432 results about "Spectral bands" patented technology

A system and method are disclosed for generating hyperspectral images from multi-modal sensor data including RGB, LiDAR, thermal, and near-infrared inputs. Training data includes hyperspectral images and corresponding multi-modal measurements. Spectral band grouping is performed based on correlation coefficients. A multi-modal decomposition network with cross-modal attention mechanisms generate reconstructed hyperspectral images by fusing complementary sensor information. A fine-tuning network creates reconstructed RGB images. A comprehensive quality assurance system analyzes spectral consistency, cross-modal coherence, and fusion artifacts to generate quality metrics. Missing data compensation strategies handle corrupted sensor inputs using information from other modalities. The system includes temporal integration for video sequences and multi-resolution processing for different sensor resolutions. Quality metrics guide network weight adjustments to improve reconstruction accuracy while maintaining robustness to sensor failures and environmental variations.

The invention relates to the technical field of underwater fish school monitoring, and discloses an underwater fish school monitoring statistical system based on image fusion. According to the system, underwater video streams and sonar reflection intensity data of different spectral bands are acquired through an underwater multi-source image acquisition module, and time-space synchronous multi-modal image data streams are generated through timestamp alignment; a fish school contour reconstruction module is used for segmenting a fish school contour boundary and fusing visible light texture and sonar geometric features to generate an underwater three-dimensional fish school distribution set; the dynamic track mapping module tracks the mass center displacement, calculates the movement rate and the direction deviation angle, and correlates the water area depth to generate a dynamic track topological graph; the behavior anomaly analysis module extracts environment data based on the track mutation node, and detects aggregation density change and direction dispersion to mark an anomaly feature cluster; and the population statistics output module integrates the data, performs classified statistics on population distribution, a quantity threshold value and a migration path overlap ratio, and finally generates a fish school quantity distribution statistics thermodynamic map.

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 discloses a lithium battery laser welding detection method, and belongs to the technical field of nondestructive testing. The method comprises the following steps: acquiring a three-dimensional path point sequence of a to-be-detected welding seam; controlling the transient thermal excitation unit to apply transient thermal excitation along the path; a high-speed multispectral imaging unit is used for synchronously collecting the thermal radiation attenuation process of the weld joint under at least two different spectral bands, and a space-time spectrum data cube is formed; and finally, inputting the data cube into an artificial intelligence model for defect identification. The system correspondingly comprises a three-dimensional visual sensor, a transient thermal excitation unit, a high-speed multispectral imaging unit and a synchronous control and data processing unit. According to the method, different types of welding defects such as air holes and incomplete fusion can be effectively distinguished by analyzing the heat diffusion characteristic difference under different spectral bands, the interference of uneven surface emissivity is inhibited, and the online detection accuracy and reliability of the three-dimensional complex welding seam are improved.

The invention relates to the technical field of remote sensing image recognition, in particular to a method for determining a moso bamboo forest snow disaster affected area based on a GEE platform. The method mainly solves the problems of low efficiency, high cost and difficulty in realizing large-range rapid evaluation caused by dependence on manual field investigation in the prior art. According to the technical scheme, the method comprises the following steps: firstly, obtaining a multi-temporal Sentinel-2 image before and after a snow disaster, and carrying out the preprocessing of the multi-temporal Sentinel-2 image; extracting spectral bands, vegetation indexes and texture features to form an initial feature set; then, a key feature variable combination is determined through statistical significance filtering and machine learning optimization; on this basis, establishing a logistic regression discrimination model and determining an optimal classification threshold; and finally, carrying out snow disaster state classification on the moso bamboo forest region by utilizing the trained model, and generating a disaster region spatial distribution diagram.

The invention provides a spectrum self-attention and cloud mask guided remote sensing image cross-modal cloud detection method. According to the method, the spectral self-attention sub-network in the remote sensing image cross-modal cloud detection network is constructed, and the dependency relationship between different spectral bands of the remote sensing image is effectively mined and utilized, so that the recognition accuracy of cloud region features is improved. According to the method, cross-modal migration from the source domain image to the target domain image is realized by using the migration sub-network of the unsupervised image domain in the remote sensing image cross-modal cloud detection network, and the multi-spectral image with the target domain features is generated, so that the defect of the source domain image in spectral information is overcome, the false detection and omission ratio is effectively reduced, and the detection efficiency is improved. And the robustness of cloud detection of the model in a complex scene is enhanced.

