593 results about "Spectrum imaging" patented technology

The invention discloses a stainless steel surface defect detection method based on machine vision, and relates to the field of material defect detection.The stainless steel surface defect detection method comprises the steps that stainless steel surface images are collected, the collected multispectral surface images are subjected to image fusion, and a multispectral composite image is generated; performing detail enhancement on the low-resolution image through a super-resolution reconstruction algorithm to obtain a high-resolution image set; positioning a defect area position from the high-resolution image set through a defect detection algorithm, and obtaining a defect position data set; extracting a historical image defect feature set and a real-time image defect feature set from the defect position data set, and constructing a defect classification model according to the historical image defect feature set; inputting the real-time image defect feature set into a defect classification model, and performing defect classification and evaluation to obtain a defect classification data set; and generating and distributing a defect detection report. By combining the multispectral imaging algorithm, the super-resolution reconstruction algorithm and the defect classification model, the automation, precision and efficiency of stainless steel surface defect detection are improved.

The invention provides an intelligent ecological restoration system, method and device for a high and steep slope of a strip mine in an arid region. Comprising a data acquisition layer which realizes real-time acquisition of multi-dimensional environmental data through InSAR satellite remote sensing, a ground sensor network and unmanned aerial vehicle multispectral imaging; the transmission layer adopts LoRa and 5G hybrid networking; the platform layer is used for constructing a slope stability prediction and restoration scheme optimization platform based on a digital twinborn model and a deep learning algorithm; and the application layer is used for remotely controlling the repairing device through a mobile terminal and a Web terminal and monitoring the repairing progress in real time. The problems that a traditional restoration technology is poor in adaptability, low in vegetation survival rate, high in ecological restoration cost, insufficient in monitoring technology application, insufficient in monitoring feedback mechanism, insufficient in intelligence, long in ecological restoration period and the like are solved.

The invention relates to the technical field of image processing, and discloses a dried persimmon quality detection method and system based on machine vision. The method comprises the following steps: performing multi-angle multi-spectral imaging through an industrial camera array to generate a standard image set; extracting quantitative dried persimmon appearance characteristics; the internal quality is subjected to nondestructive testing by using a spectrum; identifying defects and grading; analyzing a quality trend and establishing an early warning mechanism; and correlating process parameters to optimize the production process, and constructing a quality tracing system. According to the invention, comprehensive nondestructive testing of the appearance and internal quality of the dried persimmons is realized, a closed-loop system of quality early warning and process optimization is formed through data analysis and mining, and the quality stability and traceability of dried persimmon production are improved.

The invention relates to the technical field of crop disease recognition, in particular to a crop early disease recognition method and system based on time sequence feature fusion, and the method comprises the steps: obtaining an RGB image and a temperature feature image of a target leaf, and collecting multispectral feature data through a spectral imaging device; based on the time sequence image sequence, generating a coordinate transformation matrix of the first frame image by using a PnP algorithm, and aligning subsequent images according to the coordinate transformation matrix; multi-modal features are extracted by using a deep neural network, and the deep neural network comprises a 3D convolutional layer and an adaptive attention mechanism and is used for extracting features of space and time dimensions; realizing cross-modal feature fusion through a graph convolutional network based on the spectral data and the multi-modal features; inputting the fused features into a classifier, and generating the disease category and the disease severity of the target leaf based on the output of the classifier; outputting the disease transmission risk and the early warning result, and capturing the tiny dynamic change in the disease development process.

