1230 results about "Thermal radiation" patented technology

The invention provides an improved YOLOv11s safety helmet wearing detection model and an optimization method thereof, and relates to the technical field of computer vision target detection. According to the improved YOLOv11s safety helmet wearing detection model and the optimization method thereof, the improved YOLOv11s safety helmet wearing detection model comprises the following modules: a multi-modal fusion module, a space-time analysis module, a domain adaptation module, a topological optimization module and a dynamic architecture module; and the multi-modal fusion module is used for realizing feature decoupling by adopting channel separation convolution based on input RGB and near-infrared images, fusing visible light and thermal radiation features through a dynamic weight distribution algorithm, implementing affine transformation alignment on multi-scale features by utilizing a spatial transformation network, and generating a multi-modal feature graph. Through fusion of visible light and near infrared spectrum features and implementation of dynamic weight distribution, complementarity of target texture and thermal radiation features under a complex illumination condition is enhanced, and the problem of feature distortion of single-mode data in a strong backlight or low-illumination scene is solved.

The invention relates to the technical field of EPP (foamed polypropylene) production and manufacturing, and discloses an EPP foaming process parameter optimization method which comprises the following steps: monitoring temperature field and pressure changes in real time, and judging whether a thermodynamic state deviates from a preset window or not; during deviation, constructing a multi-physics field coupling model to evaluate a bubble growth dynamic unbalance risk, and identifying micro-pore non-uniform distribution characteristics through multi-source sensing data fusion and frequency domain analysis; and judging whether the technological parameters are globally coordinated and regulated based on risks and characteristics, analyzing the heat transfer and heat radiation influence of the temperature control unit, quantifying the parameter regulation sensitivity and determining the regulation priority. The system comprises a thermodynamic monitoring module, a dynamic coupling analysis module and the like. According to the invention, real-time monitoring, accurate analysis and intelligent regulation and control of the EPP foaming process are realized, the process stability and the product quality are improved, and the method is suitable for the field of EPP production and manufacturing.

The invention belongs to the technical field of construction crack detection, and particularly discloses a crack detection method and system for expressway construction, and the method comprises the steps: synchronously collecting multiband data through a multispectral thermal imaging sensor, and integrating the surface morphology, material reflection and deep thermal radiation characteristics through a multisource registration fusion technology; the crack identification capability in a complex environment is enhanced, the crack edge continuity and thermal radiation fluctuation characteristics are further analyzed through dynamic correlation, transient interference is accurately stripped, the problem of high false alarm is solved, meanwhile, a scoring model is constructed in combination with geometric and thermodynamic parameters, the damage degree and the expansion trend are quantified, and the accuracy of crack identification is improved. And finally, the crack expansion risk is pre-judged through the dynamic change trend of the thermodynamic parameters, a hierarchical early warning signal is triggered to form a sensing-anti-interference-assessment-decision closed loop chain, the whole process optimization from data fusion and interference suppression to risk assessment and maintenance response is realized, and the accuracy of road crack detection is guaranteed.

The invention provides an intelligent control method for a floodlight illumination system, and the method comprises the steps: obtaining an environment surface roughness coefficient, spectral reflection distribution and spatial geometric change data, determining the wall surface material absorptivity and shielding region coordinates, and obtaining environment reflection characteristics and shielding distribution data; analyzing shielding boundary sharpness and shielding angle distribution according to the spatial geometric change and the coordinates of the shielding area, calculating a luminous flux distribution curve of a local dark area, and judging whether the influence range of the dark area exceeds a preset threshold range or not; calculating the illumination coverage integrity according to the updated light field distribution and polarization reflection characteristics, determining the boundary sharpness of a light beam, and obtaining a light field uniformity parameter; and according to the light field uniformity parameter and the shielding thermal radiation effect, calculating and optimizing light field parameters.

