6966 results about "Visible spectrum" patented technology

The invention discloses a multi-modal visual fusion complex scene small target detection tracking method and system, and relates to the technical field of unmanned aerial vehicle target tracking, and the method comprises the steps: employing a visible light camera, an infrared thermal imager and a laser radar sensor which are carried on an unmanned aerial vehicle platform, and synchronously collecting RGB images, thermal infrared images and point cloud data; the consistency of the multi-modal data is ensured through data preprocessing and space-time alignment; constructing a lightweight double-branch network to extract multi-scale features, generating a fusion feature map by adopting adaptive weighted fusion, and generating depth information by utilizing point cloud to assist in scale estimation; a small target detection head is designed based on the fusion feature map, and precise detection is realized in combination with a feature pyramid network, adaptive scale prediction and a context awareness suppression mechanism; furthermore, through multi-mode cooperative tracking, including target association, spatio-temporal context modeling, trajectory prediction and a re-detection mechanism, tracking continuity is ensured.

The invention discloses an electrical equipment defect identification and alarm method and system based on deep learning. The method comprises the following steps: synchronously collecting and registering visible light and infrared thermal imaging images on the surface of power equipment, and constructing an instance segmentation network comprising a lightweight feature extraction network, a multi-scale feature fusion network and a frequency domain mask prediction branch; enhancing the diversity of training samples by adopting a generative adversarial strategy; based on the graph neural network, analyzing the incidence relation between the defects and the equipment topology and historical records, and deducing the defect causal relation and the risk level; generating interpretable alarm information including the thermodynamic diagram, the natural language report and the maintenance suggestion; real-time detection and deep analysis are realized by adopting an end-side cloud collaborative architecture; and the system performance is continuously improved through a closed-loop optimization mechanism. According to the method, high-precision defect detection under multi-modal data fusion is realized, the robustness and interpretability are high, and the operation and maintenance intelligence level of power equipment is remarkably improved.

A pest control system is disclosed comprising an optical, computational, and monitoring subsystem, optionally mounted on a mobile platform. The optical system may include a neutralizing laser or multi-wavelength light source, discovery and detail cameras (optionally stereo), a beam-steering mechanism, tunable focus, and optional thermal or depth sensors. The processor, such as a GPU or FPGA, identifies insect or biological targets, adjusts laser focus by depth, and controls beam activation. A monitoring system verifies safety by detecting humans or other non-target entities using environmental and thermal cameras; if detected, laser firing is inhibited. The mobile platform may use wheels, propellers, tracks, or cables, with GPS and data links for remote control. A visible light pre-flash may induce a blink reflex before firing. In some embodiments, a scouting drone transmits target coordinates to the neutralization unit, enabling coordinated, efficient, and safe laser-based pest control.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention relates to the technical field of bullet quality detection, in particular to a cartridge case surface defect lightweight detection method and system. The method comprises the following steps: acquiring a visible light image, a near-infrared image and a polarization image corresponding to a cartridge case to be detected, and generating an enhanced image by adopting a dynamic weight fusion algorithm; performing dynamic feature extraction on the enhanced image by using a feature extraction network; and segmenting the extracted feature map by adopting multi-scale segmentation to obtain a defect region, identifying a defect type corresponding to the defect region by utilizing a capsule network-based lightweight hybrid classifier, and outputting an identification result. According to the scheme, the defect detection precision and efficiency are improved, and the problem of poor environmental adaptability is better solved.

The invention discloses a small target recognition method and system for a multi-modal fusion image in a complex environment, and belongs to the technical field of computer vision and image recognition, and the method comprises the steps: obtaining a visible light image, an infrared image and environment sensor data; image registration is carried out on visible light and infrared images, and a multi-scale image feature pyramid is constructed. And respectively extracting visible light and infrared image features to obtain visible light and infrared imaging feature data. And performing multi-modal data fusion on the visible light and infrared imaging feature data based on a cross-modal attention mechanism, and adaptively adjusting a fusion weight based on environmental sensor data to generate fusion features. And performing space-time enhancement processing on the fusion feature to obtain an enhanced fusion feature. And performing target tracking detection on the small target, and outputting position and category information of the small target. According to the method, the small target recognition capability in a severe environment is remarkably improved, and high precision and robustness can still be kept in a foggy, low-visibility and dark scene.