The invention relates to the technical field of unmanned aerial vehicle dynamic dust suppression intelligent decision making based on multi-sensor data fusion, in particular to a loess photovoltaic construction dust suppression unmanned aerial vehicle spraying dynamic regulation and control device and method, and the method comprises the steps: building a dynamically updated four-dimensional concentration field through a sensor cooperation unit in combination with a turbulence diffusion model of a dust field reconstruction engine; and the strategy knowledge base is optimized to shorten the flight path planning decision-making period from the minute level of the existing offline planning to the second level response. And the anti-interference execution unit realizes accurate spray trajectory tracking under a complex wind field condition. And a real-time evaluation closed loop of the dust suppression effect is constructed by a dual-spectrum imaging and deep learning analysis technology of the efficiency feedback unit, so that a dust field model can dynamically correct boundary condition parameters. The response lag time of an existing dust suppression system is shortened, meanwhile, the spray coverage rate is increased, energy consumption is reduced on the premise that the dust suppression effect is guaranteed, and an intelligent solution is provided for photovoltaic construction flying dust treatment.

The invention discloses an intelligent rock weathering detection method based on a multispectral imaging technology, and particularly relates to the field of rock detection.The method comprises the steps that a multispectral imaging device is used for collecting a rock sample image, and then radiometric calibration, geometric correction and image enhancement processing are sequentially conducted on the image. The method comprises the following steps of: preprocessing an image, acquiring spectrum, texture and morphological characteristic parameters from the preprocessed image, collecting samples through a multi-source database, constructing a deep convolutional neural network model, inputting the acquired characteristic parameters into the model to obtain a rock weathering probability vector, judging the weathering degree according to a maximum probability criterion, and outputting a result to a user side. According to the method, multispectral imaging and deep learning technologies are fused, the defects of a traditional detection method are overcome, the rock weathering degree can be detected more accurately and efficiently, nondestructive detection and real-time monitoring are achieved, and the method has important application value in the fields of geological research, engineering construction and the like.

The invention provides an acute cerebral apoplexy early-stage intelligent screening and early warning method and system based on deep learning, and relates to the technical field of medical imaging and artificial intelligence, and the method comprises the steps: obtaining a multi-temporal angiography image of the brain of a patient through a pulse-type dynamic imaging scheme, extracting hemodynamic features through a double-flow feature extraction network, and obtaining a multi-temporal angiography image of the brain of the patient; and constructing a vasculopathy risk distribution diagram by combining a vascular structure analysis model, and marking a potential embolism risk area. Furthermore, in combination with tissue perfusion parameters obtained by multispectral imaging and brain tissue function parameters obtained by electroencephalogram signals, an ischemic penumbra region is identified by using a self-adaptive threshold segmentation model. And finally, based on the ischemic penumbra area, the tissue perfusion parameters and the brain tissue function parameters, calculating a risk level by using a hierarchical decision model with a space-time attention mechanism, and generating a corresponding clinical intervention scheme. According to the invention, the early accurate screening and risk early warning of acute cerebral apoplexy can be realized, and the clinical intervention efficiency is improved.

The invention discloses a forestry intelligent spraying system and spraying method based on multi-source sensor space perception, and relates to the technical field of intelligent control. A target forest region is scanned through a laser radar and a visual sensor carried by an unmanned aerial vehicle, a three-dimensional forest region map is constructed, and a single tree is identified and positioned by using a tree body identification model; evaluating the leaf density of the canopy; identifying diseases and insect pests by utilizing multispectral imaging, and making a pesticide proportioning decision according to the disease and insect pest types and severity; planning and generating an optimal flight path for spraying of the unmanned aerial vehicle; adjusting nozzle parameters according to the leaf density of the canopy and the severity of diseases and pests, performing variable spraying, and storing the spraying operation parameters of the unmanned aerial vehicle. Through multi-source sensor fusion, an intelligent decision algorithm and precise spraying control, autonomous obstacle avoidance, high-precision map construction, tree body recognition and canopy analysis, pest and disease damage detection and dynamic pesticide dispensing and spraying in a forestry scene are realized, the forestry spraying efficiency is remarkably improved, and pesticide waste is reduced.

