59 results about "Multispectral image fusion" patented technology

The invention discloses a metal processing detection system and method based on computer vision, and relates to the technical field of defect detection.Rotatable polarization filters are installed in front of a lens of a vision sensor, the polarization filters are arranged at four angles respectively, and four sets of polarization images are collected; calculating the polarization degree of each pixel point on the surface during metal processing by using the gray values of the four groups of polarization images, setting a polarization region interval, and dividing a metal surface reflection region into a specular reflection region and a diffuse reflection region; acquiring images of different wavebands by using a multispectral vision sensor, and constructing a multispectral image set; for the same wave band, the images of different exposure times are synthesized to obtain an HDR high dynamic range image; setting a weight for the HDR image of each wave band, and combining the weighting of the HDR image and the suppression of the polarization degree to carry out multispectral image fusion so as to obtain a final image; and a gray threshold value and an area threshold value are respectively calculated through the average value and the standard deviation, and the metal processing defects are judged by using the two threshold values.

The invention discloses a multi-spectral image fusion building surface biological attachment area detection method and device and a medium, relates to the technical field of image processing, and discloses a multi-spectral image fusion building surface biological attachment area detection method comprising the following steps: based on a to-be-detected building, obtaining a visible light image collected by a visible light camera of an unmanned aerial vehicle, the multispectral camera acquires a multispectral image sequence based on at least two different frequency bands; registering the visible light image and the multispectral image based on a calibration and preprocessing module to obtain a pixel-level aligned visible light image and multispectral image sequence; and according to a deep learning feature identification module, identifying the visible light image and the multispectral image sequence after pixel-level alignment, and obtaining a pixel-level segmentation result of the organism attachment area of the to-be-detected building. Therefore, detection is carried out based on the unmanned aerial vehicle, deep learning and an image feature fusion mode are combined, and the building surface bioattachment recognition accuracy is improved.

The invention relates to a multispectral-based printed circuit board detection method, which comprises the following steps of: firstly, acquiring and marking images and structural attributes of a printed circuit board under a multispectral channel, constructing a structured data set, and sequentially performing spatial correction, resolution unification and spectral normalization to form a consistent standard image; according to the method, space coordinate embedding and consistency modeling are utilized to realize accurate alignment of each channel image, and space and spectrum fusion parameters are optimized based on a joint adversarial learning network to obtain a self-adaptive enhanced image. On the basis, the micro-feature residual error is subjected to regional enhancement through a structural sensitive attention mechanism, and the detectability of the micro-defect is improved. And finally, the intelligent identification network realizes automatic screening and precise positioning of the micro-defects, and guarantees the detection precision and model adaptability based on a quality evaluation model and a self-correction feedback mechanism. According to the method, the PCB multispectral image fusion quality and the micro-defect detection capability can be remarkably improved.

The invention relates to the technical field of image processing, and discloses a method and system for generating a hyperspectral image based on hyperspectral and multispectral image fusion. The method comprises the following steps: respectively acquiring a hyperspectral image and a multispectral image with different resolutions and atmospheric state parameters at an imaging moment, and carrying out geometric registration on the images to obtain a registered image; analyzing according to the atmospheric state parameters, and constructing a differentiable atmospheric radiation transmission model; constructing an illumination invariance fusion network, defining a joint loss function of the illumination invariance fusion network, and optimizing the illumination invariance fusion network based on the joint loss function to obtain an optimized illumination invariance fusion network; in combination with a differentiable atmospheric radiation transmission model, taking the obtained registered image and atmospheric state parameters as input of an optimized illumination invariance fusion network, and obtaining an illumination-invariant high-resolution hyperspectral image; the method has stable ground object identification and analysis capability in different illumination scenes such as cloud shadow, time sequence and the like.

The invention relates to the technical field of water body pollution monitoring, in particular to a multispectral image fused water body pollutant accurate recognition system, which comprises a data acquisition module, a cloud processing module, a boundary processing module, a pollution recognition module, a diffusion prediction module and a visualization module, the system constructs sub-pixel representation of a water pollutant boundary by using a differential geometry manifold theory, and realizes high-precision pollutant boundary description through a multi-scale analysis and curvature flow optimization technology; enhancing pollutant characteristic expression by adopting multispectral image fusion and an optimal wave band selection technology; using a support vector machine model to accurately identify various pollutant types such as oil films, oil spots, algae blooms and the like; based on a boundary fine description result and a pollutant type identification result, the diffusion trend of pollutants is accurately predicted in combination with historical flow, wind direction and wind speed data, and the system improves the water pollutant boundary identification precision and enhances the identification capability of complex boundary forms and low-contrast regions.

