1160results about "Polarisation-affecting properties" patented technology

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 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 relates to the technical field of image data processing, and discloses a fine metal powder defect detection method and system based on image recognition, and the method comprises the steps: illuminating a metal powder region through a non-polarized light source, and collecting two images in an orthogonal polarization state through imaging equipment with a linear polarizer, according to the method, the defect morphological fingerprints are constructed through the polarization information entropy, the three-dimensional structure information is converted into light field order degree change for detection, the dimension limitation of traditional two-dimensional imaging is broken through, and the detection accuracy is improved. Hidden defects such as internal pores and aggregates can be accurately identified, and meanwhile, stable detection and material defect cross discrimination under a dynamic working condition are realized in combination with motion compensation and multiband entropy ratio verification.

The invention discloses an optical performance detection system and method for a camouflage material, and relates to the technical field of camouflage material detection, and the system comprises a structure identification and illumination control module which is used for collecting a surface image of a camouflage material sample, identifying the surface structure of the sample, starting a full-spectrum light source, and simulating step-by-step illumination from weak light to strong light; carrying out RGB imaging based on the standard illumination; and the region division module is used for applying a superpixel segmentation algorithm to the image, automatically dividing sub-regions, performing hyperspectral reflectivity scanning on each region, synchronously acquiring a light intensity image in a polarization direction, calculating a Stokes vector and constructing a polarization enhanced image. According to the method, frequency domain quantitative analysis of microcosmic interference fringes and accurate mask extraction of polarization abnormal areas can be realized, optical exposure risks of camouflage materials under complex illumination and visual angle conditions are effectively quantified through multi-angle reflection sampling and scoring function modeling, and the detection precision, the area pertinence and the automation level are remarkably improved.

The invention relates to a rail surface defect detection method and system, and the method comprises the steps: collecting a reflection response sequence of a rail surface under various illumination conditions through the controllable dynamic disturbance of incident angles and illumination intensity of different positions of the rail surface, and representing the microscopic optical response behavior of a rail surface material; for each tiny area on the surface of the rail, dynamic reflection stability is calculated, and areas with abnormal stability are marked as defect candidate areas; analyzing the difference between the defect candidate area and a normal wheel-rail contact trace, identifying whether abnormal residues or abnormal abrasion boundaries generated due to defect evolution exist or not by combining optical heterogeneity boundary characteristics, and verifying the authenticity of the defect; for the judged defect candidate region, judging whether the optical response of the adjacent region has a general change trend or not along the contact track direction and the normal micro-region fluctuation direction of the rail surface, and determining the space connectivity and the diffusion trend of the defect and a growth defect region; the system executes the steps of the method through a processor.

The invention relates to the technical field of optical detection, in particular to a glass bottle surface spraying quality detection method which comprises the following steps: under a specified wavelength, acquiring target incident light by adjusting a polarization angle, irradiating a glass bottle spraying layer, collecting a transmission spectrum signal to calculate parallel and vertical polarization transmissivity and ratio of the spraying layer, and generating polarization transmissivity. According to the method, the light with the polarization angle irradiates the spraying layer, the polarization component of the transmission light is obtained, the thickness uniformity and the internal structure are analyzed, weak areas, particle flaws and excessive spraying of the spraying layer are recognized, the defect detection precision is improved, multi-angle transmissivity data are collected, the change rate is calculated, and color deviation and thickness abnormity are accurately positioned; through cross analysis of multiple optical parameters, evaluation of adhesive force, curing quality and color consistency is more detailed, spraying abnormity is classified, multiple detection means act synergistically, interference such as environment illumination and material reflectivity is reduced, and the detection accuracy of spraying quality is improved.

Described herein are methods and systems for measuring polarization states of nonlinearly generated light by a sample illuminated in response to receiving an incident beam having a known polarization state to determine a characteristic of the sample. The system includes an optical ellipsometer configured to be included in-line of a semiconductor fabrication line and generate measured signals proportional to different polarization parameters of second-harmonic light nonlinearly generated light by the sample, allowing determination of a complete polarization state of the second harmonic light using a characteristic of the sample is determined.