The invention discloses a multi-spectral data fusion intelligent monitoring method in a reflective film production process, and relates to the technical field of reflective film production process monitoring, and the method specifically comprises the following steps: screening out all spectral band combinations with cross interference among different spectral bands based on preprocessed spectral data; calibration is carried out as a cross-band combination; establishing a wave band interference modeling matrix for each pair of spectral wave bands in the cross wave band combination, wherein the wave band interference modeling matrix is used for describing interference behavior characteristics between the spectral wave band pairs; determining the cross interference intensity of each cross band combination based on the established band interference modeling matrix, and classifying each cross band combination; and according to the classification result of each cross-band combination, respectively executing corresponding processing operations on different types of cross-band combinations. According to the invention, the problem of multispectral cross interference identification is solved, and interference quantitative evaluation and intelligent grading processing are realized.

The invention discloses a semiconductor etching end point detection method and a multi-channel detection system, and the detection method comprises the following steps: outputting detection light; expanding the detection light into a continuous spectrum band; imaging the continuous spectral band to a modulation surface of a digital micromirror device, and establishing a pixel group corresponding to each narrow-band wavelength of the continuous spectral band; performing spatial sampling on the continuous spectrum band, and distributing and writing a unique radio frequency modulation frequency f for each pixel group; respectively executing vibration type switch modulation on each pixel group at a radio frequency modulation frequency f so as to convert wavelength information of a continuous spectrum band into a time domain frequency optical signal; converting the time domain frequency optical signal into a time domain electric signal; performing frequency domain analysis on the time domain electric signal, and demodulating gas absorption light intensity corresponding to each modulation frequency; and judging the etching end point of the semiconductor according to the change trend of the gas absorption light intensity. According to the invention, parallel detection of a plurality of spectrum channels can be realized through a single detector so as to judge an etching end point.

The invention relates to a multispectral video dynamic registration method and system of a refrigeration type infrared imager. The method comprises the following steps: acquiring a multispectral video, extracting spectral feature data by adopting a spectral band separation algorithm, constructing a feature retrieval framework in combination with a time sequence information integration and spatial information extraction technology, and retrieving to obtain an initial spectral feature positioning result; performing feature enhancement and dimension reduction processing on the initial result by using a multi-dimensional information fusion technology, then recombining the initial result into a structured feature data set, acquiring real-time environment change data, registering the data set by using a preset dynamic registration model based on the real-time environment change data to generate a preliminary registration result, and calculating a registration error of the result; and adaptively updating the dynamic registration model by means of a preset closed-loop feedback control mechanism until a stable registration result is obtained. According to the method, through multi-dimensional feature processing and a closed-loop feedback mechanism, dynamic and accurate registration of a multispectral video is realized, and the registration stability and precision in a complex environment are improved.

The invention relates to the technical field of agricultural information, in particular to an intelligent crop disease and pest monitoring system based on multispectral imaging and a use method thereof, and the system comprises a multispectral data acquisition and preprocessing module, a visual intelligent analysis module, an intelligent diagnosis module and a decision support module. According to the method, a high-contribution-degree spectral band is dynamically screened through a cross-band attention mechanism, and a 3D convolutional network and mutual information constrained feature decoupling reconstruction algorithm is combined, so that adaptive fusion and noise separation of multispectral features are realized to generate sparse feature representation focusing disease and pest sensitive information, and based on an Inception-v3 and LightGBM cascaded multi-model fusion algorithm, the disease and pest sensitive information is obtained. And in combination with Monte Carlo Dropout confidence evaluation, Grad-CAM interpretability analysis and TCN time convolutional network time sequence prediction, an intelligent diagnosis system with reliability quantification, decision visualization and trend pre-judgment capabilities is constructed, and accurate identification of pest and disease damage types and severity and dynamic prediction of diffusion trends in the next five days are realized.

