1200 results about "Thermal infrared" patented technology

The invention relates to the technical field of LED display, solves the problem that the existing LED display defect detection and parameter adjustment technology is lack of multi-modal information fusion and intelligent process control capability and is difficult to meet the quality control requirement of a high-precision display product, and provides an LED display defect prediction and process adjustment method and system based on multi-modal fusion. The method comprises the following steps: performing multi-modal data fusion processing on optical image data, electrical test data and thermal infrared imaging data corresponding to a to-be-tested LED display screen to obtain fused data; inputting the fused data into a pre-trained defect recognition model to obtain a defect recognition result; according to a process parameter adjustment strategy corresponding to the defect identification result, adjusting the original process parameter to obtain a target process parameter; and according to the target process parameters, process flow correction processing is carried out, and a qualified LED display screen is produced. According to the method, the defect identification precision is improved, and the quality control requirement of high-precision LED display screen production is met.

The invention discloses a multi-modal visual fusion complex scene small target detection tracking method and system, and relates to the technical field of unmanned aerial vehicle target tracking, and the method comprises the steps: employing a visible light camera, an infrared thermal imager and a laser radar sensor which are carried on an unmanned aerial vehicle platform, and synchronously collecting RGB images, thermal infrared images and point cloud data; the consistency of the multi-modal data is ensured through data preprocessing and space-time alignment; constructing a lightweight double-branch network to extract multi-scale features, generating a fusion feature map by adopting adaptive weighted fusion, and generating depth information by utilizing point cloud to assist in scale estimation; a small target detection head is designed based on the fusion feature map, and precise detection is realized in combination with a feature pyramid network, adaptive scale prediction and a context awareness suppression mechanism; furthermore, through multi-mode cooperative tracking, including target association, spatio-temporal context modeling, trajectory prediction and a re-detection mechanism, tracking continuity is ensured.

The invention relates to the technical field of additive manufacturing, and particularly discloses a real-time defect detection and in-situ repair method and device in the additive manufacturing process, and the method comprises the steps that a multi-mode sensing system is integrated on additive manufacturing equipment, the multi-mode sensing system comprises a high-resolution CCD camera, a laser scanner and a thermal infrared imager, and data acquisition is synchronized; a hierarchical scanning strategy is adopted, two-dimensional surface images, three-dimensional point cloud data and thermal field distribution information are collected before and after each layer is machined, and the data synchronization precision is smaller than 0.1 ms; according to the invention, a multi-modal sensing system and an advanced algorithm are integrated, so that real-time detection and accurate repair of defects are successfully realized; a high-resolution CCD camera, a laser scanner, a thermal infrared imager and other sensors are adopted, a cross-scale RANSAC algorithm and a depth feature fusion network are combined, surface and internal defects are accurately detected, and laser remelting parameters are adjusted in real time for in-situ repair.

The invention discloses an egg surface microcrack detection system and method based on image analysis, and relates to the field of surface defect nondestructive detection.The method comprises the steps that a process identification interface module obtains a processing process identifier of an egg in real time; the multi-angle annular light source module is provided with a multi-band annular light source array comprising visible light and near-infrared LED lamp beads which are independently controlled, a polaroid group and a beam splitter prism are integrated, and visible light polarization images and near-infrared polarization images on the surfaces of the eggs are synchronously collected; the thermal excitation enhancement unit obtains a thermal infrared image; the process filtering module extracts the axial elongation of the mechanical stress microcrack after graded transportation, and calculates the mesh fractal dimension of the thermal stress microcrack after UV disinfection; the multi-modal feature fusion module generates a dual-channel crack probability graph; and the dynamic feedback control module outputs a micro-crack risk grade and adjusts the rotating speed of the objective table and the light source intensity. The method has the advantages that accurate judgment of mechanical and thermal stress cracks is realized, and curved surface reflection interference is broken through.