The embodiment of the invention provides a hyperspectral and multispectral image fusion reconstruction method guided by spatial-spectral diffusion priori, and the method achieves the extraction and generation of spatial-spectral priori through a two-stage design, and comprises the steps: extracting spatial and spectral priori information from a real high-resolution hyperspectral image through a spatial-spectral priori encoder in a first stage; and the spatial features of the multispectral image and the spectral features of the low-resolution hyperspectral image are fused through a bidirectional multiscale prior fusion module to complete multiscale image reconstruction. And in the second stage, through a lightweight diffusion priori generation module, the spatial-spectral priori information is generated based on the up-sampled low-resolution hyperspectral image, and the real priori information is estimated, so that the problem of priori information loss caused by lack of a real image in the reasoning stage of the existing method is solved. Through two-stage joint training and a prior injection mechanism, not only are spatial texture and spectrum details reserved, but also video memory occupation and calculation cost of a diffusion model are reduced, and efficient high-resolution hyperspectral image reconstruction is realized.

The invention relates to the technical field of multispectral image fusion, and discloses a real-time multispectral image fusion system based on deep learning. The system comprises a multispectral image spectrum module, a process spectrum synthesis module, a personalized process extraction module, a process vertex labeling module and a fusion process verification module. The multispectral image map module stores various image category templates of a map structure, and each template corresponds to a fusion flow chart containing a processing vertex and a connecting edge; the process map synthesis module fuses a plurality of templates to generate a unified hypergraph through vertex clustering and edge redirection; the personalized process extraction module analyzes a user demand text and converts the user demand text into a demand graph, and a unified hypergraph is matched with a sub-graph to serve as a basic fusion process; the flow vertex labeling module identifies adjustable vertexes and adds marks; and the fusion process verification module verifies logic through a deep learning model, detects abnormal vertexes and generates a report. The system adapts to multiple scenes, and the flexibility and reliability of the fusion process are improved.

The invention provides a multispectral image fusion method and system for security monitoring, and the method comprises the steps: carrying out the Retinex decomposition based on an obtained visible light image and an infrared image, and obtaining a visible light reflection component, a visible light illumination component, an infrared reflection component and an infrared illumination component; initial division of semantic regions is carried out based on the visible light reflection component and the infrared reflection component, and a joint semantic graph containing a plurality of semantic regions is obtained; based on the joint semantic graph, respectively carrying out regional adaptive smoothing constraint on the visible light illuminance component and the infrared illuminance component to obtain a visible light target smoothing component and an infrared target smoothing component; and performing illumination field alignment fusion on the visible light target smooth component and the infrared target smooth component based on the joint semantic map to obtain a fused illumination component. According to the method, the problems of edge blurring, artifact interference and key target structure distortion caused by uniform processing and ignoring of physical characteristic differences of semantic regions in an existing method are accurately solved.

The application provides a multispectral image fusion method based on an interpretable neural network, first, multispectral images are obtained through a multispectral sensor, a panchromatic image of the same area is obtained through the multispectral sensor, and then a data fidelity term is constructed; a residual relationship between a fusion image and the panchromatic image is established through a deep network, and used as a prior constraint term; a fusion model is constructed through the data fidelity term and the deep learning prior term; based on the fusion model, an optimization model is iteratively solved through a proximal gradient descent method; the iterative solving step is mapped into a deep network architecture, a required multispectral panchromatic image deep network model is generated through training of a training data set; and based on the generated multispectral panchromatic image deep network model, multispectral and panchromatic images of a given input are fused to generate a final fusion image. The application generates optimal parameter settings through data driving, thereby improving fusion performance.

