21473results about "Optically investigating flaws/contamination" patented technology

The invention discloses a welded pipe surface defect detection system, particularly relates to the technical field of metal pipe surface quality detection, and is used for solving the problem of defect misjudgment and missing detection caused by mixed reflection interference under single light source irradiation in the existing visual detection technology. An annular light source is adopted for time-sharing pulse triggering through a light source control module, reflected light images at different angles are synchronously collected, a feature extraction module constructs a diffuse reflection intensity ratio and mirror reflection angle distribution matrix to generate a three-dimensional reflection feature spectrum, and reflection anomaly characterization of a defect area is enhanced; the area positioning module screens candidate areas and inhibits interference by combining strength ratio circumferential deviation degree and reflection angle gradient change, and the distortion correction module compensates geometric distortion errors based on curvature radius and station movement parameters, filters pseudo defects of abnormal time sequence fluctuation, and improves the accuracy of the distortion correction. And the defect judgment module utilizes a gradient-gray scale space coupling classification model to distinguish the types of cracks, scratches and corrosion, and finally judges the defect authenticity in combination with reflection characteristic deviation vector superposition.

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 relates to the field of semiconductor packaging material defect detection, in particular to a semiconductor packaging material defect detection method based on deep learning, which comprises the following steps: acquiring a surface image, and extracting a two-dimensional contour and a feature point set; preprocessing the image, and separating a packaging material main body area; constructing a two-dimensional defect identification model based on Transform, and outputting a two-dimensional detection result; scanning suspected and unknown defect areas to obtain three-dimensional point cloud data, and extracting geometric and texture features; fusing two-dimensional and three-dimensional data through a space-time alignment model; utilizing the multi-modal fusion model to output defect positions and types; and evaluating the defect importance based on the material node connectivity and the stress distribution, and generating a visual detection report. According to the invention, high-precision detection of semiconductor packaging material defects is realized, the defect identification rate, the positioning precision and the detection efficiency are improved through multi-modal data fusion and a deep learning model, and a visual report can be generated based on material structure quantification defect importance.

The invention discloses a directional detection method and system for a power supply shell based on a multi-source heterogeneous sensor, belongs to the field of material characteristic detection, and aims to solve the problems of single dimension, weak anti-interference performance, data isomerism and poor adaptability in traditional industrial detection. Through systematic innovation of multi-source sensor collaboration, dynamic mode control, cross-modal data fusion and self-learning optimization, a high-precision, high-efficiency and high-reliability solution is provided for the precision manufacturing field, and an optical, infrared and ultrasonic multi-modal collaborative detection framework is constructed; the optical unit realizes accurate capture of surface texture and morphology through polarized light imaging and 3D structured light scanning, the infrared thermal imaging unit analyzes the heat conduction characteristic of a material, and the ultrasonic array analyzes internal structure defects to form a surface-material-internal full-dimension detection capability; and nine types of defects such as scratches, pits, weld marks, bubbles, sink marks, material layering, stress cracking, thermal stress abnormity and material pollution are covered.

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention provides a printing piece defect detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: determining an optimal detection scale through image enhancement processing, multi-scale gradient pyramid construction and mutual information calculation, extracting key points through a curvature value, and carrying out the shape discrimination. And finally, generating a laser etching compensation path based on defect characteristics and performing layered compensation processing. According to the method, the automobile part surface printing flaws can be accurately identified, the compensation strategy is adaptively generated according to the defect types, and the defect repairing precision and efficiency are improved.

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.

The invention relates to the technical field of intelligent detection, in particular to a container surface damage detection method and device based on machine vision, and the device consists of a multi-modal data acquisition module, a dynamic compensation processing module, a multi-modal data fusion module and a damage classification and positioning module. The multi-modal data acquisition module generates a high-density three-dimensional point cloud through double-laser line scanning, and acquires a multispectral image, an infrared thermogram and a real-time motion state. The dynamic compensation processing module integrates an optical flow method and acceleration data to realize sub-pixel-level motion compensation, and combines adaptive exposure control to optimize the imaging quality under complex illumination. The multi-modal data fusion module strengthens defect feature expression through space-time alignment and a double-branch collaborative attention mechanism, and model parameters are reduced through lightweight network design. And the damage classification and positioning module adopts an improved deep network to realize defect classification and complete millimeter-level three-dimensional positioning. The automatic detection requirement of a port is met, and the problems of poor container detection precision, poor adaptability and the like are solved.