The invention discloses a Bayesian learning and piezoelectric ceramic driving numerical control machine tool thermal error compensation system and method. According to the system, a distributed temperature sensor array is arranged in heat sensitive areas such as a machine tool spindle, a ball screw, a guide rail and a bearing seat, whole-field temperature information is collected in combination with a thermal infrared imager, and multi-source thermal field sensing is achieved; meanwhile, a laser interferometer and a capacitive displacement sensor are used for constructing a dynamic pose monitoring network. The intelligent decision-making unit integrates a Bayesian online learning engine, fuses a physical model and a data driving model, dynamically predicts a thermal error and generates a compensation instruction. And the piezoelectric execution mechanism carries out nonlinear pre-compensation on a driving signal through a three-section Prantl-Ishlinskii hysteresis inverse model according to the instruction, so that high-precision pose adjustment is realized. The thermal error compensation precision is remarkably improved, the adaptability of the system to complex working conditions is enhanced, the service life of equipment is prolonged, and the method is suitable for various numerical control machine tools.

The invention relates to the field of industrial automation and intelligent control, and discloses an intelligent temperature automatic control system for a digital glass mold. The method comprises the following steps: acquiring mold surface temperature and heat flow data through a thermocouple array and a thermal infrared imager, and constructing heat flux and a disturbance coefficient; forming state vectors are established in combination with the forming process parameters and the heat flow distribution, and forming stability levels are generated through support vector regression; building a heat balance deviation model based on the thermophysical parameters and the environment variables, and predicting a temperature trend; a temperature control instruction is generated through the fuzzy neural controller, and temperature dynamic closed-loop control is achieved. The system improves the temperature control precision and thermal field balance of the glass mold, and is suitable for intelligent temperature control management in the glass container forming process.

The invention discloses a tunnel convergence deformation monitoring method, and particularly relates to the technical field of tunnel monitoring, through integrating equipment such as a thermal infrared imager, a visible light camera, a distributed optical fiber sensor and a laser radar, a self-adaptive inspection system is constructed, and high-precision appearance and temperature monitoring is realized by adopting an improved ORB algorithm and a temperature field calibration technology. Performing multi-source data fusion through space-time registration and wavelet packet decomposition in combination with optical fiber strain data; and predicting a deformation trend based on an LSTM neural network, and establishing a three-level early warning mechanism. According to the tunnel convergence deformation monitoring method disclosed by the invention, the automation and intelligence level of tunnel structure safety monitoring is remarkably improved, 'perception-analysis-decision 'closed-loop management of the tunnel health state is realized, compared with a traditional method, the efficiency is remarkably improved, meanwhile, the maintenance cost can be greatly reduced, and the tunnel operation safety and economy are remarkably improved.

The invention relates to a multi-mode tumble detection method, device and equipment, and belongs to the technical field of intelligent nursing. The method comprises the steps of obtaining fall detection data; performing motion point extraction on the radar point cloud data based on time sequence analysis, screening out motion points whose positions change in continuous time, and generating motion point cloud data; projecting the moving point cloud data to a coordinate system corresponding to the thermal infrared imaging data to generate a radar point cloud projection matrix; combining the radar point cloud projection matrix with the thermal infrared imaging data, calculating the three-dimensional coordinates of the target, and generating target height change data; and when the target height change data meets a preset tumble condition, inputting the constructed time sequence multi-modal fusion data into a deep learning behavior recognition module, and outputting a classification result of tumble behaviors. According to the method, the radar sensor and the thermal infrared imaging data are fused, and time sequence analysis and deep learning behavior recognition are combined, so that the privacy of the user is protected while high-precision fall detection is realized.

The invention relates to the technical field of multi-modal visual tracking, and discloses a modal sharing information layered unwrapping fusion network for RGB-T target tracking, which comprises a double-flow feature extraction module which adopts a parameter sharing ResNet-50 network and is used for respectively extracting features of an RGB image and a thermal infrared image; the cross-modal attention module is connected with the double-flow feature extraction module and is used for realizing feature interaction enhancement of RGB (Red, Green, Blue) and a thermal mode through a bidirectional attention mechanism; and the layered unwrapping mining module is connected with the cross-modal attention module and is used for mining inter-modal deep complementary information through multi-level residual calculation. By introducing a lightweight attention mechanism and a modal alignment strategy, complementary information between modals is mined layer by layer and dynamic fusion is realized, so that the utilization efficiency of modal residual information is remarkably improved, and the stability and robustness of the system in a complex environment are enhanced.

