2177 results about "Thermographic imaging" patented technology

The invention discloses a battery charging monitoring and adjusting system based on multiple areas, and relates to the technical field of battery management. According to the system, electrochemical impedance spectroscopy, infrared thermal imaging and electric heating parameters are synchronously collected, a physical connection and heat conduction topological relation of a battery pack is constructed in combination with a graph neural network, and monomer aging distribution and a cross-regional heat diffusion path are accurately recognized. Based on multi-region data aggregation, battery health protection, temperature equalization and a power grid demand target are fused, and charging current constraint and equalization priority parameters are dynamically generated. And through a fuzzy control algorithm, an active energy transfer mode and a passive risk isolation mode are intelligently switched, the energy distribution efficiency is optimized, fault spreading is blocked, the risk level of the system is evaluated in real time, a hierarchical regulation and control instruction is triggered, and the weight is dynamically adjusted and optimized through a feedback mechanism. The problems of extensive monitoring, strategy stiffness and safety response lag of a multi-area battery system are solved, the charging safety and economy in a complex scene are remarkably improved, and the service life of the system is remarkably prolonged.

The invention discloses an intelligent photoelectric theodolite aerial target positioning and tracking system, which relates to the technical field of photoelectric detection, and comprises a multi-mode photoelectric sensor module integrating visible light, infrared thermal imaging, a laser radar and a polarized light sensor and supporting spectrum adaptive switching; the dynamic noise suppression processing module is used for eliminating environmental interference based on a time-space domain hybrid filtering algorithm; the multi-target tracking control module adopts a time-sharing partition scanning strategy and a graph neural network data association algorithm; the anti-interference servo driving module is used for realizing stable tracking under strong disturbance through inertial navigation-visual fusion compensation; and the edge computing platform is used for deploying a lightweight deep learning model to complete target recognition and trajectory prediction. According to the invention, through interdisciplinary collaboration of quantum dot materials, graph neural networks and physical equation constraints, the bottleneck of a single technology is broken through; and through closed-loop optimization of dynamic anti-interference and edge intelligence, full-link enhancement of'perception-decision-execution 'is realized.

The invention relates to the technical field of hydraulic engineering intelligent operation and maintenance, and discloses an AI unmanned aerial vehicle-based auxiliary decision-making platform for hydraulic engineering operation and maintenance, and the platform comprises a data collection module which is used for obtaining multi-modal data of a water conservancy facility through an AI unmanned aerial vehicle, the multi-modal data comprises a visible light image, thermal imaging data, multispectral data and laser radar data; the data processing and fusion module is used for carrying out formatting processing, space-time alignment and feature fusion on the multi-modal data; the health assessment and prediction module is used for assessing the health state of the water conservancy facility based on the processed data and predicting the future change trend of the facility state; and the auxiliary decision-making module is used for generating inspection priority planning of the water conservancy facilities. According to the invention, through multi-source data fusion, dynamic risk assessment and intelligent optimization distribution, the effects of accurate monitoring, efficient operation and maintenance and risk visual management of the whole life cycle of the water conservancy facilities are realized.

The invention provides a multi-source biological signal fusion moxibustion electric control method and system. In the moxibustion applying process, a multi-source biological signal flow is formed by obtaining an epidermal impedance dynamic waveform and a heart rate variability signal, a skin impedance baseline map of a physiological feature parameter library and a heart rate variability association rule are dynamically matched, and an acupoint sensitivity index and a heat tolerance threshold value are generated; triggering an infrared thermal imaging device to scan a moxibustion application area based on the sensitivity index, reconstructing a heat conduction model in combination with the epidermal layer thickness and the acupoint distribution density, comparing an epidermal impedance dynamic waveform with a skin impedance baseline map through a phase offset, and calculating a targeted thermal field compensation gradient of the acupoint area; and finally, coupling the thermal tolerance threshold and the space-time parameters of the target thermal field compensation gradient to generate a control instruction set. According to the technical scheme, accurate regulation and control of thermal field distribution and optimization of the motion path of the mechanical arm in the moxibustion applying process are achieved.

