248 results about "Spectral bands" patented technology

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 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 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.

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.

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 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 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.

An electronic device includes: memory; a color filter array (CFA); an image sensor operatively connected to the CFA, the image sensor being configured to capture a plurality of images respectively having a plurality of measured spectral densities in a plurality of spectral bands; and a processor operatively connected to the at least one memory and the image sensor, wherein the at least one processor is configured to: measure a plurality of crosstalk values in a plurality of pixels and in a plurality of K channels of the plurality of images (the K represents a number of pixels per a color of the CFA), estimate a plurality of spectral-crosstalk values by applying an estimation function to the plurality of crosstalk values, and control to store, in at least one of the memory, the image sensor, or the processor, the estimated plurality of spectral-crosstalk values.

The application belongs to the technical field of multispectral imaging system, and discloses a filter wheel control method for multispectral arbitrary spectrum imaging. The method compares the actually detected current position with the previously calibrated position, obtains the shortest path planning, avoids the detected blind area during the movement, drives the filter wheel to rotate according to the corresponding control curve stroke mode, so that the required filter aligns the optical path for imaging. The application has simple system structure requirement and simple control method implementation, can effectively and quickly complete the control of any filter in the filter wheel within half a cycle, and realizes multispectral time-sharing resident imaging.

The invention discloses an organic light emitting diode with a visible-infrared multispectral band free switching function. The organic light emitting diode sequentially comprises an anode, a hole injection / transmission layer, an infrared light emitting unit, a charge generation layer, a visible light emitting unit, an electron injection / transmission layer and a cathode, the charge generation layer sequentially comprises a P-type semiconductor layer, a transparent conductive layer, a buffer layer and an N-type semiconductor layer, the P-type semiconductor layer is arranged adjacent to the visible light emitting unit, and the N-type semiconductor layer is arranged adjacent to the infrared light emitting unit. The infrared light-emitting unit is prepared by thermally activating the delayed fluorescent material, so that the internal quantum efficiency of the organic light-emitting diode is effectively improved. In addition, the charge generation layer with high conductivity is designed to be connected in series with the light-emitting units, so that an organic lighting source with a visible-infrared multispectral band free switching function can be constructed.

This invention discloses an isotope abundance detection method and system based on the centroid method of emission spectroscopy, belonging to the fields of analytical chemistry and spectroscopic analysis technology. This method measures the centroid position of the atomic spectral lines or molecular spectral bands of the target element, utilizing the quantitative relationship between the centroid position and isotope abundance to achieve rapid, accurate, and low-cost detection of isotope abundance. This invention is the first to systematically establish a complete theoretical framework for the use of the centroid method of emission spectroscopy for isotope abundance detection, revealing the strict linearity conditions between the centroid and abundance, as well as the sources of error. This method is applicable to various excitation sources such as microwave plasma, inductively coupled plasma, and laser-induced breakdown plasma, covering both atomic and molecular spectroscopy detection modes, significantly reducing the resolution requirements of the spectrometer and exhibiting good noise interference resistance. This invention can be widely applied in fields such as nuclear engineering, semiconductor industry, environmental science, geochemistry, and medicine.

A laser system for the generation of ultrashort optical pulses of light including an oscillator emitting low power and negatively chirped optical pulses with a spectral bandwidth W1, a dispersive connecting segment to maintain the sign of the chirp of the pulses of the oscillator, an optical amplifier for amplifying the optical light pulses and a negative group velocity dispersion segment for compensating phase contributions of the whole propagation process. During the propagation from the output of the oscillator to the end of the optical amplifier, the chirp of the light pulses will change once from negative to positive chirp. After a final compression stage ultrashort optical pulses can be generated.