The invention discloses a multi-source image data fusion-based fine decoration air crack seepage quality problem detection method and device, and solves the technical problem of how to carry out comprehensive, high-precision and intelligent detection on the fine decoration surface air crack seepage quality problem. Comprising the following steps: 1) receiving visible light image data, thermal infrared image data and three-dimensional laser point cloud data of a target refined decoration surface acquired from visible light acquisition equipment, thermal infrared imaging equipment and three-dimensional laser scanning equipment respectively; 2) carrying out feature extraction on the visible light image data, the thermal infrared image data and the three-dimensional laser point cloud data; 3) obtaining point cloud projection coordinates, and then performing association fusion on the first two-dimensional feature and the second two-dimensional feature with corresponding three-dimensional features to generate fusion point cloud data containing multi-source features; and 4) based on the fused point cloud data, carrying out defect classification identification and spatial positioning to obtain a detection result. And comprehensive and high-precision detection of the quality problem of air crack seepage of the finely-decorated surface is realized.

The invention relates to a crop drought degree prediction method based on unmanned aerial vehicle remote sensing monitoring. The method comprises the following steps: S1, data collection: collecting a multispectral image and a thermal infrared image of a farmland in real time through a remote sensing sensor; s2, image preprocessing: carrying out preprocessing operation on the collected multispectral image and thermal infrared image; s3, class specific feature selection: dividing the farmland into different classes according to the types, growth stages and expected drought degree grades of the crops, decomposing a multi-class classification problem into a plurality of dichotomy problems, and constructing a deep learning model for feature learning and importance evaluation for each dichotomy problem to obtain a class specific feature selection result; a class specific feature set for each class is formed, and class specific features for different classes are fused to form a comprehensive feature set; s4, model construction and training: constructing a drought degree prediction model according to the comprehensive feature set; and S5, drought degree prediction: realizing real-time monitoring and prediction of drought according to the real-time data and the prediction model.

The invention relates to a water and fertilizer management control method based on image data processing, and the method comprises the steps: collecting a visible light image, a multispectral image and a thermal infrared image of a crop, carrying out the feature extraction, calculating the plant morphological parameters, vegetation indexes and vegetation canopy temperature distribution of the crop, creating a three-dimensional physiological feature matrix, creating an image segmentation network model, and carrying out the feature extraction. Inputting the visible light image, the multispectral image, the thermal infrared image and the three-dimensional physiological feature matrix into an image segmentation network model to segment plant organs, quantifying organ-level phenotypic parameters, and establishing a water and fertilizer decision model based on reinforcement learning; and taking real-time data of the three-dimensional physiological feature matrix, the organ-level phenotypic parameters and the soil specified parameters as state input, taking a water and fertilizer proportioning scheme as an action space, and implementing a water and fertilizer management scheme through a dynamic optimization control strategy. The technical problems that data integration of a single sensor is insufficient, phenotype analysis precision is limited, and adaptability of a decision model is poor in a traditional water and fertilizer regulation scheme are solved.

The invention discloses an intelligent AI detection method based on an infrared thermogram and automobile diagnosis, according to the technical scheme, the fireproof safety detection efficiency of an electric automobile is remarkably improved through multi-modal data fusion and intelligent analysis, and a multi-dimensional data basis is constructed through synchronous acquisition of dual-band infrared and vehicle operation parameters; secondly, a dynamic correlation model realizes accurate triggering of abnormal current on heat map enhancement, partition gradient processing is combined with dynamic threshold adjustment to optimize detection sensitivity for thermal characteristics of different parts, three-dimensional thermal field reconstruction breaks through surface detection limitation, internal potential overheat points can be identified, and the detection sensitivity is improved; and finally, closed-loop diagnosis is formed through case library intelligent matching, so that the fault positioning precision reaches a component level, and the whole scheme is coordinated through a multi-level technology, so that the problems of low infrared thermal image contrast and relatively poor detail distinguishing capability due to the fact that electric vehicle fireproof safety detection is carried out only by depending on thermal infrared images in the prior art are solved.