The invention discloses a thermal imaging temperature rise trend early warning system based on time-space sequence prediction, and particularly relates to the technical field of thermal imaging data prediction and early warning. The thermal imaging temperature rise trend early warning system comprises an image conversion module, a fluctuation feature extraction module, an edge prediction module, an anomaly characterization module and a prediction decision module; a temperature dynamic change rate and gradient intensity are calculated, edge model prediction is carried out based on a fluctuation index combination, when the fluctuation index combination does not exceed a stable interval, a lightweight deep network model deployed at a thermal imaging acquisition end is called, and when the fluctuation index combination exceeds the stable interval, a prediction decision module determines whether to switch to a high-order multi-modal model; the space-time information extraction capability is improved by constructing the temperature evolution data body, the prediction path is dynamically controlled based on the fluctuation index combination, and the prediction stability and efficiency are improved; and the abnormal activation index and the prediction offset index are combined to realize adaptive switching of model calling, so that the accuracy and adaptability of the early warning system are enhanced.

The invention relates to the technical field of fire control management, in particular to a fire rescue intelligent management method and system based on a multi-mode AI large model. The method comprises the following steps: acquiring real-time environment monitoring data of a fire scene; carrying out environmental parameter analysis on the fire scene real-time environmental monitoring data, and carrying out gas change trend prediction to generate a scene gas parameter change trend; acquiring a fire scene thermal imaging image; performing temperature distribution visual identification and continuous frame temperature change rate calculation according to the fire scene thermal imaging image so as to generate a fire source area temperature change rate; performing multi-time ignition trend prediction according to the temperature change rate of the fire source area and the on-site gas parameter change trend so as to generate a multi-time ignition trend prediction map; and identifying a to-be-rescued area point based on the fire scene thermal imaging image. According to the invention, real-time and accurate distribution of on-site fire rescue resources is realized, and the rescue management efficiency and safety are improved.

The invention relates to the technical field of agricultural science and technology, in particular to a leaf disease and pest identification method and system based on image processing, and the method comprises the following steps: setting multispectral and thermal imaging equipment, collecting multispectral images and thermal imaging data of a target frequency band, deleting images with insufficient definition and data with excessive noise, obtaining optimized multispectral images, and obtaining the leaf disease and pest identification result. And marking and classifying the data according to the collected date to form a time sequence image data set. According to the method, the network parameters are dynamically adjusted to reflect the state of each stage of leaf growth, so that the pest and disease damage prediction is not only based on static data, but also reflects the actual change of plant growth, and the real-time application value and accuracy of the prediction model are improved; the introduction of state transition learning enables the transition from health to each stage of pest and disease damage to be predicted more accurately, carries out early diagnosis and timely intervention, improves the recognition precision of the specific position of the pest and disease damage, can carry out local processing, and effectively improves the pertinence and economic efficiency of the processing.

The invention discloses a high-voltage equipment monitoring system and method based on thermal imaging and dynamic sensing fusion of a power plant, and belongs to the field of high-voltage equipment monitoring. The edge intelligent processing module is used for outputting a real temperature field matrix, separating equipment vibration characteristics from environment noise and generating a fused characteristic vector; the multi-modal analysis module is used for inputting the feature vectors into a constructed equipment topological graph neural network and outputting a health index and an abnormal probability distribution diagram of each monitoring point; the environment coupling fault analysis module is used for generating a fault diagnosis report; and the dynamic early warning module is used for executing graded early warning according to the health index and the fault diagnosis report. The fault detection rate and the operation and maintenance efficiency can be improved.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention discloses an automobile steering controller fault detection method and system, and relates to the technical field of automobile electronic control. The surface image acquisition module captures an image through a visual sensor, the quality is improved through the image preprocessing module, the convolutional neural network accurately recognizes appearance defects, and the three-dimensional contour detection and thermal imaging module is triggered to be linked to re-check an abnormal area; the three-dimensional contour module confirms patch offset or tombstone standing abnormity by using a laser scanning technology; the thermal imaging analysis module is matched with a machine learning technology to analyze welding spot temperature abnormity; the predictive fault diagnosis module predicts a defect risk by using deep learning and performs early warning in advance; and the collaborative decision optimization module integrates a multi-module data dynamic optimization detection strategy. Through integration of defect detection, re-checking and prediction, the quality detection precision, coverage and efficiency of the automobile steering controller are greatly improved, the high-quality standard and production stability of products are ensured, and the requirement of the automobile industry for efficient detection is met.