The invention provides a multi-target automatic tracking method and system based on an unmanned intelligent turntable. According to the method, electromagnetic echoes and environment depth data are synchronously collected through a detector, and an observation data set is constructed; separating thermal radiation, surface texture and motion features, establishing a four-dimensional feature space containing space-time coordinates, and identifying a shielding area; dynamically adjusting signal parameters to enhance the characteristics of the shielding area and feeding back an optimization record; calculating an optimal scanning path based on the parameter optimization value, and driving the sensor array to perform multi-angle scanning to generate a weighted data set; in combination with historical trajectory data, a trajectory prediction model is optimized in parallel through distributed calculation, turntable parameter synchronization and decision making are coordinated, and finally efficient reconstruction of a multi-target continuous trajectory in a shielded environment is realized. According to the technical scheme provided by the invention, through multi-dimensional feature fusion and dynamic parameter optimization, reconstruction of a multi-target trajectory in a shielding environment is realized, and the environmental adaptability and tracking efficiency of an unmanned system are improved.

The invention provides a method and equipment for predicting the heat exchange performance of a buried pipe ground source heat pump thermal camouflage system. The method comprises the steps that S1, a multi-physics field coupling heat exchange mathematical model of a buried pipe heat exchange system is established; s2, based on the multi-physics field coupling heat exchange mathematical model, a control equation and boundary conditions are constructed; s3, on the basis of the control equation and the boundary conditions, a multi-physics field numerical model is constructed, and simulation and calibration are carried out; and S4, based on the calibrated numerical model, designing a simulation test and carrying out main control factor analysis to obtain a prediction effect of the heat exchange performance. By establishing a rock-soil body-fluid multi-physical field coupling model, the interaction mechanism of underground heat conduction, working medium flow and ground surface heat radiation is completely represented, and the hiding efficiency of the thermal camouflage system is remarkably improved.

The invention relates to the technical field of steel rail repair, and provides a steel rail laser additive defect detection method and system.The method comprises the steps that before laser additive repair is conducted on a steel rail damage area, a local area of a steel rail base body is instantly heated, so that an instant hot spot is formed in the corresponding local area; collecting thermal radiation attenuation information of the instantaneous hot spot; according to the thermal radiation attenuation information, the actual heat dissipation rate of the local area corresponding to the instantaneous hot spot is calculated so as to achieve detection of the steel rail damage area; according to the actual heat dissipation rate, repairing process parameters used for laser additive repairing are selected from a pre-stored process parameter combination; and carrying out laser additive repair on the damaged region of the steel rail by adopting the repair process parameters. The method has the effect of improving the quality and reliability of steel rail laser additive repair.

The invention provides a target tracking method, electronic equipment and a readable storage medium. According to the target tracking method provided by the invention, by combining generation and updating of the static template and the dynamic template, the problems of time-varying characteristics and background noise in infrared small target tracking can be effectively solved. Firstly, a generated static target image template is cut by multiple scales, so that the adaptability to different scales of a target is improved; and the generation of the dynamic target image template is combined with the dynamic template of the previous frame and the temporary template of the current frame, and updating is carried out through a fusion strategy, so that the morphological change and the thermal radiation fluctuation of the target can be reflected in time, and the problem of response lag caused by the time-varying characteristic of the target is solved. And finally, inputting the static and dynamic templates into a pre-trained target tracking model for processing, thereby effectively suppressing interference of complex background noise, and accurately positioning the target.

The invention relates to the technical field of power converters, in particular to a power converter real-time fault diagnosis method and system based on edge calculation. According to the method, operation data, including electrical parameters, thermal parameters and the like, of the power converter are obtained firstly, aging and quality are ensured through synchronous filtering, limitation of a traditional single parameter is broken through, tiny abnormity and instantaneous fluctuation can be captured, and redundancy is avoided; according to the equipment structure, edge computing nodes are deployed near a data source, the transmission distance is shortened, delay is reduced, electromagnetic interference is avoided, accuracy is improved, resources with excellent computing power are matched, and local real-time monitoring is supported. Next, an edge computing node lightweight algorithm is used for localization analysis, normal data and abnormal data are quickly distinguished, identification time consumption does not need to be reduced by transmitting to a cloud end, it is ensured that abnormality is found immediately according to misjudgment and sensitivity improvement of a dynamic baseline, and finally, thermal radiation information is obtained in combination with abnormal data, and a fault is accurately positioned by associating a device structure and parameters. And refining to an element level to shorten the troubleshooting time, and analyzing the trend to realize predictive maintenance.