The invention discloses a generating device capable of generating an ultra-flat broadband micro comb. The generating device comprises a pump pulse generating module, a micro comb generating module and a micro comb generating module, the pumping pulse compression module is connected with the pumping pulse generation module; the high-Q-value microcavity module is connected with the pumping pulse compression module; and the micro comb detection and feedback module is respectively connected with the high-Q-value micro-cavity module and the pump pulse generation module, and is used for monitoring the output spectrum and power change of the high-Q-value micro-cavity module and adjusting the output wavelength of the picosecond pulse light according to the output spectrum and power change so as to stabilize the ultra-flat broadband micro comb. By arranging the high-Q-value micro-cavity, the spectral bandwidth of the micro-comb can be effectively expanded, and the ultra-flat broadband micro-comb is output; meanwhile, the defects that the hyperbolic secant single soliton micro-comb is poor in flatness and narrow in bandwidth are overcome, and the micro-comb has obvious advantages in the application field with high requirements for the flatness and the spectral bandwidth at the same time.

A sensor module for Raman spectroscopy includes a sensor package which encloses an application specific integrated circuit (ASIC), a light emitter arrangement, a light detector arrangement and a filter arrangement. The light emitter arrangement is electrically connected to the ASIC and operable to emit light with multiple excitation wavelengths to excite Raman scattering in an external probe to be placed outside of the sensor module. The light detector arrangement is operable to generate sensor signals from incident light emitted back from the external probe due to the Raman scattering. The filter arrangement is operable to filter the incident light according to a target passband. The ASIC is operable to drive the light emitter arrangement at the excitation wavelengths to shift a Raman spectral band of the external probe into the passband of the filter arrangement.

The invention provides a tree damage range intelligent identification method and system based on a satellite remote sensing image, and the method comprises the steps: determining a disaster cut image based on a satellite remote sensing image of a disaster area; fusing different spectral band data with the disaster cut image by adopting a multi-spectral band fusion technology to obtain a fused image; on the basis of the fused image and in combination with multiple indexes, a color mapping technology and a clustering algorithm are adopted for combined comparative analysis, and a post-disaster damage area preliminary screening image is obtained; inputting the post-disaster damage area preliminary screening image into a pre-constructed refined segmentation model to obtain a post-disaster tree damage detection result; performing spatial overlay analysis on the post-disaster tree damage detection result and power grid geographic data to obtain a risk identification result of a tree damage range; wherein the refined segmentation model comprises an encoder part adopting a visual architecture based on a shift window mechanism. The damage condition of trees around the power transmission line can be rapidly obtained after a disaster occurs, and the line patrol time is shortened.

The application relates to a hyperspectral remote sensing image quality grade evaluation method, which comprises the following steps: dividing an image into fixed-size blocks, then constructing a quality evaluation model, obtaining a feature map with spectral weight through a spatial-spectral attention module, and inputting the feature map into a frequency spectrum integration embedding module to generate an embedding feature map of local adjacent spectral bands; after linear projection, the embedding feature map is input into an encoder, and a skip connection mechanism is set to fuse the outputs of different encoders; the output of the last encoder is input into a linear layer and an activation function layer to obtain a quality evaluation result of the image; the quality evaluation model is trained, and the trained model is used for image quality grade evaluation. The application can perceive the good and bad degrees of a hyperspectral remote sensing image, and can dynamically detect and adjust the image output by an image processing system in a hyperspectral imager according to the quality grade of the image, thereby providing a more effective basis for parameter optimization of a real-time hyperspectral imager system.

Embodiments described herein relate to an apparatus that includes a light engine with a plurality of light sources. In an embodiment, each of the plurality of light sources is configured to emit a spectral band with different wavelength bandwidth. The apparatus may also include a beam splitter that is optically coupled to the light engine, where the beam splitter splits the spectral bands into a first optical path and a second optical path. In an embodiment, a power monitor is optically coupled to the beam splitter along the first optical path, and a reflective objective lens is optically coupled to the beam splitter along the second optical path. In an embodiment, the reflective objective lens includes a first mirror and a second mirror. In an embodiment, the apparatus further includes an optical sensor configured to measure the spectral bands after the spectral bands have reflected off of a substrate.