This invention relates to a method for inverting remote sensing data from overlapping strong and weak absorption lines of greenhouse gases, belonging to the field of satellite remote sensing technology. Specifically, it includes the following steps: Step 1: Constructing an optical system, including a satellite platform, camera 1, and camera 2; camera 1 and camera 2 are mounted at the bottom of the satellite platform; camera 1 and camera 2 are mirrored; the imaging spectral band of camera 1 moves from the weak absorption spectral band to the strong absorption spectral band along the positive push-broom direction to the negative push-broom direction; the imaging spectral band of camera 2 is in the opposite direction; the optical system moves horizontally from left to right to achieve horizontal push-brooming of greenhouse gases on the ground, obtaining a dual-camera fused image; the dual-camera differential absorption spectroscopy algorithm is used to invert the dual-camera fused image to obtain the greenhouse gas column concentration; this invention, based on the spectral characteristics of strong and weak absorption lines of greenhouse gases, adopts a detector focal plane segmentation method, and the dual cameras achieve differential absorption and inversion in the same region, which can guide the design of greenhouse gas detection micro / nano satellite payloads.

The invention relates to an intermediate infrared micro comb generation system and method, in particular to an intermediate infrared micro comb key starting type generation system and method based on a negative thermo-optic effect. In order to solve the problems that a traditional mid-infrared light frequency comb is limited in size, weight, power consumption and repetition frequency, and the mid-infrared band faces the defects that the light source tuning rate is low, the micro-cavity quality factor is low and the key device performance is poor, the invention provides the mid-infrared light frequency comb. The intermediate infrared micro comb key starting type generation system based on the negative thermo-optic effect comprises a pumping unit used for providing pumping light, a microcavity unit used for generating a nonlinear four-wave mixing process and the negative thermo-optic effect, and a detection unit used for receiving an intermediate infrared micro comb and recording time domain information and frequency domain information of the intermediate infrared micro comb. Meanwhile, the invention provides a key-starting type generation method of the mid-infrared micro comb based on the negative thermo-optical effect, which is self-starting, wide in spectral bandwidth, high in repetition frequency and high in stability based on the system.

The invention relates to a three-mirror anastigmat telescope, comprising at least a primary mirror (1), a secondary mirror (2) positioned in front of the primary mirror (1) such that the primary and secondary mirrors have optical surfaces (1.1, 2.1) opposite one another, a tertiary mirror (3) having an optical surface (3.1) opposite the optical surface (2.1) of the secondary mirror (2), and a first optronic sensor (10) positioned opposite the tertiary mirror (3). The telescope comprises a second optronic sensor (20) positioned behind the secondary mirror (2) and the optical surface of the secondary mirror (2.1) has a dichroic treatment (2') intended to separate an incident luminous flux reflected by the primary mirror (1) into a first luminous flux reflected towards the first optronic sensor (10) via the tertiary mirror (3) and a second luminous flux transmitted to the second optronic sensor (20), the first luminous flux and the second luminous flux being in different spectral bands and the secondary mirror (2) being transparent to the spectral band of the second luminous flux.

The invention provides an artificial intelligence-based industrial boiler coking prediction method and system. The method comprises the steps of infrared thermal image time sequence acquisition, multi-spectral dynamic threshold segmentation, acoustic impedance feature extraction, multi-modal working condition fusion, space-time attention prediction modeling and adaptive threshold decision triggering. According to the method, the local threshold values are adaptively calculated on different spectral bands such as short waves, medium waves and long waves, response characteristics of all temperature zones can be considered, the false detection and missing detection risks caused by the fixed threshold values are remarkably reduced, and high-temperature hot spots are extracted more accurately and reliably; acoustic impedance characteristics obtained through calculation quantitatively reflect the structure and viscosity change of a coking layer by using attenuation and time delay information of sound waves in an ash layer, a direct and fine physical basis is provided for subsequent rheological property inference, and the defect that mechanical changes of materials are difficult to capture through pure temperature monitoring is effectively overcome.

A monolithic LED IC die can include multiple emission regions independently operable by applying independent electrical input signals, such as to independently control light output from different individual ones of the multiple emission regions. Optics can include different spectral filters applied to the different light outputs from the different individual ones of the multiple emission regions, such as to define different spectral bands output by the different spectral filters. These different spectral bands can be differently encoded, and the encoded light can be used for enhanced spectroscopy. Additionally or alternatively, the different individual ones of the multiple emission regions can be selectively energized, such as to provide a tunable light source via the different spectral bands output by the optics. The monolithic LED IC die with the multiple emission regions can make the optics more compact, such as for a wearable or other compact device for spectroscopic measurement or analysis.