This application provides a method and system for smoke and fire recognition based on multispectral image fusion, relating to the field of data processing. The method acquires visible light imaging sequences, infrared thermal imaging sequences, and gimbal pose sequences of the target monitoring area and constructs scene commitment identifiers; it generates joint observation units based on unified spatiotemporal calculations and extracts multimodal increments to form cross-modal increment ledgers; it filters candidate anomalies based on structural destructiveness, thermal behavior persistence, spatial expansion, and cross-modal synergy; it performs cross-modal playback comparisons using a counterfactual surrogate fragment library and generates causal preemption chains; it combines asynchronous evidence ledgers to determine self-sustaining signs and establishes cross-regional inheritance relationship graphs; and it constructs a risk takeover ledger based on modal dominance switching trajectories and outputs graded smoke and fire alarm results. Implementing this technical solution facilitates the effective differentiation between smoke and fire anomalies and non-fire disturbances under multispectral observation conditions, thereby improving the accuracy of smoke and fire recognition based on multispectral image fusion.

The invention provides a hyperspectral and multispectral image fusion method and system. The method comprises the following steps: acquiring hyperspectral and multispectral data of the same spatial region; performing up-sampling on the low-resolution hyperspectral image by adopting bilinear interpolation; one-dimensional wavelet transform is utilized to extract reflection peak and spectrum change characteristics in a spectrum dimension, two-dimensional wavelet transform is utilized to extract texture and edge information in a space dimension, and pixel-by-pixel wavelet basis adaptive selection is realized through Softmax gating; multi-granularity feature modeling is realized for the spectral branches by adopting grouped multi-scale convolution, and a self-adaptive weighting mechanism is formed for the spatial branches through global average pooling, a convolution layer and Sigmoid activation so as to enhance spatial details and compensate spatial feature loss by utilizing MSI; a cross-scale two-way attention mechanism is introduced, and long-range dependence modeling and information interaction of spectrum and spatial characteristics are achieved; and a high-resolution hyperspectral image is generated through a feature aggregation and reconstruction module.

The application relates to the technical field of image processing, and discloses a method and system for generating a hyperspectral image based on hyperspectral and multispectral image fusion; the method comprises the following steps: collecting a hyperspectral image and a multispectral image with different resolutions and atmospheric state parameters at an imaging moment, performing geometric registration on the images to obtain registered images; analyzing the atmospheric state parameters to construct a differentiable atmospheric radiation transmission model; constructing an illumination invariance fusion network, defining a joint loss function of the illumination invariance fusion network, optimizing the illumination invariance fusion network based on the joint loss function, and obtaining an optimized illumination invariance fusion network; combining the differentiable atmospheric radiation transmission model, taking the registered images and the atmospheric state parameters as inputs of the optimized illumination invariance fusion network, and obtaining an illumination-invariant high-resolution hyperspectral image; and the application has stable ground object recognition and analysis capability under different illumination scenes such as cloud shadows and time sequences.

The application relates to the technical field of image processing, and discloses a multispectral fusion imaging camera module and an image processing method, which comprises the following steps: performing spatial mapping on infrared imaging images and near-infrared imaging images based on the spatial arrangement order of corner points and the direction of continuous edge broken lines; determining a spectral corresponding structure according to the light and dark trend changes of the same scene in three imaging channels; performing regional division on a geometric alignment result based on the spectral corresponding structure; performing direction consistency correction on geometric edges in a visible light imaging image based on a dominant contour line; performing extensibility superposition on texture lines in the spatially mapped near-infrared imaging image based on the texture direction of a texture detail area; performing superposition on the brightness levels of the visible light imaging image based on the brightness extension direction of a background smooth area; and performing connectivity checking on the boundary direction, the texture direction and the brightness extension direction. The application ensures the reliability of multispectral image fusion.

The invention discloses a building curtain wall automatic detection method and system, and relates to the technical field of building curtain wall detection based on visual inspection, and the method comprises the steps: obtaining a high-resolution image covering visible light to a near-infrared band, and classifying chemical components through a support vector machine algorithm; a chemical composition classification map containing distribution regions of corrosion products and fouling types is generated. And three-dimensional point cloud data is further obtained through a laser range finder, fused with the multispectral image and input into a convolutional neural network to predict the thickness of the deposit, and a thickness distribution diagram is generated. And generating an aging degree grading distribution diagram through the grading classification model, and determining a preferential maintenance area and a processing mode in combination with aging correlation analysis. According to the method, the types of the deposits on the surface of the curtain wall are accurately identified through an automatic technical means, the thickness and the distribution range of the deposits are accurately measured, accurate evaluation and intelligent maintenance decision making of the aging state of the surface of the curtain wall are achieved, and the maintenance efficiency and the curtain wall performance are remarkably improved.

