8114 results about "Visual inspection" patented technology

The invention provides a battery film production automation control method and system based on visual inspection, and relates to the technical field of production control, and the method comprises the steps: collecting a film image, carrying out the fusion, generating a fused film image, carrying out the noise reduction, constructing a feature point matching relation, removing wrong point pairs, carrying out the geometric correction, carrying out the sub-pixel-level alignment, and obtaining a registration image; edge enhancement is carried out based on an improved Frangian filtering algorithm, an edge region is repaired and segmented to obtain a film edge contour, three-dimensional reconstruction is carried out, three-dimensional point cloud coordinates are solved, a film three-dimensional contour model is reconstructed, a film defect detection model is constructed, and a film defect distribution diagram and defect confidence are obtained through forward propagation; performing association analysis with a production process, constructing a feature mapping model, generating a defect knowledge base and matching a process parameter optimization strategy, generating a dynamic instruction and sending the dynamic instruction to a station control unit, monitoring operation data, performing closed-loop feedback control, and obtaining a current optimal control scheme.

The invention relates to the technical field of image processing, and discloses an intelligent machine vision detection method and system based on image processing, and a storage medium. The method comprises the steps of performing candidate region generation and geometric constraint optimization processing on fusion features through a cascade bidirectional region suggestion network to obtain a target candidate region, and performing instance segmentation processing on the target candidate region through a graph convolutional network and a boundary dynamic adjustment mechanism to obtain a segmentation result; performing feature extraction processing on the segmentation result through a space-time joint attention network to obtain space-time context features; performing multi-target tracking processing on the spatio-temporal context features through depth correlation measurement and motion mode matching to obtain a trajectory prediction result; and performing abnormal mode modeling processing on the trajectory prediction result through hierarchical contrast learning and multi-modal feature fusion to obtain abnormal features, and generating a visual inspection report according to the abnormal features. According to the invention, the accuracy of intelligent machine vision detection based on image processing is improved.

The embodiment of the invention discloses a visual inspection and quality evaluation method for a small structural component of an airplane, and the method comprises the steps: S1, obtaining three-dimensional point cloud data, and carrying out the preprocessing of the three-dimensional point cloud data; s2, carrying out multi-angle two-dimensional image acquisition; s3, mapping the two-dimensional image texture obtained in the S2 to the surface of a three-dimensional model, and generating the three-dimensional model comprising surface information; s4, extracting geometric features and surface texture features from the three-dimensional model; s5, fusion and comparative analysis are carried out to identify potential anomalies and defects; s6, outputting specific abnormal types and positions; and S7, performing comprehensive quality evaluation according to an anomaly detection result in the step S6, and generating a quality evaluation report. According to the technical scheme provided by the embodiment of the invention, by fusing three-dimensional scanning and high-resolution two-dimensional image technologies and adopting the classification model based on machine learning, the comprehensiveness, accuracy and efficiency of detection are remarkably improved, so that the production quality and safety are effectively improved.

The invention discloses a visual inspection system for appearance defects of a pressure sensor, and relates to the technical field of visual inspection of defects. Comprising a light source control module, an image acquisition module, an image preprocessing module, a feature extraction module, a defect identification module, an evaluation grading module, a result display module and a man-machine interaction module which are integrated on the appearance defect AOI equipment, and all the modules are connected through electric signals. According to the visual inspection system for the appearance defects of the pressure sensor, through the high-precision industrial camera and the advanced image processing technology, precise recognition of tiny defects on the surface of a product is achieved, compared with a traditional manual inspection mode, higher inspection precision is achieved, tiny scratches and sunken defects which are difficult to perceive by human eyes can be found, and meanwhile, the visual inspection system is convenient to use. The automatic detection process greatly shortens the detection time and improves the production efficiency, and the system can detect a large number of products in a short time.

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 provides an external wall visual detection method and system based on deep learning, and the method comprises the steps: firstly obtaining an initial visual image set of a plurality of external wall image units which are collected at different shooting angles and under different illumination conditions and are provided with pixel coordinate marks, and then carrying out the feature preprocessing of the initial visual image set, and generating a standardized feature image set; then, a pre-trained outer wall defect detection model is called to carry out defect feature recognition on the standardized feature image set, a preliminary detection result is obtained, and a final detection result is determined according to confidence distribution of a defect type candidate set in the preliminary detection result and spatial position relevance of candidate areas. And finally, generating an outer wall defect detection report containing defect type statistical information and a position distribution diagram based on a final detection result, and sending the outer wall defect detection report to the target terminal equipment to trigger subsequent maintenance operation. Therefore, the accuracy and efficiency of outer wall detection are improved.

