91 results about "Infrared remote sensing" patented technology

The invention relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, and the method comprises the steps: obtaining a plurality of continuous frames of infrared images, carrying out the intelligent partitioning of a reference frame, and calculating the variance feature, the method comprises the following steps: selecting regions of interest with rich information, independently executing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, adopting an abnormal value detection algorithm to improve robustness, and finally realizing sub-pixel-level image alignment and intelligent weighted fusion. The method is suitable for real-time enhancement processing of satellite-borne infrared remote sensing images, the resource constraint requirement of an embedded platform is met while the processing quality is guaranteed, and an efficient and reliable technical scheme is provided for space remote sensing image processing.

The invention discloses a background spectrum accurate acquisition method for infrared remote measurement in a complex scene, and the method comprises the steps: carrying out the division of sky and ground object backgrounds for an input background image, and selecting different radiation spectrum generation modes based on a division result; if yes, sky radiation spectrum extraction is carried out based on an MODTRAN radiation transfer model; if the background is judged to be a single ground object background, performing background spectrum extraction by adopting ELM-EMD (Extreme Learning Mode-Empirical Mode Decomposition) which is extracted in real time based on a measurement spectrum and changes slowly at low frequency; and if the background is not single, fusion of the two spectrum extraction modes is adopted. The method has the advantages that the background classification method with the visible image as the reference is adopted, the difference of infrared radiation characteristics of the sky background and the ground feature background is analyzed, different spectrum extraction modes are selected according to the difference, and therefore accurate obtaining of the target smoke plume transmittance spectrum is achieved.

The invention provides a control system and method for a Fourier interference infrared spectral imaging remote sensing instrument, and relates to the technical field of infrared remote sensing, and the system comprises a main control module, a sub-control module, a control interface, an execution element and a spectral imaging display system. And the main control module is used for controlling the sub-control module to control the execution element through the control interface according to a logic process set by a program so as to obtain Fourier interference infrared spectral imaging remote sensing data of an observation target, and completing display and control functions of a human-computer interaction interface through the spectral imaging display system. According to the invention, centralized control management of an optical-mechanical-electrical-thermal whole system of an instrument is realized, automatic work and operation of a remote sensing instrument system are realized, and rapid acquisition and analysis of Fourier interference infrared spectrum imaging are realized by using an area array detector.

The invention discloses a deep learning-based satellite remote sensing collapsible loess foundation evaluation method, which comprises the following steps of: acquiring an optical remote sensing image, a radar remote sensing image and an infrared remote sensing image of a target area, and preprocessing; collapsibility feature weighted data are screened, and feature weights are distributed; feature extraction is carried out through a local space exhibition structure and a multi-layer Transform structure of the collapsibility foundation evaluation network; collapsibility area self-supervised collaborative learning is carried out, and self-supervised training is carried out based on spatial correlation, pseudo labels and spatial consistency regular terms; and carrying out risk grade division on the target area, and outputting a collapsibility risk result of each spatial position. According to the method, multi-mode remote sensing and deep learning are fused, high-precision intelligent partitioning of the collapsible loess foundation is achieved, and the method has the advantages of being self-adaptive, low in manpower and high in spatial resolution.

The invention provides a soybean growth monitoring method based on satellite remote sensing, and relates to the technical field of crop growth monitoring, and the method specifically comprises the steps: setting two monitoring nodes according to a soybean growth period, and collecting multispectral and thermal infrared remote sensing images and normal growth leaf area density and stem diameter sample data; a soybean area and a non-soybean area are distinguished by using a vegetation index threshold, cloud / shadow pixels are eliminated by using a multiband combination-threshold method, effective pixels are screened, and two types of remote sensing data are matched. Extracting leaf area density, canopy temperature and collaborative inversion stem diameter from the effective data; calculating the relative growth rate of the two types of indexes based on samples, and constructing a collaborative growth reference equation and a residual standard deviation; and substituting the growth rate of the planting area into an equation to obtain a theoretical value, calculating a deviation value, and judging the soybean growth state by combining the comparison between the deviation and a normal fluctuation interval and the positive and negative characteristics of the growth rate.