To provide an optical measurement device, a data management method for optical measurement devices, and a data management program for optical measurement devices, which can achieve strict traceability required in the pharmaceutical industry.SOLUTION: An optical measurement device, which stores history data combining measurement data of an optical characteristic of a sample measured by measurement means of the optical measurement device with management information on a plurality of management target components detachably mounted on an optical measurement device body, and can trace the history data when used and required, comprises: recording means attached to the management target component for recording the management information on the management target component; reading means for reading the management information recorded by the recording means when the management target component is mounted on the optical measurement device body, or when the management target component is mounted on the optical measurement device body and measured by the measurement means; and storage means for storing the history data combining the read management information with the measurement data measured by the measurement means.SELECTED DRAWING: None

The invention relates to the technical field of flame retardant particle quality monitoring, in particular to a flame retardant particle quality online monitoring and sorting system based on AI visual identification. The system comprises a polarized light field imaging module, a refractive index gradient field construction module, a dynamic sorting decision module and an online self-optimization module. A Stokes parameter image and a multispectral transmittance distribution diagram of the particles are obtained through a polarized light field imaging module; the refractive index gradient field construction module is used for converting optical data into a refractive index gradient field and accurately identifying internal defects; the dynamic sorting decision module generates a sorting instruction according to the defect characteristics; and the online self-optimization module realizes continuous optimization of the system. The method solves the problem that a traditional method is difficult to accurately identify transparent defects, can effectively identify micron-sized pure bubbles and transparent cracks, realizes defect classification and cause analysis, can meet the real-time quality monitoring requirement of a flame retardant production line, and provides an intelligent solution for product quality control.

The invention discloses an intelligent water quality monitoring and evaluating system based on image recognition, which relates to the technical field of environment monitoring, and comprises a fusion module for controlling a polarizer array to rotate based on an environment parameter set, capturing water surface optical images in different polarization directions, and performing image reconstruction through a polarization characteristic tensor decomposition method, generating a high-definition water surface image after reflection suppression; the three-dimensional diagram generation module is used for acquiring a morphology stripe image of the water surface through a DLP projection method, and generating a three-dimensional depth diagram in combination with the high-definition water surface image; the pollution identification module is used for correcting the three-dimensional depth map based on the real-time water temperature data, identifying spatial distribution of pollutants through a dynamic structure element opening and closing algorithm and generating a three-dimensional pollution distribution map; according to the method, mirror reflection noise is suppressed through multi-angle polarization image fusion, and the distinguishability of pollutant characteristics is remarkably enhanced while the image resolution is kept.

The invention relates to the technical field of road engineering and artificial intelligence, and discloses a traffic road disease detection system and method based on a cloud edge-end architecture, and the system comprises: 1, a vehicle-mounted multi-mode optical sensing terminal which is used for synchronously collecting pavement images, spectrums and three-dimensional data; the vehicle-mounted edge calculation unit is used for constructing a digital twin baseline of the road surface grid unit and executing unique double-path parallel analysis, on one hand, the health degree attenuation degree of the current state compared with the baseline is quantified, and on the other hand, whether the change is caused by a natural disease or a manual maintenance event is judged through a feature signature recognition engine; and the cloud management platform is used for storing historical health degree data and predicting a future attenuation trend through a time sequence model. According to the method, the problems that early compound diseases are difficult to recognize and the maintenance behavior is easily misreported as the pain point of the diseases in the prior art are solved, the crossing from passive detection to active prediction is realized, and the accuracy of road detection and the scientificity of maintenance decision are remarkably improved.

