508 results about "Spectrum imaging" patented technology

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 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 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 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 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.

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 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 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 relates to the technical field of image detection, in particular to a boiler scale detection method and device based on a multi-modal image, and the method comprises the steps: carrying out the panoramic scanning of the inner wall of a boiler through a dual-spectrum imaging system at a detection temperature, and synchronously collecting a visible light image and an infrared thermal imaging image; based on the scale chromaticity difference value in the visible light image, scale types are judged, and a scale classification graph is generated; based on a standard infrared thermal imaging image, obtained at the detection temperature, of the scale-free boiler, the infrared thermal imaging image is converted into a temperature anomaly distribution diagram with temperature anomaly blocks; inputting the temperature anomaly distribution diagram into a deep neural network to output a scale thickness distribution diagram, and aligning the scale classification diagram with the scale thickness distribution diagram through a spatial registration technology to ensure that pixels at the same spatial position contain scale types and thickness values at the same time; and generating a scale feature fusion graph of scale types presenting different color areas and corresponding thicknesses, thereby realizing accurate detection and quantification of the boiler scale.

The invention relates to the technical field of shoe body detection, in particular to a shoe body quality detection method based on image visual analysis, which comprises the following steps: synchronously acquiring three-dimensional contour and surface spectral information of a to-be-detected shoe body through three-dimensional scanning and spectral imaging equipment to obtain original point cloud spectral data; performing registration and fusion processing on the original point cloud spectral data to construct a registered hyperspectral three-dimensional grid model containing space coordinates and hyperspectral reflectivity values; and calculating a normal vector field and a curvature distribution diagram of the surface based on the hyperspectral three-dimensional grid model so as to quantify geometric features of the surface of the shoe body. According to the invention, through registration and fusion processing, surface spectral information collected by a spectral imaging device is reversely projected and interpolated to each space coordinate vertex of an initial three-dimensional point cloud constructed by a three-dimensional scanning device, so that a registered hyperspectral three-dimensional grid model is constructed, and a surface chemical characteristic spectrum is endowed with three-dimensional space geometric coordinates. And the detection capability is improved.

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

The invention discloses a food waste detection method and system based on image processing, belongs to the field of image recognition, and aims to realize efficient and automatic recognition and quantification of kitchen waste. According to the method, residual food images are collected at multiple periods and multiple angles in a kitchen garbage can or a dinner plate recovery area through high-resolution and multi-spectral imaging equipment, preprocessing is carried out in combination with an improved Retinex algorithm and a space self-adaptive denoising technology, and the image quality is improved. Afterwards, fine segmentation of a food area is achieved through a multi-scale super-pixel segmentation and graph segmentation algorithm, and multi-category intelligent recognition is conducted on remaining food through a recognition network fused with multi-modal features. The system further combines stereoscopic vision and Monte Carlo sampling to dynamically and accurately count the volume or weight of various residual foods. The method has the advantages of high adaptability and accurate statistical result, and can provide data support for catering management, resource recovery, nutrition evaluation and the like.

The invention relates to a multispectral imaging PCBA (Printed Circuit Board Assembly) welding spot three-dimensional defect detection method, which comprises the following steps of: acquiring image data of a welding spot under different wave bands through multispectral imaging equipment, and generating a preliminary image data set; preprocessing the image data set, extracting strength features and texture features of the welding spots, and constructing a response feature set; analyzing a feature change trend according to the response feature set, and screening a key waveband combination; multi-dimensional feature vectors are extracted through the key wave band combination, and a feature vector set representing welding spot characteristics is generated; constructing a classification detection feature matrix according to the feature vector set, and generating feature distribution data for distinguishing welding spot categories; classifying the feature distribution data through a classification algorithm to generate a classification label set of welding spot types; and performing optimization processing on the classification label set, generating a simplified feature matrix, completing fine classification, and outputting a final welding spot classification result.