The present application relates to the technical field of multispectral image fusion, and discloses a real-time multispectral image fusion system based on deep learning. The system comprises a multispectral image atlas module, a flow atlas synthesis module, a personalized flow extraction module, a flow vertex labeling module and a fusion flow verification module. The multispectral image atlas module stores a plurality of image category templates of graph structures, each template corresponding to a fusion flow graph containing processing vertices and connecting edges; the flow atlas synthesis module fuses a plurality of templates into a unified hypergraph through vertex clustering and edge redirection; the personalized flow extraction module analyzes user demand text and converts it into a demand atlas, and matches a subgraph from the unified hypergraph as a basic fusion flow; the flow vertex labeling module identifies adjustable vertices and adds labels; and the fusion flow verification module verifies logic through a deep learning model, detects abnormal vertices and generates a report. The system is suitable for multiple scenarios and improves the flexibility and reliability of the fusion flow.

The present application relates to the technical field of multispectral image fusion, and particularly relates to a multi-modal biological signal fusion method and system, multi-modal biological signal acquisition: at least two different types of biological signals are collected; time synchronization processing: a unified time axis T_ref=[0, 10ms, 20ms, …, T_end] with a resolution of 10ms is constructed, and alignment of each modal signal with the unified time axis is realized through a three-level synchronization strategy; cross-device standardization processing: the biological signals after time synchronization are subjected to standardization processing, and characteristic differences among different devices are eliminated; signal quality calculation: the signal quality Q(t) of each modal signal at time t is calculated in real time; dynamic weight updating: based on the real-time quality Q(t) of each modal signal, the fusion weight of each modal is dynamically updated; the present application has the advantages that: through a three-level mixed time synchronization algorithm, a unified time axis is constructed, and precise alignment of multi-modal signals with different sampling rates and different timing characteristics is realized.

The invention discloses contact appearance automatic detection equipment and a detection method thereof, relates to the technical field of contact appearance detection, and aims to solve the problem that tiny defects are difficult to effectively recognize in the prior art. Comprising a multispectral imaging module and a deep learning processing module. The multispectral imaging module comprises a multi-light source assembly and a multi-camera synchronous acquisition assembly and is used for acquiring a spectral image of the contact; and the deep learning processing module comprises a multispectral image fusion sub-module and a defect identification sub-module, and is used for analyzing and detecting the quality of the coating on the surface of the contact according to the spectral image and transmitting a detection result to the interaction module. The method has the advantages of improving the fine defect identification accuracy and reducing the false detection rate.

The application discloses a multispectral image fusion method and device based on multi-target segmentation, and equipment, and relates to the technical field of image processing. Different fusion methods are adopted for different targets in the feature domain according to the category of multi-target segmentation, so that the image conforming to the human eye visual perception is generated, and the significant target in the infrared image is effectively highlighted. The method comprises the following steps: collecting a visible light image and an infrared image, performing image registration processing on the visible light image and the infrared image to obtain a registered target visible light image and a target infrared image; performing multi-target semantic segmentation on the target visible light image and the target infrared image by using a multi-target segmentation network to generate a multi-target segmentation image; extracting a first deep feature corresponding to the target visible light image and a second deep feature corresponding to the target infrared image based on a multispectral image fusion network; and generating a target fusion image by fusing the first deep feature, the second deep feature and the multi-target segmentation image.

This invention relates to the field of visual inspection technology for electroacoustic devices, and discloses a method and system for quality inspection of horn drum paper based on visual inspection. The method separates a base layer, a detail layer, and a noise layer through multispectral image fusion and multi-scale decomposition. A three-dimensional mesh model of the horn drum paper is reconstructed based on the base layer, and the detail layer is mapped to the surface texture of the model, realizing the correlation analysis of defects in three-dimensional morphology and texture features, thus improving the ability to identify minute three-dimensional defects. By analyzing the local energy distribution of candidate defect regions in the noise layer and combining it with the local curvature of the mesh for authenticity judgment, it can accurately distinguish between real material damage and imaging artifact interference, reducing the false detection rate. Finally, repair instructions are generated based on the determined defect set. This method overcomes the limitations of two-dimensional image detection, has higher accuracy and reliability, and is suitable for automatic quality inspection on production lines.