The invention provides a multi-modal crowd counting method and system, and the method comprises the steps: obtaining an image, and extracting the image features of the image, the image comprising a visible light modal image and a thermal infrared modal image; image features of the visible light modal image and the thermal infrared modal image are subjected to attention through spatial frequency in a spatial frequency guiding module layer by layer to generate a target attention map; fusing the image features of the visible light modal image and the thermal infrared modal image of each level and the target attention map through an adaptive dynamic fusion module to obtain each multi-modal fusion feature; according to the multi-modal crowd counting method, a prediction density map is generated from each multi-modal fusion feature through a multi-scale progressive fusion module, the multi-scale progressive fusion module is composed of cavity space pyramid pooling and Swin Transform Block, and through the modules designed in the steps, the problems that multi-modal crowd counting errors are large and counting precision is low can be effectively solved.

The invention relates to the technical field of glacier information processing, in particular to a glacier surface moraine identification method based on multi-source remote sensing parameters, which comprises the following steps: performing radiation-atmosphere joint correction and geometric-reprojection on optical, thermal infrared, synthetic aperture radar and digital elevation data to generate physical consistency data; deducing the surface temperature under the driving of short-wave albedo, downward radiation and turbulent flux by using a cross-modal visual conversion network and a symmetric neural operator, and obtaining a thickness-existence degree posterior through a normalized flow; an uncertainty field is constructed according to information entropy and variance, a representative pixel unmanned aerial vehicle radar sampling and value measurement backflow fine tuning model is selected by means of quantum annealing, and then a spatial continuous thickness field is output through anisotropic diagram diffusion. According to the method, the future thickness is integrated according to mass conservation in combination with day-by-day weather forecast, and the future thickness is coupled with the existence degree index to form a burst risk grid, so that high-precision and iterable moraine dam early warning is realized.

The invention discloses a power distribution equipment on-line monitoring system based on multi-modal data fusion, and relates to the field of power distribution equipment management, and the system comprises a sensing module which is used for collecting an electrical signal, a thermal infrared signal, a mechanical vibration signal and an acoustic signal generated in the operation process of power distribution equipment through a multi-type sensing channel, converting the collected various signals into processable equipment state original data to form an equipment operation state original data set; according to the invention, through accurate acquisition of multi-dimensional signals, combination of space mapping and time delay compensation, data quality is optimized, the reliability of original information is ensured, key features are extracted according to working conditions, subtle state changes are captured by means of temperature field analysis and a variable-resolution spectrum technology, the comprehensiveness, accuracy and response timeliness of equipment operation monitoring are effectively improved, and the real-time performance of equipment operation monitoring is improved. Misjudgment and missed judgment are reduced, and fault risks are avoided in advance.

The invention discloses a soil water and salt dynamic monitoring and quality evaluation method based on multispectral remote sensing, and relates to the field of geological monitoring, and the method comprises the steps: carrying out the scattering deviation correction and roughness correction of an observation spectrum through a satellite remote sensing image, an unmanned aerial vehicle multispectral image, a ground sample and meteorological driving data; the soil intrinsic reflectivity, the vegetation coverage component and the salt crusting component are obtained; a water-salt inversion model is established by combining thermal infrared and red edge wave band information, and multi-temporal soil volumetric moisture content and surface salinity index are inverted; by introducing meteorological conditions and vegetation dynamic characteristics, a water-salt coupling partial differential equation and a graph space-time constraint model are constructed, and continuous space-time distribution of soil water content and salinity is obtained; extracting salinity, moisture, vegetation response and soil health indexes, establishing a soil quality comprehensive evaluation model, and outputting high-risk plaques and a treatment priority sequence. The method realizes dynamic monitoring and quality grading evaluation of soil water and salt, and is suitable for saline-alkali soil treatment and ecological restoration.