The invention relates to the technical field of unmanned aerial vehicle measurement, in particular to an unmanned aerial vehicle measurement system based on low-altitude meteorological high-precision positioning, which comprises a low-altitude meteorological sensing unit, a positioning fusion calculation unit, a cooperative calibration control unit and a self-adaptive measurement unit. The positioning fusion calculation unit uses an atmospheric delay correction model and fuzzy adaptive Kalman filtering to compensate an atmospheric delay error and adjust a positioning noise matrix to output accurate track coordinates, and cooperates with the calibration control unit to establish a relation between a humidity gradient and a positioning height error, obtain an optimal compensation coefficient and convert the optimal compensation coefficient into a flight control instruction, and finally control the flight control instruction. And the adaptive measurement unit controls the multispectral imager to switch wavebands and adjust the scanning angle of the laser radar according to the flight path and the meteorological parameters, so that the aerosol vertical profile synchronous inversion is realized, and the positioning accuracy of the unmanned aerial vehicle and the reliability of measurement data under the complex meteorological condition are improved.

The invention relates to the technical field of hyperspectral imaging, in particular to a skin cosmetic residue grade dynamic monitoring method based on a hyperspectral imaging technology, which comprises the following steps: step 1, acquiring skin surface hyperspectral image data and preprocessing to eliminate environmental interference and noise; 2, compensating and correcting the preprocessed hyperspectral data based on the established skin heterogeneity correction model; 3, extracting multi-dimensional spectral features in the corrected data, and vectorizing correlation model input to predict the residual level; and step 4, outputting a dynamic monitoring result of the cosmetic residue grade for guiding safe use evaluation, and compared with the traditional and prior art, the method solves the problems of long time consumption, high cost, strong subjectivity, low detection precision and the like. By optimizing instrument channels, compensating spectral errors and constructing a quantitative model, rapid nondestructive testing is achieved, the detection accuracy and real-time performance are improved, and reliable technical support is provided for cosmetic safety assessment.

Aiming at the problems of low efficiency, strong subjectivity, insufficient modeling ability, complex hyperspectral data noise, weak waxiness spectrum difference and the like of a traditional rice grain waxiness discrimination method, the invention provides a hyperspectral rice grain waxiness discrimination method based on multi-branch collaborative modeling. The method comprises the following steps: S1, acquiring glutinous and non-glutinous rice grain images by using a hyperspectral imaging system to obtain spectral data; s2, preprocessing spectral data through a combined method of SG smoothing, an asymmetric weighted penalty least square method and multivariate scatter correction to reduce noise and interference; s3, constructing a multi-branch modeling architecture which comprises a CNN local feature extraction module, an SRU spectrum sequence modeling module and a HorNet global high-order modeling module, and outputting a discrimination result through a classifier after multi-branch features are subjected to fusion and pooling attention weighting; and S4, evaluating the performance. According to the method, high-precision lossless discrimination of the waxiness is realized, and a technical support is provided for germplasm screening and quality evaluation in rice breeding.

The invention relates to the technical field of aerial target detection, in particular to an aerial target detection method based on image recognition. Comprising the steps of image acquisition and preprocessing, target detection, target classification, target tracking, data storage, alarm and feedback and the like. According to the invention, technologies of deep learning, multispectral imaging and the like are combined, the defects of a traditional detection technology are effectively overcome, and the detection precision of low-altitude, low-speed and small targets is improved; the requirement of real-time monitoring is met while the detection precision is ensured; effective detection can be carried out at night and in a complex environment, and the adaptability of the system under different illumination conditions is improved; a target tracking algorithm is optimized, continuous and stable tracking of multiple targets can be realized, and target motion trails can be accurately output; the system integrates detection, identification, storage and alarm, and is suitable for various application scenes such as airport security, important facility protection, city management and the like.

The invention discloses a coarse cereal aflatoxin detection method combining hyperspectral imaging and deep learning, and belongs to the field of agricultural product quality safety detection. The method comprises the following steps: firstly, acquiring hyperspectral imaging data of coarse cereal grains, extracting space and spectral information, forming basic data, and performing preprocessing, space-time registration and feature alignment; building a hyperspectral space-spectrum double-branch feature fusion convolutional neural network deep learning model, and importing the processed data to complete iterative training; and finally, performing same-standard data acquisition and processing on to-be-detected coarse cereals, importing the trained model, and realizing aflatoxin detection and pollution area positioning through pixel-level analysis. According to the method, deep fusion of space and spectral features is realized, the detection accuracy and generalization ability are effectively improved, the detection result is stable, reliable and traceable, and the fine detection requirement of aflatoxin in coarse cereals is met.