The invention discloses a wild animal target detection method based on improved YOLO11. According to the invention, double-dynamic convolution is designed to improve the feature extraction and nonlinear expression capability of the feature extraction module on the wild animal image so as to flexibly adapt to the feature difference between visible light and infrared images; a multi-scale expansion attention mechanism is introduced to improve the multi-scale target detection precision; unified-IoU loss is introduced to solve the problem of unbalanced quality of a prediction frame; and a DySample dynamic up-sampling operator is introduced, so that the detail recovery capability is improved, and the feature distortion is reduced. The problems that a traditional YOLO feature extraction module cannot effectively process an image shot by a field infrared camera, and an infrared image depends on thermal radiation, is low in contrast, weak in texture, large in environmental thermal noise and large in feature difference with a visible light image and is difficult to adapt are solved, and traditional CIoU loss easily pays attention to and fits a low-quality prediction frame. And a sampling module cannot effectively reconstruct low-resolution infrared image features.

The invention relates to the technical field of fire coal control, and discloses a thermal power plant fly ash floating black online detection control method and system, and the method comprises the steps: 1, employing microwave, infrared and laser sensors for cooperative work, detecting carbon dielectric characteristics through microwave dust interference resistance, identifying thermal radiation difference through infrared, and analyzing particle distribution through laser; 2, on the basis of the detection requirements in the step 1, triple protection of air curtain barrier, ultrasonic cleaning and hydrophobic coating is designed, dust condensation is blocked, and it is ensured that the light transmittance of the sensor is larger than 90%; the invention provides a multi-mode sensing fusion-anti-interference current stabilization-closed loop optimization full-link solution for the industrial problem of signal distortion caused by optical window dew formation and dust deposition, airflow disturbance and environmental interference in fly ash floating black online detection. The light transmittance of the optical window is greater than 93% by combining triple protection of air curtain barrier, ultrasonic cavitation stripping and nano hydrophobic coating.

The invention provides an unmanned aerial vehicle fire monitor fire source positioning system based on infrared thermal imaging, and the system comprises a fire scene situation collection module which obtains thermal image data and high-precision point cloud data; the fire diffusion model building module is used for generating a radiation intensity distribution diagram; the thermal image processing module is used for generating a structured fire source risk feature image; the LSTM time sequence model processing module is used for generating a smooth diffusion thermodynamic diagram to predict the spreading direction and speed; the combat map generation module is used for realizing data visualization; and the unmanned aerial vehicle flight path planning module realizes comprehensive coverage of a fire monitor striking path. Aiming at the problem that the prior art depends on static fire source data and is difficult to capture time-space dynamic evolution of a fire behavior, the method comprises the following steps: constructing a multi-dimensional fire behavior diffusion model, and fusing fire scene air flow vector projection analysis, thermal radiation background concentration dynamic superposition and LSTM time sequence modeling modes; accurate prediction of the diffusion path of the fire from the low-risk area to the high-risk area is realized, and instant support is provided for fire extinguishing action.

The invention discloses a multi-modal imaging method and system based on acoustic-thermal coupling, and relates to the field of artificial intelligence, and the method comprises the steps: firstly obtaining thermal radiation data and ultrasonic reflection data of a target equipment region, and obtaining thermal radiation embedded features and ultrasonic embedded features through feature representation learning; performing multi-stage compensation on the thermal radiation embedding characteristic to generate a thermal field compensation reference characteristic of each stage; synchronously carrying out equal-stage multi-stage compensation on the ultrasonic embedded features, coupling the ultrasonic compensation result of the previous stage with the thermal field reference features of the corresponding stage between every two continuous stages of compensation, and combining the ultrasonic embedded features and coupling output in the later stage of compensation; and finally, performing imaging by using the acoustic-thermal coupling characteristics after multi-stage compensation to obtain an acoustic-thermal coupling imaging result fusing the thermal distribution characteristics of the target equipment region and the ultrasonic reflection spectrum characteristics. According to the method, deep fusion of acoustic and thermal information is realized through multi-stage feature compensation and dynamic cross-modal coupling, and the comprehensiveness and accuracy of equipment area detection are improved.