The invention discloses an infrared target detection method based on a bidirectional receptive field attention feature network, which is realized by constructing a model comprising a feature coding network, a feature fusion network and a detection head, and improves the detection precision and model generalization ability of an infrared remote sensing image, especially a small target. A receiving field attention convolution module is introduced into the feature coding network, different spatial positions in a receptive field are independently weighted, fine-grained feature extraction is enhanced, and the problem that multi-scale target feature extraction is insufficient is solved; further, the feature coding network optimizes feature extraction and fusion operations by alternately stacking RFAConv modules and standard convolution modules, so that key features of a target are reserved and enhanced to the greatest extent, and the problem of poor adaptability of a complex background is solved; the feature fusion network adopts a bidirectional feature pyramid network, high-level semantics and low-level detail information are bidirectionally and efficiently fused, and the problem of information loss in the fusion process is solved.

The invention discloses a lightweight unmanned aerial vehicle infrared remote sensing target detection method and device based on knowledge distillation, and belongs to the technical field of infrared weak and small target image recognition and deep learning model compression. In order to solve the problems of low detection precision, high omission ratio, high calculation complexity and difficulty in deployment at edge equipment of an existing unmanned aerial vehicle infrared remote sensing image weak and small target detection algorithm, an infrared remote sensing image data set is constructed, and a basic network model containing an intra-scale feature efficient interaction and fusion module is constructed; a teacher model and a student model are designed, a lightweight student network is trained by using a channel dimension knowledge distillation mechanism, and optimization is performed by using a WIoU loss function based on a distance attention mechanism and a soft-NMS, so that multi-scale feature fusion and model compression are realized. According to the method, the accuracy and robustness of weak and small target detection are improved, the model parameter quantity and the calculation quantity are reduced, and the method is suitable for real-time application in the fields of environmental monitoring, military reconnaissance and the like.

The invention provides a transformer substation noise traceability and diffusion simulation method fusing multi-source remote sensing data. Belongs to the technical field of power environment protection and remote sensing science. The method comprises the following steps: acquiring thermal infrared remote sensing image data of a transformer substation, after preprocessing, finding out a temperature anomaly region of main noise source equipment by using a temperature anomaly recognition algorithm, determining the position of the main noise source equipment in combination with equipment layout information, and generating position data of the noise source equipment; a laser radar is used for scanning a transformer substation and surrounding areas to obtain high-precision point cloud data, the high-precision point cloud data are processed to generate high-precision digital elevation model data, and a transformer substation three-dimensional scene model containing detailed topographic features and noise source positions is constructed in combination with noise source equipment position data. Temperature abnormity of main noise source equipment is identified by means of thermal infrared images, noise sources of specific equipment such as a main transformer and an electric reactor can be accurately positioned, equipment-level traceability is realized, the situation that traceability is difficult in a traditional method is changed, and a clear direction is provided for targeted noise treatment.

This invention provides a method, apparatus, and device for generating high-fidelity infrared remote sensing images. The method includes: acquiring a visible light remote sensing image to be processed; cropping the visible light remote sensing image to obtain a cropped visible light remote sensing image; and inputting the cropped visible light remote sensing image into a pre-trained infrared image generation network to generate a corresponding high-fidelity infrared remote sensing image. Specifically, during the training process of the pre-trained infrared image generation network, visible light remote sensing image samples are used as input data, while radiation spectral characteristic mapping data is embedded as prior information into the design of the pre-trained infrared image generation network. A specific loss function is designed to guide the learning direction of the pre-trained infrared image generation network. This allows the interpretability of the physical model to enhance the generalization ability and reliability of the deep learning model, thereby improving the fidelity and reliability of the high-fidelity infrared remote sensing image ultimately generated by the pre-trained infrared image generation network.

The invention discloses a multi-mode remote sensing image weak and small target detection method based on super-resolution. The method comprises the following steps that firstly, feature fusion is carried out on input RGB and infrared remote sensing images through BFM to generate comprehensive features, and the BFM can effectively make up for the defect that weak and small target information is insufficient in a single mode; the features are then sent to a backbone network to generate multi-level features. Secondly, a backbone network in the SA-YOLO is responsible for further fusing the multi-level features in a feature pyramid network, and then classification and regression are carried out in a detection head; a branch network in SA-YOLO selects low-level and high-level features output by a backbone network, and local texture and semantic information are fused to carry out super-resolution reconstruction. According to the method provided by the invention, the detection precision of the weak and small target in the multi-mode remote sensing image is effectively improved, and the method has a wide application prospect in the aspects of military reconnaissance, civil monitoring, disaster early warning and the like.

