11246 results about "Optical polarization" patented technology

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

The invention belongs to the technical field of welding quality detection, and particularly relates to a visual image-based welding seam defect detection method, which comprises the steps of image acquisition, image preprocessing, data analysis, data output, defect classification and decision making, system closed-loop optimization and the like. According to the method, by synchronously collecting two-dimensional images, three-dimensional shapes and heat distribution data of metal welding seams and adopting a polarization filter and annular LED light source combination scheme, multi-dimensional conjoint analysis of physical defects and thermodynamic characteristics is achieved, basic characteristic data are extracted through primary processing, quantifiable defect coefficient indexes are generated through secondary processing, and the detection accuracy is improved. And finally, generating a comprehensive defect index through a multi-modal fusion algorithm, constructing a well-arranged intelligent analysis decision chain, and establishing a self-evolution mechanism of data acquisition-analysis decision-model iteration through real-time interaction of a detection result and an algorithm model. The system can continuously optimize the detection threshold value and the characteristic weight parameter according to the actual working condition of the production line, and the continuous improvement of the detection sensitivity is kept.

A method and device for inverting ocean-atmosphere optical parameters and storage medium. The method includes: obtaining a vector apparent reflectance ρl, ρQ, ρU of water-leaving radiance at top of atmosphere and observation geometry of a multi-angle polarized satellite, and inputting the vector apparent reflectance ρl, ρQ, ρU and the observation geometry into a trained ocean-atmosphere optical parameter model, to output a group of ocean-atmosphere optical parameters, the output ocean-atmosphere optical parameters comprise: PBOAw, FAOD / CAOD, FRm / CRm, W, CHL and SPM, the ocean-atmosphere optical parameter model is an XGBoost machine training model. In this way, the ocean-atmosphere optical parameters can be quickly and stably inverted.

The invention belongs to the technical field of iron stick yam image recognition detection, and discloses an iron stick yam intelligent system and method based on image recognition, and the method comprises the steps: dynamically adjusting the polarization direction to generate a polarization suppression image, and generating a space mapping relation; based on the polarization suppression image, segmenting the main body area of the iron stick yam, filling and optimizing the hole edge, generating a high-precision contour mask, further extracting the texture difference between wrinkles and cracks, generating a direction sensitive characteristic pattern, and evaluating morphological defects; constructing a comprehensive feature vector, training a Gaussian mixture model to generate reference distribution, calculating a quality deviation index, and judging a risk level; constructing a double-branch feature vector, outputting a preliminary comprehensive quality score through a complementary aggregation model, introducing a planting density-curvature physical model, obtaining a final quality score, dynamically adjusting a quality judgment threshold, dividing quality grades, and triggering a response strategy according to confidence grade to form a closed loop; and comprehensiveness and accuracy of Chinese yam quality evaluation are improved.

The invention discloses a quartzite surface defect detection method, system and device, and relates to the technical field of image processing, and the method comprises the following steps: fixing a to-be-detected quartzite on a detection platform, and carrying out preprocessing; constructing a double-path polarization imaging light path; polarization parameter optimization is carried out based on the optical anisotropy characteristic of quartz stone, so that a polarization state difference is generated between a defect area and a normal area; under the condition of polarization parameter optimization, synchronously acquiring a first polarization image and a second polarization image through a double-path polarization imaging light path; performing polarization difference calculation on the first polarization image and the second polarization image to generate a polarization difference image; and carrying out contrast enhancement processing on the polarization difference image, identifying a defect area, carrying out defect classification, and outputting a quartz stone surface defect detection result. By optimizing polarization imaging parameter configuration and combining image processing, high-precision detection and intelligent classification of quartz stone surface defects are achieved, and the automation level and reliability of detection are improved.

The invention relates to the technical field of visual inspection, and particularly discloses an optical communication filter appearance defect detection method and related device.The optical communication filter appearance defect detection method comprises the steps that polarization image sets of an optical communication filter to be detected at different polarization angles are obtained; preprocessing the polarization image set to obtain a defect enhancement image set; performing defect detection on the defect enhancement image set based on a pre-trained multispectral attention fusion network; according to the method, multi-polarization information, special preprocessing and a deep learning network combined with frequency domain-space feature fusion and a cross-modal attention mechanism are utilized, the detection capacity of the defects which are tiny, low in contrast and interfered by film layer textures on an optical communication filter is improved, and the omission ratio is reduced.

