the feature value is lower than a preset inspection adaptation feature threshold value or not; analyzing a field-of-view missing feature set of the unmanned aerial vehicle based on the aerial image data of the unmanned aerial vehicle if the field-of-view missing feature set is lower than the preset routing inspection adaptive feature value threshold; performing flight path planning processing on the unmanned aerial vehicle based on the field-of-view missing feature set; according to the method, the unmanned aerial vehicle is subjected to flight path planning processing on the basis of the field-of-view missing feature set, so that when an aerial picture is deviated, fuzzy or blind areas, judgment and automatic adjustment are performed in real time, self-adaptive optimization of the flight path is realized, and the road slope inspection efficiency is remarkably improved.

The invention discloses a re-parameterization unmanned aerial vehicle target detection method based on multi-core fusion and omnidirectional connection, and the method comprises the following steps: obtaining an unmanned aerial vehicle aerial image data set, and constructing a detection model comprising a backbone network, a feature fusion network and a deformable task decoupling detection head; the backbone network extracts multi-scale and multi-direction features by six parallel paths in a training stage through a wide-branch re-parameterization convolution module, and re-parameterization is carried out in a reasoning stage to obtain single-path convolution; the feature fusion network performs down-sampling through a spatial deep convolution module and reduces spatial information loss, and realizes cross-stage omnidirectional feature interaction and double attention enhancement in combination with an omnidirectional kernel cross-stage partial connection module; and the deformable task decoupling detection head decouples the classification and regression features, optimizes feature expression, weights the classification features and then performs aggregation decoding. The model is trained to be used for a test set to output a detection result, detection precision and reasoning efficiency can be balanced, and the model adapts to a complex aerial photography scene of an unmanned aerial vehicle.

The invention discloses a city-level live-action three-dimensional modeling method based on air-ground multi-source data, and the method comprises the following steps: 1) respectively constructing a ground point cloud and an air point cloud, respectively extracting semantic tags, local geometric features and color features of a ground image and an air image, and carrying out the back projection of the semantic tags, local geometric features and color features to the corresponding ground point cloud and air point cloud; the method comprises the steps of (1) obtaining a ground point cloud and an aerial point cloud, (2) respectively calculating fusion multi-modal features of the ground point cloud and the aerial point cloud, (3) realizing cross-point-cloud feature interaction through a Transform point cloud registration model, and (4) selecting a three-dimensional reconstruction area. According to the invention, accurate registration of different-source point clouds with large air-ground view angle difference and low overlapping degree can be realized; the problems that an existing oblique photography urban three-dimensional model is lack of details and insufficient in precision in ground and building facade areas, and a live-action three-dimensional model is complex in updating process, long in period and low in automation degree are solved.

The invention relates to a robust unmanned aerial vehicle detection method based on dynamic feature fusion and context attention, and belongs to the technical field of image processing. Aiming at the problems of small target feature loss, semantic gap, background noise interference and the like caused by a fixed convolution kernel scale, one-way feature fusion and a static attention mechanism in an existing unmanned aerial vehicle aerial image target detection method, the method comprises the following steps: constructing a detection model comprising a backbone network, a neck network and a detection head network; a feature rearrangement and extraction module is designed in the backbone network to enhance feature learning, an enhanced double-flow feature fusion pyramid is designed in the neck network to optimize multi-scale feature fusion, and a dynamic multi-scale context attention mechanism is designed in the detection head network to suppress irrelevant background noise. The method effectively improves the accuracy and robustness of small target detection, and achieves a clearer and more stable detection effect in a complex environment.

The invention relates to a computer vision and target detection technology, in particular to an enhanced target detection method and device based on feature fusion and a medium. The method comprises the following steps: acquiring aerial image data; extracting a multi-level initial feature map through an initial feature extraction network; respectively extracting local features and global features of the initial feature map of each level through a local feature extraction network and a global feature extraction network which are deployed in parallel; self-adaptively fusing the local features and the global features through a gating fusion module to generate a fused feature map, and calculating a feature competition map and generating a spatial dimension gating weight map by adopting a local-global feature competition mechanism oriented to an aerial photography scene to realize feature weighted fusion of spatial positions one by one; and performing multi-scale fusion on the multi-level fusion feature map, and finally outputting a target detection result. According to the invention, the detection precision and robustness of the multi-scale target in the aerial image are effectively improved.