The invention relates to the technical field of multi-sensor identification, in particular to a coal gangue detection and classification method and system based on multi-sensor fusion discrimination, and the method comprises the following steps: obtaining visible light and infrared images, calculating brightness and intensity judgment feature conditions, executing edge detection to extract gray segments, and fusing textures and a thermal field to generate a vector set. And performing clustering analysis to finish classification judgment, and outputting a coal gangue detection classification result. According to the method, a precise trigger mechanism is established through brightness and thermal radiation double-feature screening, boundary recognition sensitivity is enhanced through gray abrupt change point division, salient region extraction capacity is enhanced through weighted fusion of texture energy and gray gradient, and a cross-modal consistency feature group is constructed through combination of two-dimensional vector construction and similarity screening. The recognition expression integrity is improved, static threshold classification is replaced by vector distribution clustering, accurate mapping and classification decision making of material attributes in a complex scene are achieved, and the stability and the recognition rate of a coal gangue detection result are guaranteed.

The invention discloses a forestry fireproof early warning method and system based on the Internet of Things, and relates to the technical field of forestry monitoring, prevention and control, a heterogeneous sensing network is constructed through millimeter wave radar and multispectral imaging, and omnibearing monitoring in a complex environment is realized by combining terrain shielding coefficient Dmxs and signal coverage intensity Xgql analysis. The thermal radiation intensity Rsqd, the combustible drying index Gzzs and the airflow disturbance coefficient Qlys are fused to improve the hidden fire source identification precision; a LoRa relay and a Mesh network are adopted to ensure communication stability of a weak signal area, data transmission is optimized through edge calculation, and high-risk area resources are preferentially scheduled based on hierarchical response of a joint early warning index Lyzb; the risk assessment module calculates a fire danger index Hxzs in real time to predict fire spreading, the intelligent decision-making module comprehensively assesses hidden fire source probability Yhgl and a positioning error Wcsp to generate an early warning strategy, and the emergency linkage module automatically executes hierarchical response to form an intelligent monitoring-early warning-disposal closed-loop management system.

The invention provides a forest disaster detection method and device, and belongs to the field of forest disaster monitoring, and the method comprises the steps: collecting an infrared image, a stereoscopic vision image and positioning data of a target forest region in real time; an ASSA module is introduced in the feature extraction stage of the YOLOv11, and an infrared image is input into the improved YOLOv11 for flame target recognition; recognizing the stereoscopic vision image through a smoke and fire recognition algorithm, and detecting flame and smoke targets; determining a fire detection result by combining the identification results of the flame target and the smoke target; under the condition that the fire disaster is determined to occur, an HITNet algorithm is adopted to carry out ignition point positioning through a stereoscopic vision image, and the geographic coordinates of a fire disaster point are determined based on positioning data; and sending alarm information based on the fire point geographic coordinates. Therefore, the capability of extracting the flame heat radiation characteristics of the tiny fire points is effectively enhanced, and the detection result of the forest fire is improved.

The invention relates to the technical field of recycled asphalt mixture recovery, in particular to a mixing uniformity monitoring method adaptive to a recycled asphalt mixture, which comprises the following steps: S101, performing time-space fusion on infrared thermal radiation field dynamic capture and microwave dielectric property synchronous monitoring bimodal sensing to jointly perform mixing defect alarm; s102, based on the dynamic sequence of the infrared thermal radiation field, judging the particle motion consistency through the standard deviation of the motion direction angle, integrating the infrared temperature field and microwave dielectric data, constructing a three-dimensional feature vector, comprehensively evaluating the uniformity through a weighting function, judging defects according to the maximum contribution term, and generating an optimization instruction; and S103, setting a grading standard based on the multi-modal features, and feeding back and adjusting the process according to a grading result. According to the invention, infrared thermal radiation and microwave dielectric characteristics are combined, temperature uniformity and material component distribution are evaluated synchronously, and the problem of monitoring that the thermal uniformity of the recycled asphalt is not equal to the component uniformity is solved.