The invention discloses a rapid polarized light acoustic imaging device which comprises an excitation source, a light beam shaping module, a rapid polarization modulation module, an energy feedback module, a rapid scanning module and an acquisition processing module. And receiving photoacoustic signals corresponding to the exciting light in four different polarization states for calculation, and extracting anisotropy information of the sample. Compared with multiple times of scanning a sample or modulating the polarization state by rotating a half-wave plate, the problems of dislocation of multiple times of polarization acquisition points and false positive calculation are avoided, meanwhile, the time interval generated by continuously modulating the laser pulses is short, and energy fluctuation among the four pulses and errors caused by fluctuation are reduced. According to the invention, the limitation of the precision and speed of polarized light acoustic microscopic imaging is overcome, the microstructure characteristics in the target sample can be rapidly, accurately and highly sensitively detected, and the method has great application prospects in biomedical imaging and high-precision material detection.

The invention discloses a melamine paper surface defect detection system based on optical detection, and relates to the technical field of industrial optical detection, and the melamine paper surface defect detection system comprises a polarized light signal generation module which employs a high color rendering index white light source to irradiate the surface of melamine paper, obtains a surface reflection light beam array carrying different polarization states, and modulates the surface reflection light beam array into eight groups of independent polarized light signals; the multispectral imaging and registering module inputs eight groups of independent polarized light signals into a free-form surface prism group and performs light splitting to generate a diffuse reflection texture image, a mirror reflection image and a near-infrared perspective image, and constructs a three-channel space registering image matrix; and the light path calibration module is used for extracting defect coordinate data and confidence score based on the pixel-level defect thermodynamic image, and performing light path compensation by adopting a real-time feedback regulation mechanism to obtain a dynamically calibrated stable optical imaging signal.

The invention relates to the technical field of image data processing, and discloses a method for detecting impurities in metal powder based on image recognition, which comprises the following steps of: irradiating the metal powder by using polarized light, synchronously acquiring an orthogonal polarization component image of the reflected light, and realizing impurity detection by calculating a differential image. Qualified powder spontaneously forms a uniform dark field in a differential image by utilizing the essential difference of response of metal powder and impurities to polarized light, and the impurities generate remarkable signals due to optical anisotropy, so that the detection blind area of the traditional method on the microscopic impurities is broken through, the method further performs time sequence analysis by multiplexing total luminous flux data, and the detection accuracy is improved. Synchronous identification of impurity morphological characteristics is realized, component and morphological two-dimensional detection capability is formed, the system realizes adaptive threshold adjustment based on statistical characteristics of differential images, and long-term operation stability is ensured.

The invention discloses a defect detection system for a metal-composite material mixed structure device. The defect detection system comprises an industrial control computer, a mechanical scanning arm, a polarization detection probe, an ultrasonic detector assembly and a power supply, the invention further provides a defect detection method for the metal-composite material mixed structure device. The defect detection method specifically comprises the following steps: acquiring polarization image information of the metal surface; solving polarization image parameter information; carrying out surface defect detection; metal internal ultrasonic detection information is obtained; performing internal defect detection; and performing multi-mode data fusion detection. According to the invention, two information sources with complementary advantages, namely polarization and ultrasound, are selected as detection information sources of the multi-modal detection method, so that all-directional defect detection and visualization on the surface-interior of the metal-based composite material mixed structure device are effectively realized; and a feasible technical method is provided for engineering application of polarized light / phased array ultrasonic multichannel integrated detection.

The invention belongs to the technical field of light polarization analysis, and discloses a method for determining the sex of an embryo in an egg based on polarization image recognition. Comprising the following steps: acquiring a transmitted spectrum image sequence of a hatching egg to be detected at a plurality of polarization angles, wherein the transmitted spectrum image sequence covers a transmitted spectrum image from visible light to a near-infrared band; extracting a polarization sensitive wave band set of the embryonic tissue according to the polarization state change characteristics of the embryo area in the transmitted spectrum image at different polarization angles; constructing an embryo gender characteristic spectrum based on the polarization degree distribution difference and the spatial texture characteristics of each wave band in the polarization sensitive wave band set; determining an optimal wave band combination for sex determination by analyzing the relevance between a polarization anisotropy index of a specific wave band in the embryo sex characteristic spectrum and an embryo development stage; according to the polarization response mode corresponding to the optimal wave band combination, the sex of the embryo in the egg is judged, and the resource investment and the waiting cost are greatly reduced.