The invention relates to the technical field of topographic change analysis, in particular to an unmanned aerial vehicle-based topographic change detection system, which comprises a slope direction sensing track control module, a texture structure extraction module, a crack evolution track construction module, a direction trend comparison module and a patrol recheck positioning module. According to the method, a continuous elevation point column of an unmanned aerial vehicle scanning area is extracted, laser reflection point coordinates are fused, a space relation of transition point distribution is constructed, dynamic adjustment of a ground-imitated flight path is achieved, and a texture structure area with continuous directivity is recognized in combination with a high-angle image boundary communication relation; texture boundary evolution is compared at different time nodes to form a crack path, the stability of the path and the slope direction is judged through an included angle sequence, recognition and sorting of areas with the consistent direction are completed, a space comparison result is registered in a three-dimensional coordinate system, terrain change areas are accurately marked, and rapid positioning and continuous tracking of high-risk areas are achieved.

A method for detecting printing defects in a photolithography mask that will print on a wafer when using the photolithography mask in a specific photolithography system to print semiconductor structures on the wafer, the method comprising: acquiring a first aerial image of the photolithography mask using a mask inspection system; generating a second aerial image of the photolithography mask by applying a machine learning model (26) to the first aerial image, wherein the machine learning model is trained to map a first aerial image acquired by a mask inspection system to a second aerial image that emulates the application of the specific photolithography system to the photolithography mask; and detecting printing defects in the photolithography mask by comparing the second aerial image to a reference image.

The invention discloses a building height detection method and device and a storage medium, and relates to the technical field of image data processing, and the method comprises the steps: generating a digital elevation model of a target region and a digital orthoimage of the target region based on an aerial image; inputting the digital orthographic image into a building detection model for building identification to obtain an identification result of the building in the target area; according to an identification result of the building, establishing world coordinates of the building in the digital orthographic image; and determining a building elevation value and a ground elevation value of the building in the digital elevation model according to the world coordinates, and determining the height of the building according to the building elevation value and the ground elevation value. The technical problems of poor precision and low efficiency of building height detection in urban wide-area complex areas are solved, and the precision and efficiency of building height measurement are improved.

The invention provides a ship target detection method based on nonlinear network enhancement, a medium and equipment, and belongs to the field of artificial intelligence. The method comprises the following steps of: performing type conversion, data division and enhancement on image data in a data set by adopting the ship data set acquired in a real river channel scene; a nonlinear network enhanced YOLO ship detection model is constructed; adopting a multi-task joint loss function to train the YOLO ship detection model; and inputting a port monitoring image and a sea surface aerial image into the trained YOLO ship detection model, outputting a category number, a confidence value and bounding box coordinates of each prediction box, and forming a visual image. According to the method, a nonlinear network enhanced YOLO ship detection model is constructed, and the modeling expression capability of the network on a ship target in a complex scene is effectively improved, so that the robustness and the accuracy of target detection are enhanced, and particularly, the performance is better under the conditions of small target detection and image degradation.

The invention discloses a three-dimensional reconstruction method and device based on an aerial image. The method comprises the following steps: collecting a plurality of aerial images of a to-be-reconstructed region and corresponding position data and attitude data; generating an initial three-dimensional point cloud according to the plurality of aerial images and the corresponding position data and attitude data; and converting the initial three-dimensional point cloud into a Gaussian original body, carrying out iteration on the Gaussian original body to obtain a Gaussian sputtering three-dimensional model, and generating a three-dimensional reconstruction result by using the Gaussian sputtering three-dimensional model. According to the scheme, through combination of traditional point cloud reconstruction and Gaussian sputtering modeling, efficient conversion from the aerial image to the high-precision three-dimensional model is realized. The method not only improves the reconstruction precision of the three-dimensional model, but also has high stability and calculation efficiency when processing complex terrains and large-scale scenes.

A method and system for constructing a three-dimensional (3D) aerial survey of a city street scene include obtaining a plurality of video frames from a calibrated multi-camera setup covering a 360-degree view mounted on a moving vehicle. The plurality of video frames is split into a plurality of 3D parts containing a subset of the plurality of video frames and preprocessing the subset of the plurality of video frames of each part of the plurality of parts to obtain a calculated information. Further, constructing, by the processing circuitry, a 3D representation of each part of the plurality of parts based on the calculated information to obtain a plurality of local 3D reconstructed scene intervals. The method includes stitching and filtering, by the processing circuitry, the plurality of local 3D reconstructed scene intervals to construct the 3D city street scene.