The invention is suitable for the technical field of earth surface monitoring, and provides an earth surface deformation monitoring method and system based on a time sequence InSAR, and the method comprises the following steps: carrying out the SAR image screening and interferogram generation, and collecting corresponding meteorological data and thermal infrared data; performing space-time adaptive atmospheric phase correction, taking the air pressure vertical gradient and the temperature anomaly as driving factors of atmospheric delay, and separating an atmospheric phase through a space-time weighted model; dynamic deformation modeling is carried out, and deformation is decomposed into linear deformation and nonlinear deformation; the method comprises the following steps: taking a mining area road network as a geometric constraint, unwrapping a coherent region by adopting a minimum cost flow algorithm, converting an unwrapping phase into sight-line-direction deformation, and calculating horizontal and vertical deformation components in combination with InSAR sight-line-direction deformation and a digital elevation model. The method adapts to a complex deformation mechanism of a mining area by capturing linear and nonlinear deformation. Through sight line deformation and DEM geometric projection, vertical and horizontal deformation separation is realized, and the deformation direction is determined.

Disclosed in the present invention are a multi-source aviation remote sensing data acquisition apparatus, system and control method, which are applied to the technical field of aerial survey remote sensing. The apparatus comprises: a flight platform and an airborne device disposed on the flight platform. The airborne device comprises: a multi-sensor ultra-compact integrated device, an airborne integrated synchronization control device, and a GNSS receiving antenna. The multi-sensor ultra-compact integrated device and the airborne integrated synchronization control device are connected by means of cables, and the GNSS receiving antenna is disposed on an upper end face of the flight platform. The multi-sensor ultra-compact integrated device is used for acquiring multi-source remote sensing data; and the airborne integrated synchronization control device is used for controlling the operation of the multi-sensor ultra-compact integrated device. The present invention ultra-compactly integrates a LiDAR, a hyperspectral camera, a thermal infrared camera, and a visible-light camera on the basis of a high-precision inertial navigation system, and achieves efficient synchronous acquisition of texture, spectral, temperature, and geometric information by means of using an integrated control subsystem.

The invention relates to the technical field of geological disaster detection, in particular to an unmanned aerial vehicle multi-sensor image fusion slope geological disaster detection system which comprises a data acquisition module, a manifold registration module, a feature fusion module, a weight optimization module, a fusion execution module, a disaster detection module and the like. RGB images, thermal infrared images and laser point cloud data of a slope are collected through an unmanned aerial vehicle, the surface of the slope is modeled as a Riemannian manifold, and high-precision space registration of heterogeneous data is achieved; constructing a feature manifold based on a manifold learning method, and extracting and fusing multi-scale features; evaluating the information amount of different areas by adopting a differential entropy theory, and generating a self-adaptive weight distribution diagram; performing weighted fusion on the registered multi-source data to generate a fused image; geological disaster features such as cracks, abnormal vegetation and water seepage points on the surface of the slope are recognized based on the fused image, and high-precision recognition and early warning of the geological disaster of the slope are achieved.

The invention discloses a distributed photovoltaic power station surveying method and system based on an unmanned aerial vehicle, and the method comprises the steps: dynamically planning an obstacle avoidance and supplementary shooting route, and collecting multi-modal data; visible light, thermal infrared and laser radar data are fused and analyzed, missing images are complemented in a generative mode, and defects are diagnosed in a combined mode; generating a dynamic shadow thermodynamic diagram by fusing the historical weather and the real-time cloud picture; and a report is automatically generated and AR interaction visualization is realized. The system correspondingly comprises a route planning module, a defect diagnosis module, a shadow prediction module and an analysis display module. Through the generative AI and spatio-temporal data fusion technology, the exploration efficiency and the defect recognition precision in a complex environment are remarkably improved, and intelligent operation and maintenance decision support of the whole life cycle of the photovoltaic power station is achieved.