The invention provides a traditional Chinese medicine physique dynamic change identification system. The system comprises a data acquisition module, a feature extraction module, a deep learning analysis module, a physique analysis and prediction module and a diagnosis report generation module. The data acquisition module is used for acquiring tongue image video streams, pulse condition waveforms and body surface infrared thermal imaging data and recording environment temperature and humidity, user behavior logs and medication information. The feature extraction module is used for extracting various feature data. And inputting the multi-dimensional data set into a deep learning analysis module, and calculating the transformation relation probability of the traditional Chinese medicine constitution types. The physique analysis and prediction module judges the current physique type based on the probability data and predicts the physique evolution trend in combination with historical data, and finally the diagnosis report generation module outputs a diagnosis report containing physique evolution analysis and conditioning suggestions. According to the method, multi-dimensional physiological, environment and behavior data can be integrated, dynamic identification of traditional Chinese medicine physique is realized, the accuracy of physique analysis is improved, and a personalized health management scheme is provided.

A method and system for evaluating the survival state of a wounded person on the basis of incomplete parameters. The method comprises: acquiring visual information, audio information and thermal imaging multi-modal data of a target; inputting the multi-modal data into a feature extraction network, so as to obtain multi-modal feature data; and combining the multi-modal feature data with physiological indexes, so as to form a multi-modal data set, and on the basis of a trained survival index model, obtaining an evaluation result for the survival state of the target. The present invention provides a more comprehensive and more accurate evaluation result for the survival state of a wounded person, so as to implement rapid and accurate triage of wounded persons at a disaster site.

The invention discloses a method, device and system based on acoustic-thermal multi-mode fusion imaging, and relates to the technical field of artificial intelligence, and the method comprises the steps: firstly, collecting ultrasonic and thermal imaging data of target industrial equipment through an acoustic imager, and obtaining acoustic and thermal imaging features respectively; performing feature integration processing to obtain a first acoustic-thermal joint feature so as to determine a first modal integration unit corresponding to the acoustic feature, completing cross-modal fusion to obtain a first feature descriptor, similarly obtaining a second acoustic-thermal joint feature, a second modal integration unit and a second feature descriptor, and determining a fault evaluation result in combination with a fault type label. And then calibrating image data to construct a three-dimensional acoustic-thermal fusion substrate image and marking a result to obtain a diagnosis fusion image so as to realize accurate diagnosis of the target industrial equipment.

The invention discloses a multi-dimensional anti-bird intelligent recognition method and system based on thermal imaging, and relates to the technical field of intelligent monitoring and ecological protection. Multi-modal data is collected through thermal imaging, visible light, millimeter wave radar and a voiceprint sensor, after PTP protocol synchronization and Kalman filtering preprocessing, 3-5-second tracks of birds are predicted by using an LSTM network, and the anti-bird intelligent recognition method and system based on the thermal imaging are obtained. And the cross-modal features are fused through a Transform architecture, so that 95% of classification accuracy is realized. A bird repelling strategy is generated in real time through edge calculation, and the model is updated through cloud federal learning. The system integrates an oil-electric hybrid unmanned aerial vehicle and a ground device, supports dynamic path planning based on a thermodynamic diagram and differentiated repelling of directional sound waves, laser stroboflash and the like, has the night recognition accuracy rate of 92% and the bird repelling response time of 0.8 second, and is suitable for scenes of electric power, airports and the like. Through multi-dimensional perception, dynamic modeling and eco-friendly expelling, the problems of poor environmental adaptability, single strategy and the like of a traditional scheme are solved, and the anti-bird efficiency and the ecological safety are improved.

The invention discloses a photovoltaic module cleaning method and device based on image processing and electronic equipment, and belongs to the technical field of photovoltaic power generation. The method comprises the following steps: acquiring operation information, a visible light image, a near-infrared image and thermal imaging data of a photovoltaic module; the operation information, the visible light image, the thermal imaging data and the near-infrared image are input into a hot spot detection model, a hot spot detection result, output by the hot spot detection model, of the photovoltaic module is obtained, the hot spot detection result comprises a hot spot area and a hot spot level, and the hot spot detection model is obtained based on a plurality of first training samples; the first training sample comprises sample operation information, a sample visible light image, sample thermal imaging data, a sample near-infrared image and a hot spot detection label of the sample photovoltaic module; acquiring environment information and dust information of dust attached to the photovoltaic module, wherein the dust information comprises a dust type and an attachment form; the environment information, the dust information and the hot spot detection result are input into a dust cleaning prediction model, cleaning parameters of each hot spot output by the dust cleaning prediction model are obtained, and the dust cleaning prediction model is obtained based on a plurality of second training samples; the second training sample comprises sample environment information, sample dust information, a hot spot detection label and a cleaning parameter label of the sample photovoltaic module; and cleaning the photovoltaic module based on the cleaning parameters. According to the method, different types of dust can be effectively cleaned, and the dust cleaning effect of the photovoltaic module is improved.