The application discloses a unmanned aerial vehicle (UAV) agricultural condition monitoring system and method under multispectral image fusion, and belongs to the technical field of visual detection, wherein the system comprises: an image acquisition module, which is used for acquiring a target remote sensing image set of a target agricultural area; an image extraction module, which is used for extracting a first remote sensing image; an image preprocessing module, which is used for obtaining a first target image; an image splicing module, which is used for obtaining a target agricultural condition monitoring image of the target agricultural area; and an agricultural condition visual monitoring module, which is used for performing agricultural condition visual monitoring on the target agricultural area. The application solves the technical problem of insufficient agricultural condition monitoring precision and poor real-time performance caused by radiation distortion and spatial registration error of traditional visual detection technology in multispectral data fusion, and achieves the technical effects of improving the spatiotemporal resolution of multispectral visual detection and enhancing the precision of agricultural condition identification through multispectral image radiation correction and accurate geographical coordinate splicing.

The application discloses a method and system for generating hyperspectral images based on hyperspectral and multispectral image fusion. Y The method comprises the following steps: performing subspace decomposition to extract left low-rank vectors U of the hyperspectral image to be generated; Z The method comprises the following steps: performing subspace decomposition to extract left low-rank vectors U of the hyperspectral image to be generated; U And right low-rank vectors V of the hyperspectral image to be generated are generated through the trained deep generation network; V The left low-rank vectors U and the right low-rank vectors V are fused to generate the hyperspectral image X The system comprises optical elements and imaging sensors for acquiring input hyperspectral images and multispectral images. Y The system comprises optical elements and imaging sensors for acquiring input hyperspectral images and multispectral images. Z The application can generate hyperspectral images based on hyperspectral and multispectral image fusion, and reduce the cost and efficiency of hyperspectral image acquisition.

The invention relates to the field of image processing, and provides a multispectral image fusion method, system and device based on a mask auto-encoder, and the method comprises the steps: carrying out the feature extraction of a to-be-processed image; generating a complementary binary mask according to the random masking rate, masking the to-be-processed image to obtain a masked image, and reconstructing the masked image to obtain a reconstructed image; performing multi-scale feature extraction on the reconstructed image to obtain multi-scale features, performing mark sequence flattening on the multi-scale features to obtain a mapping vector so as to obtain a correlation matrix, obtaining a correlation mask matrix according to the correlation matrix, and obtaining correlation attention output through the correlation mask matrix; negation is carried out on the complementary correlation matrix to obtain a negation correlation matrix, so that a negation correlation mask matrix is obtained, and complementary attention output is obtained through the negation correlation mask matrix; and carrying out residual block splicing on the correlation attention output and the complementary attention output. According to the invention, a clear target image can be obtained.

The application discloses a kind of multispectral image fusion composite insulator operating state evaluation method and system, method includes: according to insulator structure parameter and operating voltage to construct standard state atlas;From ultraviolet image, extract actual discharge distribution, from infrared image, extract actual temperature distribution, from visible light image, extract actual water droplet distribution;Actual atlas is compared with standard atlas point by point, calculate discharge deviation degree, temperature deviation degree and hydrophobic deviation degree;Three kinds of deviation degrees are carried out spatial superposition analysis, identify high co-occurrence area, moderate co-occurrence area and isolated deviation area;According to co-occurrence mode, the deterioration type is reversed, including end pollution accumulation type, grading ring failure type, internal defect type, overall aging type and local damage type;Output deterioration type, deviation degree spatial distribution and risk level.The physical interpretable inversion of deterioration type is realized, and the problem that the prior art evaluation lacks benchmark and cannot distinguish deterioration type is solved.

The invention belongs to the technical field of image processing, and relates to a deep Kalman filtering-based high-multispectral image fusion method, which comprises the steps of constructing a training sample, creating a deep Kalman filtering-based high-multispectral image fusion network, carrying out network training, and carrying out high-multispectral image fusion by utilizing a trained network model. And taking the preprocessed hyperspectral image and multispectral image as joint input, and generating a fusion result image with both high spatial resolution and hyperspectral information through a nonlinear mapping relation of a network. The network is composed of an encoder module, an implicit feature fusion module, an optical flow estimation module, a Kalman gain estimation module and a Kalman recursion fusion module. According to the method, the spectral dimension dynamic evolution relation between wavebands is explicitly modeled through Kalman filtering, and the expression consistency of the fused image in the spectral dimension is improved; according to the method, the spatial detail reconstruction capability can be effectively enhanced; and the model parameter scale and the redundancy calculation overhead are remarkably reduced while the spatial spectrum modeling precision is ensured.

