348 results about "Multispectral data" patented technology

The invention relates to the technical field of remote sensing monitoring, in particular to an intelligent forest disease and pest monitoring method and system based on unmanned aerial vehicle remote sensing, and the method comprises the following steps: collecting multispectral data by an unmanned aerial vehicle, extracting reflectivity and smoothing the reflectivity, carrying out differential recognition on abnormal pixels, extracting curves and screening significant changes, segmenting scab boundaries, and classifying health states. And generating a pest and disease map layer prediction trend. According to the method, the reflectivity time sequence is constructed, differential processing is carried out, the vegetation change trend is dynamically captured, abnormal areas are identified by combining slope offset and persistence analysis, significant pixels are screened according to main peak wavelength offset, boundary information is extracted, the scab positioning precision is improved, and the recognition resolution of the lesion state is enhanced through reflectivity combined analysis; accurate description of disease spot dynamic changes is realized, static image dependence limitation is broken through, monitoring time continuity and space response capability are enhanced, and disease and insect pest change capture efficiency and state classification accuracy are effectively improved.

The invention relates to the field of forest protection, and discloses an unmanned aerial vehicle intelligent monitoring system for forest protection. Comprising a multispectral data dynamic acquisition module, an environment field real-time modeling module, an image intelligent enhancement module, a multi-scale frequency domain feature extraction module, a Bayesian anomaly probability inference module, a space-time correlation verification module, an unmanned aerial vehicle cluster scheduling module and a heterogeneous hardware acceleration module. According to the system, real-time monitoring and accurate identification of the hidden ecological damage behavior are realized through a closed-loop process of multi-modal sensor collaborative acquisition, environmental parameter dynamic prediction, image illumination invariance transformation, cross-scale texture fingerprint extraction, particle filter dynamic threshold optimization, multi-constraint space-time clustering, resource allocation optimization and reconfigurable hardware acceleration. According to the invention, on the basis of an image preprocessing architecture based on illumination reflection separation and edge preserving enhancement, the feature distortion bottleneck of a traditional algorithm in a complex illumination environment is broken through, and a high-fidelity data base is provided for hidden ecological damage detection.

The invention discloses an intelligent management method for liquidation receipts based on multi-modal data fusion, and particularly relates to the field of data analysis. Comprising the steps of S1, multispectral data acquisition in a limited illumination environment, S2, cross-modal feature decoupling and recombination, S3, space-time heterograph neural network analysis, S4, multi-scale attention decision fusion, S5, resistance enhancement verification, and S6, incremental management based on knowledge distillation. According to the method, the physical anti-counterfeiting capability is remarkably improved, the paper material, the ink components and the surface structure are deeply analyzed through a multispectral sequence acquisition mechanism, and hidden tampering behaviors such as color fading and chemical altering of the thermo-sensitive paper are accurately identified. Cross-modal deep correlation analysis is achieved in a breakthrough mode, a physical-semantic decoupling technology and a space-time heterogeneous graph network are adopted for modeling, and non-dominant laws such as commodity position offset and tax rate anomaly are effectively captured.