The invention relates to the technical field of image target detection, and discloses an unmanned aerial vehicle aerial image small target detection model construction method comprising the following steps: preparing an aerial image data set, preprocessing the aerial image data set, and generating an aerial image sample set; the method comprises the following steps: establishing a basic model on the basis of a YOLOv8 network model, removing a P5 detection layer in a head network Head of the basic model, introducing a P2 detection layer, replacing a specified position of a Conv module in a backbone network Backbone by adopting an ACMConv feature enhancement module, replacing a specified position of a C2f module in a neck network Neck by adopting a C2fMixStructure mixed structure module, and constructing an improved model by adopting a lightweight enhanced detection head structure; and dividing the aerial image sample set into a training set, a verification set and a test set in proportion, training and verifying the improved model, and generating an unmanned aerial vehicle aerial image small target detection model based on AMLP-YOLOv8. According to the invention, the method has higher perception capability when extracting fine target features, and improves the stability and robustness of small target detection of the aerial image of the unmanned aerial vehicle.

The invention discloses a smart city planning three-dimensional scene reconstruction optimization system combined with semantic segmentation, and belongs to the technical field of three-dimensional scene reconstruction optimization. The system comprises a data sensing module which collects aerial images and laser point clouds, and complements a sheltered area to obtain city modeling data; the preprocessing module performs denoising, data registration and data fusion on the city modeling data to generate texture point cloud data; the semantic understanding module realizes multi-modal semantic segmentation of the texture point cloud data through a fine-tuned SAM network and an improved RandLA-Net, and semantic tag data is obtained through restoration and optimization; the reconstruction optimization module constructs an initial three-dimensional grid model based on the texture point cloud data, and optimizes a ground feature boundary and a missing region of the model in combination with a semantic tag; and the output application module converts the optimized model into a standard format and outputs the model. Through deep coupling of semantic segmentation and reconstruction optimization, the precision of the three-dimensional scene model is improved, and reliable support is provided for smart city planning.

The invention discloses an unmanned aerial vehicle small target detection method based on feature enhancement and selective attention and a related device, and relates to the technical field of computer image target detection, and the method comprises the steps: obtaining an aerial image of an unmanned aerial vehicle, adjusting the image to a preset resolution, and obtaining an adjusted image; inputting the image into a backbone network of a network architecture, extracting a multi-level feature representation through a plurality of CALBlock feature extraction modules, and enhancing features of the multi-level feature representation through an EMIT edge enhancement architecture to obtain an enhanced multi-level feature representation; inputting the enhanced multi-level feature representation into a neck network of the network architecture, and performing cross-scale feature fusion and enhancement through an FSAFPN structure to obtain a multi-scale enhanced feature map; and inputting the multi-scale enhanced feature maps into a detection network of a network architecture, and performing target detection by using a decoder to obtain target category probability distribution and bounding box coordinates.

The invention provides an engineering machinery working environment layered modeling and planning method and system, and the method comprises the steps: carrying out the modeling based on the scanning point cloud data of an engineering machinery working region, the geometric point cloud of an object in the working region, and the aerial photographing image and aerial photographing point cloud data of the working region; a bottom geometric perception layer, a middle semantic planning layer and an upper topological navigation layer are obtained, and a corresponding model is reconstructed when an object moves, so that the contradiction between high-precision calculation power waste and low-precision modeling insufficiency caused by a current single-precision map is solved, the modeling calculation efficiency is improved, and the method can be expanded to different complex scenes.

A georegistration (a.k.a. georectification) of an image captured by a camera in an aerial vehicle, such as a satellite, is based on identifying multiple features using descriptor sets, and sending to a ground station only the descriptors of the identified features and the associated locations in the captured image, without sending of the captured image itself, thus requiring a low communication bandwidth. Using a database of geosynchronized reference images, the ground station uses the received descriptors sets and the associated image locations to localize the features on a selected geosynchronized reference image from the database, and forms a mapping function that map any locations in the captured image to geographical coordinates on Earth. The mapping may be used to geosynchronize an additional feature identified in the aerial vehicle, or to geo synchronize a region that may be cropped from the captured image and sent to the ground station.