The invention relates to the technical field of bridge monitoring, in particular to a bridge crack real-time monitoring method based on edge calculation, which comprises the following steps of: acquiring bridge crack data by using a hyperspectral imaging camera, a thermal imaging device and a laser radar, adaptively adjusting edge calculation nodes by using an edge learning technology, and calculating the edge calculation nodes according to the edge learning technology. A deep learning model is used for crack analysis, an anomaly monitoring model is established, an augmented reality technology is introduced to assist field investigation, and through comprehensive application of technologies such as multi-source data acquisition, edge calculation, deep learning and augmented reality, the problems that a traditional bridge crack monitoring method is low in efficiency, low in precision, incapable of real-time monitoring and the like are solved. The system has the advantages of comprehensive data acquisition, real-time and efficient processing, strong adaptive ability, comprehensive abnormity monitoring, high system reliability and visual and efficient field investigation.

The invention discloses a textile fabric polyester fiber detection method based on spectral imaging and curve fitting, and relates to the technical field of hyperspectral imaging analysis, and the method comprises the steps: carrying out the characteristic decomposition and principal component analysis of a reflectivity function after baseline correction, and obtaining a characteristic curve; extracting feature information of the hyperspectral image, and fusing the feature information of the hyperspectral image with the feature curve to generate a multi-dimensional feature vector; and constructing a hyperspectral fiber content detection model, inputting the multi-dimensional feature vector and the feature parameter vector into the hyperspectral fiber content detection model, and generating the polyester fiber content. Not only are the problems of background noise and drift interference solved, but also the multi-dimensional characteristics of the hyperspectral data are fully mined. The content of the polyester fibers in the waste textile fabrics can be efficiently detected on line. Finally, according to the method, the detection precision and reliability are remarkably improved, and an efficient, environment-friendly and accurate technical means is provided for resource utilization of the waste textile fabrics.

The invention relates to the technical field of agricultural information, discloses a corn precise topdressing prescription map generation method based on unmanned aerial vehicle multispectral remote sensing, and effectively solves the key problems of insufficient monitoring precision, poor environmental adaptability, low decision intelligent level and the like in the traditional fertilization technology. Through the unmanned aerial vehicle multispectral imaging technology, efficient and accurate monitoring of the corn nitrogen nutrition condition is achieved; in combination with a machine learning algorithm, an intelligent fertilization decision model adapted to different environmental conditions is established, and the fertilization precision in a complex farmland scene is remarkably improved; and a multi-source data fusion technology is adopted, and crop growth, soil characteristics and meteorological factors are comprehensively considered, so that the fertilization scheme is more scientific and reasonable. Compared with a traditional method, the method has the advantages that accurate variable fertilization can be realized according to actual needs of crops, the utilization rate of the fertilizer is increased, environmental pollution is reduced, and reliable technical support is provided for sustainable development of modern agriculture.

The invention relates to the technical field of fire-fighting engineering, and discloses a real-time fireproof monitoring data transmission system for fire-fighting engineering construction, which comprises a fire parameter analysis module, an environmental data analysis module, a fire source positioning module, a model construction module and a safety database. The fire parameter analysis module generates a temperature change record table through gridding temperature measurement nodes, estimates the smoke diffusion rate, and evaluates the thermal stress and deformation risk of the building structure; the environment data analysis module associates the air velocity with the smoke diffusion rate and corrects the parameter weight of the evaluation model; the fire source positioning module generates a fire source three-dimensional positioning map by using multispectral imaging equipment; the model construction module constructs a dynamic fire assessment model according to multi-dimensional data and outputs a comprehensive threat level; the security database stores building material parameters, historical case characteristics, and dynamic threshold rules. According to the system, through multi-module cooperation and multi-data fusion, the accuracy and timeliness of fire-fighting monitoring are improved, and the system is suitable for safety management of fire-fighting engineering.