The invention discloses a zinc coating shedding performance detection method and system, particularly relates to the technical field of metal surface treatment quality detection, and is used for solving the problems of high misjudgment rate and insufficient detection precision caused by coating surface pseudo defect interference in the existing method. The method comprises the following steps: synchronously acquiring a multispectral reflection image and three-dimensional morphology data of the surface of a zinc coating through an industrial vision imaging device, extracting grain boundary distribution information based on multispectral data, and identifying radial pseudo defects in combination with a curvature manifold geodesic line energy gradient direction of the three-dimensional morphology; and further performing micro-crack topology analysis and grain boundary spatial correlation verification on the candidate region, eliminating texture interference and grain boundary overlapping regions, and finally outputting a high-confidence fall-off defect detection result, so that the identification capability of real defects under a complex galvanized surface is remarkably improved, and the method is suitable for automatic detection requirements of a high-speed continuous production line.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention relates to the technical field of vegetation remote sensing recognition, in particular to a multispectral and visible light remote sensing image fusion segmentation method and system and a medium. The method comprises the following steps: constructing a double-branch model comprising a visible light branch encoder, a multispectral branch encoder, a multi-scale feature fusion module and a shared decoder; inputting the visible light data into a visible light branch encoder to obtain a spatial feature map; inputting the multispectral data into a multispectral branch encoder to obtain a spectral feature map; the multi-scale feature fusion module applies a channel-space joint attention weight to an input feature map, then performs channel splicing according to scales, compresses the feature map to an original channel number by adopting 1 * 1 convolution, and then applies a channel attention weight to obtain a multi-scale fusion feature sequence; and the shared decoder analyzes the multi-scale fusion feature sequence and outputs an invasive plant segmentation mask image of the target detection area, so that high-robustness invasive plant identification under a complex background is realized.

This invention relates to a pair of intelligent, multimodal smart eyeglasses designed for adaptive vision, biometric ocular monitoring, and predictive emergency response. The system integrates multilayer lenses filled with electrochromic gel and surrounded by elastic structures to enable dynamic focal length modulation in response to user visual behavior. An array of sensors—including visible light, ultraviolet, micro-cameras, infrared emitters, gyroscopes, and ultrasonic transceivers—collects multispectral data to evaluate environmental and physiological parameters. Real-time data processing is performed by an embedded quantum microcontroller, which manages light transmittance, detects eye fatigue, maps ocular surfaces, and identifies hemodynamic abnormalities such as fainting, seizure, or ocular stroke. Advanced imaging techniques, such as ultrasonic ptychography and wave diffraction analysis, enable non-invasive 3D mapping of ocular layers and internal eye motion. The system also supports wireless communication with external platforms including smartphones, vehicles, and brain-machine interfaces, enabling proactive safety interventions. These features work in tandem to support early diagnosis of critical conditions, protect vision, and personalize user experiences through machine learning and adaptive feedback mechanisms.

The invention relates to the technical field of satellite-borne hyperspectral image processing and analysis, and solves the technical problem that the huge spectral and spatial resolution difference between low-resolution hyperspectral data and high-resolution multispectral data cannot be fully considered in the existing method. The forest hyperspectral satellite image-oriented deep and shallow double-branch super-resolution method comprises the steps of constructing a double-branch network architecture, performing feature fusion reconstruction on output features of the double-branch network architecture, and obtaining high-resolution hyperspectral data with low spectral variation characteristics of forest vegetation in spaceborne hyperspectral image data. According to the method, the problem of modal difference between low-resolution hyperspectral data and high-resolution multispectral data is effectively solved by learning on different feature levels and scales, and the model is enabled to pay more attention to low-spectral variation characteristics of different forest vegetation in a satellite image through a plurality of feature attention mechanisms. And the requirements of subsequent fine monitoring tasks of various forest resources can be met.

The invention belongs to the technical field of crop growth prediction, and particularly relates to a crop growth prediction method and system based on multispectral unmanned aerial vehicle monitoring. The method comprises the following steps: acquiring multi-dimensional data of real crops in different growth stages under different soil water contents and disease and insect pest states, determining the contribution degree of each vegetation index to crop growth through a factor analysis algorithm, carrying out dimension reduction on hyperspectral data by using a principal component analysis algorithm, fusing with the multi-spectral data, constructing a multi-spectral resolution characteristic space, and carrying out multi-spectral analysis on the hyperspectral data. The method comprises the following steps: firstly, obtaining a real plant height growth fitting function of crops through analogue simulation by combining LiDAR data and vegetation indexes, thirdly, calculating a real growth vegetation index space and obtaining a real growth state space of a standard staged growth period, and finally, inputting the calculated space and function into a model constructed by a reinforcement learning algorithm for training, and accurate prediction of the crop growth state is realized.