The invention relates to the technical field of fusion devices, and discloses a flange structure of a cold shield sector of a nuclear fusion device, the cold shield device and the nuclear fusion device, and the flange structure of the cold shield sector of the nuclear fusion device comprises a first panel; the second panel and the first panel are arranged at an interval; the flanging assembly is arranged between the first panel and the second panel and comprises a first flanging and a second flanging, the first flanging is connected with the first panel, the second flanging comprises a main body part and a flat plate part which are connected, the main body part is connected with the second panel, and the flat plate part is arranged at the ends, away from the second panel, of the main body part and the first flanging; the surface, opposite to the second panel, of the flat plate part is a polished surface; the connecting piece is connected with the first flange and the second flange in a fastening manner; and the first insulating part is arranged between the first flange and the second flange in an insulating manner. According to the flange structure of the cold shield sector of the nuclear fusion device, the heat shielding effect can be improved, and the heat radiation influence on low-temperature components is reduced.

The invention belongs to the technical field of aerospace engineering, and discloses a flexible radiator which comprises a radiation assembly, the radiation assembly comprises a plurality of radiation plates which are rotationally connected in sequence, each radiation plate comprises a liquid cooling pipe set and a radiation film arranged on the liquid cooling pipe set, and cooling liquid circulates in the liquid cooling pipe set. The material density is low, the structural mass is effectively reduced, a plurality of radiant panels are rotationally connected in sequence, the radiant panels are in a folded state during emission, then a radiator with enough radiation area can be emitted under the condition that the size of a spacecraft is limited, and the heat dissipation requirement of high-power electronic equipment is met.

The invention discloses a tunnel structure full life cycle damage detection and safety evaluation method, and belongs to the technical field of damage detection and safety evaluation. The method comprises the following steps: completing installation configuration of a mobile detection platform along the axial direction of a tunnel; generating a moving track and a detection point location distribution scheme of the mobile detection platform; controlling the mobile detection platform to sequentially move to each detection section position according to the detection path, and synchronously starting each detection module to perform continuous data acquisition in the moving process; generating a tunnel three-dimensional mathematical model, a lining structure thickness distribution diagram, a heat radiation anomaly distribution diagram and a crack damage distribution diagram; equally dividing the tunnel three-dimensional model into N detection segments according to a preset interval, and counting the lining thickness mean value, the number of heat radiation abnormal points, the crack length density and the damage level of each segment as safety evaluation indexes; statistical analysis and trend judgment are conducted on the safety indexes of all the segments, abnormal segments exceeding a safety threshold value are recognized, and the risk level of the abnormal segments is determined.

The invention discloses an unmanned aerial vehicle and bird target identification method and system based on multiple modes, and belongs to the technical field of computer vision and target identification. The method comprises the steps of collecting an RGB image, an infrared image and a continuous frame image of a monitoring area, extracting visual features, thermal radiation features and motion features after preprocessing, fusing multi-modal features by adopting an adaptive weight fusion strategy, detecting a potential target through a multi-scale feature fusion detection head, and obtaining a target detection result. The multi-mode classification module judges the target category and triggers early warning; the system comprises a data acquisition module, a preprocessing module, a feature extraction and identification module, a decision and early warning module and a database management module. Through multi-modal fusion, a self-adaptive weight strategy, multi-scale detection and multi-branch classification, the method can effectively overcome the limitation of a single modal, improves the recognition accuracy, the small target detection capability and the environmental adaptability in a complex environment, and meets the requirements of real-time recognition and early warning.