The invention relates to the technical field of image analysis, in particular to a heat exchanger welding quality visual inspection method and system, and the method comprises the following steps: collecting original image data from a welding image, carrying out the preprocessing of the original image data, including noise removal and brightness adjustment, and obtaining a preprocessed image; according to the method, in the whole process from welding image data collection to defect recognition result generation, preprocessing operation based on high-frequency noise pixel filtering and gray value frequency adjustment is carried out on original image data, interference information in an image is effectively reduced, and the reliability of the image data is improved; meanwhile, continuous iteration deconvolution operation and pixel detail optimization are carried out on the basis of preprocessing, the distinguishing precision and texture detail discernibility of the welding image are improved, the discernibility of the welding defect under different scales is enhanced through multi-scale-level image feature decomposition, and the precision of image feature data is comprehensively improved.

The invention discloses an automobile leather defect detection method and system based on visual inspection, and relates to the technical field of industrial visual inspection, and the method comprises the steps: reconstructing the three-dimensional shape of a leather surface and generating a three-dimensional point cloud picture by analyzing the parallax relation and illumination direction reflection characteristics among multi-view automobile leather images; automobile leather surface curvature change characteristics of the three-dimensional point cloud picture are extracted, multi-scale texture analysis is carried out, and potential defect areas are identified and marked; through a three-dimensional shape measurement method, defect three-dimensional shape characteristics of the potential defect area are extracted, and defect three-dimensional geometric parameters are calculated; by analyzing defect geometrical characteristics and spatial distribution rules of the defect three-dimensional geometrical parameters and utilizing a preset grading judgment rule to divide defect grades, an automobile leather quality evaluation report containing defect three-dimensional coordinates is generated; according to the method, through combination of curvature-texture multi-scale fusion detection, the recognition capability of complex surface defects is remarkably enhanced.

The invention discloses an aluminum alloy surface defect detection method and system using deep learning, and relates to the related field of detection performed in combination with deep learning, and the method comprises the following steps: carrying out image acquisition on an aluminum alloy finished product, constructing a surface image data set to extract multi-scale surface features, and constructing a feature enhanced image set to carry out defect detection, and a defect detection result is obtained to dynamically optimize aluminum alloy production process parameters, and a process adjustment instruction is generated and fed back to a production system. The technical problems that traditional detection mostly depends on manual visual inspection or a machine vision system based on rules, imaging distortion, small defect missing detection and process adjustment are caused are solved, and the technical effects that deep learning is conducted through the double-branch network, the defect detection efficiency and the production yield are improved, and control over the aluminum alloy manufacturing quality is met are achieved.

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 discloses an intelligent welding forming method and system for a green building steel heating radiator, and the method comprises the following steps: carrying out the surface defect recognition of a steel heating radiator base material based on an AI visual inspection system, recognizing a qualified base material, and automatically matching the type of a welding material from a material database according to the material and thickness parameters of the qualified base material. Welding parameters are intelligently matched through AI visual inspection and a neural network model, a laser and friction stir hybrid welding process is combined, traditional manual operation is replaced, the welding efficiency and precision are improved, and the problems of uneven welding seams and the like are solved; welding data are analyzed in real time through a multi-mode AI model, parameters are dynamically adjusted, intelligent defect recognition and repair welding are achieved in cooperation with 3D visual inspection, and the quality stability is improved through whole-process monitoring; smoke dust is treated through an environment-friendly process, acid pickling is replaced with mechanical rust removal, efficient recycling of materials is achieved through waste recycling, a green manufacturing system is constructed, and the sustainable development requirement of green buildings is met.

The invention discloses a glass bottle surface defect detection method based on visual detection, relates to the technical field of surface defect detection, and aims to solve the problem of inaccurate glass bottle surface detection. According to the method, information combination is carried out on the types, the number and the severity of the defects, weight distribution is carried out, then comprehensive scoring is carried out, various factors can be comprehensively considered in the mechanism, a more comprehensive and accurate scoring result is obtained, and defect boundaries are extracted through a contour detection method. According to the method, the defect position can be accurately positioned, the defect boundary can be accurately extracted, a reliable data basis is provided for subsequent analysis and processing, defect candidate areas are generated through a threshold segmentation method, screening is carried out through size filtering, shape matching, texture analysis and other methods, candidate areas without defects can be eliminated, and the defect detection accuracy is improved. And an image of the defective glass bottle can is accurately obtained.