The invention discloses a visual detection system and method for tiny flaws of industrial products, and belongs to the technical field of product detection, multi-source image data of a target industrial product under multiple detection angles and illumination conditions are acquired, and an image information matrix is established; performing region segmentation and texture enhancement on the image, and extracting local texture direction inconsistency parameters; carrying out normalization analysis on the pixel ratio under different spectrum channels, and calculating a multispectral reflectance ratio abnormal index; constructing a deep convolution recognition model; reasoning the image by using the model, and outputting a defect judgment result and a confidence score; judging whether the area is a flaw area based on a dynamic threshold mechanism, and outputting a detection report containing flaw position information and a visual heat map; according to the method, multi-dimensional fusion identification of texture structure disturbance and spectral response abnormity is realized, the micro defect identification precision is effectively improved, and the method has high robustness, automation and engineering practicability and is suitable for high-precision quality control requirements of various industrial scenes.

The invention relates to a part surface defect detection and process optimization method and system, and solves the problems that defect detection has defects, missing detection and erroneous judgment are easy to occur, and subsequent process improvement faces huge challenges even if defects are detected, and the method comprises the following steps: inputting a feature set into a double-branch fusion deep learning model, the first branch identifies defect types and quantization parameters by fusing three-dimensional features and two-dimensional features, and the second branch calculates the correlation degree between the defect features and each process through association rule mining and a random forest algorithm; when the three-dimensional features and the two-dimensional features both meet a preset defect threshold value and the association degree of a certain process exceeds a preset value, determining that the process is a root process; and analyzing a deviation value between the key parameter of the source process and the defect quantization parameter, and correcting the parameter through a dynamic adjustment mechanism according to the deviation degree. The method has the advantages that the defects of the part are accurately detected, the procedure is traced, parameters are dynamically adjusted, closed-loop optimization is formed, and the quality of the part is improved.

The invention discloses a wharf armor block quality detection method, and belongs to the technical field of concrete structure detection.The method comprises the steps that a surface model is constructed through a movable three-dimensional laser scanning device, and a high-risk defect area is recognized; performing encryption scanning and impact echo detection in the high-risk area by adopting a frequency-adjustable dual-mode flaw detection device to generate a three-dimensional defect distribution diagram; a core sample is drilled in the defect area for compressive strength testing, and the material performance is analyzed in combination with a non-contact infrared spectrum; and establishing a dynamic evaluation model, calculating a safety coefficient by integrating surface defects, internal defects and material discreteness data, and generating a three-dimensional visual report. The problems that a traditional detection method is low in efficiency, insufficient in internal defect positioning precision, one-sided in material performance evaluation and the like are solved, full-dimensional detection from the surface to the interior and from the macroscopic level to the microscopic level is achieved through a multi-source data fusion and closed-loop feedback mechanism, and the reliability of structural safety evaluation and the scientificity of maintenance decision are remarkably improved.

The invention discloses an intelligent quality detection system based on machine vision, relates to the technical field of data processing, and solves the problem that light source parameters are difficult to automatically adjust by combining and utilizing a control algorithm according to environmental parameter influence coefficients. After multi-scale fusion alignment is difficult to carry out on image data, optimized image information is obtained by adopting a Q-learning algorithm and combining image quality scores; defect identification detection and three-dimensional deformation detection are difficult to carry out; and the quality abnormal coefficient is difficult to analyze and control feedback and visualization are difficult to carry out. Through adaptive light source control and image optimization, in combination with defect detection and three-dimensional deformation evaluation, efficient and accurate quality detection is realized, and through real-time feedback and visualization, the defect and deformation state of an object can be conveniently and rapidly known.

The invention relates to the technical field of stamping part detection, and discloses a stamping part size and defect synchronous detection method and system. The method comprises the steps that the multi-sensor measurement module is used for collecting line laser scanning morphology data, infrared thermal image strain data and structured light projection contour data of a stamping part; multi-sensor data registration is achieved through a feature point matching algorithm, and a three-dimensional space coordinate mapping relation is generated; calculating the thermal expansion compensation amount of the material in combination with a thermal deformation correction model; filtering the structured light projection contour data, and extracting key contour feature points and defect region boundaries; inputting the related data into a multi-source data fusion model to obtain a dimensional deviation and defect fusion detection result; based on this, a measurement path is updated through a dynamic path planning algorithm, and a synchronous detection scheme is output. The device can synchronously detect the size and defect of the stamping part, improves the detection precision and efficiency, achieves the quality grade classification, and is high in adaptability.