The invention relates to the technical field of leaf nitrogen content estimation, in particular to a method for establishing a grassland plant nitrogen content monitoring model based on a remote sensing technology. The method comprises the following steps: controlling an unmanned aerial vehicle platform carrying a narrowband multispectral camera and a linear polarization camera, and collecting a spectral image sequence and a polarization image group at an observation window with a solar zenith angle of 30-40 degrees; extracting a reflectivity value of each wave band in the spectral image sequence, calculating a reflectivity first-order derivative and determining a red edge position offset; extracting horizontal and vertical polarization components in the polarization image group, calculating a polarization degree and constructing a polarization degree difference index; and identifying the center of the fecal spot by searching a pixel with a local maximum polarization degree difference index and red edge position offset blue shift exceeding a preset threshold value. According to the method, excrement spot recognition and remote sensing description of the nitrogen influence range thereof are achieved through double-index complementation of the red edge position offset and the polarization degree difference index.

The invention relates to the technical field of machine vision and optical detection, in particular to a tea color automatic evaluation method and system based on machine vision. The method comprises the following steps: constructing a stable and repeatable optical measurement environment and calculating radiation baseline parameters; performing multi-band polarization sequence imaging on the tea sample to obtain original data with spectrum and structural characteristics; separating mirror surface and diffuse reflection components on the surfaces of the tea leaves, and performing spectrum consistency correction by combining a radiation baseline; performing multiband spectrum reconstruction and normalization processing on the basis of the corrected diffuse reflection image, mapping to a CIE Lab color space, extracting pixel-level brightness, chromaticity and color distribution statistical indexes, and generating a color feature vector in combination with spatial weighting matrix optimization; a final score is calculated by constructing a quantitative evaluation and grade judgment model and integrating perception color separation, drying calibration color separation and a penalty term, and a grade result is output. According to the method, objective, accurate and automatic evaluation of the color of the tea leaves is realized.

The invention relates to the technical field of coating detection, and particularly provides an aluminum pigment thickness uniformity analysis and detection method and system.The method comprises the steps that a multispectral image and a polarization image of an aluminum pigment coating are obtained, and geometrical shape information of a base material is obtained; analyzing whether the aluminum pigment coating has a non-uniformity defect or not according to the multispectral image and the polarization image; when the non-uniformity defect exists, acquiring the defect type and the optical characteristic of the non-uniformity defect according to the multispectral image and the polarization image; acquiring a target light source parameter and an image sensor target receiving angle according to the defect type, the optical characteristic and the geometrical shape information; adjusting a light source and an image sensor of the aluminum pigment thickness uniformity analysis and detection equipment according to the target light source parameter and the target receiving angle of the image sensor; the method comprises the following steps: acquiring a coating image by using aluminum pigment thickness uniformity analysis and detection equipment, and acquiring non-uniformity defect characteristics according to the coating image; according to the method, adaptive adjustment of detection parameters can be realized.

The invention relates to the technical field of atmospheric correction and remote sensing, in particular to an aerosol optical thickness obtaining method and system, and the method comprises the steps: obtaining the high-frequency historical aerosol optical thickness of a target region through the inversion of historical multi-angle multi-band polarization data; performing space-time matching on the high-frequency historical aerosol optical thickness and historical data to obtain a training data set; and obtaining the high-frequency aerosol optical thickness of the target area by using the trained neural network model. According to the invention, through the inversion data of the multi-angle multi-band polarization data, the preset neural network model is trained with the geographic data, the meteorological data and the ground base station network data, the relationship between the geographic data, the meteorological data and the ground base station network data and the aerosol optical thickness is established, and a plurality of results are fused, so that the space-time coverage is improved, and the accuracy of the aerosol optical thickness is improved. The defects of a traditional aerosol optical thickness product in time and space continuity are effectively overcome.