The invention relates to a multi-source remote sensing image-based visual large model construction method, device and equipment and a medium, and the method comprises the steps: researching a multi-source remote sensing image-based visual large model for solving the problems that the image feature difference of an optical remote sensing image, an SAR remote sensing image and an infrared remote sensing image is large, and the cross-source generalization of the model is weak; by fusing multi-modal data complementary features and establishing a cross-source target unified representation space, the bottleneck that a single model is insufficient in adaptability to different image source features is solved, the relevance of target texture, geometry and radiation characteristics in a multi-source image is emphatically mined, a multi-level feature interaction and adaptive fusion mechanism is designed, and the multi-source image feature fusion is realized. The semantic consistency understanding of the model on the cross-source target is enhanced, a visual large model with strong generalization ability is finally constructed, a high-precision supervision signal is provided for an on-satellite lightweight small model, and the stability of the cross-source remote sensing image target detection performance is ensured. According to the method, the dependence on the target domain annotation data can be reduced, so that the model keeps good generalization ability.

This invention provides a method for creating an infrared small target detection dataset, including S1, segmenting a satellite infrared remote sensing image to obtain at least two background images with different environments; S2, creating simulated target points; S3, adding simulated target points to the background images and fusing them with the background images; and S4, expanding the dataset of simulated target points. This invention uses space-based remote sensing satellite images as a background, providing a large data foundation for remote sensing infrared small target detection; it achieves diversity in complex backgrounds, motion characteristics of small targets, morphological diversity of small targets, and type diversity of small targets, making the simulated data closer to the required real data and greatly enriching the content of the dataset.

The application relates to the technical field of image processing, and discloses an infrared image enhancement method and system based on local phase correlation, wherein the infrared image enhancement method based on local phase correlation is used for acquiring continuous multiple infrared images, performing intelligent block division on a reference frame, calculating variance features, selecting an information-rich region of interest, independently performing phase correlation operation in each region to extract a local translation vector, obtaining global displacement estimation through weighted fusion, improving robustness by using an outlier detection algorithm, and finally realizing sub-pixel level image alignment and intelligent weighted fusion. The application is suitable for real-time enhancement processing of spaceborne infrared remote sensing images, can guarantee processing quality while meeting the resource constraint requirements of an embedded platform, and provides an efficient and reliable technical scheme for space remote sensing image processing.

The invention is suitable for the technical field of infrared telemetering gas detection and data processing optimization, and provides a multi-source infrared telemetering gas identification optimization method and system, and the method comprises the steps: sorting samples from small to large according to the average value of the actual conveying speed corresponding to a speed increase event in each sample, and analyzing the change trend of the compensation residual optimization degree along with the actual conveying speed based on the sample feature information so as to judge whether a turning interval which firstly ascends and then descends exists or not, and if yes, adjusting the set conveying speed of the belt type conveying device based on the actual conveying speed of the speed increasing event of each sample in the turning interval. According to the method, a technical thought of reversely optimizing the infrared telemetering methane recognition performance by utilizing micro speed fluctuation caused by the maintenance period is provided for the first time by systematically analyzing a slight speed increase event naturally generated by the belt type conveying device in the maintenance period.

The application discloses a kind of multi-wavelength pumped mid-infrared upconversion spectral detection methods, its characteristics are that the method uses multi-wavelength narrowband laser as the pump source of nonlinear conversion, simultaneously adopt multi-period quasi-phase matching crystal as nonlinear upconversion medium, realize mid-infrared signal broadband and efficient conversion, and combine high-performance near-infrared spectral measurement and control element, finally obtain wide band, high resolution, ultra-sensitive mid-infrared spectral detection.The application has the characteristics of large bandwidth and high efficiency compared with existing upconversion spectral technology, simultaneously can high fidelity resolution mid-infrared spectral information, for realizing wide band, high resolution, ultra-sensitive mid-infrared spectral measurement provides effective means, is expected to be applied to molecular spectroscopy, trace substance detection, infrared remote sensing and atmospheric monitoring and other important fields.