The invention discloses a PCB three-proofing coating quality detection method and system. The method comprises the steps that a visible light reflection image of the surface of a PCB and scattering spectrum data of a near-infrared band are acquired; performing spatial filtering processing on the visible light reflection image to extract interface area pixels, calculating coating layer thickness gradient distribution based on Mie scattering characteristics in scattering spectrum data, and fusing to generate an edge gradient distribution map; and according to the gradient amplitude change rate in the edge gradient distribution map, segmenting the effective coverage area of the coating layer by adopting a self-adaptive dynamic threshold algorithm, and extracting curvature extreme point density and boundary fractal dimension parameters at the segmentation boundary. According to the invention, three technical barriers of difficult microdefect capture, unmeasurable interface performance and delayed failure risk in traditional detection are solved, and two-dimensional accurate diagnosis of the structural compactness and interface reliability of the coating layer is realized.

The invention discloses a power transmission line connection fitting fault detection method based on acoustics and electromagnetic induction, and belongs to the technical field of intelligent inspection and nondestructive detection of power transmission lines. A multi-mode sensor is carried by an unmanned aerial vehicle, ultrasonic wave, vibration, images and power frequency electromagnetic field data are synchronously collected, and a multi-dimensional original data set is constructed. Signal quality is improved by adopting wavelet noise reduction, beam forming and sound image fusion technologies, and flight vibration and electromagnetic interference are effectively suppressed by combining an adaptive filtering algorithm and a physical shielding structure. And acoustic, image and electromagnetic characteristics are extracted and normalized and fused, a dynamic threshold reference library is established, and intelligent grading discrimination of abnormity, defects and faults is realized through multi-stage early warning logic. A detection result automatically generates a report and is mapped to a three-dimensional line model, and operation and maintenance system linkage is supported. According to the method, synchronous identification of surface and internal defects is realized, the anti-interference capability is high, the detection accuracy is high, the inspection efficiency and safety are remarkably improved, and the method is suitable for intelligent operation and maintenance of the high-voltage transmission line.

The invention discloses a metal surface quality detection method and system, and relates to the technical field of metal surface quality detection. The method is used for solving the problems of low microdefect detection precision, weak technological parameter relevance and closed-loop control deficiency of the high-reflection surface. The metal surface is irradiated through multi-angle coherent light field serialization, the phase offset of interference fringes is analyzed to generate three-dimensional shape data, and reflection noise interference is restrained. Defect depth gradient is extracted based on dynamic segmentation of process parameter constraint, deposition temperature and pressure deviation are quantified through deconvolution calculation, and process deviation feature distribution is constructed. Finite element simulation is utilized to generate a process-morphology mapping atlas library, cross-domain invariance features are extracted through depth constraint manifold alignment and comparative learning, and a causal correlation model of defect types and process parameters is established. And dynamically adjusting process parameters according to the weight gradient, and reflowing data to update the manifold rule. And high-precision three-dimensional defect detection, process deviation traceability and adaptive parameter optimization are realized.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The embodiment of the invention discloses a circuit board defect automatic detection method and system based on machine vision, which are used for improving the precision and efficiency of circuit board defect detection and repair, and the method comprises the following steps: obtaining a surface image data set of a target circuit board; performing image preprocessing operation on the surface image data set to generate a preprocessed image data set; inputting the preprocessed image data set into a pre-trained defect feature extraction model, and performing multi-level feature fusion processing on each piece of image data through the defect feature extraction model to generate a multi-dimensional defect feature set; determining a defect positioning information set of the target circuit board according to a matching degree calculation result between the multi-dimensional defect feature set and a preset defect feature template; and transmitting the defect positioning information set to a defect repair control terminal, and triggering the defect repair control terminal to generate a repair path planning instruction according to the defect positioning information set.