The invention relates to the technical field of visual inspection, in particular to a multi-mode industrial product dynamic defect detection system based on edge calculation. According to the method, by introducing a multi-modal image construction and enhancement mode, three-dimensional acquisition and enhanced expression of detail information of the surface of the wind power blade are realized, and by means of inter-modal image synchronization and a space registration mechanism, the consistency extraction capability of defect information under different sensor view angles is improved; through combination of density change trend analysis and direction mutation identification means, surface micro cracks, edge damages and other structural changes can be accurately identified in continuous frames, and further through multi-source discrimination and mutual elimination comparison of abnormal indexes, environmental interference and misjudgment risks are effectively eliminated, and the accuracy of the detection result is improved. Therefore, a stable defect track area with direction consistency and distribution continuity is screened out, accurate detection and partition identification of the surface defects of the wind power blade under the dynamic condition are achieved, and the reliability and precision of defect positioning are remarkably improved.

A bridge apparent disease inspection method, inspection system and inspection apparatus, relating to the technical field of bridge maintenance. The method comprises: obtaining, in real time, an inspection image set photographed by a monocular camera in a bridge inspection process; dynamically identifying a target disease in the inspection image set by using a visual inspection algorithm; selecting a two-dimensional code positioning point, determining spatial position coordinates of the two-dimensional code positioning point, and calculating a first relative position between the monocular camera and the two-dimensional code positioning point on the basis of an eight-orientation two-dimensional code positioning method; determining a relative distance and a relative angle between the monocular camera and the target disease, and obtaining a relative position between the target disease and the monocular camera by means of calculation by using the relative distance and the relative angle; and obtaining spatial position coordinates of the target disease by means of calculation by using spatial position coordinates of the monocular camera and the relative position between the target disease and the monocular camera. The technical problems in the prior art that bridge apparent disease inspection is mainly manual inspection, the danger coefficient is high, and the inspection result is affected by subjective factors are solved.

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

The invention discloses a full-automatic laser welding forming system, which relates to the technical field of laser welding, and comprises a laser generation and light beam modulation module, a material conveying and positioning module, a welding execution module, a visual inspection and monitoring module, an intelligent self-adaptive control module and an energy recovery and reutilization module, the material module accurately conveys and positions materials; the welding module robot is accurate in operation; the visual module detects welding seams; the intelligent module performs self-adaptive control; the energy module recycles energy. The welding quality is improved, and defects are reduced; the production efficiency is improved and the cost is reduced; operation is simple, and manpower requirements are reduced; energy is saved, environment is protected, and green concept is met; real-time monitoring is achieved, and the product quality and the management level are guaranteed.

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

The invention discloses an industrial visual detection system based on multi-modal fusion, which belongs to the technical field of industrial data acquisition, processing and processing, and comprises a multi-modal data acquisition and calibration module, a scene image capture module, a visual information synthesis module, an algorithm optimization and training module and a multi-modal image output module, the multiple modules are integrated into the industrial visual detection system for multi-modal data acquisition, processing and fusion, high-quality original data are provided for subsequent image analysis, images of industrial scenes can be automatically captured under different illumination conditions, an original image database containing various illumination and scene conditions is constructed, and the visual detection system has the advantages of being simple in structure, convenient to operate and high in practicability. The system can operate stably, high-precision analysis and processing of the image data set in the industrial scene are realized, the corresponding fusion image is generated, the industrial visual inspection report is generated based on the fusion image, and the adaptability and reliability of the industrial visual inspection system are improved.

The invention relates to the technical field of industrial image recognition, in particular to an automatic visual inspection system applied to charging pile production. The method comprises the following steps: the image enhancement module is used for acquiring an omnibearing production monitoring image; carrying out image detail convolution enhancement on the omnibearing production monitoring image, and carrying out topological structure fitting to generate a component topological structure diagram; the twinborn model module is used for performing production process action recognition on the assembly topological structure diagram based on the omnibearing production monitoring image, performing three-dimensional simulation modeling and constructing a dynamic production twinborn model; and the deviation identification module is used for identifying the position deviation of the dynamic production twin model assembly according to a preset production planning log, then carrying out reference positioning deviation calculation, and generating assembly position deviation parameters. According to the invention, efficient and accurate charging pile production diagnosis detection is realized, so that the production process of the charging pile is optimized.