The application discloses an agricultural multi-source image low-delay transmission and intelligent processing method and system, and belongs to the technical field of image processing, and comprises the following steps: a double-branch parallel network is constructed; target data containing RGB images and near-infrared spectral data are synchronously collected through an industrial-grade device, the target data are input into the double-branch parallel network, visual feature vectors and spectral feature vectors are respectively extracted, and the visual feature vectors and the spectral feature vectors are input into a cross-modal attention layer; weights are dynamically calculated with the aid of a cross-modal attention mechanism, global semantic feature vectors fused with double-modal information are generated, and local attention graph elements pointing to key spectral bands of lesions are simultaneously output; an edge node-cloud two-stage transmission architecture is adopted, the global semantic feature vectors and the graph elements are preprocessed by the edge node and then transmitted to the cloud; a trained generative model is deployed on the cloud, and a visualized lesion feature map is reconstructed according to the received preprocessed data; and the method realizes efficient fusion of double-modal features and low-delay transmission, and improves the lesion feature recognition accuracy.

This invention discloses a method for predicting soil organic carbon (SOC) content based on UAV multispectral data, belonging to the field of soil remote sensing monitoring technology. The method constructs a multi-source heterogeneous fusion dataset by collecting soil visible-near-infrared spectral data and environmental data such as topography and climate. The SG-FD algorithm is used to preprocess the spectra to enhance feature signals. A dynamic deep separable convolutional network (DS-DCNN) is constructed, and a scale pyramid dynamic weight module is used to adaptively extract key spectral bands. Environmental driving factors are deeply fused to achieve high-precision prediction of forest soil SOC content. Finally, the SHAP framework is introduced to analyze the contribution of each factor. This invention overcomes the limitations of traditional single-spectral modeling and effectively solves the problems of low SOC inversion accuracy and poor model interpretability in complex forest environments, providing support for forest carbon storage estimation.

The application belongs to the field of image data processing, and particularly relates to a geometric and radiation compensation measurement method applied to farmland extraction, which comprises the following steps: obtaining remote sensing image data of a target area, pre-processing the remote sensing image, constructing a label, cutting and cropping, and dividing a training set and a test set; under the condition of fixed spatial resolution, the application establishes a unified compensation measurement mechanism between spatial resolution and spectral band number, realizes quantitative description of farmland extraction performance, solves the problem that the existing technology cannot uniformly measure the synergistic effect of spectral radiation information and spatial geometric information, and provides method support for two-dimensional quantitative analysis of farmland extraction precision; by constructing an Iso-IoU joint regression model, mathematical expression of the compensation relationship between spectral radiation information and spatial geometric information is realized, and the farmland extraction capability under different remote sensing configuration conditions can be quantitatively evaluated.

The present application relates to the field of optics, in particular to a broadband achromatic superlens with a large field of view. The superlens is composed of a cylindrical base embedded with a non-equal-height circular hole array and a nano-pillar array with a multi-ring structure. The non-equal-height circular hole array serves as the light entrance surface, and the nano-pillar array serves as the light exit surface. The circular hole array on the base has different depths and radii, and the nano-pillar array unit structure layer is composed of multiple ring bands, each ring band corresponding to a wavelength to achieve broadband achromatism. The nano-pillars arranged in the unit structure layer have various structures, such as cylindrical, annular, cross-shaped, etc. Different nano-pillar structures are used from the center to the outside, thereby reducing the mutual interference between adjacent ring bands. The materials of the unit structure layer and the base are transparent or low-absorption materials, and appropriate materials are selected according to different wavelength ranges. The superlens proposed in the present application can focus light beams of any wavelength range under a large field of view and a large spectral bandwidth.

This invention relates to the field of detection, and in particular to an online method and system for detecting impurities in calcium carbonate ore. Specifically, it includes: acquiring a continuous multispectral image sequence of calcium carbonate ore on a conveyor belt, and performing radiometric correction and spectral normalization preprocessing; identifying dominant spectral clusters of pure calcium carbonate ore by statistically analyzing the pixel spectra of the preprocessed images, and selecting a subset of high-confidence pixel spectra as the initial background training set; training a background dictionary, applying a set of sparse regularization constraints during training, and using the correlation between spectral bands to constrain the atomic energies of the dictionary to continuous or strongly correlated band groups; for each pixel of the detected image, using a sparse approximation algorithm combined with the background dictionary, calculating a sparse reconstruction coefficient vector and a reconstruction residual vector, and obtaining the reconstruction residual and coefficient activation uncertainty measure, respectively; if the reconstruction residual exceeds a first threshold, or the uncertainty measure exceeds a second threshold, it is determined to be an impurity point, and the location and quantity are summarized to generate an online impurity distribution map.