The invention relates to the technical field of multispectral image fusion, in particular to a bearing gland machining surface microdefect three-dimensional shape reconstruction and analysis method based on multispectral image fusion. The method comprises the following steps that a multispectral imaging device is used for conducting synchronous imaging on the machined surface of the bearing gland, multispectral two-dimensional image data of different wavebands are obtained, and the multispectral two-dimensional image data are preprocessed; based on the preprocessed multispectral two-dimensional image data, multispectral feature vectors reflecting the micro-defects of the machined surface are extracted; and carrying out fusion processing on the multispectral image by adopting a weighted fusion method based on the multispectral feature vector to form a fused spectral image. According to the method, two-dimensional image data under different wavebands are obtained by using a multispectral imaging technology, and the data are subjected to preprocessing, feature extraction and weighted fusion to form a fused spectrogram.

The invention relates to the technical field of image processing and analysis, and discloses a physical information constraint-based unsupervised hyperspectral and multispectral image fusion method, which comprises the following steps of: constructing a dynamic search unsupervised generative network which combines depth image prior and dynamic adaptive search to obtain an unsupervised hyperspectral and multispectral image fusion result; through input low-resolution hyperspectral image and high-resolution multispectral image data, super parameters such as the number of network layers, the size of a convolution kernel, the number of channels and an up-sampling mode are adaptively adjusted, and optimal network structure search and high-resolution hyperspectral image generation are synchronously completed. According to the unsupervised hyperspectral and multispectral image fusion method based on the physical information constraint, high-quality fusion of hyperspectral and multispectral images is realized through a three-in-one design of'unsupervised generation network + dynamic structure search + physical constraint '.

The invention discloses an unmanned aerial vehicle agricultural condition monitoring system and method under multispectral image fusion, and belongs to the technical field of visual inspection, and the system comprises an image collection module which is used for collecting a target remote sensing image set of a target agricultural region; the image extraction module is used for extracting a first remote sensing image; the image preprocessing module is used for obtaining a first target image; the image splicing module is used for obtaining a target agricultural condition monitoring map of the target agricultural area; and the agricultural condition visual monitoring module is used for performing agricultural condition visual monitoring on the target agricultural area. According to the invention, the technical problems of insufficient agricultural condition monitoring precision and poor real-time performance caused by radiation distortion and space registration errors during multi-spectral data fusion in the traditional visual detection technology are solved, and the purposes of accurate splicing of the multi-spectral image radiation correction and the geographic coordinates are achieved. The spatial-temporal resolution of multispectral visual detection is improved, and the accuracy of agricultural condition recognition is enhanced.

The application discloses a hyperspectral and multispectral image fusion method based on dilated convolution and cross attention, and is specifically implemented according to the following steps: step 1, the original data is preprocessed, and a hyperspectral and multispectral image dataset is generated by simulation, and then the generated hyperspectral and multispectral image dataset is divided into a training set and a test set; step 2, a hyperspectral and multispectral image fusion network model based on dilated convolution and cross attention is constructed; step 3, the hyperspectral and multispectral image fusion network model based on dilated convolution and cross attention is trained; and step 4, the trained hyperspectral and multispectral image fusion network model is used to test the test set. The application solves the problems of the prior art, such as difficulty in considering spatial and spectral information in feature extraction, difficulty in balancing calculation efficiency and accuracy, and insufficient feature fusion.

This invention relates to the field of multispectral image fusion technology, specifically to an immunochromatographic quantitative algorithm and system based on multispectral image fusion. The algorithm acquires the detection area of ​​an immunochromatographic test strip and a visible light image Im(x,y) of the same detection area. Cross-modal registration is performed between the acquired multispectral signal Sp(λ) and the acquired visible light image Im(x,y). Through image contour recognition and spectral acquisition area calibration, the C and T regions of the test strip are accurately located and matched to obtain the registered target detection areas T and C. Baseline correction and feature extraction are performed on the multispectral signal Sp(λ) of the registered T and C regions to calculate the spectral feature vector Spf. The advantages of this invention are: by fusing multispectral signals and image signals, it compensates for the information limitations of a single modality, and combined with a robust temperature compensation and batch compensation mechanism, it achieves precise quantitative detection at the μg / mL or ng / mL level.