The utility model is suitable for the technical field of electric power engineering, and provides an electric power engineering construction investigation device which comprises an unmanned aerial vehicle body, a camera, a spectrograph, an infrared remote sensing analyzer and a glass protective cover are installed on the bottom face of the unmanned aerial vehicle body, and a cleaning mechanism is further arranged on the bottom face of the unmanned aerial vehicle body. The cleaning mechanism comprises a mounting shaft rotationally mounted on the glass protective cover, sweeping rods fixedly mounted on the mounting shaft and a driving assembly for driving the multiple sweeping rods to synchronously rotate, the sweeping rods are in clearance fit with the outer side face of the glass protective cover, and wiping strips are arranged on the inner walls of the sweeping rods; the glass protective cover provides protection for the camera, the spectrograph and the infrared remote sensing analyzer, and equipment is prevented from being impacted by foreign objects and polluted by dust, water vapor, rain, snow and the like. And the cleaning mechanism can clean the glass protective cover to ensure that the surface of the glass protective cover is clean, so that internal equipment can obtain clear and accurate data, and the problem of equipment performance reduction caused by lack of protection and cleaning of an existing device is solved.

The invention provides a broadband bidirectional reflectance distribution function (BRDF) measurement method and system of source-to-direction hemispherical reflectance (DHR), belongs to the technical field of bidirectional reflectance distribution function measurement, and is used for reflection spectrum distribution measurement of materials at different wavebands and detection angles. Existing researches on the aspects of BRDF measurement, modeling, application and the like mainly aim at visible light and near-infrared bands, spectrum BRDF measurement is directly achieved through a broadband light source, the method has the advantages of being rapid, simple and convenient, however, when an incident signal is decomposed to a wavelength point, energy is weak, and the measurement signal-to-noise ratio is affected. According to the invention, the BRDF measurement absolute value is traced to the direction hemisphere reflectivity by using the relation between the BRDF and the reflectivity and adopting a BRDF relative measurement method, so that the measurement of incident light and a detection solid angle is avoided. In the aspect of detection, a band-pass spectrum is selected by using an optical filter, and broadband BRDF measurement is carried out. According to the method and the system, complete measurement of the BRDF is realized, and the method and the system have important significance in the fields of infrared remote sensing and radiation temperature measurement.

A diaphragm device for simulating an infrared target and a control circuit thereof relate to the technical field of infrared remote sensing external field radiometric calibration, and comprise a driving assembly, a base, a diaphragm disc, a vertical rod and photoelectric switches, a step-shaped cylinder is arranged at the center of the base, and a plurality of photoelectric switches are uniformly distributed along the circumferential direction of the edge of the base. A vertical rod is arranged between any two optoelectronic switches, the diaphragm disc is rotationally connected to the step face of the cylinder, a plurality of diaphragm pieces are arranged in the circumferential direction of the diaphragm disc, the diameters of diaphragm holes in the centers of the diaphragm pieces are different, a shielding rod is fixed to the position, between any two diaphragm pieces, of the diaphragm disc, and the shielding rod is fixed to the position, between any two diaphragm pieces, of the diaphragm disc. The lower part of the shielding rod penetrates through the diaphragm disc and is in contact with the vertical rod; the diaphragm disc is driven by the driving assembly to rotate, and elastic sheet assemblies are correspondingly arranged on the two sides of the upper face of the diaphragm disc. According to the invention, the automatic switching of different diaphragm apertures is realized, the precise control of the diaphragm aperture position is realized through the cooperative work of the driving circuit, the decoding precision is high, and the operation is convenient.

This invention relates to the fields of data processing and remote sensing information processing technology, specifically to a remote sensing carbon source and sink monitoring and assessment method based on a multimodal hybrid expert model. The method includes: feature extraction from multi-source remote sensing data (hyperspectral, visible, and infrared); multimodal fusion of the extracted features; construction of a large-scale multimodal hybrid expert carbon source and sink model using a hybrid expert partitioning sparsity processing mechanism to detect and segment carbon source and sink changes, obtaining a carbon source and sink distribution map; assessment of annual carbon emissions / absorption rates for different types of land cover using a combination of source and sink distribution and ground measurement data; and calculation of regional carbon emissions and absorption based on land cover type area and annual carbon emissions / absorption rates to obtain carbon flux assessment results. This invention can be applied to carbon metering, regional carbon emission inventory compilation, and carbon neutrality effectiveness assessment, providing technical support for achieving the "dual carbon" goal and improving the effectiveness of remote sensing carbon source and sink monitoring and assessment.

