318 results about "Polarization imaging" patented technology

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention discloses a laser radar and multispectral polarization imaging fused target identification system and method. The system comprises a sensor configuration and data preprocessing module, a cross-modal feature extraction and fusion module and a multi-task output and optimization module. The sensor configuration and data preprocessing module performs time-space synchronization processing on the collected original optical signals and laser signals in the environment to obtain multispectral image data and laser radar point cloud data; the cross-modal feature extraction and fusion module performs feature extraction and fusion enhancement processing on the multispectral image data and the laser radar point cloud data, and outputs high-dimensional semantic enhancement point cloud representation containing image semantics and point cloud geometry; and the multi-task output and optimization module processes the high-dimensional semantic enhanced point cloud representation and outputs a three-dimensional target recognition result, target speed information and a pixel-level depth map. Through module design and data processing, the defects in the prior art are overcome, and the accuracy of target recognition is improved.

The invention discloses a surface defect detection device and method based on polarized optical dark field imaging, and belongs to the technical field of industrial defect imaging detection, the device comprises a polarization camera, an imaging lens, a polarization annular LED light source and a sample to be detected which are coaxially arranged; wherein the polarization camera and the imaging lens form a polarization imaging system, the polarization annular LED light source emits linearly polarized light with an inclination angle, and the linearly polarized light is reflected and scattered on a sample to be detected; the linearly polarized light enters the surface of a to-be-detected sample, a small amount of reflection and most of scattered light of the to-be-detected sample are collected by the imaging lens and the polarization camera through a circular hole in the center of the polarization annular LED light source, and finally imaging is achieved. The defect depth can be calculated according to the defect width and the defect inclination angle, so that the defect depth is inversed by the low-frequency linear polarization degree, and the problem that the defect depth information cannot be effectively extracted in the prior art is solved.

The invention discloses a deep foundation pit crack feature coding detection method based on image recognition, and aims to solve the problems that a two-dimensional image is difficult to accurately characterize a three-dimensional crack, and the opening width and the slab staggering height are inaccurately estimated, the deep foundation pit crack feature coding detection method comprises the following steps of: calibrating and collecting through short time sequence and multi-polarization imaging, segmenting the crack to extract a center line, and dividing a left side domain and a right side domain; carrying out local reconstruction in a mask neighborhood by adopting a nerve radiation field or three-dimensional Gaussian sputtering, establishing a left implicit surface and a right implicit surface, fusing a polarization solution line, applying occupancy priori and seam-crossing repulsive potential in a gap region, combining a reference object to obtain an absolute scale, measuring a millimeter-level opening and a dislocation along a central line section, and generating a feature code; and threshold value and time sequence comparison judgment is carried out, and the technical effects of high-precision three-dimensional measurement, reliable risk assessment and traceable management of the deep foundation pit crack are achieved.

The invention provides an imaging method and system based on a polarization imaging lens and visual collaborative optimization, and a medium, and the method comprises the steps: integrating a four-way polarization filter array on the surface of an imaging sensor, and synchronously collecting a four-channel polarized light intensity image through single-frame exposure based on the imaging sensor; obtaining a three-channel RGB image, and inputting the four-channel polarized light intensity image and the three-channel RGB image into a polarization-RGB fusion neural network; polarization features and RGB features are extracted, heterogeneous information coupling optimization is carried out, and fusion features are obtained; constructing a polarization-color joint decoding network, carrying out polarization and RGB information collaborative decoding on the fusion features, training a polarization decoding process and a material classification task together based on an end-to-end joint optimization strategy, and outputting a material classification result; high-efficiency acquisition of original polarization information on a hardware level is guaranteed through the polarization filtering array; and an end-to-end joint training strategy is adopted to realize optimal decoupling and fusion of perception information.

The invention relates to the technical field of image recognition, in particular to a plate defect recognition and detection method based on an image recognition model. The method comprises the following steps: aiming at the technical problems of insufficient texture direction identification precision and difficulty in defect type distinguishing in a traditional plate surface detection method, the invention provides an intelligent detection solution based on polarization imaging. A multi-angle polarization measurement system is constructed through a fixed polarizer and a rotatable analyzer, and omnibearing polarization characteristic collection of the surface of a steel plate is achieved. A polarization intensity change model is established based on the Malus law, the polarization degree and polarization angle parameters of each pixel point are extracted, and a polarization parameter spatial distribution diagram reflecting the surface microstructure is constructed; through statistical analysis of polarization angle distribution characteristics, a rolling direction dominant angle is identified by adopting a peak detection algorithm, and accurate classification and severity evaluation of defects are realized in combination with anisotropy characteristic analysis.