The invention discloses a fire situation analysis method based on multi-dimensional data fusion, and particularly relates to the field of fire image analysis, and the method comprises the steps: analyzing the change of the direction retention rate between adjacent frames through extracting the main direction texture vectors of a building and a vegetation region, and recognizing object state change candidate segments; in the candidate area, combining main direction disturbance and image definition reduction to construct a spatial scoring graph, performing nonlinear amplification on the spatial scoring graph, and extracting a gradient increasing path to generate a structure damage main path set; then calculating a directional included angle between paths and a space coincidence rate, constructing a trend consistency aggregation channel graph, and extracting a spreading principal axis; and finally, superposing a temperature rise area in the thermal infrared image with a spreading principal axis, extracting a dual response area to generate a fire behavior boundary prediction layer, and realizing fire behavior spreading path prediction based on fire scene related image structure damage information analysis.

The invention discloses a target identification method for multi-sensor data fusion, particularly relates to the technical field of data fusion, and comprises a dynamic weight fusion module, a double-branch neural network and an abnormal sensing compensation system. Data are synchronously collected through a visible light camera, a thermal infrared imager and a millimeter wave radar, and a standardized feature map is generated through sensor specificity preprocessing; the dynamic weight fusion module generates an adaptive weight matrix based on the real-time confidence score and the environmental parameters, emphasizes infrared data when illumination suddenly changes, and improves intelligent distribution of radar weights in a rain and fog environment; the combined feature map after weight fusion is input into a double-branch neural network, a channel self-calibration branch suppresses interference noise, and a target category and coordinates are output after residual connection optimization; when the recognition confidence is insufficient, the abnormal compensation system starts visible light Wiener filtering restoration, generative adversarial network infrared compensation and radar multi-frame accumulation algorithms, and the system reliability is ensured when a single sensor fails.

The invention discloses a multi-modal fusion defect detection method and system, and belongs to the technical field of intelligent detection and machine vision, and the system comprises a visible light sensor, a thermal infrared sensor, a hyperspectral sensor, a multi-band light source trigger control system, a modal preprocessing and alignment module, a cross-modal feature fusion module, and a defect detection and output module. According to the invention, three sensors are used to construct a multi-modal visual perception system, and a multi-band light source triggers a control system to complete image acquisition; after multi-modal information is subjected to preprocessing and cross-modal alignment through the modal preprocessing and alignment module, multi-modal fusion is achieved through the cross-modal attention module and the multi-scale feature fusion pyramid structure, and finally defect recognition and output are conducted through the defect detection and output module. According to the method, the defect identification precision can be obviously improved, and the method has obvious advantages especially for low-contrast and early-stage hidden crack defects, and has good expandability and deployment suitability at the same time.

The invention discloses a negative pressure pipeline leakage point detection and targeted repair system and method, and the method achieves the confirmation of a pipeline leakage point through a detection end, and achieves the repair of the pipeline leakage point through a repair end. The detection end comprises a distributed optical fiber, an unmanned aerial vehicle and a control platform; through distributed optical fibers laid along a pipeline, pipeline vibration and temperature abnormity are monitored in real time, a leakage area is preliminarily positioned, then infrared scanning is performed on a suspicious area through an unmanned aerial vehicle carrying a high-precision thermal infrared imager, a temperature field distribution diagram is generated and uploaded to a control platform, and the control platform outputs leakage point coordinates through a data fusion algorithm; the repairing end comprises self-repairing gel and a magnetic control robot; the small cracks with the width smaller than 1 mm are externally sprayed with self-repairing gel, and laser curing is conducted after the cracks are sucked in through negative pressure; for large cracks with the width larger than or equal to 1 mm, the magnetic control robot is put into the pipeline, resin is sprayed to the inner wall of the pipeline, and ultraviolet curing is conducted. The system and the method can remarkably improve the maintenance efficiency of the negative pressure pipeline.

The invention relates to the technical field of leather production, and discloses a leather production line-oriented multispectral-thermal imaging composite defect detection device and a self-optimization method, and the leather production line-oriented multispectral-thermal imaging composite defect detection device comprises a visible light camera, an infrared thermal imager, an ultraviolet camera and a temperature sensor, the visible light camera, the thermal infrared imager, the ultraviolet camera and the temperature sensor are respectively connected with the computer through cables to obtain data, the computer is used for storing and processing the data and sending out an execution instruction, the visible light camera can clearly capture various defects on the leather surface, and the thermal infrared imager can penetrate through the leather surface to evaluate the uniformity of the leather. The ultraviolet camera can detect chemical components and defects on the surface of leather, the temperature sensor can accurately measure the surface temperature of the leather, composite defect detection is carried out on the leather through the equipment, errors are reduced, the detection effect is improved, and data are stored and evaluated by connecting a cable with a computer. And the accuracy and integrity of defect detection are improved.