The invention discloses an alanine aminotransferase detection device and method based on a liquid crystal microcavity array, and belongs to the field of optical sensing. The device comprises a functional liquid crystal microcavity array, an excitation optical fiber and a collection optical fiber, the microcavity array is composed of liquid crystal microcavities doped with fluorescent dye and stearic acid, and the multiple microcavities are independently distributed and serve as optical sensing units; the excitation optical fiber is used for synchronously exciting the microcavity to generate an echo wall mode, and the acquisition optical fiber acquires resonance spectrum signals of each channel in real time; the functionalized liquid crystal microcavity is converted into a bipolar configuration from a radial configuration in an enzymatic reaction, echo wall mode spectrum red shift is caused, spectrum shift time is in negative correlation with enzyme concentration, and dual-channel detection is realized by matching with conversion time of a polarized light pattern. According to the detection structure based on the liquid crystal microcavity array provided by the invention, high-flux, real-time and label-free rapid detection on the concentration of alanine aminotransferase can be realized.

The invention discloses an intermediate infrared polarization spectrum imaging method and device. The method comprises the following steps: carrying out joint coding on an intermediate infrared light beam carrying to-be-measured target space, spectrum and polarization information by adopting an intermediate infrared polarization spectrum modulator consisting of metasurface units which are arranged in an array and have non-correlation response characteristics; projecting the coding light field to a two-dimensional detector array to generate a two-dimensional coding image under single exposure; and decoding the two-dimensional coding image through a reconstruction unit so as to reconstruct and obtain a space-spectrum-polarization four-dimensional data cube of the target to be measured. The problem that polarization information cannot be rapidly obtained in the prior art is solved, target full information can be obtained under single exposure, and the system has the advantages of being high in imaging speed, simple in structure, easy to miniaturize and the like.

The invention discloses a passive imaging-based multi-spectral polarized light transmission characteristic test method, which relates to the technical field of optical engineering, and comprises the following steps of: performing space-time alignment and weighted fusion on a multi-spectral polarized image data set by using a multi-dimensional image fusion method to generate a polarization coupling matrix; inputting the polarization coupling matrix into a non-uniform polarized light transmission model, performing depolarization feature extraction by a multi-scale convolutional layer, performing inter-spectrum polarization correlation analysis by an attention mechanism layer, outputting a multi-spectrum polarized light transmission characteristic test scheme, inputting the multi-spectrum polarized light transmission characteristic test scheme into an optical simulation engine, and performing optical simulation on the multi-spectrum polarized light transmission characteristic test scheme. And driving the LabVIEW unit to execute polarized scattered light imaging simulation of the smoke environment. The advanced multi-dimensional image fusion technology and the non-uniform polarized light transmission model are adopted, the image data fusion and imaging simulation precision is improved, the problem of transmission characteristic inversion errors caused by registration deviation is effectively solved, and meanwhile the reliability and precision of transmission characteristic testing in the complex environment are greatly improved.

The invention discloses a machine vision-based enzyme freeze-dried powder quality detection method, and belongs to the technical field of machine vision, and the method specifically comprises the following steps: in a closed optical cavity, synchronously collecting a multispectral polarization sequence of a sample through annular multispectral polarization illumination and microscopic imaging; performing short-time airflow pulse disturbance and continuous imaging on the sample, and constructing a polarization time sequence texture map by means of inter-frame polarization retention difference; according to Stokes parameters, calculating a deskewness graph and a specular reflection component graph, and carrying out joint normalization on the deskewness graph and the specular reflection component graph and the polarization time sequence texture graph to form a multi-channel feature stack; constructing a double-branch encoder, and performing comparative learning on the feature stack to generate an impurity prototype dictionary; using the dictionary to match the feature stack pixel by pixel, and tracking and inhibiting powder agglomeration false detection through a topological constraint connected domain; and recovering a height map by using an oblique incidence fringe phase shift method, performing pixel-level fusion with the candidate image, retaining targets with height abrupt change and consistent polarization characteristics, and outputting an impurity mask.