The invention relates to the technical field of bionic navigation orientation and positioning, in particular to a carrier course angle measuring method based on polarized light imaging in a non-Rayleigh atmosphere. Comprising the steps of real-time sky polarized light imaging, atmospheric polarization mode resolving based on Mueller matrix imaging, non-Rayleigh polarization information interference removal, polarization orientation resolving based on Rayleigh scattering, space projection conversion and sun position information resolving. Alignment information of the sun in a carrier coordinate system is obtained by utilizing a Rayleigh scattering model and actual sky polarization imaging, and then a carrier course angle is obtained according to the orientation of the sun in a local geographic coordinate system, so that an orientation function is realized; meanwhile, the error influence caused by image registration and time-sharing rotation measurement of the multi-view-field polarization detector and the interference of environmental factors on a single point are avoided, the method can adapt to sunny and cloudy weather, is also applicable to cloudy and other non-Rayleigh weather, and is wide in application range.

The invention discloses a broadband full-Stokes polarization imaging device based on non-orthogonal four-channel polarization beam splitting, which comprises a dielectric substrate, a non-orthogonal polarization beam splitting metasurface and a focal plane detection array, and is characterized in that the non-orthogonal polarization beam splitting metasurface is composed of a plurality of sub-wavelength unit structures; the sub-wavelength unit structure is a single-layer anisotropic medium nano-column array and is configured to perform intrinsic-loss-free non-orthogonal four-channel beam splitting on incident light and form four spatially separated focuses on the same focal plane, and polarization states corresponding to the four channels form a regular tetrahedron measurement base on a Poincare sphere; therefore, full Stokes parameter measurement is completed by four pixels; the sub-wavelength unit structure forms a super-pixel unit, and the super-pixel unit is aligned and integrated with a focal plane detection array in a two-dimensional array form to form a compact polarization camera facing focal plane segmentation. The device is simple in structure, easy to integrate with the CMOS front end, small in device size, low in power consumption and high in mass production feasibility.

The invention provides a wall surface damaged structure detection method based on polarization imaging, and relates to the field of image recognition, and the method comprises the steps: collecting damaged wall surface single-channel gray scale digital images in four polarization directions of 0 degree, 45 degrees, 90 degrees and 135 degrees to construct a polarization imaging data set; taking the 90-degree polarization direction image as input, and combining with a scale smoothing kernel and a non-Gaussian anisotropy kernel to obtain a multi-scale polarization structure enhanced feature map; generating a polarization angle feature and a polarization degree feature based on the polarization intensity image, and constructing a local structure reconstruction tensor; constructing a multi-direction geometric perception kernel by using the polarization degree features, extracting damage response features, and fusing the damage response features with the polarization intensity image to output a damage probability graph; and integrating the above steps into a unified wall surface damage structure detection model, and updating model parameters through an iterative optimization training strategy to realize accurate detection of a damage area.