A system and method are disclosed for generating hyperspectral images from RGB (red-green-blue) images. A set of data includes training hyperspectral images and their corresponding RGB images. A spectral band grouping is performed on the training hyperspectral images based on a correlation coefficient of spectral bands. A decomposition network is used to generate a reconstructed hyperspectral image. A fine-tuning network is used to create a reconstructed RGB images. The difference between an input RGB image and a corresponding reconstructed RGB image is used to adjust one or more weights of one or more of the networks.

The invention discloses a hyperspectral image classification method based on light spectrum hybrid adaptive band selection, which belongs to the technical field of remote sensing image processing, and comprises the following steps: based on a data cube sample set and label vectors, carrying out hierarchical random sampling in proportion, and dividing a training set and a test set; inputting the training set into a hyperspectral image classification model based on light spectrum hybrid adaptive band selection, and performing forward propagation to record an optimal weight; inputting a test set into the model, performing forward propagation by using the optimal weight, outputting a pixel-level category probability through a convolution integral category head, and determining a prediction label; the forward propagation comprises the steps of establishing learnable weight vectors for all spectral bands at the first layer of a data loader, performing band-by-band weighting on each cube sample, extracting spatial spectral features by using a light spectrum mixed structure combined with a local convolution global converter, performing cross attention band screening on high-level spatial spectral tensor, and performing data processing on the high-level spatial spectral tensor. And dynamically calculating the importance weight of the spectral band, and updating the learnable band weight vector by using the importance weight of the spectral band.

The invention provides a machining part optical detection method and system based on industrial vision, and relates to the technical field of industrial vision detection. Comparing the optical response of each region in the image data with a pre-stored standard spectral response characteristic of a typical pollutant, and identifying the existing region and type of the pollutant; according to the identified pollutant information, influence distribution information is generated; according to the influence distribution information, carrying out real-time image correction compensation processing on the pollutant existence area in the image data to obtain target image data; the three-dimensional shape data of the workpiece is obtained based on the target image data and compared with the preset three-dimensional digital model to judge the quality of the workpiece, the problems that in the prior art, traditional manual quality inspection is low in efficiency and high in misjudgment rate are effectively solved, and the interference of by-products generated in the machining process on the accuracy of an optical detection system is overcome.

In a projection exposure method for exposing a substrate arranged in the area of ​​an image plane of a projection lens with at least one image of a pattern arranged in the area of ​​an object plane of the projection lens with radiation from a wavelength range around a design wavelength < 260 nm, a projection lens (PO) is used which is optically corrected such that at the design wavelength a focus is generated in a design focus position (FOC0) and for other wavelengths from the wavelength range offset focus positions are generated from an axially extended focus area (ΔFOC) around the design focus position (FOC0).The projection lens has a wavefront manipulation system (WFM) for controllably influencing the wavefront of the projection radiation extending from the object plane to the image plane, wherein the wavefront manipulation system (WFM) has at least one manipulator which, in response to control signals from a control unit within a usable setpoint range, undergoes setpoint changes that lead to changes in the wavefront. The method comprises the following steps: . Coating the substrate (SUB) with a radiation-sensitive photoresist layer (RS) having a layer thickness (SD); exposing the photoresist-coated substrate with the image of the pattern using radiation that, under the control of a control unit, has different wavelengths from a wavelength range with a spectral bandwidth around the design wavelength according to a predetermined temporal sequence; synchronizing a wavefront manipulation system (WFM) control with the temporal sequence of radiation of different wavelengths such that a lateral chromatic aberration caused by switching between different wavelengths is at least partially compensated.

A flame detection system is disclosed. The flame detection system includes: an image capture unit configured to capture an image of a field of view (FOV); a flame detector configured to capture a first set of spectral band data and a second set of spectral band data; and a processor configured to classify the one or more images and the first set of spectral band data, assign a weight to each of the second set of spectral band data, receive a flame count for each of the second set of spectral band data, determine a weighted count for each of the flame counts, and determine a weighted count for each of the flame counts. A number of weighted counts exceeding its respective threshold is determined, and a flame is determined to be present within the FOV when the number is greater than or equal to a number threshold, and a flame is determined to be absent within the FOV when the number is less than the number threshold.