Techniques for determining a presence of an object, especially an object such as animal or debris, in a path of a vehicle, are discussed herein. For example, sensors of various modalities, which may include multispectral sensors, may capture data representing an environment the vehicle is traversing. In examples, one or more trained machine learned (ML) models, operating on a vehicle computing system, may detect and / or classify objects in the environment, based on input data of one or more modalities or spectral bands. The ML models may be pre-trained using training data including real sensor data, synthetic data, and / or augmented data, along with auto-generated annotations. In some examples, hyperspectral data may be used to identify materials associated with detected objects. A confidence score associated with the detection of the object may also be computed. The vehicle may be controlled based on detection of the object and its classification.

The invention relates to a data analysis technology, and discloses a spectral feature and artificial intelligence fused grain heavy metal detection method and system.The method comprises the steps that a near-infrared diffuse reflection spectrum, a laser-induced breakdown spectrum and a surface enhanced Raman spectrum of a grain sample are obtained, a matrix accumulation area thermodynamic diagram is constructed according to the near-infrared diffuse reflection spectrum, and the matrix accumulation area thermodynamic diagram is obtained; the method comprises the following steps: optimizing a laser-induced breakdown spectrum based on a thermodynamic diagram of a matrix accumulation area, carrying out dual-channel spatial-temporal feature extraction on a surface enhanced Raman spectrum and the optimized laser-induced breakdown spectrum to obtain a fusion feature vector, carrying out weighted fusion optimization on the fusion feature vector based on a signal-to-noise ratio of multispectral data to obtain an optimized fusion feature vector, and finally obtaining a fusion feature vector. And carrying out migration adaptation on a pre-trained grain detection model by using a pre-marked new grain sample, and carrying out heavy metal content identification on the grain sample by using the adaptive grain detection model according to the optimized fusion feature vector to obtain a heavy metal detection result. The precision of grain heavy metal detection can be improved.

The invention belongs to the technical field of lake remote sensing monitoring, discloses a lake water level and water reserve dynamic monitoring method based on satellite-borne active and passive remote sensing information fusion, and effectively solves the problem that lake information in remote areas is difficult to efficiently and accurately obtain. Comprising the following steps: S1, preprocessing laser data; s2, preprocessing the multispectral data; s3, taking the ICESat-2 data as a core, combining with a multispectral image covering a corresponding region, and constructing a lake bottom DEM inversion data set based on neighborhood features; s4, constructing and training an elevation inversion convolutional neural network model; s5, predicting and generating a lake bottom DEM by using the trained elevation inversion convolutional neural network model; and S6, calculating the absolute water volume of the lake according to the DEM at the bottom of the lake, and carrying out lake water level-water reserve monitoring. According to the invention, long-time-sequence hydrological observation of remote region lakes can be effectively realized with low cost.

The invention relates to the technical field of geological early warning, in particular to a dangerous rock mass instability analysis method, system and equipment based on a space-time diagram neural network, by fusing unmanned aerial vehicle LiDAR, multispectral data, meteorological radar data and the space-time diagram neural network (ST-GNN), the system realizes sub-meter spatial resolution and minute-level time response, and the stability of dangerous rock mass instability analysis is improved. The four-dimensional (time and space) analysis result of the instability probability of the dangerous rock mass is obtained through high-precision space-time modeling, the problems that a traditional geological disaster early warning system is low in resolution ratio, slow in response and high in misinformation are solved, the comprehensiveness, accuracy and reliability of instability prediction of the dangerous rock mass are improved, and the early warning effect is good. And full-chain intelligent closed-loop management of real-time data acquisition-dynamic prediction-early warning push-feedback optimization is supported, the emergency decision time is shortened by real-time rainfall superposition risk thermodynamic diagrams, and the attenuation rate of long-term prediction precision is reduced by dynamically fusing newly added geological data and instability events through incremental learning.