The invention provides a small target detection method and system based on aligned visible light and infrared images, and relates to the field of target detection, and the method comprises the steps: obtaining a visible light image and an infrared image of a to-be-detected small target; inputting a visible light image and an infrared image into the trained detection model, firstly, respectively performing multi-scale feature extraction on the visible light image and the infrared image by adopting a double-branch structure, and in the extraction process, performing multi-scale feature extraction on the visible light image and the infrared image through interactive collaborative learning of the visible light image and the infrared image; the method comprises the following steps: firstly, carrying out feature alignment, modal interaction correction and multi-scale feature enhancement between two modals to obtain enhanced visible light features and infrared features, then carrying out feature fusion, and finally, carrying out small target positioning and classification by utilizing the fused features. According to the method, feature level alignment of image pairs is realized by using deformable convolution and modal interaction correction, so that the features of visible light and infrared images are effectively and fully interacted and fused, and the accuracy of a detection algorithm is improved.

The invention discloses a wildlife detection method fusing an unmanned aerial vehicle thermal infrared image and a visible light image, and belongs to the field of small target wildlife identification, and the method comprises the following steps: S1, obtaining a preprocessed TIR-RGB image pair set; s2, an FDM-YOLO double-source target detection model improved based on YOLOv81 is constructed, and the improved FDM-YOLO double-source target detection model is trained based on the preprocessed TIR-RGB image pair set obtained in the step S1; and S3, inputting an image acquired in real time into the improved FDM-YOLO double-source target detection model trained in the step S2, and outputting a wild animal detection result. By adopting the wild animal detection method fusing the thermal infrared image and the visible light image of the unmanned aerial vehicle, high-precision, real-time and robust detection of a small target of a wild animal in a complex field environment is realized by improving the FDM-YOLO model.

The invention discloses a lightweight multi-source unmanned aerial vehicle target detection method and system based on a DEYOLO framework, and relates to the field of target detection, and the method comprises the steps: obtaining an unmanned aerial vehicle visible light image and an unmanned aerial vehicle infrared image which are registered, and inputting the images into a pre-trained target detection model; the model comprises a double-flow feature extraction network module which is used for extracting an unmanned aerial vehicle visible light image and an unmanned aerial vehicle infrared image to obtain a visible light feature map and an infrared feature map; the bimodal adaptive feature weighting module is used for performing bimodal adaptive feature weighting and adding on the visible light feature pattern and the infrared feature pattern to obtain fusion features; the lightweight bimodal attention enhancement module is used for performing feature enhancement on the fusion features; and the detection head is used for detecting the enhanced features. According to the method, the calculation complexity is effectively reduced, and the detection precision and the reasoning speed of the model on the low-slow small target and the robustness of the model on a complex scene are remarkably improved.

The invention provides an unmanned aerial vehicle full-time perception image reconstruction method based on multi-mode cooperative reinforcement learning and degeneration decoupling. A frequency perception feature modulation model, a dual-mode dual-domain transformation module and a dynamic bidirectional guide mechanism are included. According to the system, firstly, feature information of different frequency bands is adaptively separated and modulated through a frequency sensing feature modulation model, and decoupling and compensation of composite unknown degradation are achieved; realizing cross-domain interaction and information fusion of visible light and infrared characteristics in a spatial domain and a channel domain by using a bimodal dual-domain transformation module; and finally, realizing collaborative enhancement of cross-modal degradation perception through a bidirectional dynamic guide mechanism, and generating an unmanned aerial vehicle visible light reconstruction image and an infrared super-resolution image with higher structural consistency and texture fidelity. According to the method, deep fusion and degeneration decoupling of multi-modal information can be realized in a complex degeneration environment, and the imaging quality and the environmental adaptability of an unmanned aerial vehicle full-time sensing system are remarkably improved.