The invention discloses a fluorescent dye for shoe soles as well as a preparation method and application of the fluorescent dye, and belongs to the technical field of fluorescent materials for shoes. The fluorescent dye has a structure as shown in a formula 1. The preparation method comprises the following steps: synthesizing an intermediate through Buchwald-Hartwisting arylation reaction, and carrying out Suzuki coupling to obtain a target product. The dye inhibits aggregation fluorescence quenching through a rigid plane structure and a deuterated group, the HOMO-LUMO energy gap is regulated to 2.8-3.2 eV, the dye has the broad band emission of 450-600 nm, and the fluorescence quantum efficiency reaches 0.55-0.75. When the brominated butyl rubber is applied to a sole formula, the brominated butyl rubber comprises the following components in parts by weight: 40-60 parts of brominated butyl rubber, 0.1-2 parts of fluorescent dye and the like, and a finished product is prepared by mixing, vulcanizing and foaming. Compared with a traditional dye, the fluorescence intensity is improved, the photooxidation resistance is enhanced, the dissolution rate is reduced by 60% or above, and the dye has excellent humidity and heat resistance and material compatibility.

The invention provides a dry-farming oat growth vigor monitoring method and system based on unmanned aerial vehicle hyperspectral remote sensing, and relates to the technical field of dry-farming oat growth vigor monitoring. The reflectivity of each image pixel under different spectral band conditions is calculated through the absolute radiation brightness and the known reflectivity of the reference plate, the average reflectivity of the unit grids in different spectral bands is obtained, a linear product is adopted to construct a growth score index, the growth score index of each grid unit is clustered by adopting a K-means clustering method, and the average reflectivity of each grid unit is calculated. And outputting the clustering condition of the growth vigor of the dry farming oats of the grid units, and setting a judgment threshold according to the clustering condition of the growth vigor of the dry farming oats to realize growth vigor classification. A reference plate is introduced to calibrate the reflectivity, the pixel-level absolute reflectivity is calculated through a radiation transfer equation, and the influence of illumination condition changes is eliminated; k-means clustering is adopted to automatically divide growth vigor levels, and a threshold value can be generated in a self-adaptive mode based on data distribution.

The invention relates to the technical field of image processing, and discloses a facial health state assessment method and system based on layered optical modeling. The objective of the invention is to solve the problems of poor face health state evaluation precision and robustness caused by rough optical modeling, mixed reflection components, weak environmental adaptability and insufficient multi-dimensional health index fusion capability in the prior art. The method comprises the following steps: synchronously acquiring a user face video sequence comprising at least two different spectral bands through a multispectral imaging device; constructing a multilayer optical transmission model of the skin tissue, and separating a specular reflection component and a diffuse reflection component from the face video sequence; extracting a physiological time sequence signal related to subcutaneous blood flow activity from the separated diffuse reflection component, and calculating heart rate, heart rate variability and blood oxygen saturation parameters; and fusing the physiological parameters and expression and micro-expression features extracted from the facial video sequence, and outputting quantitative evaluation results of fatigue degree, pressure level and emotional state through a pre-trained deep neural network model. The system comprises a multispectral image acquisition module, a layered optical modeling module, a reflection component separation module, a physiological parameter inversion module and a health state evaluation module. According to the technical scheme, the signal-to-noise ratio and stability of physiological signal extraction in a complex illumination environment can be remarkably improved, the adaptability to individuals with different skin colors and dynamic illumination conditions is enhanced, and more accurate and more robust judgment of the high-order health state is achieved.

The invention discloses a gradient-guided panchromatic sharpening method for unlocking spatial textures, which comprises two image processing branches: a guide feature extraction branch for capturing gradient features from gradient amplitudes and generating auxiliary gradient features grads and refined guide gradient features Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr, Gsr and Gsr; gradient features extracted from gradient amplitudes can provide complementary information to promote optimization and improve the overall performance; one branch is a gradient-guided fusion branch, the other branch is a gradient-guided fusion branch, PAN and MSI are combined with extra information of gradient features, and in the fusion branch, a multi-image cross attention module is designed to gradually integrate image features with different spectral bands and resolutions, so that the PAN and MSI fusion process is effectively guided, and the panchromatic sharpening performance is remarkably improved.