The invention discloses an intelligent control system for a diamond grain curing sintering furnace, belongs to the technical field of industrial furnace control, and aims at solving the problems that existing sintering control depends on experience, process monitoring is not comprehensive, real-time optimization is difficult to achieve, and product quality consistency is difficult to guarantee. The system comprises a sensor module, a multispectral imaging device, a data preprocessing module, a liquid neural network module, an attention fusion module, a user interaction module, a thermal structure coupling module and a real-time feedback control module. A liquid neural network is used for processing time sequence dynamics, multi-dimensional features such as temperature, humidity, spectrum and mechanical force are fused through a multi-head attention mechanism, and in combination with an internal state result of user input and thermal structure coupling analysis, a real-time feedback control module adopts a closed-loop strategy to calculate optimal firing parameters and generate a control signal; and the actuating mechanism is driven to perform self-adaptive adjustment.

The invention discloses an automobile part surface defect detection method based on a large model. The method comprises the following steps: acquiring feature data of a target surface by using a multi-dimensional imaging device; feature extraction, clustering analysis and abnormal mode recognition are carried out through the large model; extracting an abnormal region and generating a statistical model; comparing the statistical model with standard sample data to determine the position of an abnormal region; defect types are classified and identified, and resource allocation is dynamically optimized and calculated to adapt to detection complexity; and finally outputting defect types and positions. According to the method, the spectral imaging technology and the deep learning algorithm are combined, high-precision detection, positioning and classification of defects such as cracks, scratches and bubbles are achieved, a visual detection report is generated, the method is suitable for real-time quality control in large-scale production, the detection efficiency and accuracy are improved, and manual errors and cost are reduced.

The invention provides an intelligent online analysis and monitoring system and method for circuit board production, and the system comprises an image collection module, a multispectral imaging module, a three-dimensional scanning measurement module, a copper thickness test module, a data collection module, an intelligent analysis and recognition module, and a self-adaptive algorithm optimization module. The system is used for carrying out real-time image acquisition on each key station in the circuit board production process by adopting a high-resolution industrial camera. Through high-definition image acquisition, multispectral imaging, three-dimensional scanning and measurement, comprehensive data acquisition in the circuit board production process is realized, the problem of limited data acquisition range in the prior art is solved, accurate detection and classification of circuit board defects are realized by applying an intelligent analysis algorithm, and the production efficiency is improved. The accuracy and efficiency of defect detection are improved, automatic adjustment and optimization of algorithm parameters are achieved through the self-adaptive algorithm optimization module, it is ensured that the algorithm can continuously adapt to changes of the production environment, and the stability and reliability of the system are improved.

The invention relates to the technical field of intelligent forestry management, and particularly discloses a forest farm intelligent comprehensive management and control system, which comprises a space-air-ground collaborative sensing module, a fire danger dynamic modeling module and an intelligent decision execution module. The method comprises the following steps: acquiring temperature field data through multispectral satellite remote sensing, constructing a high-precision terrain model through a laser radar, extracting vegetation features through hyperspectral imaging, extracting a thermal anomaly structure by adopting topological continuous coherence analysis and a Morse-Smal complex method, and establishing an adaptive fire danger model in combination with a quantum annealing optimizer. The system can generate a fire danger thermodynamic diagram in real time and divide risk levels, and drives the unmanned aerial vehicle to perform priority patrol and obstacle avoidance path planning. The problems of difficulty in pseudo hot area identification, low fire danger prediction precision, non-intelligent path planning and the like in a complex environment are solved, and the intelligent level of forest farm fire prevention and control is improved.

The concrete-filled steel tube arch bridge detection system and method based on multi-modal fusion and quantum optimization are used for bridge structure health monitoring. The system comprises a multi-modal data acquisition module, a quantum optimization data fusion module, a defect self-growth simulation damage prediction module, a low-power-consumption intelligent sensor network and a quantum safety remote monitoring module. The multi-modal data acquisition module is fused with an ultrasonic radar, a millimeter wave radar, a hyperspectral image, a LiDAR and a wireless sensor, and bridge surface and internal damage detection is realized; the data weight is dynamically adjusted by adopting a quantum optimization algorithm, and the fusion precision is improved; in combination with a graph neural network and deformable grid calculation, crack propagation and fatigue damage development are predicted, the power consumption of the sensor is reduced through an energy collection technology, and the safety of remote monitoring data is ensured by adopting a quantum safety remote monitoring module. Compared with the prior art, the method improves the detection precision, prediction capability and data safety, and is suitable for health monitoring of various bridges and infrastructures.