The invention relates to the technical field of three-dimensional reconstruction, in particular to a near-infrared assisted low-light scene three-dimensional reconstruction method based on 3D Gaussian splashing, which is characterized in that generation of a Gaussian ellipsoid is dominated by a near-infrared image with a high signal-to-noise ratio, and a stable geometric basis is provided for a visible light information missing area; in the rendering stage, a near-infrared rendering image and a normal visible light rendering image are respectively generated through Gaussian ellipsoid shared geometric parameters and respective opacity and color attributes of two modes; through cross-modal structure similarity loss, a normal visible light image which is forcibly rendered is aligned with a near-infrared light image in structure, and clear near-infrared structure information is used to strictly constrain the recovery process of the color of the visible light image, so that the accuracy and authenticity of a recovery result are ensured.

The invention discloses a multi-mode fruit sugar degree nondestructive testing method, device and system and a medium, and the method comprises the steps: preprocessing an input fruit near infrared spectrum image and a fruit visible light image to obtain a one-dimensional near infrared spectrum input image tensor and a two-dimensional visible light input image tensor; a pre-trained multi-modal deep learning fusion model is utilized to obtain the predicted fruit sugar degree, and the multi-modal deep learning fusion model is pre-trained to establish an input near infrared spectrum input image tensor and a visible light input image tensor. The method comprises a spectral feature extraction network, an image feature extraction network, a multi-modal feature fusion network and a regression prediction network. The objective of the invention is to solve the problem of limited prediction precision caused by the fact that single spectral information is susceptible to noise, illumination conditions, peel thickness, water content and other factors, and improve the accuracy of fruit sugar degree nondestructive testing.

The invention discloses a regional termite detection method and system based on multispectral fusion, and the method comprises the steps: carrying a multi-mode spectrum collection device through a movable detection platform, and synchronously collecting a visible light image, a near-infrared hyperspectral image and Raman spectrum data of a to-be-detected region; preprocessing and feature extraction are carried out on the modal data, and the modal data are converted to a frequency domain to obtain frequency features; based on the physical and biochemical characteristics of the termites and the nests thereof, calculating the matching degree and the credibility of each modal feature, and performing adaptive weighted feature fusion according to the matching degree and the credibility to generate comprehensive spectral features; inputting the fusion features into a pre-trained termite identification and risk assessment model to realize precise identification, positioning and threat level assessment of termite individuals, termite paths and nests; and carrying out trajectory analysis on termite activities by combining a multi-target tracking algorithm, and giving out early warning based on identification and tracking results. According to the invention, early-stage, lossless and accurate detection and active early warning of termites are realized, and the detection efficiency and reliability are significantly improved.

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

The invention discloses an infrared image power transmission equipment target identification method and system based on YOLOv7, and relates to the technical field of electric power automation, and the method comprises the following steps: obtaining continuous frames of infrared images and visible light images of power transmission equipment, and carrying out the preprocessing; based on the processed continuous frame infrared image and visible light image, constructing a space-time multi-mode input tensor; processing the input tensor through an improved YOLOv7 model, and outputting a first target frame and a corresponding frame-level feature vector; performing cross-modal feature fusion optimization based on the frame-level feature vector to obtain a joint optimization feature; performing confidence coefficient correction on the first target frame by using the joint optimization feature to obtain a second target frame; and outputting a target recognition result based on the second target frame. The method is used for solving the defects of a traditional power transmission equipment identification method in the aspects of information fusion effect, detection result stability and result credibility.