The invention relates to the technical field of semiconductor manufacturing, and discloses a semiconductor wafer etching method with a detection function. The method comprises the following steps: acquiring initial image data of a wafer surface through an optical detection device, and extracting geometric features and defect distribution information; processing through a multi-scale feature fusion algorithm, identifying an abnormal region and classifying defect types; dynamically adjusting process parameters of etching equipment according to defect conditions, and planning a self-adaptive etching path; during etching, a real-time monitoring module is used for collecting data, and etching parameters are corrected through a feedback control mechanism; and after etching, the etching precision is secondarily detected and verified by means of a multispectral imaging technology. According to the method, defects can be accurately positioned, the etching process is optimized, the etching precision and quality are effectively improved, the rejection rate is reduced, and the efficiency and reliability of semiconductor wafer manufacturing 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 provides a PCBA circuit board defect detection method, apparatus and device, and a storage medium. The method comprises the steps of obtaining three-dimensional point cloud data of the surface of a circuit board by projecting multispectral composite structured light; performing feature fusion on the multi-modal image of the circuit board according to the three-dimensional point cloud data to generate a fused multi-channel feature map; performing non-rigid registration on the acoustic impedance distribution data and the three-dimensional point cloud data to generate an acousto-optic fusion defect probability distribution diagram; performing multi-scale edge detection on the fused multi-channel feature map to generate an optimized binary edge map; and performing scattering transformation on the optimized binary edge graph, and determining the defect type of the circuit board according to the acousto-optic fusion defect probability distribution graph. Through implementation of the scheme, non-rigid registration is performed by using the acoustic impedance distribution data and the three-dimensional point cloud data, the acousto-optic fusion defect probability distribution diagram is generated, the surface three-dimensional information and the internal acoustic impedance data are combined, internal hidden defects are effectively identified, and comprehensive detection of surface and internal defects is realized.

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 provides a visual inspection system and an application method thereof. The system comprises an image acquisition module used for acquiring a multi-angle optical image and laser three-dimensional point cloud data; the data preprocessing module is responsible for denoising, geometric correction and multi-modal data alignment; the feature extraction module extracts texture, edge and defect features through a convolutional neural network; the defect detection module identifies cracks, scratches and foreign matters based on feature fusion; the adaptive optimization module dynamically adjusts a detection threshold value and classifier parameters; the result output module generates a detection report and marks defect positions; the feedback calibration module corrects the weight of the detection model according to an artificial rechecking result; the equipment control module triggers the sorting device to remove defective products; and the performance monitoring module counts the detection accuracy and the system response delay. According to the invention, the detection accuracy and the system stability can be improved.

The invention provides an industrial production part detection method based on machine vision, and the method comprises the steps: collecting multi-dimensional image data through a multispectral industrial camera when a part passes through a detection region, and carrying out the preprocessing of the multi-dimensional image data; constructing a three-dimensional feature space according to the structured light projection, and mapping the preprocessed multispectral image data into the three-dimensional feature space to carry out space registration operation to obtain a to-be-detected image; extracting composite parameters in the to-be-detected image; performing coarse screening identification on the parts through the geometric parameters and the texture parameters, and removing the parts with appearance defects; and carrying out microcosmic fine judgment identification on the roughly screened parts through material characteristic parameters, and rejecting the parts with quality defects. According to the method, the three-dimensional feature space is constructed through fusion of multispectral imaging and structured light projection, and composite analysis of geometry, texture and material characteristic parameters is combined, so that the problem of single detection dimension of a traditional method is solved, and the recognition accuracy and efficiency of complex defects are improved.

The invention relates to the technical field of PCB (Printed Circuit Board) defect identification, and discloses a method and a system suitable for identifying true and false defects of a PCB, and the method comprises the following steps: firstly, obtaining to-be-detected image information of the PCB, obtaining actual gray value distribution data of each detection area in real time under a preset detection condition, obtaining a gray difference index through difference analysis, and judging whether the gray difference index is out of a threshold interval or not. If yes, surface texture data (texture definition, uniformity and direction value) and edge contour data (contour smoothness, continuity and curvature value) of the abnormal area are obtained, a texture abnormal index and a contour deformation index are obtained through analysis, and then a preliminary defect probability value, a depth defect probability value and a comprehensive defect probability value are obtained; and taking corresponding defect identification measures after processing according to the screening rule. The system comprises an image acquisition module, a difference analysis module and the like, can improve the defect identification accuracy and efficiency, and has good real-time performance and adaptability.