This invention discloses a wireless network-based intelligent fire monitoring method and system, relating to the field of fire detection. The method includes: deploying infrared thermal imaging monitoring nodes based on a wireless mesh network; periodically monitoring through these nodes to obtain infrared image data; acquiring temperature data from the infrared image data and calculating surface humidity data; obtaining a two-dimensional image of fire hazard level distribution based on the surface humidity and temperature data; and conducting manual or robotic patrols based on the two-dimensional fire hazard level distribution image. This invention uses infrared remote sensing to calculate the surface humidity and ground temperature of combustible materials within the detection area, combined with multi-node collaborative monitoring via a wireless mesh network and a quasi-effective timing correction algorithm. This fundamentally transforms fire prevention from a passive response to proactive prevention, significantly improving early warning accuracy and the efficiency of resource utilization in fire control.

This application relates to the field of infrared remote sensing instrument calibration technology, and provides a high-dynamic-range, large-aperture, high-precision on-board blackbody radiation source and its temperature control method. The main body of this on-board blackbody radiation source is temperature-controlled by a refrigerator, exhibiting a large dynamic range, capable of temperature control from 200K to 310K. Through a three-layer structure consisting of the radiation source cavity, a cooling inner shell, and an outer shell, zoned temperature control via a heat transfer bracket, and a three-layer thermal insulation installation design, the temperature uniformity and stability of the blackbody radiation source are significantly improved, with temperature uniformity exceeding 30mK. Simultaneously, it possesses high emissivity (spectral emissivity reaching 0.998) and an aperture as large as 310mm, meeting the requirements for large field-of-view, long-distance use.

A high-precision radiometric calibration method for very high sensitivity infrared sea imaging data is proposed. Firstly, the blackbody calibration data on the infrared remote sensor satellite are used to calculate the radiometric calibration coefficient and detect the blind pixels. Then, the image data are corrected by the primary relative radiometric correction and blind pixel compensation correction. Secondly, a radiometric correction method based on small sample statistics is proposed to remove the residual stripe noise after the primary correction, which further improves the radiometric quality of the infrared image. Finally, the blind pixels in the image are detected and removed.

The application provides a near real-time fire point dynamic monitoring method, device, equipment and medium, relates to the technical field of infrared remote sensing monitoring, and the method comprises the following steps: acquiring thermal infrared remote sensing data and land cover product of a forest and grass monitoring area, and performing data preprocessing; the thermal infrared remote sensing data is segmented to obtain tile data, and cloud extraction is performed based on the tile data; the extracted cloud is used for mask of the thermal infrared remote sensing data, and the medium infrared band and the far infrared band are segmented to obtain an isothermal graph spot layer; the isothermal graph spot layer is masked through water body and building area corresponding to the land cover product; the masked isothermal graph spot is comprehensively identified based on the statistical characteristics of the isothermal graph spot and the comparison characteristics of the isothermal graph spot and adjacent background graph spots, and a fire point detection result in the forest and grass monitoring area is obtained. The application alleviates the technical problems of pixel-by-pixel identification, large amount of calculation, low efficiency and limited precision in the prior art.

The utility model relates to the technical field of measurement, in particular to an infrared telemetering scanning device, which comprises a transverse support, a clamping type telescope is mounted on the transverse support, one end of the transverse support is rotatably connected with a longitudinal support through a pin shaft, the longitudinal support is in transmission connection with a longitudinal motor, the longitudinal support is connected with a transverse motor, and the transverse motor is connected with the clamping type telescope. A through hole is formed in the longitudinal support, an output shaft of the transverse motor penetrates through the through hole to be in transmission connection with the transverse support, a galvanometer control mechanism is arranged in the longitudinal support and the longitudinal motor, and the galvanometer control mechanism reflects light to a detector. The utility model has the advantages of small volume, simple structure, low power consumption and good stability.

This invention discloses a lightweight method, device, and medium for small target recognition in infrared remote sensing images. The method includes the following steps: Step S1: Constructing and preprocessing an infrared remote sensing image dataset; Step S2: Constructing a lightweight backbone network HLNet, training the HLNet based on the infrared remote sensing image dataset, and obtaining a trained HLNet network; Step S3: Constructing a lightweight multi-scale feature fusion network LSF, inputting the feature map output by the trained HLNet network into the LSF for multi-scale feature fusion, and obtaining an enhanced multi-scale feature map; Step S4: Inputting the enhanced multi-scale feature map into a detection head module for small target recognition, and obtaining the recognition result. This invention can effectively recognize small infrared targets in complex backgrounds while reducing model complexity.