The invention relates to the field of image processing, and discloses a wind turbine generator blade surface defect intelligent detection method based on image processing, which is used for improving the accuracy of wind turbine generator blade surface defect detection. Comprising the steps that pulse laser thermal excitation infrared thermal imaging and a multi-angle polarization light source array polarization imaging technology are fused, a bimodal data set is constructed, and thermal anomaly features of a dynamic thermal image sequence are extracted; and photoelastic stress characteristics are obtained through phase analysis and local energy filtering. Generating a fusion defect indication diagram by utilizing heat conduction blocking and stress concentration space coincidence strengthening mechanism fusion features, obtaining a defect probability distribution diagram, calculating a predicted risk value, matching a maintenance strategy library according to the risk value, outputting a maintenance scheme code, and generating a three-dimensional maintenance guide diagram. According to the invention, intelligent and accurate detection and maintenance guidance of blade surface defects are realized, and the comprehensiveness and accuracy of defect detection are improved.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention discloses a bionic visual positioning system and method based on multispectral perception and a storage medium, and the system comprises a front-end multidimensional optical perception module, a mesopic vision preprocessing module and a rear-end fusion perception and target positioning module, the front-end multi-dimensional optical sensing module is used for performing optical imaging, spectral decomposition and polarization imaging; the mesopic vision preprocessing module is used for carrying out image preprocessing and optical flow estimation; and the rear-end fusion perception and target positioning module is used for feature fusion and target identification and positioning. According to the bionic visual positioning mechanism, through a bionic compound eye structure + multispectral + polarization + optical flow fusion perception strategy, the perception bottleneck of a traditional visual system in a high-dynamic and complex illumination environment is effectively solved, and the bionic visual positioning mechanism has the advantages of high robustness, low delay, wide field of view, multiple modes and the like; the method is especially suitable for application scenes such as automatic driving, unmanned aerial vehicles, robots and the like with extremely high requirements on real-time performance and reliability.

The invention discloses an end-to-end polarization hyperspectral image classification method and system, belongs to the technical field of deep learning and optical imaging, and solves the technical problems of low reconstruction process speed, limited precision, low information utilization rate in a classification process and weak feature extraction capability in the prior art. The method comprises the following steps: carrying out target shooting based on a snapshot type space coding hyperspectral polarization imaging system, and carrying out system coding on a shot image to obtain two-dimensional aliasing data; reconstructing the two-dimensional aliasing data into a polarization hyperspectral data cube by using the trained reconstruction network; training the classification network based on the polarization hyperspectral data cube to obtain a trained classification network; and performing joint fine tuning on the trained reconstruction network and the trained classification network to obtain a polarization hyperspectral image classification model, and performing polarization hyperspectral image classification by using the polarization hyperspectral image classification model. The method is used for realizing high-quality reconstruction and accurate classification of the polarization hyperspectral image.

The invention discloses a bridge underwater safety detection method based on opto-acoustic dual modes, and relates to the technical field of underwater engineering detection, and the method comprises the following steps: obtaining water area particle size distribution and concentration data by using a multi-frequency acoustic suspended sediment concentration profiler, establishing an environmental parameter database, configuring a shipborne detection device according to the initial environmental parameter database, and detecting the underwater safety of a bridge according to the shipborne detection device. The method comprises the following steps: setting a three-axis stabilized platform carrying a polarization imaging camera and a multispectral LED array, deploying a multi-beam detection module and ADCP equipment, and establishing a unified coordinate system; multi-angle original intensity images are collected through a polarized optical imaging module, and multi-exposure fusion is carried out after processing is carried out through a mutual information minimization algorithm. An optical image and an acoustic point cloud are deeply fused by adopting a graph neural network, the detection precision is improved through dynamic weight optimization of an attention mechanism and a space-time registration algorithm, a multi-index fusion model is constructed based on fuzzy logic and a D-S evidence theory for adaptive early warning decision, upgrading from single-parameter alarm to multi-dimensional decision is realized, and the false alarm rate is reduced.

The present invention provides a graphic recognition method and obstacle avoidance system for intelligent agricultural robots, which relates to the technical field of image pattern recognition. By introducing polarization imaging, the present invention effectively suppresses water droplet highlights, avoids misjudging optical artifacts as diseases, and significantly improves recognition accuracy. For low-confidence targets, the original micro-area thermal excitation physical review mechanism can confirm their true physical properties online, solving the problem of recognition uncertainty; in addition, the dynamic risk volume constructed based on multi-dimensional information, combined with the adaptive speed control of path risk foresight, realizes differentiated intelligent obstacle avoidance for rigid and flexible obstacles. While ensuring safety, this method greatly improves the robot's travel efficiency and smoothness in complex environments.

Embodiments can relate to systems and methods for generating hyperspectro-polarimetric images. The system can include a metasurface having a plurality of super pixels configured to encode spectral and polarization image data into spatial intensity distributions. The system can include an image capture device having at least one image sensor and a plurality of subsets of pixels configured to capture the spatial intensity distributions encoded by the metasurface. The system can include a processor in communicative connection with the image capture device. The processor can be configured to receive pixel data from the image capture device, generate captured image input data based on the pixel data, and access at least one computational reconstruction model configured to decode the captured image input data, and generate image output data based on the decoded captured image input data.