The invention relates to an intelligent inspection data processing method and system. The intelligent inspection data processing method comprises the following steps: analyzing an infrared image in an inspection scene to obtain an image resolution, a temperature matrix and a pseudo-color image of the infrared image; synchronizing annotation information of the infrared image and the visible light image in the inspection scene to obtain multi-modal annotation data; based on preset reference object information, performing defect quantitative analysis on the multi-modal labeling data, and generating a real physical size quantitative result of the defect; and carrying out associative storage on the image resolution, the temperature matrix, the pseudo-color image and the quantification result. According to the invention, the whole-process optimization of the inspection data can be realized, and the processing stability, the defect detection accuracy and the system practicability are effectively improved.

The present invention belongs to the technical field of coal gangue recognition and sorting, and in particular, relates to a coal gangue recognition method based on visible-near-infrared spectrum and image multi-modal information fusion. The method includes: S1 collecting spectral information and image information about a sample to be recognized; S2 preprocessing the spectral information and the image information respectively; S3 extracting spectral features from a spectral data set by using a spectral feature extraction neural network model; and extracting image features from an image data set by using an image feature extraction neural network model; S4 inputting the spectral features and the image features obtained from feature extraction into a two-stream fusion network; S5 inputting extracted spectral features, extracted image features and a comprehensive feature into a spectral branch classifier, an image branch classifier and a fusion branch classifier, respectively; S6 calculating importance weights of a spectral branch, an image branch and an image-spectrum fusion branch; and S7 subjecting the importance weights and corresponding confidence to multiply-accumulate operation to obtain a score matrix of coal gangue, and using the score matrix to achieve coal gangue recognition.

A neurosurgery system for probing brain tissue of a patient for tumorous tissue. The system including a suction tool, an excitation source, an optical instrument, and a controller. The suction tool including a suction cannula defining a lumen, an optical fiber configured to transmit fluorescence emitted by the brain tissue; and an indicator configured to selectively emit visible light. An excitation source is configured to emit an excitation light having a wavelength to induce the fluorescence in the tumorous tissue. The optical instrument is coupled to the optical fiber. The optical instrument configured to convert the fluorescence emitted by the brain tissue and transmitted by the optical and configured to determine that the brain tissue is tumorous based on the electrical signal and activate the indicator based on the determination.

The invention discloses an offshore small target radar detection method and system, and relates to the technical field of radio detection. The method comprises the following steps: acquiring radar echo data, a visible light image, an infrared image, an AIS signal and navigation attitude data in real time; performing sea clutter suppression and pre-filtering on the radar echo data, and correcting target plots in combination with a historical radar plot set to obtain a radar single-source target track set; performing target detection on the visible light image and the infrared image to obtain an optical target detection set and an infrared target detection set; the radar single-source target track set, the optical target detection set, the infrared target detection set, the AIS signals and the navigation attitude data are associated and fused, and finally deception feature recognition and consistency verification are performed to obtain an updated fused target track set; according to the method, man-made confrontation and cheating behaviors on the sea can be accurately identified, and reliable support is provided for safety monitoring on the sea, maritime affair supervision and emergency response.

The invention provides a lightning trajectory data fusion detection based single-tree accurate early warning system for lightning fire, and relates to the technical field of meteorological disaster monitoring and early warning and forest lightning fire prevention and control. The lightning trajectory data fusion detection based single-tree accurate early warning system comprises an electromagnetic radiation receiving module which adopts an antenna array composed of at least four directional antennas to receive electromagnetic radiation signals generated by lightning, the antenna array is connected with a signal conditioning circuit, and the signal conditioning circuit is connected with the electromagnetic radiation receiving module. The signal conditioning circuit amplifies the received weak electromagnetic signal by 100-1000 times, processes the weak electromagnetic signal with a filtering bandwidth of 10 kHz to 1 MHz, and transmits the weak electromagnetic signal to a data acquisition card with a sampling frequency of 5-20 MS / s and a sampling precision of 12-16 bits. By integrating lightning electromagnetic radiation continuous sampling, infrared, visible light and ultraviolet multispectral tracking shooting and atmospheric electric field early warning multi-modal data, a lightning track is accurately detected, and by combining forest environment information, accurate early warning of a single tree where a lightning fire may occur is achieved.