The invention relates to the technical field of machine vision technology and agricultural disease and insect pest diagnosis, in particular to a rice disease and insect pest identification method and system based on the machine vision technology, and the method comprises the following steps: S1, obtaining the polarization direction distribution of reflected light on the surface of a leaf, and generating a polarized light compensation image; s2, performing feature layer fusion to generate a multispectral fusion image; s3, based on the polarization phase difference value of each pixel point in the multispectral fusion image, segmenting an independent leaf area; s4, performing multi-dimensional feature extraction on the segmented leaf area; s5, performing Euclidean distance calculation, and determining a nearest neighbor matching result; and S6, according to a feature similarity threshold value in a matching result, determining the disease and pest species. According to the invention, through fusion of multi-source spectral information and structured feature comparison, high-precision identification of rice diseases and insect pests and scab space positioning are realized, and the intelligent level and practicability of disease diagnosis are improved.

The invention provides an adjustable spectrum-based liquid crystal display system and a control method thereof, relates to the technical field of display systems, and aims to overcome the limitations of poor environmental adaptability, easiness in causing visual fatigue, single user experience and the like of an existing display. The system creatively integrates a liquid crystal panel, a high-precision multi-main-color Micro-LED backlight, a pixel inner sensing array, an external multi-mode sensing module, a dynamic addressable micro-optical array, a dynamic polarization modulation layer and an optional transparent energy collection layer. The intelligent controller carries out fusion processing on the real-time biological state, environment information and application context data of the user obtained by the multi-source sensor, and carries out accurate state evaluation and future trend prediction. And determining an optimal space-time-direction-spectrum-polarized light field output characteristic and an intelligent energy management strategy through a multi-objective optimization decision engine, and driving each optical regulation and control component to execute in real time. The visual comfort and health level are remarkably improved, and the information fidelity and task performance in a complex environment are ensured.

The invention relates to the technical field of image processing, and discloses a vehicle surface defect identification method and system based on artificial intelligence. The method comprises the following steps: acquiring a vehicle surface image through polarization filtering multi-angle imaging; processing the standardized image by applying HSV color conversion and texture equalization; constructing three-dimensional deformation features in combination with parallax analysis, and extracting defect feature vectors; and identifying defects by using the position-aware convolutional neural network, and outputting a severity thermodynamic diagram. According to the invention, in a complex illumination environment, tiny defects of vehicle surfaces with various colors and materials are accurately identified and evaluated, and accurate three-dimensional positioning and severity quantification of the defects are realized at the same time.

The invention provides a self-adaptive polarization modulation-based inter-satellite laser communication and signal scheduling method, which relates to the field of laser communication, and comprises the following steps of: acquiring polarization state information of emitted and received light beams, calculating a mismatch degree, generating a compensation parameter, constructing a polarization state prediction model by using a deep learning neural network for pre-correction, and calculating the mismatch degree; a polarization modulator is adopted to perform real-time modulation and establish a link quality evaluation model, carrier synchronization and channel compensation are realized based on an adaptive optical carrier recovery technology of coherent detection, a link state is monitored in real time, and a training data set is updated.

The invention relates to a method and a system for inhibiting strong mutual reflection based on polarization complementary logic coding. The method comprises the following steps: acquiring calibration parameters of a camera and a projector; generating a polarization phase shift stripe based on the calibration parameter, designing a high-frequency complementary logic code word, and embedding the high-frequency complementary logic code word into a polarization channel through polarization modulation to obtain a composite stripe pattern; the method comprises the following steps: calculating to obtain a linear polarization degree image through an acquired multi-angle polarization image, extracting a high-reflection region from the linear polarization degree image by adopting an adaptive threshold segmentation method, and carrying out binarization processing on the linear polarization degree image to obtain a binarized linear polarization degree mask; and on the basis of a decoding range of a binary linear polarization degree mask constraint high-reflection region, decoding the composite stripe pattern to obtain absolute phase level information, and performing noise suppression on the absolute phase level information by adopting a self-adaptive threshold algorithm. The method can effectively solve the influence of mutual reflection and high dynamic range of the surfaces of the measured object, and improves the precision and stability of three-dimensional reconstruction.