The invention relates to the technical field of scene matching navigation, and provides a multi-mode scene matching navigation positioning method and device for a self-adaptive variable-structure unmanned aerial vehicle. According to the method, geographic coordinates corresponding to all pixel points in an aerial image are determined according to pose information in the aerial image of the unmanned aerial vehicle, and the geographic position of the unmanned aerial vehicle with errors is corrected based on difference information between the pixel coordinates of the central point of the aerial image of the unmanned aerial vehicle and the geographic position corresponding to the center of a camera. Based on the corrected geographic position of the unmanned aerial vehicle, determining a reference image pixel coordinate corresponding to the angular point pixel coordinate of the aerial image, and obtaining a simulated aerial image under the view angle of the unmanned aerial vehicle through perspective transformation so as to determine a homography transformation matrix; according to the method, the satellite reference image pixel coordinates of the interest point in the aerial image are determined based on the homography transformation matrix and the perspective transformation matrix, and finally the longitude and latitude of the interest point are determined, so that the limitation of the traditional method in view angle difference compensation, computing resource constraint and cross-modal processing is solved, and the universality, robustness and positioning accuracy of the system are improved.

The invention discloses an unmanned aerial vehicle image target detection method based on collaborative feature fusion, and the method comprises the steps: carrying out the detection of a to-be-detected unmanned aerial vehicle image through employing a trained unmanned aerial vehicle image target detection model, and obtaining a detection result; the model comprises a backbone network, a neck network and a detection head network; the backbone network is integrated with a multi-scale dynamic double-domain coupling module, a multi-scale feature map is extracted through frequency domain-space domain combined processing, and the multi-scale feature map serves as input of a neck network after target edge features are enhanced; the neck network adopts a collaborative feature pyramid network to fuse features layer by layer, linear deformable convolution is used in a P2 layer to enhance details, a wide-area sensing module is combined with large kernel convolution to capture a long-range context in a P3 layer, and finally, the detection head network outputs a target detection result. The method solves the technical problems that the extraction precision of small target features in the aerial image of the unmanned aerial vehicle is not high, background noise interference is serious, and multi-scale target fusion is insufficient.

The invention discloses an aerial photography small target detection method combining windmill convolution and weighted multi-branch fusion, and the method comprises the following steps: obtaining an unmanned aerial vehicle aerial photography image, and constructing a training set and a verification set; constructing an aerial photography small target detection model based on the improved YOLO11; constructing an optimal aerial photography small target detection model according to the training set and the verification set; and inputting the new unmanned aerial vehicle aerial image into the optimal aerial small target detection model to obtain an aerial small target detection result. A backbone network of YOLO11 is improved by using a windmill convolution module and a C3K2HSD module based on a hidden state mixer and state space duality, a WMFPN mechanism based on a learnable weight is designed, aerial photo small target feature information can be more effectively extracted and utilized, the overall detection accuracy is improved, and the detection efficiency is improved. Experimental results show that the method has excellent detection performance in a complex aerial photography scene.

The invention belongs to the technical field of aerial image target detection, and particularly relates to an unmanned aerial vehicle aerial image multi-scale target detection method based on improved YOLOv10, and the method comprises the following steps: S100, obtaining a data set of a disclosed unmanned aerial vehicle aerial image, and carrying out the preprocessing of the data set; s200, configuring an operation environment of model training; s300, improving a backbone network, a neck network and a head network based on a YOLOv10s network model, and training the backbone network, the neck network and the head network by using the obtained data set; s400, performing performance verification on the improved model by using the obtained data set; according to the technical scheme, the multi-scale feature extraction capacity of the model is improved, the multi-scale feature fusion capacity of the model is enhanced, the small target detection capacity is improved, meanwhile, light weight is achieved, and the sensitivity of the model to different-scale targets is reduced.

The invention discloses a space-air-ground collaborative rock-soil landslide monitoring and early warning system, which relates to the technical field of disaster monitoring and early warning and comprises an air remote sensing monitoring module, a ground sensing module, a data communication module, a multi-source information fusion module and an intelligent early warning decision module. The aerial remote sensing monitoring module is used for collecting aerial image data based on the unmanned aerial vehicle; the ground sensing module is used for collecting ground sensing data; the multi-source information fusion module is used for fusing and aligning aerial image data, ground sensing data and geological model multi-dimensional information, extracting surface crack morphological characteristics by adopting a deep learning algorithm, analyzing a deformation time sequence generated by radar interferometry and identifying an abnormal motion mode; the intelligent early warning decision module is used for analyzing the landslide evolution stage and simulating the geologic body deformation process and conducting early warning decision, the problem of signal blind areas of a traditional monitoring system in extreme landforms can be effectively solved, and total factor analysis and advanced early warning of the disaster evolution process are achieved.