The invention provides a multi-mode remote sensing target tracking and positioning and intention discrimination method and device. The method comprises the following steps: acquiring a plurality of visible light image frames and a plurality of infrared light image frames, and carrying out frame alignment operation on each visible light image frame and each infrared light image frame to obtain a plurality of groups of effective image frame pairs; for each group of effective image frame pairs, determining tracking identification information of each detection object in the effective image frame pairs based on the effective image frame pairs and a pre-trained multi-modal detection tracking model; for each detection object, determining longitude and latitude tracks of the detection object based on the tracking identification information and a back projection mapping function; and determining the behavior intention of each detection object based on a behavior recognition model and the longitude and latitude tracks of each detection object. The accuracy of target tracking and behavior intention recognition in the remote sensing video can be improved.

The invention relates to the technical field of remote sensing image processing, and provides a remote sensing multi-modal target detection method and device for gated cross attention fusion, and the method comprises the steps: obtaining a multi-modal remote sensing image pair; and inputting the multi-modal remote sensing image pair into the target detection network model, and outputting a target detection result. According to the remote sensing multi-mode target detection method based on gating cross attention fusion, firstly, the edge information of each mode is explicitly enhanced, then, the information interaction and reconstruction between the two modes are realized by using a bidirectional cross attention mechanism, a key region can be adaptively highlighted, redundant or noise characteristics can be inhibited, and the detection accuracy is improved. The reconstructed visible light feature map and invisible light feature map not only maintain respective modal advantages but also have complementarity, dynamic weighted fusion is performed on different modal features through a gating weighted fusion mechanism, target discrimination features in the multi-modal fusion feature map are highlighted, accurate and stable target detection is realized, and the target detection accuracy is improved. And the advantages of the multi-modal image can be fully played in a complex scene.

The invention belongs to the technical field of electrical equipment detection, and discloses a defect image enhancement method fusing reasoning and generation, which integrates visible light, infrared and laser radar data through a multi-modal feature fusion network, breaks through the limitation that a contrast file CN114281093A only depends on a visible light image, and improves the detection accuracy. The dynamic attention mechanism can flexibly deploy visual, spatial and semantic feature weights according to defect types, key features can still be captured in complex environments such as strong light and shielding, and meanwhile, the spatial form of the defects is analyzed by means of three-dimensional point cloud; by means of the design, missing detection caused by insufficient characteristics of tiny parts such as hardware fittings and pins is effectively avoided. Aiming at the problem of distortion of a sample generated by a traditional data enhancement method in a comparison file, the sample quality is guaranteed through double mechanisms of reasoning constraint and physical verification, defect features output by a reasoning model directly constrain feature distribution of the generated sample, and meanwhile, a material mechanics rule is introduced to verify the physical rationality of the generated sample.

The invention discloses a multi-modal lightweight forest fire detection method suitable for a vertical take-off and landing fixed-wing unmanned aerial vehicle platform, and the method comprises the steps: carrying out the feature alignment and weight fusion of a visible light image and an infrared image, which are synchronously collected by an unmanned aerial vehicle, through a multi-modal early fusion method, and obtaining a fusion feature; carrying out lightweight down-sampling and multi-scale feature extraction on the fusion features to obtain a multi-scale feature set; performing cross-scale feature fusion on the multi-scale feature set by adopting a bidirectional feature pyramid network, and extracting dynamic change information to obtain time sequence features; performing multi-scale context enhancement on the time sequence features by using a spatial pyramid method based on cavity convolution, and performing global context and local texture modeling by using a lightweight Transform module to obtain high-level semantic features; and based on the high-level semantic features, a target detection frame, fire level classification, prediction uncertainty estimation and a pixel-level fire probability graph are generated in parallel through a multi-task output header.