The invention provides a shield tunnel longitudinal seam joint recessive damage diagnosis method based on multi-mode ultrasonic guided waves, and the method comprises the steps: applying orthogonal polarization in a specific region of a longitudinal seam joint, and forming a space-time coupled guided wave interference pattern in the whole region of the longitudinal seam joint; the acquired guided wave interference map is utilized to separate baseline interference characteristics and abnormal disturbance components caused by hidden damage through multi-physics field reverse decoupling, and the abnormal disturbance components are represented as energy abnormal attenuation and phase distortion in a specific polarization direction; constructing a three-dimensional damage sensitive vector field in the longitudinal seam joint; and carrying out dynamic manifold topology analysis by utilizing the generated three-dimensional damage sensitive vector field, identifying topological characteristics in the three-dimensional damage sensitive vector field, and tracking a migration path and an intensity evolution trajectory of the topological characteristics along with the change of the detection condition. According to the method, full-dimension diagnosis and evolution prediction of the hidden damage of the longitudinal joint connector of the shield tunnel are realized.

The invention provides a polarization three-dimensional reconstruction method and a polarization three-dimensional reconstruction system based on a prior guide diffusion model, which apply a diffusion model in the field of polarization three-dimensional reconstruction and improve the recovery capability of complex details and the robustness of noise interference resistance. According to the method, a two-stage training mode is adopted, and the generation quality and the calculation efficiency are balanced through step-by-step optimization. In the first stage, a VQGAN codec is independently trained to learn high-efficiency low-dimensional potential representation of an image, and direct high-cost calculation in a pixel space is avoided; in the second stage, learnable parameters of the VQGAN are frozen, a diffusion model is trained on a trained potential space, gradual denoising is guided through a priori condition, and potential features are generated and mapped back to an image space. The diffusion model effectively fuses the physical constraint of the polarization clue and the data prior in the gradual denoising process, and the surface normal with rich details can still be stably generated in the case of noise interference or information loss. Experimental results show that the method provided by the invention is excellent in surface normal reconstruction in a plurality of complex scenes.

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

The invention provides a multi-layer optical film detection method, device and equipment and a storage medium, and the method comprises the steps: obtaining a polarization phase difference signal of transmission light and reflected light through multi-angle incidence irradiation of a multi-band programmable laser source, and recording the polarization ellipticity parameter change; then combining confocal microscopic interference and polarization analysis to determine a space coordinate and a stress distribution diagram of a stress abnormal region; continuously irradiating in a photo-thermal coupling environment through a broadband light source, and monitoring interference signals, polarization states and temperature changes to obtain dynamic evolution characteristics; and finally, performing a simulation test on the multi-layer optical film in a system environment with preset optical axis arrangement and light beam energy distribution, obtaining an in-situ polarization state and interference pattern data, and evaluating the optical performance of the film layer. According to the invention, potential defects can be positioned under high resolution, and the reliability and imaging quality of the film layer under complex application conditions can be accurately evaluated.

The invention discloses an industrial area atmospheric environment monitoring management system and method, and relates to the technical field of industrial Internet of Things environment monitoring, and the method comprises the steps: triggering polarization imaging to scan a target region based on the time-space information of an abnormal event signal, obtaining the polarization characteristic data of a pollution plume, and generating a spatial distribution characteristic parameter; inputting the sound pressure abnormal indexes and the spatial distribution characteristic parameters into an industrial equipment leakage causal model, calculating a pollution source confidence coefficient, and when the pollution source confidence coefficient reaches a process safety dynamic threshold value, obtaining an industrial equipment traceability result and a leakage level; according to the traceability result of the industrial equipment and the leakage level, a hierarchical control instruction is obtained, meanwhile, the actually measured attenuation rate of the industrial pollutants is calculated, and the dynamic response effect is verified; according to the method, cross-modal fusion modeling is carried out on acoustic abnormal information and pollutant spatial distribution characteristics, so that the technical limitation that pollution source positioning is carried out by depending on a static empirical formula traditionally is broken through.