The invention discloses a multi-modal fusion identification method and system for rice leaf type diseases and insect pests. The method comprises the following steps: respectively carrying out feature extraction on image data and non-imaging multispectral data of a detected rice area to obtain image features and spectral features; performing cross attention feature extraction on the image features and the spectral features, and performing adaptive gating feature fusion on the obtained features to obtain modal fusion features; according to the gating feature fusion structure provided by the invention, the fusion weight is dynamically adjusted according to different input features so as to improve the flexibility of model information fusion, so that the fusion features containing different modal effective information are obtained, and the accuracy of rice leaf type disease and pest identification is improved. In addition, the spectral reflectivity data and the spectral vegetation index are subjected to feature extraction at the same time, feature encoders corresponding to the spectral reflectivity data and the spectral vegetation index are stacked for multiple times, and full extraction of spectral features is achieved.

The invention discloses a ground wire adaptive visual trajectory tracking control method for unmanned aerial vehicle inspection, relates to the technical field of unmanned aerial vehicle inspection, and solves the problem that it is difficult to effectively fuse geometric, texture and multispectral features to construct a ground wire lightweight model. And a three-dimensional coupling constraint system is difficult to be combined to adapt to a dynamic scene of ground wire inspection. Comprising the following steps: collecting an initial visual image and multispectral data of a ground wire, extracting and splicing geometric, texture and state features, and constructing a ground wire lightweight model based on knowledge distillation to output a state evaluation result; the flight controller combines the model and geographic information to construct a three-dimensional coupling constraint system, calls a dynamic programming algorithm to generate an initial trajectory and sets a constraint deviation real-time monitoring mechanism; multi-dimensional deviation is calculated, and comprehensive deviation is obtained through weighting of the incidence matrix; and if the deviation exceeds a threshold value, starting a lightweight prediction controller to solve an optimal control problem and adjust flight parameters to generate a new trajectory, otherwise, flying along an initial trajectory.

The invention relates to the technical field of ecological environment protection, in particular to a mangrove forest carbon sink monitoring and metering method based on an unmanned aerial vehicle, a radar and an AI technology, and the method comprises the steps: 1, obtaining the laser radar data of a mangrove forest through a laser radar carried by the unmanned aerial vehicle; step 2, acquiring elevation data in a mangrove forest vegetation layer area, and obtaining point cloud data after topographic error correction; 3, obtaining a multispectral image of the mangrove forest, and obtaining a multispectral data matrix; 4, identifying forest growth data features, constructing a mangrove forest carbon sink prediction model, and predicting the mangrove forest carbon sink amount; step 5, marking the image region with the NDVI value higher than a preset NDVI threshold value as a blade over-dense region; and according to the area of the overdense leaf region and the multispectral data matrix, calculating a light depression factor by using a photosynthetic depression factor formula, determining the carbon sink deviation of the overdense leaf region by using a regional carbon sink deviation formula, and obtaining a real carbon sink value of the mangrove forest according to a carbon sink calculated value obtained by prediction.

The invention relates to a real-time monitoring method for soil remediation by utilizing multi-spectrum of an unmanned aerial vehicle and sensing coupling of the Internet of Things, and belongs to the technical field of soil environment monitoring and remediation. The method comprises the following steps: constructing a space grid model of a monitoring area; a space-air-ground integrated monitoring network is arranged, image data are obtained through multi-spectral remote sensing of an unmanned aerial vehicle, and soil environment parameters are collected through a ground Internet of Things sensor; preprocessing and fusing the multi-source spatio-temporal data, and establishing a spatio-temporal matching model; constructing an inversion model of the soil heavy metal content, the organic matter content and the pollutant degradation degree based on the fusion data; and the repair efficiency is dynamically calculated and visualized, and real-time evaluation and early warning of the repair process are realized. According to the invention, space-air-ground data collaboration is realized, the real-time performance, accuracy and space coverage of monitoring are remarkably improved, the defects of high cost, low efficiency and limited data dimension of a traditional method are overcome, and whole-process and intelligent decision support is provided for soil remediation.