The invention belongs to the technical field of hydraulic engineering detection, and particularly provides a hydraulic metal structure surface coating defect detection method and a hydraulic metal structure surface coating defect detection device based on image processing. In a wet and dry alternating environment of a hydraulic metal structure, a multispectral imaging device is used for acquiring an image of a metal structure surface coating, and the acquired multispectral image is preprocessed; a complete and clear image to be detected is obtained; extracting features related to coating defects from the preprocessed to-be-detected image, wherein the features comprise spectral features, texture features and color features; and inputting the extracted features related to the coating defects into a pre-trained classification model, and classifying the coating defects through the classification model. According to the method and the device, the state of the coating can be comprehensively described, and the type and the degree of the coating defect can be accurately identified.

The invention relates to an emotion accompanying robot system based on multi-dimensional perception and an interaction method, and belongs to the technical field of intelligent robots. The system comprises a sensing module which captures micro-expression, voice, body temperature, touch and other multi-source data through a biological radar array, multispectral imaging and a touch sensing fabric; the decision-making module quantifies the emotion intensity by using an emotion state calculation engine, constructs a user personalized emotion file in combination with the dynamic knowledge graph, and generates a dynamic emotion graph; and the execution module realizes anthropomorphic emotion expression through bionic face driving, joint compliance control and thermal feedback. The interaction method comprises the steps of data capture, emotion file construction, dynamic graph generation, interactive execution and feedback adjustment, definition of an emotion intensity quantification formula, a graph edge weight model and the like. According to the method, physiological signals, environment situations and bionic expression are fused, multi-modal precise emotion perception and safe interaction are achieved, man-machine naturalness is improved, the method is suitable for elderly accompanying, psychological counseling and other scenes, and the emotion accompanying effect is enhanced.

An ophthalmic imaging system provides an automatic focus mechanism based on the difference of consecutive scan lines. The system also provides of user selection of a focus point within a fundus image. A neural network automatically identifies the optic nerve head in an FA or ICGA image, which may be used to determine fixation angle. The system also provides additional scan tables for multiple imaging modalities to accommodate photophobia patients and multi-spectrum imaging options.

The invention relates to a polarization spectrum imaging device and a polarization spectrum reconstruction method, in particular to a miniature high-energy-efficiency diffraction polarization spectrum imaging device and a polarization spectrum reconstruction method thereof, and solves the technical problems that an existing polarization spectrum imaging device is low in energy utilization rate and regulation and control precision and large in size. According to the miniature high-energy-efficiency diffraction polarization spectrum imaging device provided by the invention, the diffraction spectrum lens module, the electronic control rheological lens module and the polarization module are directly and compactly integrated by utilizing the macro imaging module, so that the space coupling distance can be reduced, and the imaging quality can be effectively improved; the diffraction spectrum lens module realizes time-sharing spectrum regulation and control through the electronic control rheological lens module, and the polarization module performs area array space division polarization regulation and control, so that 0-degree, 45-degree, 90-degree and 135-degree polarization information under different spectrum channels can be directly obtained, the absorption effect of traditional light filtering on spectrums is avoided, the energy utilization rate is improved, and the energy consumption is reduced. And the number of polarization regions can be reduced, and the regulation and control precision is improved.

According to the BIM-based construction process monitoring and early warning system provided by the invention, the data acquisition dimension and precision are remarkably improved through multi-source sensing fusion of millimeter-wave radar, multispectral imaging and voiceprint recognition; feature vector voxel units carrying material characteristics and process constraints are adopted, so that risk early warning has space-time relevance and process interpretability; through a composite risk calculation model containing environmental interference correction and time-varying gradient, the problem that a traditional threshold value method is poor in adaptability to complex working conditions is solved; the holographic early warning mechanism realizes upgrading from sound-light alarm to touch sense-three-dimensional projection cooperative interaction; the double-circulation self-optimization system not only guarantees real-time control, but also realizes block chain evidence storage of empirical data, and finally forms a construction monitoring closed-loop system with space-time perception, intelligent decision, accurate early warning and sustainable evolution capabilities.