The invention relates to the technical field of tree height statistics, in particular to a mangrove forest tree height estimation method and device, equipment and a medium. According to the method, the spectral band, the topographic feature and the radar backscattering feature of the boundary area where the mangrove forest is located are collected, and the tree height mean value of the mangrove forest in the boundary area is estimated through the spectral band, the topographic feature and the radar backscattering feature. As the topographic features and radar back scattering are satellite remote sensing data acquired by the radar, the spectral band is data acquired by the spectral imager sensor, and the radar can acquire the satellite remote sensing data of any boundary area where the mangrove forest is located, even if the boundary area is located in a shallow sea area, the data can be accurately acquired. Therefore, the method for estimating the height of the mangrove forest is not limited by the growing environment of the mangrove forest. And the tree height is estimated by adopting the multi-source satellite remote sensing data, so that the accuracy of the tree height estimation value can be improved.

The invention discloses a vegetation function character hyperspectral inversion method and device based on deep learning, and belongs to the field of artificial intelligence and ecological remote sensing cross fusion. The method comprises the following steps: integrating multi-source ground spectral measurement data and corresponding field actually-measured plant functional traits in a global scale range, and constructing a canopy scale spectrum-character deep learning data set; according to the method, complementary advantages of a convolutional neural network, a Transform and a Kolmogorov-Arnold network are comprehensively utilized, and a CTRN model capable of simultaneously estimating various vegetation functional traits is constructed; training is carried out based on the data set and the model, and the optimal model obtained through training is applied to automatic inversion of global-scale vegetation function characters; a model interpretability method is further adopted to identify key spectral bands, and internal correlation between vegetation characters and spectral response is revealed. According to the method, high-precision and high-efficiency intelligent estimation of global vegetation function characters is realized, and the generalization ability and interpretability of a remote sensing inversion model are remarkably improved.

A computer-implemented method comprising: receiving, as input, spectral image data acquired with respect to a unit of tomato; calculating, from the spectral image data, spectral reflectance values associated with the unit of tomato in at least one specified combination of spectral bands; deriving, from the spectral reflectance values, a normalized difference spectral index (NDSI) value; and determining, based on the NDSI value, a value of at least one specified quality parameter associated with the unit of tomato.

The present invention relates to a method for determining a texture of an object (12), the method being implemented by a computer (16) of a system (10) for determining the texture of an object (12) and comprising a step of: - obtaining a set of images of the object (12), each image of the set being an image of the object (12) in a respective acquisition spectral band, each acquisition spectral band having at least one overlap with another acquisition spectral band, - selecting a reference image from the images of the obtained set, - subtracting the reference image from an image obtained from the one or more other images of the set of images, and - determining the texture image from the subtracted image.

The application discloses a kind of passion fruit plant health monitoring methods using spectral analysis, the method is by simultaneously collecting the spectral data of the top of plant new leaf, middle functional leaf and fruit, it is compared with the standard health spectrum model of prestorage, based on the diagnosis of magnesium deficiency of top leaf 530nm reflection peak, the diagnosis of nitrogen deficiency of middle leaf red edge position blue shift, the diagnosis of water deficiency of middle leaf water index rise, specific waveband spectrum distortion early warning disease and fruit spectrum form evaluation quality, realize multi-index comprehensive diagnosis;The application realizes multi-index diagnosis of plant nitrogen, magnesium nutritional status, water stress, potential disease and fruit quality by simultaneously collecting and analyzing the reflection characteristics of the top of passion fruit plant new leaf, middle functional leaf and fruit in specific spectral band, so as to overcome the problem of the prior art relying on single vegetation index or chlorophyll measurement, which leads to the problem of partial diagnosis information.