The application discloses a kind of dam leakage passage digital twin systems and diagnostic methods based on flow-heat coupling inversion reconstruction, the diagnostic method includes: obtaining the time series thermal infrared remote sensing observation data of dam to be measured, environmental driving data and digital twin dam model;Under the assumption of no leakage, seepage-heat coupling simulation is carried out, and the benchmark temperature field that is continuous in space-time is generated;Based on the difference between time series thermal infrared remote sensing observation data and benchmark temperature field, extract thermal infrared anomaly index;Seepage-heat coupling inversion model containing physical constraint and structured constraint is constructed, and three-dimensional leakage parameter field inside dam is obtained by minimizing the difference between observation and simulation inversion;It is written back to digital twin dam model to carry out seepage stability calculation, and the leakage risk classification early warning result of dam is obtained.The application effectively solves the problem of ill-conditioned and environmental interference of thermal infrared inversion, realizes the structured reconstruction and risk quantitative evaluation of dam leakage passage.

The application provides an infrared remote sensing image target classification method and device, and relates to the technical field of infrared target detection and identification. The method comprises the following steps: obtaining a target slice containing a ship target from an infrared remote sensing image; based on the target slice, extracting infrared radiation features and gray statistical features of the ship target; associating and fusing the infrared radiation features and the gray statistical features of the same target slice to generate double-domain fusion features which take into account spatial details and thermal physical mechanisms; and based on the double-domain fusion features, determining the category of the ship target.

The invention relates to the technical field of infrared remote sensing, in particular to a multiband high-speed infrared image excitation generation system which comprises a simulation image generation unit, a data processing unit, a data comprehensive display unit, a data transmission unit and a data conversion unit. The simulation generation unit generates an infrared simulation image containing a plurality of wave bands and target information according to the input parameters. And the data processing unit transmits the infrared simulation image to the data comprehensive display unit and the system to be tested through the data transmission unit and the data conversion unit, receives target identification information and sends the target identification information to the data comprehensive display unit together. And the data comprehensive display unit displays the infrared simulation image and marks a target and a target identification result. The invention also comprises a method. Simulation images and targets are more vivid and practical, target parameters can be flexibly set, and powerful support is provided for robustness and stability testing of a detection system. According to the invention, the high-speed transmission rate of system data is realized, and the high-speed data transmission requirement can be met.

The invention discloses a semantic understanding method based on cross-modal image target detection, and relates to the technical field of target detection, and the method comprises the steps: carrying out the feature extraction of a visible light remote sensing image and an infrared remote sensing image through a multi-branch convolutional neural network; performing multi-scale fusion operation on the extracted features through a feature pyramid network; inputting the feature map obtained by the fusion operation into a region proposing network, generating a visible light remote sensing image feature candidate region and an infrared remote sensing image feature candidate region, and extracting features from the candidate regions; matching the candidate region features through a cross-modal similarity measurement network to obtain an association matching result; determining a visible light semantic feature map and an infrared semantic feature map according to an association matching result; and obtaining a target detection result and target semantic description based on the visible light semantic feature map and the infrared semantic feature map. Therefore, the target detection accuracy and the target description accuracy are improved.

The application relates to a long-wave infrared remote sensing imaging spectrometer objective, which is composed of a meniscus positive lens A, a meniscus negative lens B and a meniscus positive lens C arranged in sequence along the same optical axis; the convex surface of the meniscus positive lens A and the meniscus negative lens B faces the object side; the convex surface of the meniscus positive lens C faces the image side; an aperture diaphragm is located on the front surface or the rear surface of the meniscus positive lens A, and the chief ray of each field of view is parallel to the optical axis when exiting the lens; and infrared radiation emitted by an object is imaged on the focal plane of the objective. The application effectively corrects the aberration of the lens and realizes the output of a telecentric light path; when the temperature changes, the focal plane displacement of the objective matches the expansion and contraction of the aluminum alloy lens barrel, and the imaging position is relatively stable.