The invention discloses a scattering characteristic adaptive polarization imaging non-uniform fog transmission method, which comprises the following steps of: acquiring polarization images at different angles, and calculating Stokes vectors, linear polarization degrees and dual-channel polarization intensity images of the images; constructing a non-uniform fog scattering feature, and estimating a dual-channel non-uniform scattering intensity image; estimating background light at infinity, and calculating a dual-channel polarization intensity transmissivity image; constructing an attenuation matrix of the polarization dual-channel variable-scale degradation model; a polarization dual-channel variable-scale degradation imaging model is constructed, and preliminary restoration of a dual-channel non-uniform fog scene is solved; and iterating the optimal polarization variable-scale degradation matrix to obtain a final restored image of the fogless scene. According to the scattering feature self-adaptive polarization imaging non-uniform fog transmitting method, the problems of overexposure and the like caused by target polarization information loss in a non-uniform fog scene are solved, complete removal of global non-uniform fog of the scene is realized, and target scene information is restored more clearly.

The utility model relates to the technical field of super-structure lenses, and particularly discloses a fully-integrated polarization imaging device based on a polarization beam splitting super-lens. The fully-integrated polarization imaging device based on the polarization beam splitting superlens comprises a nanopillar array, a transparent medium substrate and an image sensor, a nanorod array is arranged on one side surface of the transparent medium substrate; the image sensor is arranged on the other side, far away from the nanorod array, of the transparent medium substrate; the image sensor is provided with at least two pixel areas; the light beams output from the nano-pillar array pass through the transparent medium substrate and then are split and focused to the pixel area. According to the utility model, the nanorod array, the transparent medium substrate and the image sensor are integrated together, so that the interference of an external light path is reduced, the imaging stability and reliability are improved, the structure is compact, and the size is small. The nanorod array can accurately control polarization and phase of light, high-resolution imaging is achieved, and the problems that an existing polarization system is low in integration degree, large in size and low in resolution are solved.

This invention relates to the field of optical imaging and detection technology, specifically providing a broadband snapshot-type polarization interferometry imaging detection system and method for breast tumors. A broadband light source illuminates a breast tumor tissue sample. The reflected signal light is collected and collimated by a pre-optical system, then sequentially passes through a first and a second ray shaping unit for two shearing and two polarization state transformations, resulting in two beams of linearly polarized light and two beams of circularly polarized light. These beams are imaged by an imaging lens to an area array detector to obtain a polarization interferogram, which is then demodulated to obtain all Stokes parameters of the breast tumor tissue sample. This invention achieves broadband snapshot imaging of breast tumors with all Stokes parameters without using an analyzer, doubling the light throughput compared to traditional polarization imaging instruments using analyzers. Furthermore, this invention has advantages such as compact structure, no moving parts, easy assembly and adjustment, and no need for image registration.

The invention discloses a polarization degree and light intensity image fusion method based on bidirectional cross attention. The method belongs to the technical field of polarization imaging, computational imaging and image processing. The technical problems that in the prior art, dynamic balance between structural detail keeping and polarization saliency enhancing is generally difficult to achieve, and particularly under the condition of a complex background or a low signal-to-noise ratio, texture missing or noise artifacts are prone to occurring in a fusion result are solved. A fusion mechanism capable of establishing a two-way information interaction relationship between a light intensity image and a polarization degree image is provided, and further processing is carried out aiming at a noise problem, so that more stable and more reliable polarization fusion imaging is realized.

Embodiments described herein relate to an apparatus that includes a light engine with a plurality of light sources. In an embodiment, each of the plurality of light sources is configured to emit a spectral band with different wavelength bandwidth. The apparatus may also include a beam splitter that is optically coupled to the light engine, where the beam splitter splits the spectral bands into a first optical path and a second optical path. In an embodiment, a power monitor is optically coupled to the beam splitter along the first optical path, and a reflective objective lens is optically coupled to the beam splitter along the second optical path. In an embodiment, the reflective objective lens includes a first mirror and a second mirror. In an embodiment, the apparatus further includes an optical sensor configured to measure the spectral bands after the spectral bands have reflected off of a substrate.