The invention relates to a broiler instance segmentation method based on thermal imaging and RGB fusion, and belongs to the field of image processing, and the method comprises the steps: collecting image data, and carrying out the preprocessing of the image data; inputting the labeled training data after data preprocessing into the constructed network model as a training set for training to obtain a trained and optimized network model; and inputting the acquired image data into the trained and optimized network model again, extracting feature information of two modes of RGB and thermal imaging in the image, fusing the feature information of the two modes through a cross-modal attention mechanism, and optimizing cross-modal information interaction in combination with a deformable attention mechanism and multi-source position coding, so as to obtain the multi-source position code. And finally, generating a final broiler instance segmentation mask through a decoder. According to the method, accurate segmentation and tracking of broiler individuals are realized, and the robustness and the recognition capability of the model in a complex agricultural environment are further improved through temperature information of a thermal imaging image.

The invention provides a thermal wave imaging detection method based on nonlinear frequency modulation microwave excitation induction, and the method comprises the steps: constructing a composite excitation module of Gaussian beam and dynamic phase compensation, decomposing high-frequency / low-frequency sub-pulses for alternate emission, synchronously collecting infrared non-uniform sampling and microwave polarization diversity data, and carrying out the detection of the thermal wave imaging. And through feature fusion of a time domain CNN and a frequency domain graph neural network, propagation parameters are iteratively corrected in combination with a thermal diffusion equation model, and defect and deep structure detection is realized. According to the method, the composite excitation structure is combined with high-frequency / low-frequency sub-pulse alternate emission, the micro-defect detection resolution and the deep structure penetrating capacity are remarkably improved, the feature robustness is enhanced through multi-modal data acquisition and cross-modal neural network fusion, detection depth self-adaptive optimization is achieved through a depth compensation model and Kalman filtering, and the detection precision is improved. The parameter precision is improved with the assistance of a terahertz spectrum coupling matrix and Landweber iterative inversion, and finally an advanced detection system integrating efficient excitation, multi-dimensional perception and intelligent analysis is formed.

The invention discloses a dam termite cave positioning method based on transient electromagnetism carried by an unmanned aerial vehicle. The method comprises the following steps: constructing an unmanned aerial vehicle transient electromagnetic detection system; preprocessing the received secondary induction electromagnetic field signal, and obtaining a comprehensive electromagnetic signal according to the response difference of the multi-frequency signal; inputting the integrated electromagnetic signals into a pre-trained deep learning neural network model, identifying characteristic signals related to termite acupoints in the integrated electromagnetic signals, and outputting position coordinates of suspected termite acupoints; a dam surface thermal imaging image obtained by a thermal imaging module is combined for secondary verification, and termite cave position information is transmitted to a ground control terminal in real time through a communication module carried by the unmanned aerial vehicle. According to the invention, efficient, accurate and lossless positioning of the termite cave of the dam is realized, underground electromagnetic signals and surface heat distribution information of the dam are rapidly obtained, and accurate termite cave position coordinates are output.

The invention relates to the technical field of robots, and discloses a multi-source data fusion method of a tracked robot, which realizes dynamic environment adaptation and autonomous decision optimization through a multi-source sensing cooperation mechanism, and comprises the following steps: dynamically adjusting exposure parameters of a visual module based on environment illumination intensity, and avoiding overexposure or underexposure of an image through a feedback adjustment mechanism; fusing data of the accelerometer and the gyroscope to generate attitude angle compensation parameters; the heat dissipation system is intelligently regulated and controlled by comparing infrared thermal imaging and temperature sensor data; dynamic task priority distribution is realized; optimizing an advancing route to reduce complex terrain navigation deviation, in addition, establishing a multi-source sensing coordination mechanism, monitoring health indexes such as motor temperature and vibration frequency in real time, and triggering abnormal state judgment; track optimization is implemented in combination with pavement mechanical characteristics and a kinematic model; and executing the staged energy management strategy. According to the method, the environment sensing precision and the motion control reliability of the tracked robot in a complex environment are remarkably improved.