A pharmaceutical hyperspectral reconstruction method based on a coded aperture snapshot spectral imaging (CASSI) system includes: collecting and processing original pharmaceutical hyperspectral images to obtain augmented pharmaceutical hyperspectral images; performing simulated spatial encoding on the augmented pharmaceutical hyperspectral images to obtain encoded measurement images; performing spectral inverse shift on the encoded measurement images, then performing inverse encoding to obtain inversely encoded three-dimensional hyperspectral images, using the augmented pharmaceutical hyperspectral images as target images, and constructing a training set and a testing set according to the inversely encoded three-dimensional hyperspectral images and the target images; constructing a deep symmetric neural reconstruction network, and training and testing the deep symmetric neural reconstruction network; and deploying a tested deep symmetric neural reconstruction network onto the CASSI system, real-time collecting pharmaceutical measurement images using the snapshot coded imaging system, and performing computational reconstruction on the pharmaceutical measurement images to obtain reconstructed three-dimensional hyperspectral images.

The invention discloses a barn pest monitoring and early warning method, system and device based on big data and a medium, particularly relates to the technical field of pest monitoring, and is used for solving the problem that the distribution trend of newly appearing pests in a barn cannot be accurately monitored in the prior art. The method comprises the following steps: dividing a granary into a plurality of monitoring areas, arranging an independent spectrum sensor network, carrying out real-time monitoring based on a spectrum analysis technology, carrying out contrastive analysis with a granary pest library, rapidly identifying new pests, and after the new pests are monitored, generating a distribution trend chart in real time through a spectrum imaging technology, according to the method, a trend chart is obtained, whether early warning needs to be sent out is judged according to the trend chart, and the data quality can be mastered more comprehensively by evaluating the spectral distortion hidden danger and randomness of spectral data in a high-risk monitoring area, so that the accuracy evaluation of the distribution trend chart of newly-emerged pests is improved, and the reliability and accuracy of a granary pest monitoring and early warning system are remarkably improved.

The invention relates to the technical field of optical detection and image processing, in particular to a mobile phone lens window glass defect detection method. Comprising the following steps: acquiring a high-resolution image of a window glass sample by imaging equipment by adopting a multispectral imaging technology; inputting the acquired image into a pre-trained deep learning model for preprocessing; determining whether defects exist or not according to an output result of the deep learning model, marking the positions and types of the defects, and generating a detection report containing defect geometric parameters; if the defect exists, the severity of the defect is judged according to preset standards and geometric parameters of the defect, and the standards comprise the size, the shape, the depth and the position of the defect; and the detection result and the image data are stored in a cloud database to support subsequent quality analysis and process improvement. By combining the multispectral imaging technology, the deep learning model and the fuzzy logic system, the quality detection level of the window glass is comprehensively improved.

The invention relates to a leather material cutting robot and a cutting method thereof, and belongs to the technical field of material cutting, the leather material cutting robot comprises a human-computer interaction interface, a CAD / CAM system, a multi-mode visual perception unit, a central processing system, a dynamic compensation cutting unit and a quality closed-loop system, and the multi-mode visual perception unit comprises a high-resolution multispectral imaging module and a 3D scanning module. According to the method, the problems that a traditional leather material cutting robot visual recognition system cannot recognize leather thickness changes, material waste is easily caused by subcutaneous defects, natural curling deformation of leather is difficult to adapt, and the edge precision error is large are solved, the rejection rate can be further reduced through multispectral and 3D defect recognition, and the production efficiency is improved. Three-dimensional dynamic compensation enables edge errors to be further reduced, and real-time force control further reduces the occurrence rate of crush damage.