The invention discloses a visual detection system for an automobile door panel assembly, and relates to the technical field of automatic detection, and the visual detection system comprises the steps: installing a high-precision industrial camera, and carrying out the synchronous collection of the multi-view image data of an automobile door panel; preprocessing the acquired multi-view image data of the automobile door panel; carrying out detail enhancement on the preprocessed multi-view image data of the automobile door panel by adopting a super-resolution algorithm; integrating the enhanced multi-view image data of the automobile door panel into an automobile door panel panorama through binocular stereoscopic vision matching, and constructing an automobile door panel three-dimensional model; the automobile door panel three-dimensional model is compared with a standard automobile door panel three-dimensional model, and the buckle defect in the automobile door panel is recognized; and carrying out position marking and defect type analysis on the identified defect points, and generating an automobile door panel detection report according to the defect type analysis. According to the invention, details are enhanced by using a super-resolution algorithm, defects are accurately positioned by using a three-dimensional model, and the detection efficiency and accuracy are improved.

The invention discloses a polishing track regulation and control method and system for automatic polishing of hub burrs, and relates to the technical field of electronic engineering.The method comprises the steps that a hub is scanned through visual inspection, the material of the hub is analyzed, a three-dimensional model of the hub is captured, and all burrs are positioned; the burr characteristics are analyzed through a DBSCAN algorithm, and a corresponding polishing path is generated; the robot arm starts grinding according to the grinding path, and data in the grinding process are collected in real time; burr detection is continuously conducted on the surface of the hub through the visual sensor, after burrs are found, the robot triggers an alarm mechanism, the polishing path is adjusted according to the specific position of the burrs, and the hub is polished again till detection is qualified. According to the method, the industrial camera is started, comprehensive hub surface data collection is achieved, the optimal polishing path is generated, optimization of path selection is achieved, instant response and processing of abnormal conditions are achieved through an alarm mechanism and path adjustment, and closed-loop control and high-quality guarantee of the polishing process are achieved through repeated verification and adjustment.

An AI-based method for visual inspection of parts manufactured on a shop floor includes acquiring a set of real images of nominal parts manufactured on the shop floor to create training datasets. A self-supervised pre-trainer module is used to pre-train a loss computation neural network in a self-supervised learning process using a first dataset on pretexts defined by real-world conditions pertaining to the shop floor. The first dataset is labeled by automatically extracting pretext-related information from image metadata. A main anomaly trainer module is used to train a main anomaly detection neural network to reconstruct a nominal part image from an input manufactured part image in an unsupervised learning process using a second dataset. The main anomaly training measures a perceptual loss between an input image and a reconstructed image by measuring a difference between feature representations thereof at one or more layers of the pre-trained loss computation neural network.

The invention provides a supporting cable production monitoring method and system for 3D visual detection, and relates to the technical field of image processing and intelligent detection, and the method carries out multi-source fusion processing based on collected cable three-dimensional space image data, and comprises coordinate alignment, point cloud reconstruction and multi-scale feature extraction, and construction of a unified space geometric model. On the basis, the center line of the cable is extracted, coordinates of starting and ending points are calculated, and then geometric evaluation parameters such as the axial deviation rate and the straightness are obtained. The method has the advantages of high precision, high robustness and high universality, and is suitable for real-time detection and quality control of slender components such as cables and hoses in the industrial production process. According to the invention, the technical problems of inaccurate three-dimensional structure extraction, insufficient defect identification, difficult defect cause tracing and untimely early warning response in the prior art can be solved.

The invention relates to the field of image processing, and particularly discloses a welded pipe surface defect detection method based on robot visual inspection. The method comprises the steps that a robot carries a binocular camera and an annular LED light source and moves at a constant speed in the axial direction of a welded pipe to collect orthographic and inclined views, and a three-dimensional point cloud is constructed; and establishing a parameterized mapping function based on the point cloud, and converting the 3D coordinate into a 2D expansion surface coordinate. In the convolutional neural network, a first layer is inserted into a spatial transformation network to correct distortion of the expanded image, deformable convolution is adopted to extract edge, local deformation and specific defect response features, and standard convolution is combined to extract global features; and fusing multi-scale features and adding an attention mechanism to improve the weight of a defect region, and outputting a defect category and a bounding box offset after generating a candidate box. The method effectively solves the problems of stretching, deformation and defect distortion of welded pipe curved surface imaging, reduces the imaging difference of the same defect, and remarkably improves the defect positioning precision and recognition accuracy.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention discloses a chip surface defect visual detection method and system, and the method comprises the steps: carrying out the high-sensitivity imaging through the dynamic adaptation of a quantum imaging enhancement module to the surface reflectivity, and achieving the sparse efficient processing of a defect signal through the combination of event-driven vision and a pulse neural network; based on a risk thermodynamic diagram generated in real time, intelligently scheduling detection precision and speed resources, and utilizing multi-modal feature decoupling and a federal knowledge graph to accurately associate defects and process roots; the digital twinning module synchronously predicts a defect evolution path and drives online adjustment and optimization of process parameters, and meanwhile the dynamic compensation module corrects optical defocus in real time. According to the invention, full-detection coverage of nanoscale defects is realized, a defect transfer chain is actively blocked while the detection efficiency is improved, and the chip manufacturing yield and reliability are remarkably improved.