The invention discloses an intelligent laser testing method, which relates to the field of laser testing, and comprises the following steps: judging whether a laser has a fault or not by monitoring the fluctuation amplitude of optical power at the initial stage of starting a to-be-tested laser to emit a light beam; after the laser is not judged to have a fault, the influence of environment heat radiation or aerosol particles on the light beam is positioned by collecting temperature gradient distribution data of a light beam propagation path and combining with air particulate matter concentration data; and obtaining a light beam drift direction, a drift speed and energy uniformity on a light beam propagation path based on the light spot image, determining an abnormal position according to changes of the light beam drift direction, the drift speed and the energy uniformity, and associating the abnormal position with the temperature gradient and the particulate matter concentration in real time. The method effectively distinguishes the fault of the laser from the environmental interference, accurately locates the abnormal position on the beam propagation path, and determines the main environmental interference factors causing the dynamic quality degradation of the beam, thereby providing an accurate basis for the performance evaluation of the laser and the optimization of the application environment.

Apparatuses and methods for drying an object are provided. An apparatus for drying an object may include a first airflow channel that has an airflow inlet and an airflow outlet. The apparatus may also include one or more radiation energy sources configured to provide thermal radiation toward the object. The apparatus may also include an airflow generating element configured to provide an airflow by effecting the airflow into the first airflow channel through the airflow inlet and direct a first portion of the airflow in the first airflow channel toward the airflow outlet. The apparatus may further include an airflow guide assembly configured to provide a second airflow channel through which a second portion of the airflow in the first airflow channel is guided to a target region. The target region abuts at least one of the one or more radiation energy sources.

The invention relates to the technical field of kitchen safety monitoring, and discloses a kitchen gas stove abnormity early warning system based on infrared thermal imaging multispectrum. The system comprises an infrared thermal imaging acquisition module, a dynamic feature extraction module and an abnormal behavior identification module. The infrared thermal imaging acquisition module synchronously captures the surface temperature field distribution of the gas cooking range through a multispectral infrared sensor array, obtains flame form data in combination with a visible light image, and generates a multi-channel fused infrared thermal imaging characteristic spectrum through processing. The dynamic feature extraction module adopts a three-dimensional convolution kernel to scan continuous time sequence frames, identifies flame morphological feature differences, calculates thermal radiation intensity energy distribution offset, and outputs dynamic feature vectors. The abnormal behavior recognition module extracts a correlation mode through a deep residual network, compares current features with a historical normal working condition feature library, and generates a preliminary early warning signal containing an abnormal type code and a risk level when the attenuation rate of flame spectral energy in a specific infrared band exceeds a first threshold value.

The invention provides a thermal radiation shielding assembly and a thermal radiation shielding system of a star imitator, the thermal radiation shielding assembly comprises a plasma container and a Dewar tank surrounding the periphery of the plasma container, and a first thermal radiation shielding part is arranged in an annular cavity formed between the plasma container and the Dewar tank; the first heat radiation shielding part is fixedly arranged on the periphery of the plasma container in a surrounding mode and comprises a cold pipe assembly, a first heat insulation assembly, a first cold shield assembly and a first supporting assembly; the cooling pipe assembly comprises a cooling pipe which surrounds and is attached to the peripheral wall of the plasma container to extend; the first heat insulation assembly extends around the plasma container, and the inner side surface of the first heat insulation assembly is attached to the peripheral wall of the cooling pipe assembly. The first cold shield assembly and the first heat insulation assembly extend around the plasma container at an interval; the first cold shield assembly is fixed to the outer wall of the plasma container through the first supporting assembly to form multiple heat radiation shielding layers, and high-temperature heat radiation from the plasma container can be isolated.