The invention belongs to the field of image fusion, and particularly relates to a polarization spectrum image fusion method based on non-subsampled contourlet transformation. The method specifically comprises the following steps: S1, collecting polarization spectrum images through a polarization spectrum imaging system, and obtaining spectrum images and light intensity images I of four different linear polarization angles (0 degree, 45 degrees, 90 degrees and 135 degrees); and S2, corresponding Stokes parameters of the image are calculated, and corresponding Q, U and DoLP images are obtained. S3, performing non-subsampled contourlet transform decomposition on the Q image and the I image, averaging low-pass sub-bands, performing preliminary fusion on high-pass sub-bands through an amplitude maximum rule to obtain a polarization feature S, finally performing non-subsampled contourlet transform decomposition on the S image and the intensity image, and fusing the low-pass sub-bands obtained through decomposition by adopting an improved pulse coupling neural network method; and fusing the high-pass sub-bands by adopting a weighted fusion rule based on local mode spectrum characteristics.

The invention discloses a hyper-spectral imaging chip, device and system based on an adjustable and reconfigurable metasurface, the chip comprises a metasurface unit array, a metasurface reconstruction module, a metasurface adjustment module, a CMOS image sensor and a spectrum reconstruction and identification module, the metasurface unit array comprises a plurality of metasurface units arranged on a silicon layer; the metasurface reconstruction module is used for adjusting the number of metasurface units included in the reconfigurable metasurface unit array; the metasurface adjusting module is used for being connected with the silicon layer of the metasurface unit array through a wire and changing the spectral response of the metasurface unit by adjusting the voltage of the wire; the CMOS image sensor is used for detecting optical signals modulated by the reconfigurable metasurface unit array; and the spectrum reconstruction and identification module is used for performing hyper-spectrum reconstruction and imaging according to the optical signal detected by the CMOS image sensor. The system is adjustable in spectral response, and has ultrahigh spectral resolution, real-time dynamic monitoring capability and adaptive imaging capability.

The invention discloses a polarization spectrum modulation type multispectral stereo imaging method which solves the problem that in the prior art, an existing polarization spectrum imaging scheme does not achieve three-dimensional reconstruction of a target and achieves the purpose that the three-dimensional reconstruction of the target is not achieved under the condition that active illumination or special marks are not needed. Synchronous high-precision reconstruction of physical attributes and three-dimensional geometry of the surface of the target is realized; the method comprises the following steps: uniformly modulating polarization information and spectral information of incident light of a target through a three-way optical filter and a polaroid to obtain modulated incident light information; performing aliasing acquisition of a plurality of polarization spectrum components on the modulated incident light information through a multi-color detector combination to obtain aliasing light intensity; performing linear solution on the aliasing light intensity to obtain each polarization spectrum component vector, calculating the polarization degree and the polarization angle of each channel, and further deriving a surface zenith angle and an azimuth angle; reconstructing the three-dimensional morphology of each spectrum channel through normal gradient field frequency domain integration; and finally, fusing the multi-spectral three-dimensional data to obtain a multi-spectral three-dimensional imaging result.

The invention discloses a multi-element antibiotic rapid detection method based on high-throughput array spectrum and deep learning, and belongs to the technical field of water environment new pollutant detection. The method comprises the following steps of antibiotic spectrum sensing system building, combined chemical probe screening, high-throughput spectrum experiment, deep learning stoichiometric model development and antibiotic intelligent detection system development. According to the invention, a high-throughput combined array spectrum sensing technology is adopted, holographic spectrum imaging is taken as a core to obtain fingerprint image data, the experiment efficiency is greatly improved, a high-throughput experiment and a machine learning model are combined, the sampling range is expanded, more types of chemical mixture combinations can be covered, and the detection accuracy is improved. The deep learning model is good at extracting complex features from a large amount of data, fingerprint spectrums of different types of antibiotics can be accurately identified and distinguished, high sensitivity and high specificity can be kept even in a complex water environment, and the accuracy and response speed of pollutant detection in a complex chemical system are improved.