The invention discloses a concrete crack depth detection method and system based on multi-modal data fusion. The method comprises the following steps: synchronously obtaining a visible light image sequence and a thermal imaging image sequence of a concrete crack; the thermal imaging image sequence is obtained based on adjustable thermal excitation; and through a preset multi-modal data registration algorithm, according to the visible light image, predicting a registration displacement vector field to generate a pseudo-infrared image corresponding to the enhanced visible light image, and migrating a temperature field of the thermal imaging image at the same moment and under the same picture to the pseudo-infrared image to generate a fusion modal image, obtaining a fusion modal image sequence; and through a preset heat conduction inversion model and a temperature attenuation characteristic curve generated based on the fusion modal image sequence, obtaining crack depth data and generating a three-dimensional crack map so as to visually present concrete crack depth detection data. According to the invention, the universality and accuracy of concrete crack depth detection can be improved.

The invention provides a rescue robot autonomous identification and positioning system and method based on industrial vision, and relates to the technical field of robots, and the system comprises a cooperative scanning module which is used for constructing a robot coordinate map; the data fusion module is used for establishing a data fusion frame with a unified time sequence to obtain multi-modal image data; the human body recognition module is used for mapping the thermal imaging image to a visible light image and executing human body local recognition to obtain human body structure features; the target marking module is used for determining a rescue target and marking the rescue target in the robot coordinate map; and the path planning module is used for performing path planning, constructing an updated path and performing rescue through the rescue robot. According to the method and the device, the technical problem of relatively low rescue efficiency caused by difficulty in accurately positioning the rescue target due to shielding and interference in a complex environment in the prior art can be solved, and the precision of positioning the rescue target is improved by combining industrial vision, so that the rescue efficiency is improved.

The invention discloses a switch cabinet fault diagnosis method and system based on thermal imaging and voiceprint. The method comprises the following steps: acquiring historical operation data, voiceprint data and thermal imaging data of a switch cabinet and an operation site picture of the switch cabinet; carrying out noise reduction processing on the acquired voiceprint data and historical operation data by adopting an infinite Kalman filter; constructing a space-time attention mixed expert model to carry out training prediction on the voiceprint data set and the historical operation data set, and obtaining predicted voiceprint data and historical operation data at the next moment; constructing an improved MaxViT-DF model to classify the collected thermal imaging data and the operation site picture, and training to obtain a fault feature image and a fault-free image; and fusing fault feature data output by the space-time attention hybrid expert model and the MaxViT-DF model by adopting multi-modal orthogonal sequence fusion. According to the invention, the accuracy and efficiency of fault diagnosis of the switch cabinet are improved, operation and maintenance personnel can find and process potential problems, and the reliability and safety of a power system are improved.

The invention relates to the technical field of cylindrical lithium ion battery production, and discloses a method for detecting insufficient welding of a cylindrical lithium ion battery, and the method comprises the following steps: S1, appearance inspection; s2, initial internal resistance benchmark test; s3, applying a short-time large-current pulse to the battery by using a pulse generator, simulating an actual load condition, and recording an instantaneous voltage drop and current response curve of the voltage of the battery end during the pulse period; s4, performing infrared thermal imaging identification and data processing; s5, learning the picture and establishing a model; s6, comparing the test picture and the data with the model; s7, dynamic internal resistance calculation; s8, cold solder joint query and sorting processing; and S9, retesting the electrical performance. According to the invention, the technologies of dynamic internal resistance measurement, nondestructive X-ray or ultrasonic detection, rate discharge test and the like are adopted, so that efficient, accurate and nondestructive detection of the cold solder joint of the cylindrical lithium ion battery is realized, and the production efficiency and the detection precision are remarkably improved.

A pest control system is disclosed comprising an optical, computational, and monitoring subsystem, optionally mounted on a mobile platform. The optical system may include a neutralizing laser or multi-wavelength light source, discovery and detail cameras (optionally stereo), a beam-steering mechanism, tunable focus, and optional thermal or depth sensors. The processor, such as a GPU or FPGA, identifies insect or biological targets, adjusts laser focus by depth, and controls beam activation. A monitoring system verifies safety by detecting humans or other non-target entities using environmental and thermal cameras; if detected, laser firing is inhibited. The mobile platform may use wheels, propellers, tracks, or cables, with GPS and data links for remote control. A visible light pre-flash may induce a blink reflex before firing. In some embodiments, a scouting drone transmits target coordinates to the neutralization unit, enabling coordinated, efficient, and safe laser-based pest control.