Provided is an inspecting apparatus for semiconductor device including a chamber, a substrate stage within the chamber and configured to load a test wafer, the test wafer including a plurality of wiring layers, a milling device configured to partially remove the test wafer, and a measuring device including a body portion, a light source configured to irradiate an incident light, an objective lens, a lens assembly having a plurality of optical filter and an optical splitter, wherein the lens assembly is configured to introduce the incident light such that the incident light is emitted on the test wafer; and a polarization camera configured to scan the test wafer by receiving reflected light reflected from the test wafer, wherein the measuring device is configured to measure polarization characteristic of the reflected light and uniformity of the test wafer.

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 relates to the technical field of road engineering detection, in particular to a road disease recognition method and system based on electromagnetic waves and photographic imaging, and the method comprises the steps: firstly obtaining electromagnetic wave detection data and photographic imaging data of a road region, then synchronously detecting the distribution position and thickness of a road thin water film, generating a rasterized water film feature map, and finally obtaining a road disease recognition result. Then, according to the water film characteristic pattern, dynamically correcting bimodal data interference, adjusting a reflected wave amplitude value and a time domain delay parameter for electromagnetic wave detection data, compensating a gray difference and an edge gradient value for photographic imaging data by applying a local reflection suppression algorithm, and finally, fusing the corrected data and associating the state of the water film characteristic pattern to eliminate coupling misjudgment. The system comprises a data acquisition unit, a water film detection unit, a double-domain correction unit and a fusion decision unit, the problem of insufficient identification precision caused by a thin water film is solved, the road disease identification precision is improved, and a reliable basis is provided for road maintenance.

The invention relates to the technical field of spinning quality detection, and discloses a jute spinning product quality detection method and system based on an intelligent sensor. The method comprises the following steps: acquiring real-time process parameters of a jute spinning production line, and synchronously capturing yarn surface temperature field distribution in combination with an infrared thermal imager; performing space-time alignment processing on the yarn tension fluctuation data and the temperature field distribution to generate a fused dynamic process characteristic matrix; according to the tension gradient change rate and the temperature field abnormal region coordinates in the dynamic process characteristic matrix, dividing potential occurrence sections of yarn quality defects; a microstructure image of the yarn in the potential generation section is extracted, a fiber arrangement angle deviation value is measured through a polarized light interferometer, and fiber molecular bond vibration frequency data is obtained in combination with a Raman spectrometer; and inputting the fiber arrangement angle deviation value and the molecular bond vibration frequency data into a cascade quality analysis model, and outputting a quantitative index set of the internal structure defects of the yarns.

The invention provides a photoelectric detection technology-based foreign fiber removing machine impurity detection method, and belongs to the field of data processing. The method comprises the following steps: obtaining a plurality of interception intervals; obtaining an abnormal factor of each interception interval according to the distribution difference of the signals to be detected in different interception intervals; clustering the to-be-detected signals in each interception interval to obtain an upper bound and a lower bound of a normal clustering cluster and a normal signal range in each interception interval; the final upper bound and the final lower bound of the normal signal range are obtained by combining the difference between the abnormal factors of each interception interval and the abnormal factors of other interception intervals; and obtaining the to-be-cleared degree of each interception interval according to the distribution of the to-be-detected signals in each interception interval. The invention aims to solve the problem that when foreign fibers are cleared through a K-means clustering algorithm, all data need to be clustered every time new data is added, so that abnormity confirmation is carried out on the new data, and the detection efficiency is too low.