The invention relates to the technical field of rack plating line body detection, and discloses a full-automatic intelligent continuous rack plating line body detection method and device.The method comprises the steps that real-time multi-modal data collection is conducted on the rack plating line body operation process, and rack plating line body state data and electroplating liquid multispectral data are obtained; joint feature extraction is carried out, and feature vectors representing the relation between the rack plating process and the electroplating liquid components are obtained; constructing a mapping model of rack plating production quality and recovery parameters; executing a variable genetic factor-based multi-objective optimization algorithm, creating a hierarchical cooperative control strategy, and generating rack plating line body production parameters and linkage control parameters of electroplating liquid recovery equipment; according to the method, accurate prediction of optimal recovery parameters under different working conditions is achieved, and the problem that traditional fixed parameter control is insufficient in prediction capacity in the face of complex change working conditions is solved.

The invention provides a multispectral radiation temperature measurement method and system, and belongs to the field of signal acquisition and processing in an infrared radiation temperature measurement technology. The problems of poor inversion precision and applicability of multispectral radiation temperature measurement are solved. The multispectral radiation temperature measurement method comprises the following steps of: measuring radiation information of the surface of a to-be-measured target by using a multi-wavelength pyrometer; the beam splitting system obtains radiation information of different spectrum channels; photoelectric conversion: converting the optical signal into an electric signal; constructing a target equation and a constraint optimization condition according to the brightness temperature model; carrying out multispectral data processing by utilizing an improved multi-population mixed pigeon inspired optimization algorithm; and the final real temperature and emissivity values are obtained. The device is mainly used for multispectral radiation temperature measurement.

The invention discloses an infrared and visible light dynamic temperature measurement method based on multispectral fusion. The method specifically comprises the following steps: S1, synchronously acquiring infrared thermal radiation data and visible light image data through a multi-modal data synchronous acquisition module; s2, performing feature matching and region segmentation on the acquired data based on a multispectral data fusion module; s3, environment interference and heat conduction errors are corrected through a dynamic compensation algorithm module; s4, a visual temperature distribution diagram is generated based on the temperature field reconstruction module, and abnormal temperature early warning is triggered, the method is based on a transfer learning method, on the basis of an original task domain model, small sample transfer to a new task domain is achieved, the limitation of sample dependence is relieved, and the universal generalization of the model is improved.

The invention belongs to the field of crop yield estimation, and discloses a corn yield remote sensing estimation method and system based on multispectral data coupling radiation transmission and a crop growth model, and the method comprises the steps: obtaining and preprocessing a Sentinel-2 multispectral remote sensing image, and obtaining the reflectivity data of a corn planting region; constructing a PROSAIL forward simulation spectrum library, and performing domain correction on the simulation spectrum by using an auto-encoder and a residual error alignment network; establishing a machine learning inversion model based on the corrected simulated spectrum and the actually measured spectrum, and generating regional leaf area index (LAI) distribution; key agronomic parameters are obtained, a localized WOFOST crop growth model is constructed, the LAI state of the model is assimilated by adopting ensemble Kalman filtering at a remote sensing observation moment, and the crop growth process is dynamically corrected; and advancing the model to a mature period, outputting the dry weight of the corn kernels, and realizing remote sensing estimation of the regional corn yield. The method can effectively improve the LAI inversion precision and yield prediction reliability, and is suitable for the fields of agricultural monitoring, grain evaluation, agricultural condition management and the like.