The invention discloses an intelligent security and protection monitoring system and method fusing multi-modal data analysis, and relates to the field of intelligent monitoring, and the method comprises the following steps: collecting a video stream, infrared thermal imaging data and an environment audio signal in real time through a heterogeneous sensor array; operating a lightweight neural network at an edge computing node to carry out space-time alignment and feature level fusion on the multi-source data, and generating an enhanced environment sensing matrix; a dual-channel anomaly detection mechanism is adopted, a first channel identifies short-term sudden abnormal events through a dynamic threshold self-adaptive model, and a second channel early warns potential risk behaviors through a time sequence prediction model; according to the system and the method, a dynamic privacy protection mechanism is adopted, behavior analysis is completed on the premise of ensuring biological feature safety, and transformation from passive monitoring to active early warning is realized in combination with a space-time prediction model; the problem of multi-view target tracking is solved through a distributed consensus algorithm, and the monitoring reliability in a complex scene is remarkably improved.

The invention discloses a nondestructive testing method for internal defects of a concrete structure fusing big data feature extraction and deep learning. According to the method, through multi-source data collaboration and dynamic feature fusion, the accuracy and robustness of concrete structure defect detection are remarkably improved. In a data processing link, ultrasonic electromagnetic induction infrared thermal imaging data and the like acquired by a multi-source nondestructive testing technology are subjected to collaborative preprocessing, so that the influence of noise interference and environmental fluctuation is eliminated, and standardized input is provided for feature extraction. The dynamic weight distribution network further combines the relevance of each modal feature in a historical defect sample, adjusts fusion weights of different modals in real time, reinforces ultrasonic features with great contribution to cavity recognition or infrared features sensitive to cracks, effectively compresses redundant information, and improves the accuracy of cavity recognition. According to the method, features and data-driven deep features of the fused feature vectors are manually designed at the same time, so that the limitation of single-modal data is avoided, and a model can more accurately capture multi-dimensional features of defects.

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 nondestructive testing method for infrared thermal imaging spatio-temporal information fusion, which relates to the technical field of nondestructive testing and comprises the following steps: establishing a linear laser heat source scanning infrared thermal imaging nondestructive testing system; collecting a dynamic infrared thermal image sequence; and calculating and analyzing dynamic infrared thermal image sequence data, and carrying out data processing on the acquired dynamic infrared thermal image sequence by using an infrared thermal imaging spatio-temporal information fusion post-processing method to obtain a full-field thermal response image with surface crack defects. According to the method, spatio-temporal information can be fused to obtain data with higher resolution, more comprehensive and accurate full-field thermal response characteristics are obtained while the calculation efficiency is improved, the method has better flexibility and wider applicability, and the purpose of nondestructive detection of metal surface defects is achieved.

The invention provides an outer wall leakage point positioning detection method and system based on infrared thermal imaging, and relates to the technical field of computer vision, and the method comprises the steps: firstly collecting a multi-period infrared thermal imaging data set of a target building outer wall, covering a surface temperature distribution matrix at different environment temperatures, and then carrying out the dynamic temperature correction processing, the method comprises the following steps of: forming a standardized temperature distribution set for eliminating the influence of environment temperature fluctuation, extracting a thermal characteristic parameter set containing the characteristics of temperature gradient distribution and the like from the standardized temperature distribution set, calling a pre-trained thermal anomaly detection model to carry out leakage risk probability mapping on the thermal characteristic parameter set, and generating a leakage point probability distribution diagram; and finally, performing region segmentation processing according to the leakage point probability distribution diagram and a preset dynamic judgment threshold value, outputting a leakage point positioning coordinate set and a corresponding leakage strength grade identifier, and realizing accurate positioning and strength grade identification of the leakage points of the outer wall.

The invention relates to an unmanned aerial vehicle image crack detection method, system and device and a storage medium, and the method comprises the steps: carrying out the multi-angle data collection of a to-be-detected building through a sensor group on an unmanned aerial vehicle, obtaining the multi-modal data of the to-be-detected building, and enabling the sensor group to comprise a visible light camera, a thermal imaging sensor and a laser radar sensor; preprocessing the multi-modal data of the to-be-detected area to obtain preprocessed multi-modal data; inputting the preprocessed multi-modal data into a pre-trained crack detection model for comparative analysis to obtain a detection result; and generating a detection report according to the detection result, and uploading the detection result and the detection report to an Internet of Things platform in real time. The method has the effect of improving the accuracy of crack detection.