The invention discloses a cable operation environment optimization method based on cable trench collection information, and relates to the technical field of intelligent operation and maintenance of a power system, and the method comprises the steps: collecting an original parameter data set in a cable trench; a pulse current signal is collected at a cable grounding wire connection point, and a standard pulse discharge capacity sequence is generated through band-pass filtering and amplitude integral conversion; based on the original relative humidity measurement value and the environment temperature value, calculating a compensated relative humidity value through a multi-scale heat conduction-diffusion coupling equation; calculating a cable insulation risk index through a quantum annealing algorithm by using the compensated relative humidity value and the standard pulse discharge capacity sequence; the dynamic measurement deviation of the temperature and humidity sensor is subjected to double compensation of thermal radiation and molecular adsorption by constructing a multi-scale heat conduction-diffusion coupling equation, so that the reliability of environmental state sensing is greatly improved, and the operation safety, stability and intelligent operation and maintenance level of a power cable system are effectively enhanced.

A heat radiation structure includes a vapor chamber provided along a surface of a die, a mesh interposed between the die and the vapor chamber, and a liquid metal impregnated in the mesh. In the mesh, a peripheral portion has a higher material density per unit volume than a central portion. In the mesh, the central portion may be formed of a single layer, and the peripheral portion is formed of two layers. The mesh may be a resin material.

The invention discloses an intelligent fire detection and quick response method based on image recognition. The method comprises the following steps: S1, synchronously acquiring and preprocessing multi-source image data; s2, carrying out multi-modal feature fusion and early fire identification; s3, intelligent grading response based on deep reinforcement learning: constructing a fire behavior grade dynamic evaluation network, inputting fused multi-modal features, outputting fire behavior grade probability distribution and a crisis index, judging a fire behavior grade according to a dynamic threshold value, and triggering a predefined grading response strategy; s4, performing cross-scene adaptive transfer learning; and S5, carrying out system self-diagnosis and dynamic optimization. According to the invention, through time-space synchronous acquisition and deep fusion of visible light, infrared and smoke multi-mode data, vision, thermal radiation and smoke characteristics of flames are integrated, and common interferences such as real fire behavior and lamplight, light reflection, a moving heat source, water mist and dust are effectively distinguished by using an improved lightweight CNN, a self-adaptive background temperature model and an environment airflow model.

The invention provides an intelligent target identification method and system based on an unmanned intelligent turntable. According to the method, an unmanned intelligent rotary table is used for collecting a thermal imaging video at night, a thermal radiation map is generated through compensation, and a background heat conduction coefficient is extracted. After the high-temperature heat source is positioned, calculating a centroid trajectory, and combining a background coefficient and an environment gradient to establish a dynamic reference temperature field; the heat source is divided into a contact sub-region and a non-contact sub-region according to the heat exchange rate difference, and the boundary mutation of the contact sub-region is marked. And analyzing the collaborative response of the temperature gradient change rate and the boundary deformation rule of the contact sub-region to judge an abnormal behavior. If abnormal, tracking parameters are generated based on the mass center acceleration to adjust the posture of the rotary table to keep the target in the middle, and non-contact subarea temperature diffusion is utilized to verify continuity so as to optimize tracking. According to the method, intelligent discrimination and adaptive closed-loop tracking of night abnormal behaviors are realized by analyzing the dynamic thermodynamic characteristics of the heat source contact area and the deformation rule cooperative response.

The invention relates to the technical field of image processing, in particular to an optical thin film quality detection method based on image processing, which comprises the following steps: S101, acquiring a transient light field image sequence of an optical thin film in a dynamic stress field; s102, performing spectral analysis on the brightness time sequence of each pixel point, extracting a dominant frequency component as a brightness oscillation period, processing time domain fluctuation data by adopting a Fourier transform algorithm, and calculating a local optical path difference variation based on the brightness oscillation period of each pixel point in the image sequence; and S103, projecting the phase distortion field to a preset film elastic modulus distribution model, establishing a phase distortion-stress distribution-defect mapping relation, and identifying a modulus mutation region. A dynamic stress field is generated through the synergistic effect of mechanical vibration and thermal radiation, remarkable stress concentration can be generated at the micro-defect position, the deformation difference of the micro-defect is amplified, and therefore the sensitivity of defect detection is improved.