The invention discloses a waste plastic classification method and system based on visual identification, and particularly relates to the technical field of plastic classification. Reflection suppression is realized through an industrial camera equipped with a polarization filter and a multi-angle cross polarization light source, and the structure and color characteristics of a shielded area are effectively restored by combining brightness equalization, reflection area detection and image restoration technologies; and then semantic segmentation and target classification are completed by using an improved deep neural network model, a control signal is generated based on an identification result, and an execution mechanism is driven to realize accurate sorting of multiple types of plastics, so that the identification robustness and the sorting efficiency of the system under a complex illumination condition are improved.

The invention discloses a magnetic material defect detection system based on image recognition, which adopts a first acquisition module, a first determination module, a second determination module, a second acquisition module and a third acquisition module, and is characterized in that the first acquisition module is used for performing multi-angle illumination control on the surface of a magnetic material by adopting a polarized light imaging device; mirror reflection generated by the strong light reflection characteristic is inhibited by adjusting the angle of a polaroid and the incident angle of a light source, original image data with uniform illumination distribution are obtained, and if it is detected that an overexposure area exists in an image, exposure parameters and the polarization angle are automatically adjusted to obtain a high-quality image suitable for follow-up processing; and the first determination module is used for performing multi-scale wavelet transform decomposition according to the acquired high-quality image, identifying a surface defect area by analyzing texture features and edge information in different frequency components, and judging that the surface defect area is a potential defect area if a wavelet coefficient in the high-frequency component exceeds a preset threshold value. The detection performance is obviously improved.

The invention discloses a package defect elimination method based on visual inspection, and relates to the technical field of image processing and computer vision, and the method comprises the following steps: setting a polarized light source with a fixed angle on a package production line, and uniformly illuminating a target package area to enable the incident light polarization direction and the package surface reflection direction to form a deviation angle; an industrial camera provided with a polarization filter is used for carrying out image collection on a packaging target area, and the polarization direction of the filter is perpendicular to the polarization direction of a light source so as to restrain reflection interference to the maximum extent. Reflection interference is eliminated through polarized light, and the image quality is improved; by combining a neural network and feature fusion scoring, the defect recognition accuracy and adaptability are improved; a closed-loop elimination control and data tracing mechanism is introduced, the elimination precision is guaranteed, quality responsibility investigation is supported, and the intelligence and reliability of the packaging defect detection and elimination system are integrally improved.

A right-hand circularly-polarized patch antenna comprising a ground plane and a patch connected to each other with one or more wires for which the wire shape and location of the end points are selected such that they do not cause an antenna mismatch, and the electrical current carried in the wires produces an extra electromagnetic field subtracted from the patch field in the nadir direction.

The invention discloses a photovoltaic glass defect detection method and system based on image recognition, particularly relates to the technical field of visual inspection, and is used for solving the problem that defect recognition is interfered by optical artifacts caused by a film layer interference effect on the surface of coated glass. The method comprises the following steps: extracting interference characteristics through a reflection image to generate an optical artifact distribution diagram, positioning an interference region in a transmission image frequency domain space, constructing a process response template in combination with a sedimentary pulse frequency, and executing polarization coherence screening and time-varying deconvolution collaborative filtering to eliminate artifacts; based on energy ratio dynamic decision reinspection or defect classification process implementation, inherent optical noise and real physical defects of a material are effectively separated, and the accuracy and reliability of coated glass defect detection are remarkably improved.

The invention provides an image recognition system and method based on deep learning, and the system comprises a self-adaptive optical collection module, a heterogeneous preprocessing pipeline, a hierarchical reconfigurable convolutional network, a multi-dimensional training optimization engine and a cross-modal verification output interface, aperture parameters are dynamically adjusted through deep reinforcement learning; the heterogeneous preprocessing pipeline comprises a quantum noise modeling non-local mean noise reduction unit, a double-discriminator generative adversarial network enhancement unit and a dynamic normalization unit; the hierarchical reconfigurable convolutional network adopts a staged feature distillation structure and comprises a separable convolution module, a mixed pooling layer and a three-dimensional attention fusion module. According to the invention, through a multi-modal data fusion and dynamic optimization mechanism, the image acquisition quality in a complex illumination and noise scene is improved, the adaptability of the model to different environments is enhanced, and all modules work cooperatively to realize an end-to-end efficient identification process.