The invention provides a mineralogical substance composition testing method and system based on multispectral data fusion. The method comprises the following steps: separating out a first characteristic interference signal caused by the oxidation state of an iron element and a second characteristic interference signal caused by the oxidation state of a manganese element by adopting wavelet transform in a superposition region of a Raman spectrum signal and a visible light absorption spectrum; performing spatial weighting on the intensity of the first feature interference signal and the intensity of the second feature interference signal to construct a multi-dimensional feature vector, inputting the multi-dimensional feature vector to a pre-trained residual neural network, and combining a radioactivity attenuation parameter measured by a gamma spectrometer to determine the radioactivity attenuation of the first feature interference signal and the second feature interference signal. And generating a mineralogical substance composition test result containing a mineral phase, an isomorphic substitution ratio, color cause analysis data and a radiation safety level. According to the method, the collaborative interpretation of the multispectral data and the radiation parameters can be realized, the mineral component analysis precision is improved, the color formation mechanism is synchronously analyzed, and the radioactive risk level is quantitatively evaluated.

The invention discloses a beet multispectral data dimension reduction processing method based on principal component analysis, and relates to the technical field of agricultural information processing, and the method comprises the following steps: (a) carrying out centralized preprocessing on original beet multispectral data; (b) extracting principal components by adopting a double-path parallel mechanism: executing standard principal component analysis on a path I, and screening the principal components according to a variance contribution rate on the basis of a sample covariance matrix; according to the beet multispectral data dimension reduction processing method based on principal component analysis, through a double-path principal component fusion mechanism and orthogonal verification design, the inherent contradiction that global structure reservation and discriminant feature enhancement are difficult to consider in a traditional dimension reduction method is overcome while the dimension of beet multispectral data is reduced.

The invention relates to the technical field of geological survey, in particular to an unmanned aerial vehicle multispectral geological survey method and system, and the method comprises the steps: fusing a multispectral image, a digital elevation model, geophysics and historical geological data, systematically constructing a geological feature priori knowledge model, including lithology, construction and alteration feature libraries, and mapping with multispectral data; multi-scale geologic features are extracted through adaptive wavelet transform and morphological analysis, and feature weight adaptive adjustment is achieved; geological units are accurately divided by adopting geological scene perception superpixel segmentation and combining geological boundary constraint and similarity recursion combination; identifying an interference mode, generating an adaptive filtering matrix, and enhancing image quality; cooperatively interpreting multi-source information by using a deep auto-encoder network to generate a high-precision geological interpretation map and a confidence map; geological professional knowledge is introduced, so that the geologic body recognition accuracy is remarkably improved; the adaptive flight control strategy ensures the consistency of complex terrain data, and improves the precision and efficiency of geological survey.

The invention provides a method and system for on-line determination of molten steel carbon content in converter steelmaking, and relates to the technical field of converter steelmaking, the method comprises the following steps: obtaining multi-source process data, the multi-source process data comprising exhaust gas components, furnace mouth flame multispectrum and process parameters; performing time synchronization and feature extraction on the multi-source process data to generate a feature vector; inputting the feature vector into a carbon content prediction model to obtain a carbon content prediction value and an uncertainty estimation value thereof; the molten pool temperature is obtained, and based on the molten pool temperature, the waste gas components and the technological parameters, a theoretical carbon content value is calculated through a thermodynamic carbon content calculation model; and carrying out weighted fusion on the carbon content predicted value and the theoretical carbon content value to generate a fused carbon content as a target carbon content. According to the method and system for online determination of the carbon content of the molten steel in converter steelmaking, the precision and reliability of online determination of the carbon content of the molten steel can be effectively improved, and powerful support is provided for intelligent production of converter steelmaking.

The invention relates to the technical field of image filtering extraction, in particular to a vegetation growth path extraction method and system based on a remote sensing image, and the method comprises the steps: obtaining the multispectral remote sensing data of a vegetation region at each time point, obtaining a spectral information interference influence difference value according to the correlation between each pixel and the spectral data of pixels in a local region, and obtaining a spectral information interference influence difference value; the method comprises the following steps: clustering all pixels at the same time point, calculating a normalized vegetation index of each pixel according to filtering parameters when filtering multispectral data of the pixels in each cluster by a technology, and constructing a screened and adjusted characteristic value in a process of extracting a vegetation growth path by adopting an optimal path search algorithm, and adjusting the initial screening value of each pixel when the initial seed point is selected, further screening the initial seed point, and setting the moving direction of each pixel to extract a vegetation growth path. According to the invention, the accuracy of vegetation growth path extraction is improved.