The invention discloses a keyboard key defect detection method and system based on machine vision, and the method comprises the following steps: collecting the multi-modal data of keyboard keys, and carrying out the fusion preprocessing of reflection component separation, dynamic gamma correction and multi-modal constraint alignment; constructing a double-flow deep neural network for defect detection, wherein the first branch network adopts an improved U-Net architecture embedded with a CBAM attention module to extract texture features; the second branch network adopts a PointNet + + architecture to process three-dimensional geometrical characteristics; realizing cross-modal feature association through a feature fusion layer, wherein a fusion weight is adaptively adjusted according to a material type; and outputting a detection result based on the cascade classifier, wherein the method comprises the following steps: positioning a suspected defect area by a first-stage YOLOv5 network; the second-stage ResNet50 network is used for completing defect classification; and the dynamic threshold segmentation algorithm adjusts the judgment boundary according to the material type. According to the invention, high-precision and high-efficiency detection of keyboard key defects is realized.

The invention relates to the technical field of machine tool casting detection, and discloses an automatic detection system for surface defects of machine tool precision castings. The system comprises a surface information acquisition core module, a first defect identification core module, a second defect identification core module and a defect type fusion core module. The surface information acquisition module is used for synchronously acquiring real-time optical images and process parameter data in production aiming at the surface of the casting part, and constructing a defect diagnosis characteristic spectrum and an auxiliary text according to the real-time optical images and the process parameter data; the first defect recognition module inputs the atlas and the auxiliary text into a pre-training double-flow convolutional neural network to generate a first classification result of defect types; a second defect identification module extracts defect mechanism characteristic values from the atlas and matches the defect mechanism characteristic values with a pre-stored defect mechanism knowledge base to obtain a second classification result; and the defect type fusion module fuses the two types of results to determine a target defect type. The system solves the problems of single detection information and identification deviation in the prior art, improves the detection accuracy and real-time performance, and meets the requirements of different production scenes.

The invention belongs to the technical field of concrete crack detection, and particularly discloses and provides a high-strength concrete construction crack detection system and method.The system comprises a multi-parameter cooperative sensing module, a construction crack recognition module, a crack risk analysis module and a crack early warning processing module. According to the invention, the temperature, humidity, strain and multispectral image data from a plasticity stage to a hardening stage are collected in real time through the multi-parameter cooperative sensing module, and crack characteristics are extracted by combining staged differential signal processing and a deep convolutional neural network, so that the detection precision of micro cracks and internal defects is remarkably improved. Meanwhile, multi-physics field data are fused, a crack evolution risk rate model is innovatively constructed, dynamic probability prediction before crack initiation is achieved, a grading disposal scheme is automatically triggered through risk grade division and a progressive early warning mechanism, drying shrinkage and temperature crack causes are effectively distinguished, the prevention and control efficiency is remarkably improved, and the method is suitable for large-scale popularization and application. And full-life-cycle accurate monitoring of the high-strength concrete is supported.

The invention relates to an intelligent vacuum coating quality monitoring method based on artificial intelligence, which comprises the following steps: collecting process parameters in real time through a coating equipment sensor, dynamically monitoring vacuum degree change, deposition rate deviation and temperature gradient, and standardizing data flow to obtain a quantitative trend curve of process parameter fluctuation; combining the defect microcosmic feature description set with gas flow fluctuation and power supply voltage jitter to obtain a correlation analysis result of process parameters and defect forms, and calibrating defect types according to a defect feature library to obtain an updated defect classification basis; and inputting real-time new surface image data through the updated defect classification basis, performing defect detection for illumination reflection differences and stripe deflection angles, obtaining a preliminary defect classification result, and adjusting classification weight parameters.

The invention relates to the technical field of concrete member detection, and discloses a concrete member defect identification and labeling system, which comprises an image acquisition equipment matching module, a defect feature analysis module and a real-time labeling regulation and control module, and a defect classification priority judgment module capable of being additionally arranged. The image acquisition equipment matching module calculates and matches the optimal equipment through the adaptive characteristic value based on the image resolution, the equipment acquisition precision, the working distance and the illumination compensation parameter; the defect feature analysis module performs quantitative analysis on features such as textures, crack forms and hole distribution of zoning images by using algorithms such as multi-scale image segmentation and frequency domain transformation; the real-time labeling regulation and control module dynamically adjusts the labeling position according to the defect position offset, the size change rate and the illumination fluctuation parameters; and the defect classification priority judgment module divides defect grades according to crack width, hole density and the like. The system improves the automation level and accuracy of concrete member defect detection, and is suitable for constructional engineering member quality detection.