The present invention relates to a solar azimuth estimation method and system based on multi-channel feature enhancement and region-aware attention, belonging to the technical field of intelligent navigation for low-altitude economy unmanned systems. Aiming at the problem of decreased accuracy in solar azimuth estimation based on polarization images under complex cloud cover conditions, the present invention proposes a deep learning framework integrating multi-channel features and direction-aware attention. First, based on polarization light field information acquired by a division-of-focal-plane polarization camera, a three-channel composite input feature composed of a polarization intensity map, an adaptive threshold gradient map, and high-frequency residual edge information is constructed. Second, a ResNet backbone network embedded with a squeeze-and-excitation mechanism is adopted, and a direction-aware polarization attention module is introduced to achieve adaptive fusion of multi-scale features through luminance guidance, deep feature enhancement, and a gradient edge branch.

The invention discloses a bone joint lesion area automatic labeling method and system based on image processing, and belongs to the field of bone joint lesion area labeling. According to the invention, a bone joint reflection signal is obtained by combining the polarization-state-adjustable laser light source with polarization-sensitive optical coherence tomography, and a three-dimensional birefringence distribution diagram is generated through matrix analysis. And after non-local mean filtering is carried out on the distribution map, a lesion edge contour is extracted based on a dynamic gradient amplitude, and a binary mask is generated. Mask pixels are converted into network nodes containing space coordinates, gradient directions and birefringence, and a topological network is constructed based on the Euclidean distance of adjacent pixels. The weight is calculated through the node intersection density and the birefringence, and a graph attention network is utilized to aggregate multi-layer features to extract lesion topological features. The features are mapped to a three-dimensional space to construct a lesion probability distribution field, an interactive labeling result is generated through contour surface extraction and transparency mixed rendering, and the accuracy of labeling the complex lesion area of the bone joint can be improved.

The invention relates to the technical field of optical fiber communication monitoring, in particular to a passive optical fiber multi-parameter digital twin drive abnormal root cause positioning method and system. Collecting temperature, stress, acoustics and polarization signals, and constructing a time domain, frequency domain and energy domain coupling feature tensor and time sequence data set; performing nonlinear dimension reduction and feature decoupling by using a beta-VAE model, and dynamically quantifying contribution of each parameter to anomaly by using an integral gradient to form a contribution degree vector group; constructing a PINN digital twinborn model embedded with heat conduction and elasto-optical effects, and predicting a normal fluctuation range under contribution vector weighting and data-physics dual constraints; modeling measurement and prediction deviations under the guidance of contribution vectors, and outputting an abnormal measurement score, confidence, a position and a time sequence; and a causal graph neural network is constructed, topology and abnormal events are fused for tracing causes, a fault source is identified, and the model is subjected to closed-loop calibration. According to the invention, data driving and a physical mechanism are fused, and accurate detection and root cause positioning of the abnormity of the optical fiber system are realized.

The invention belongs to the technical field of image processing and robots, and particularly relates to an image processing method and device and a robot, and the method comprises the steps: obtaining a structured light coding image, a polarization imaging image and a binocular depth image; constructing a multi-dimensional feature vector by fusing decoded contour phase, polarization analysis material characteristics and depth image data; dynamically calibrating the multi-dimensional feature vector according to the ambient light and the pose of the robot, correcting color deviation through an adaptive white balance algorithm, compensating a camera installation error, and obtaining a calibrated descriptor; based on the calibrated descriptor, utilizing an improved Hough transformation and template matching algorithm to identify the object category attitude, and calculating six-degree-of-freedom pose parameters through a triangulation method; and according to the six-degree-of-freedom pose parameters, a collision-free grabbing path is generated in combination with a robot kinematics model and obstacle information. Therefore, the problems of insufficient image analysis capability, single compensation mechanism, lack of path optimization capability and the like in the prior art are solved.