The invention discloses a wildlife detection method fusing an unmanned aerial vehicle thermal infrared image and a visible light image, and belongs to the field of small target wildlife identification, and the method comprises the following steps: S1, obtaining a preprocessed TIR-RGB image pair set; s2, an FDM-YOLO double-source target detection model improved based on YOLOv81 is constructed, and the improved FDM-YOLO double-source target detection model is trained based on the preprocessed TIR-RGB image pair set obtained in the step S1; and S3, inputting an image acquired in real time into the improved FDM-YOLO double-source target detection model trained in the step S2, and outputting a wild animal detection result. By adopting the wild animal detection method fusing the thermal infrared image and the visible light image of the unmanned aerial vehicle, high-precision, real-time and robust detection of a small target of a wild animal in a complex field environment is realized by improving the FDM-YOLO model.

The invention discloses a projectile drop point detection method, system and equipment based on dual-light end-to-end fusion, and a medium. The method comprises the following steps: collecting visible light imaging data and thermal infrared imaging data of all target regions; preprocessing the data to obtain visible light and thermal infrared images of corresponding target region time-space registration; projecting a ground target area on the obtained image after space-time registration to a corresponding reference projection target surface; inputting the registered image into a multi-modal fusion detection model, and judging whether an explosion phenomenon is detected in the image frame by frame to obtain a bounding box corresponding to an explosion target in the image; according to the explosion bounding box, resolving and correcting the position of the drop point by adopting a drop point resolving algorithm, and determining the image coordinate of the drop point in the explosion area; and mapping the drop point to a reference projection target surface, obtaining position coordinate information of the drop point in the reference projection target surface, and recording and outputting relative position information of the drop point on the reference projection target surface. The detection precision is improved, and the target scoring task is completed.

The invention relates to the technical field of vehicle image recognition, and discloses a multi-modal data enhanced vehicle recognition method and system based on a generative adversarial network, and the method comprises the steps: collecting vehicle multi-modal data (a visible light image, a thermal infrared image and three-dimensional laser point cloud data), and constructing a vehicle multi-modal data set; performing preprocessing and feature alignment on the data set to obtain standardized multi-modal data; constructing a cross-modal generator based on an adaptive attention mechanism, learning inter-modal feature association through a dynamic weight distribution module, and generating vehicle feature fusion data; designing a dual discriminator structure consisting of a perception consistency discriminator and a semantic fidelity discriminator, and optimizing the generator by adopting an alternate adversarial training strategy; generating a supplementary data sample for the complex scene by using the optimized generator, and constructing an enhanced data set; and constructing a multi-mode cooperative vehicle identification model based on the enhanced data set, and realizing high-precision vehicle attribute identification.

The invention relates to an auxiliary inspection method, system and device for a box-type substation, in particular to the field of box-type substation monitoring, and the method achieves the efficient and accurate inspection of box-type substation equipment through the combination of a millimeter wave radar, an RGB-D camera, a thermal infrared imager, a vibration sensor, load current monitoring equipment and other sensors. By generating a three-dimensional topological graph and superposing sensor data, comprehensive state monitoring can be performed on equipment, electromagnetic intensity field and temperature field distribution of the equipment can be accurately predicted based on physical field distribution and a neural network model, in addition, by dynamically correcting a prediction result and calculating a fault risk index, the risk of equipment fault can be identified in real time, and the reliability of the equipment fault is improved. And the enhanced inspection instruction is triggered when the threshold value is exceeded, so that the stability and the safety of the equipment in the operation process are ensured, the intelligent degree and the fault early warning capability of inspection are effectively improved, and the maintenance efficiency of the transformer substation is optimized.