The embodiment of the invention relates to the technical field of visual inspection, and discloses a surface defect detection method based on machine vision, which comprises the following steps: performing parameter analysis on an image quality parameter corresponding to a casting surface image to determine an image accuracy index of the casting surface image; if the image precision index of the casting surface image is not less than a precision index threshold; if the area characteristic parameters are smaller than a preset area standard characteristic threshold value, defect detection is conducted on the corresponding casting area image, and if the area characteristic parameters are not smaller than the preset area standard characteristic threshold value, the corresponding casting area image is marked as a to-be-detected defect image; and comparing the regional defect parameter with a surface defect standard index preset in a casting detection library. According to the surface defect detection method based on machine vision in the embodiment of the invention, the casting surface image is divided into a plurality of areas, and feature extraction is performed on each area, so that the surface condition of the casting can be analyzed more meticulously, and the detection accuracy is improved.

The invention provides a visual detection method and related system for a complex assembly structural member, and the method comprises the steps: determining a target multi-scale decomposition algorithm corresponding to a preset detection type and a first algorithm control parameter corresponding to the target multi-scale decomposition algorithm when the target detection type is the preset detection type; performing multi-scale decomposition on the target area according to a target multi-scale decomposition algorithm and the first algorithm control parameter to obtain a low-frequency component part and n high-frequency component parts; determining a second feature extraction algorithm corresponding to the preset detection type and a second algorithm control parameter of the second feature extraction algorithm; performing feature extraction on the m high-frequency component parts according to a second feature extraction algorithm, a second algorithm control parameter and m feature type labels to obtain m second feature sets; and determining a target detection result according to the m second feature sets and a target detection verification standard.

The invention provides a surface roughness measuring method and device based on a multispectral laser speckle image, and belongs to the field of surface quality visual inspection. Comprising the following steps: respectively acquiring speckle images formed by irradiating the surface of a workpiece with lasers of N wavelengths at different angles, and constructing a multispectral laser speckle data set; the speckle images of N wavelengths at a single angle are spliced according to channels and subjected to feature fusion, and then a multispectral laser speckle fusion image is generated; the fused images of the multiple angles form an image sequence, and single images in the sequence are subjected to multi-scale feature extraction and fusion to generate single-angle features; the multi-angle features generate global illumination information through a multi-angle feature aggregation module, and finally a roughness value is calculated through a multi-layer perceptron. Multi-spectral laser speckle coding surface features are adopted, multi-angle information is fused through the multi-angle feature aggregation neural network, and the relation between the light source wavelength, the incident angle and the surface three-dimensional shape and roughness is comprehensively considered, so that high-precision measurement is achieved.

The invention discloses a ship plane segmented welding multi-robot path planning system based on visual inspection, and relates to the technical field of ship manufacturing, the ship plane segmented welding multi-robot path planning system comprises a multispectral visual inspection module which is mounted on a portal frame and comprises a laser scanning sensor and an infrared thermal imager, the system is used for collecting three-dimensional point cloud and temperature distribution data of ship plane segments in real time. According to the ship plane segmented welding multi-robot path planning system based on visual inspection, the problem of path planning misalignment caused by assembly errors, thermal deformation and complex working conditions in large ship plane segmented welding is fundamentally solved through a multispectral visual real-time perception and material self-adaption dynamic correction mechanism. A composite correction vector and space-time grid collaborative pre-judgment technology is adopted, the welding seam track precision is effectively improved, it is ensured that the weld leg size strictly reaches the standard in the high-difficulty processes such as wrap angle welding and vertical position welding of the maximum-size workpiece, the defects such as air holes and cracks are eliminated, and the rework rate and the quality risk are greatly reduced.