The invention provides an AI-based photovoltaic module construction quality tracing system and method, and the system comprises an image collection module which obtains visible light, infrared and depth information, and forms original image data; the preprocessing module is used for carrying out geometric distortion correction, multispectral registration and noise suppression and outputting standardized image data; the feature extraction module is used for performing multi-scale feature fusion on the standardized image data to generate a multi-dimensional feature vector; the defect detection module is used for analyzing the multi-dimensional feature vector, detecting the defect type of the photovoltaic module and outputting a defect position coordinate; the construction parameter matching module is used for retrieving a construction process database according to the defect position coordinates and outputting a construction quality evaluation result; and the quality tracing module is used for performing hash coding, generating a tracing code and storing the tracing code to the block chain. According to the invention, the AI intelligent quality inspection robot is introduced and multispectral imaging is combined, so that real-time quality inspection of the photovoltaic module in the construction process is realized.

The invention discloses a meteorological satellite multispectral imaging observation-based subtropical high voltage parameter extraction method, which comprises the following steps of: S1, projecting target data into a static meteorological satellite observation view field to calculate a subtropical high voltage influence area, and performing space-time matching on the subtropical high voltage influence area, historical observation data and sea-land template data; s2, randomly sampling the space-time matching data set, selecting time or space discrete points, and dividing a plurality of sampling areas according to different sky states; s3, sample sets of the multiple sampling areas are divided into a training set and a test set, an XGBoost classification decision tree is adopted to establish an identification inversion model, and Bayesian iterative optimization is adopted to establish an identification optimal model; s4, inputting real-time static observation data, and identifying the subtropical high-voltage influence area by using the model to obtain the influence probability of each point; s5, dividing according to a probability threshold value; s6, performing projection conversion on a division result, and applying Canny edge detection on a specified range to extract a subtropical high-voltage outer boundary; and S7, calculating the area index or the strength index of the subtropical high pressure.

The invention relates to the technical field of material detection, in particular to a cross-linked polyethylene insulation material impurity full-detection device and system for a high-voltage cable and application, the detection device carries out detection based on single-row continuous flowing insulation material particles, and the detection device comprises a near infrared spectrum imaging unit, a visible light imaging unit and an image processor, and identifying black spot impurities and heterochromatic particles. The full-detection system provides a detection channel for installing full-detection equipment and comprises a current limiting pipe body and a camera obscura structure, and reflection spectrum and surface color information are collected in the camera obscura structure; the full-inspection system further comprises a buffer stock bin, a finished product stock bin, a pneumatic sorting module, a cyclone separator, a cooling fluidized bed and a metal separator. Impurity identification reliability is guaranteed through double optical detection, full-process online detection of insulation material impurities can be achieved, limitation of traditional sampling detection is broken through, full-detection coverage of insulation material particles is achieved, the impurity missing detection risk is eliminated, and the strict requirement of an ultrahigh-voltage cable for insulation material cleanliness is fundamentally met.

The invention relates to the technical field of industrial vision multispectral imaging detection, in particular to a defect detection method and system based on multispectral fusion imaging, and the method comprises the steps: synchronously collecting a multi-channel spectral image of the surface of an object to be detected, and generating an enhanced feature map fusing multispectral information; identifying and preliminarily marking a suspected defect region by applying a region segmentation algorithm based on anomaly detection; extracting a multi-dimensional spectral response curve of each region in each original spectral channel, and constructing a spectral feature vector; matching the vector with a standard template of a pre-established defect spectral feature database, and classifying and confirming defect types; and carrying out contour refined analysis, calculating the geometric dimension and position of the defect, and generating a structured detection report. According to the method, the sensitivity of defect detection and the classification accuracy are improved through accurate matching of multispectral fusion and spectral features, and automatic and high-precision defect identification is realized.

The invention provides an image processing method based on underwater robot formation operation, and the method comprises the steps: determining the position and observation range of an observation target object according to the task demands of the underwater robot formation operation, and deploying underwater robots around the target object to form an annular observation network; the method comprises the following steps: dynamically adjusting the pose of a robot and camera parameters in an image acquisition process, acquiring a plurality of visual angles and a plurality of spectral images so as to obtain omnibearing target object image data, and preprocessing the image, including denoising, enhancing and correcting; the definition, the contrast ratio and the integrity of an image are analyzed, the collected image data are subjected to quality evaluation, the motion trail and the sampling strategy of the robot are adjusted in a self-adaptive mode, a dead angle area and a shielding part are subjected to emphasized observation, and supplementary image data are obtained for the dead angle area and the shielding part.