The invention discloses a dynamic optimization method and device for an automatic fiber placement path of a composite material and a medium, and belongs to the technical field of automatic fiber placement of composite materials. The method comprises the steps that a three-dimensional model of a component to be laid is obtained, and curved surface curvature distribution characteristics and fiber laying angle constraints are obtained based on the three-dimensional model; processing the curved surface curvature distribution characteristics based on a path planning algorithm to generate an initial fiber placement path; on the basis of the tension mapping table and the temperature compensation coefficient, the fiber placement head is driven to execute reverse pre-tension control; acquiring actual fiber trend data according to a preset sampling period based on a polarization laser polarization instrument, and extracting a fiber angle deviation of the actual fiber trend data; processing the fiber angle deviation and the resin viscoelasticity data based on a Bayesian optimization algorithm so as to dynamically correct the laying path and generate an updating instruction; and adjusting the spatial pose and tension of the fiber placement head according to the updating instruction. Based on the method, dynamic multi-target fiber placement path optimization and real-time feedback control are realized.

The invention relates to the technical field of unmanned aerial vehicle detection and identification, in particular to an unmanned aerial vehicle detection method and system based on visible light polarization imaging. According to the method, a channel state sequence with aligned time sequences is constructed by obtaining multi-source environment sensing data, cross-layer optimization parameters are dynamically calculated and decomposed into calculation and transmission characteristic quantities, and edge nodes are scheduled to cooperatively generate resource scheduling instructions; physical resources are matched to construct a virtual instance topology, a control instruction is generated through online reasoning of a dual-channel graph neural network training model, and finally the control instruction is fed back to the polarization sensor to achieve link calibration. According to the system, through fusion of polarization imaging and cross-layer optimization technologies, the accuracy and real-time performance of unmanned aerial vehicle detection in a complex environment are remarkably improved, meanwhile, computing resource consumption is reduced, and efficient spectrum multiplexing and power adaptive adjustment in a dynamic environment are achieved.

The invention discloses an intelligent steel pipe surface defect recognition system based on deep learning, and particularly relates to the technical field of pipe surface defect analysis. An annular polarization light source array and a high-frame-rate CMOS sensor are adopted to synchronously collect visible light and near-infrared multi-polarization images; a surface normal is calculated based on Stokes parameters, mirror surface suppression and diffuse reflection enhancement are realized, a defect candidate area is generated by fusing multi-scale Laplacian pyramid residual error and Renyi entropy segmentation threshold positioning, multi-physical quantity registration is completed through white light interference and infrared thermal imaging, a six-channel feature cube is constructed, and a three-dimensional image is obtained. According to the method, space, spectrum and thermal characteristics are jointly extracted in the multi-head attention convolutional neural network, the confidence coefficient is evaluated in combination with Jensen-Shannon divergence, and the polarization angle and the focal length are dynamically adjusted according to the confidence coefficient, so that closed-loop parameter self-optimization is realized, and the micro-scale pitting corrosion and millimeter-scale crack detection precision is remarkably improved.

The invention discloses a special environment laser radar system and method based on polarization modulation and laser coding. The laser radar system comprises a polarization modulation laser emitting unit, a multi-channel receiving unit and a compressed sensing reconstruction unit. The polarization modulation laser emission unit comprises a laser, a beam expanding module, a spatial light modulator, a micro galvanometer array, a spatial filter and a first focusing lens which are arranged in sequence; the multi-channel receiving unit comprises a second focusing lens, a polarization beam splitter and a single-pixel detector array which are arranged in sequence; and the compressed sensing reconstruction unit comprises a compressed sensing processing module, an image reconstruction module and a loop adaptive control module which are arranged in sequence. The anti-interference capability in a complex environment is enhanced through polarization modulation, the detection resolution and the signal-to-noise ratio are improved in combination with laser coding, the imaging quality and stability of the laser radar in a special environment are greatly improved, and the method can be widely applied to the fields of military reconnaissance, disaster rescue, unmanned driving and the like.