The invention discloses a railway comprehensive defect detection method and system based on multi-source sensor fusion. The method comprises the following steps: carrying out joint calibration on static external parameters by adopting a multi-modal calibration plate; a high-precision timestamp and vehicle multi-source motion parameters are utilized to construct a motion compensation model to carry out adaptive dynamic distortion correction calibration; synchronously acquiring multi-source sensor data, and based on static external parameters and adaptive dynamic distortion correction, obtaining a corrected visible light image, an infrared image and a laser radar three-dimensional point cloud; complementarity features are extracted from the data of the three modes, adaptive fusion and optimization are carried out through an attention mechanism, and multi-scale fusion features are output; segmenting different railway facility areas by using multi-scale fusion features, extracting corresponding area features, and calling detection algorithms of corresponding facilities to identify defects of different railway facilities; according to the method, high-precision calibration, feature-level fusion and railway facility special defect detection are fused, and the automation level and the detection accuracy of railway inspection are improved.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

The invention provides an unmanned aerial vehicle visible light-infrared image cooperative enhancement network and method based on dual-branch cooperation and frequency adaptive fusion, and relates to the technical field of unmanned aerial vehicle multi-modal image enhancement and super-resolution restoration. According to the method, visible light image enhancement and infrared image super-resolution reconstruction are respectively carried out by adopting a heterogeneous double-branch architecture; a frequency adaptive fusion module is embedded in a visible light branch, and spectrum decomposition and feature refining of degradation sensing are realized through a learnable frequency mask; in the infrared branch, a long-range dependency relationship is captured through a residual Transform module; bidirectional cross-modal guidance is realized through a multi-modal feature interaction module, the module integrates wavelet convolution transformation, frequency perception fusion and a cross-modal Transform mechanism, and feature alignment and semantic complementation of space-frequency double domains are realized. According to the method, the visual quality, the detail recovery capability and the cross-modal collaborative robustness of the visible light and infrared images of the unmanned aerial vehicle under complex illumination, weather and degradation conditions can be effectively improved.

The invention relates to an organic matter near infrared spectrum inversion method and device for removing moisture based on radiation. The method comprises the steps that after a soil sample is pretreated, multiple moisture content gradients are regulated and controlled, the natural moisture permeation state is simulated, visible light-near infrared spectrum data and the organic matter content are collected, and a soil spectrum library is constructed; quantifying the interference weight of moisture on the spectrum by using a physical radiation transmission model to obtain a preliminary correction spectrum; moisture and organic matter features are extracted through a deep learning multi-branch architecture, a moisture characterization index is constructed, cross-domain feature alignment is realized in combination with a domain adversarial network, and an accurate correction spectrum is obtained; all the modules are integrated to construct a soil moisture elimination reconstruction spectrum model, after layered verification and iterative optimization, a to-be-detected soil spectrum is input into the model, organic matter content near infrared spectrum inversion is completed, and a result is output. According to the method, moisture interference is removed through combination of physical radiation and deep learning, the inversion precision is improved, and the method is suitable for rapid detection of organic matters in various moisture-containing soil.

The invention belongs to the field of target identification, and relates to a multi-modal target identification method and device for an unmanned aerial vehicle, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-modal data, and carrying out the preprocessing of the multi-modal data, the multi-modal data comprises visible light image data, infrared thermal imaging data, laser radar point cloud data and synthetic aperture radar data; carrying out hierarchical cross-modal feature extraction on the basis of the preprocessed multi-modal data; multi-scale cross-modal feature fusion based on attention guidance is carried out; predicting the position and category of each target in the image based on the fused multi-scale features; assigning a unique ID to each target by associating detection results in continuous frames to form a motion track, and correcting an identification result of the current frame by using spatio-temporal context information; and lightweight model optimization and embedded real-time deployment are carried out. The problems of target shielding and losing can be solved, current frame identification can be further optimized, and tracking continuity and accuracy are ensured.