The invention discloses an intelligent forestry surveying and mapping method and system based on regional feature feedback, relates to the technical field of forest surveying and mapping, and provides the following scheme: extracting spectrum and texture features of a target forest image through satellite remote sensing data, realizing recognition and grading division of feature regions by using an intelligent segmentation algorithm, and obtaining a target forest image. A priority monitoring grid of the content evaluation indexes is generated; and scheduling the unmanned aerial vehicle to scan the priority monitoring grid for the first time, and integrating image data of the unmanned aerial vehicle and the satellite. According to the method, spectrum and texture features of a target forest region are extracted through satellite remote sensing data, a non-uniform priority monitoring grid containing a feature density index is generated, an unmanned aerial vehicle is scheduled to scan a high-priority grid for the first time, satellite multi-spectrum data, an unmanned aerial vehicle high-resolution image and LiDAR point cloud data are fused, and the target forest region is obtained. The tree species are identified, the health index thermodynamic diagram is generated, high-danger areas are rapidly identified, the scanning range of the unmanned aerial vehicle is remarkably compressed, and the monitoring efficiency is improved.

The invention relates to the technical field of plant disease and insect pest detection, and discloses a plant disease and insect pest identification method based on a multispectral image acquisition system. Specifically, multispectral image data of a plant sample is obtained through a multispectral image acquisition system, geometric calibration is performed on the multispectral image data, noise and atmospheric influence in the multispectral image data are eliminated, and target multispectral image data are obtained. An improved competitive self-adaptive reweighted sampling method is used for extracting key characteristic wave bands related to diseases from target multispectral data, and the characteristic selection efficiency is remarkably improved. And finally, feature extraction and classification decision are performed on the key feature wavebands by using a multi-modal classification model, so that the model interpretability and classification precision can be enhanced, and the method has significant advantages in early disease detection and can effectively identify early disease symptoms.

The present invention relates to an intelligent tree barrier detection method and system based on multimodal perception. The method uses sensors carried by drones to collect data, including RGB images, LIDAR point clouds, multispectral data, etc. First, the data is preprocessed to form a standardized input. Then, a Hough transform is performed on the LIDAR point cloud to extract the position of the power line and generate a three-dimensional line model. Then, a deep learning algorithm is used to locate the tree bounding box from the RGB image, and the high-risk point cloud area is screened in combination with the line model. Then, a three-dimensional tree model is constructed, and the minimum safe clearance distance between the power line and the tree is calculated in real time through a collision detection algorithm to obtain a three-dimensional tree model with a safety distance annotation. Finally, a spatial risk assessment is performed based on this model. The present invention can significantly improve the reliability and efficiency of power line inspections.

The invention relates to the technical field of structural safety detection of steel structural members, in particular to a steel structural damage identification and repair method, system and device and a medium, and the method comprises the steps: obtaining a multi-source image data set and a standard corrosion test block database of a steel structural member through an unmanned aerial vehicle, the multi-source image data set comprises a polarized visible light image, a long-wave infrared temperature matrix and hyperspectral reflectivity data, and the standard corrosion test block database comprises spectral feature vectors of different corrosion grades and corresponding physical parameters; and carrying out sub-pixel level registration and feature extraction on the multi-source image data set. According to the method, the unmanned aerial vehicle synchronously carries the three cameras to carry out real-time multi-dimensional data acquisition, the problem of data splitting in traditional single-mode detection is solved, the multi-spectral data acquisition efficiency and the sub-millimeter level hidden damage identification precision are remarkably improved, and dynamic environment interference is inhibited in real time through an intelligent parameter inversion mechanism.