The embodiment of the invention relates to the technical field of computer vision, in particular to a water surface oil spill detection method based on infrared polarization imaging and polarization characteristic enhancement, which comprises the following steps: shooting a target area within a specific observation angle by using an infrared polarization camera to obtain an infrared polarization mosaic image, the polarization information difference between the spilled oil in the target area and the water surface is maximized; inputting the infrared polarization mosaic image into a polarization feature enhancement network for denoising and demosaicing processing to obtain a multi-dimensional polarization information image so as to enhance the contrast ratio of spilled oil of the target area to the water surface; and inputting the multi-dimensional polarization information image into a detection backbone network, firstly extracting oil spill characteristics through a middle layer of the detection backbone network, then carrying out oil spill detection through a detection head of the detection backbone network, and finally obtaining and outputting an oil spill detection result of the target area. According to the method, the precision and speed of water surface oil spill detection are greatly improved.

The invention belongs to the technical field of optical imaging and automatic detection, and particularly relates to a multispectral polarization imaging automatic scanning system. According to the system, through combination of multispectral polarization imaging and an AI algorithm, rapid positioning and classification of a target are realized, and the precision and efficiency of trace detection and material evidence identification are improved on the premise that a sample is not damaged. The multispectral polarization imaging automatic scanning system comprises a single-axis detachable slide rail, a polarization camera, a multispectral light source module, a small darkroom with an open bottom, a control panel and a main control computer. The two ends of the single-axis detachable sliding rail are supported by the adjustable tripod, the portable small darkroom is fixed below the movable end of the sliding rail through mechanical connection, the polarization camera and the multispectral light source are installed in the darkroom, and the darkroom is driven by the stepping motor to conduct single-axis scanning motion. The multispectral light source module supports switching of more than six wave bands within the wave band of 450-950 nm, wave band switching can be completed within 5 ms, and stable illumination intensity (0-500 lux) is provided. The process provided by the invention comprises the steps of equipment deployment, scanning path configuration, light source regulation and control, image acquisition, data analysis and the like, and a high-precision detection report can be quickly generated in various scenes. Through cooperative work of the control panel and the main control computer, the system realizes automatic image acquisition, target detection and report generation, is widely applied to the fields of trace detection, material evidence identification, industrial quality inspection and the like, and has the characteristics of automation, high efficiency and high precision.

The invention discloses an underwater polarization imaging method based on a deep learning algorithm, and the method employs a data collection module, a deep learning module and an image output module, and comprises the steps: collecting a large amount of polarization image data in different underwater scenes through the data collection module; inputting the data into a deep learning module to train a deep learning model to obtain a trained deep learning model; and inputting a newly collected underwater polarization image into the trained deep learning model to obtain a clear underwater imaging picture. Through mass data training, the model generalization ability is high, a good imaging effect on a new scene is achieved, meanwhile, parameters can be adjusted in real time, the imaging quality can be optimized, and high-quality image support is provided for underwater detection and other activities.

The invention discloses an image fusion method and device based on infrared polarization imaging, and relates to the technical field of image processing. The method comprises the following steps: acquiring infrared polarization image data of a target scene; based on a polarization bidirectional reflection distribution function, decomposing the image into three kinds of feature original element images including total radiation intensity, linear polarization degree and polarization angle; processing according to a preset task mode to obtain a feature primitive image; calculating the regional information abundance measurement of each feature primitive image and the regional complementarity measurement between every two feature primitive images; and generating a fusion weight map through a normalized exponential function, and outputting a fusion image after carrying out weighted fusion on the feature primitive image. Through physical feature decomposition, task targeted processing and quantitative measurement fusion, the problems of low richness and low accuracy of fused image information in a traditional method are solved.