The invention relates to the technical field of unmanned aerial vehicle water conservancy exploration, in particular to an unmanned aerial vehicle water conservancy exploration intelligent system and method based on multi-source data fusion, and the system comprises an image collection module which is used for collecting water surface information and outputting an original image signal through an airborne multispectral polarization imaging unit; and the image preprocessing module is used for executing geometric correction based on the original image signal so as to output a corrected image signal. According to the method, a fusion weight calculation mechanism is introduced among multiple spectral bands, and the target detection depth and the spectral signal-to-noise ratio model are combined, so that the adaptive weight fusion of the multi-source spectral data is realized, and the complementarity of information of different bands is effectively enhanced; the spectrum contrast ratio, the detail fidelity and the target prominence of the generated exploration image are obviously improved, and higher accuracy is provided for quantitative analysis and feature recognition of the water body environment.

The invention provides a hyperspectral spectrum data fusion method and a hyperspectral spectrum data fusion system capable of widening a spectral band, and the method comprises the steps: carrying out the wavelength domain matching of Vis-NIR hyperspectral data obtained by a ground object spectrometer based on the visible light band of a hyperspectral image, and storing the resampled Vis-NIR hyperspectral data in a spectrum library. Performing similarity measurement on the spectral vector of each pixel in the hyperspectral image and all standard spectrums in the spectrum library to generate a spectral feature fingerprint map and a spectral response coefficient map; and performing spectrum reconstruction on each pixel in the hyperspectral image data in combination with the spectral feature fingerprint map and the spectral response coefficient map. And finally, obtaining a hyperspectral image cube after broadened spectrum fusion. According to the scheme, on the basis of wavelength domain matching, the spectral feature fingerprint map, the spectral response coefficient map and the like, the hyperspectral image cube obtained through final fusion covers a wider spectral range, and the original high spatial resolution is reserved.

The invention discloses a field operation portable low-light puncture infusion guide system which comprises a multispectral imaging module used for collecting an image containing a plurality of spectral bands by utilizing the penetrating power and reflection characteristic difference of light rays with different wavelengths to human tissues; the near-infrared blood vessel sensor is used for acquiring the contour and the position of a blood vessel; the low-light sensor is used for converting the optical signal into an electric signal based on a photomultiplier or a CMOS (Complementary Metal Oxide Semiconductor) low-illumination imaging technology and generating a low-light image through amplification and noise reduction processing; the image fusion processor is used for carrying out image fusion on the blood vessel image and the low-light-level image based on a feature-level or pixel-level image fusion algorithm; the AR display module is used for superposing a virtual image which is generated by image fusion and contains the position and the direction of the blood vessel into a real scene; the method has the advantages that the multispectral imaging technology and the low-light sensing technology are combined, richer tissue information is obtained, the limitation of a single imaging mode in the low-light environment is solved, and the blood vessel visualization effect is improved.

The invention discloses a multispectral microscope blood cell automatic classification and counting system. The system is composed of a multispectral illumination and microscopic imaging module, a spectrum and geometric calibration module, a multispectral preprocessing and cell segmentation module, a multispectral discrimination index and spectral band weight adaptive updating module, a cell graph structure classification module, a man-machine interaction module, an online adaptive learning module and the like. The method comprises the following steps: acquiring a blood smear image by a system under a narrow-band multi-spectrum condition, constructing cellular spectrum-morphological characteristics by combining a multi-scale segmentation result after noise suppression, flat field correction and background deduction, generating a discrimination index with a self-adaptive spectrum band weight, and completing joint classification and counting on a cell map; and meanwhile, carrying out constrained increment updating on the spectral band weight and the classification model by utilizing an artificial correction result of the low-confidence-coefficient cells. Compared with a traditional single-channel microscopic imaging and static classification method, the method has higher classification accuracy and counting stability under different dyeing and imaging conditions, and the workload of manual recheck can be reduced.

Optical device 100 comprises: separating element 102; in aspects, dispersive element 104; and optical elements 106. The separating element separates an optical signal into spectral bands, each having