The invention relates to a metal surface weld feature positioning method and system based on polarization imaging, and the method comprises the steps: collecting a plurality of polarization information images of a metal surface in different polarization directions, and calculating the Stokes parameter images of the polarization information images; processing the plurality of polarization information images to obtain strong reflection light component images; segmenting the strong reflection light component image to obtain an initial target laser line image; segmenting the second Stokes parameter image to obtain an initial background laser line image; performing morphological operation on the initial background laser line image to obtain a final background laser line image; performing logical operation on the initial target laser line image and the final background laser line image to obtain a final target laser line image; and reconstructing a laser line track and positioning welding seam feature points. According to the method, the dependence of the traditional technology on a large number of manual parameters is avoided, and the robustness, the adaptive capability and the generalization performance in the complex illumination and reflection environment are greatly improved through the adaptive image processing flow.

The invention belongs to the technical field of photoelectric detection imaging, and particularly relates to a dim target enhancement device and method based on range gating polarization imaging, the device comprises a transmitting system and a receiving system, the transmitting system comprises a pulse laser, a signal modulation circuit and a transmitting optical system; the pulse laser is used for emitting laser beams; the signal modulation circuit is used for generating a high-frequency modulation electric signal and modulating a laser beam emitted by the pulse laser; the emission optical system is used for focusing and adjusting the light beam direction and the emergent light polarization state; the receiving system comprises a receiving optical system, a focal plane polarization detector, a synchronous control board, a signal processing board, an image processing board and display control equipment; according to the device, the pulse illumination module, the time synchronization control module and the polarization imaging module are integrated, and an image definition evaluation algorithm is combined, so that synchronous acquisition and fusion of target polarization information and distance information are realized.

The invention discloses a polarization imaging system based on a super-structure micro-lens array and a construction method of the super-structure micro-lens array, and relates to the technical field of polarization imaging. The polarization imaging system comprises at least one refraction lens, a metasurface full Stokes polarization micro lens array and a pixel plane, and the pixel plane is formed by arranging four detector pixel arrays; the metasurface full-Stokes polarization micro-lens array is composed of a plurality of single micro-lens arrays, and a polychromatic light source focuses when incident to the refraction lens and then is transmitted to the metasurface full-Stokes polarization micro-lens array. The metasurface full Stokes polarization micro-lens array splits light in different polarization states and focuses the light to a pixel plane, and real-time polarization imaging is carried out on an object. According to the invention, the energy utilization efficiency of a single polarization is improved from 1 / 4 to 1 / 3, which has positive significance for polarization imaging and also has positive significance for function integration and volume miniaturization of an optical imaging system.

A building material defect visual detection system driven by artificial intelligence is characterized in that a multispectral imaging module is used for performing high-resolution image acquisition on the surface of a building material under different illumination conditions and wave bands, and comprises a visible light camera, a near-infrared camera and a polarization imaging unit; the texture, color, reflectivity and microstructure information of the surface of the building material can be synchronously obtained, the self-adaptive illumination control unit is linked with the multi-spectral imaging module, and the intensity, angle and spectral distribution of a light source are automatically adjusted according to the type of the material of the building material, so that the contrast ratio of a defect area and a normal area is enhanced. Through deep linkage of the multispectral imaging module and the self-adaptive illumination control unit, the system quickly identifies the material type through first frame image analysis or RFID reading at the beginning of detection, and calls an optimal illumination parameter combination from a preset strategy library, for example, low-angle polarized light is started for a metal plate to suppress glare, so that the material type can be quickly identified. Near-infrared penetration of the composite panel is enhanced to reveal internal debonding.

The invention discloses a self-reference three-phase polarization imaging system and method based on an integrated photon neural network, and the method comprises the steps: setting a fixed phase difference in a silicon-based photon chip to achieve three-phase projection and single-port reading, outputting a three-phase interference signal at the same time, obtaining all phase and polarization information in one-time imaging to achieve full-polarization measurement, and carrying out the single-port reading of the silicon-based photon chip; according to the invention, the effective resolution ratio is consistent with that of an original detection array, power and temperature drift is eliminated, single-frame acquisition has no time sequence error and motion artifact, a stable and compact single-port self-reference reading mode is realized through a three-phase interference measurement architecture constructed on a chip in combination with channel normalization and rotation correction, and complete Stokes parameters and polarization information can be measured. According to the system provided by the invention, the MZI is adopted, so that the phase bias can be flexibly adjusted, and different bandwidth requirements can be met by adjusting an interference path. The silicon-based photon platform has a broadband response characteristic and can cover a visible wave band, so that the device can keep stable interference performance in a communication wave band.