8255 results about "Visual detection" patented technology

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 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 discloses an unmanned aerial vehicle small target aerial photography detection method and system based on RT-DETR, a medium and equipment, and belongs to the technical field of unmanned aerial vehicle visual detection, aerial photography images are collected based on an unmanned aerial vehicle, the images are input into a trained target detection model, and target position and category information is obtained. The method comprises the following steps: firstly, extracting low-layer features of an image, and simultaneously capturing information of a channel dimension and a space dimension based on an efficient multi-scale attention mechanism; and outputting features through the convolution residual block. Key features are processed based on a single-scale feature interaction module; for the coded feature map, fusing information of a shallow layer and information of a deep layer step by step in an up-sampling and transverse connection mode; in a down-sampling stage, context semantic information of a target is reserved, and global and local information interaction is enhanced. According to the method, the accuracy and robustness of small target detection are remarkably improved, and the practicability and deployment value in actual application scenes such as unmanned aerial vehicle aerial photography and remote sensing monitoring are expanded.

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 relates to the technical field of computer vision detection, in particular to an unmanned aerial vehicle target detection method based on frequency-space joint attention and dynamic fusion, and the method comprises the steps: obtaining an unmanned aerial vehicle image data set, carrying out the preprocessing, and dividing a training set and a test set; constructing a target detection model, inputting the training set into the target detection model to extract image features, sequentially performing frequency domain detail enhancement, spatial domain salient region extraction and multi-scale feature adaptive fusion based on the image features, and establishing a feature sequence; screening the feature sequence to obtain an initial target query, and finishing target classification and positioning on the initial target query through a decoder; training a target detection model by using the training set, and inputting the test set into the trained target detection model to generate a detection result; on the premise that the real-time reasoning advantage of RT-DETR is kept as much as possible, the problems that in an unmanned aerial vehicle scene, a target is prone to missing detection, the scale change is large, the background is complex, and the target is fuzzy are effectively solved, and the detection precision is improved.

The invention relates to the technical field of multi-image fusion recognition, in particular to a data fusion method and system for CCD visual detection, and the method comprises the following steps: obtaining a horizontal pixel row calculation gradient construction trend sequence, repairing an edge fracture to generate an integrity index, extracting a gray value to detect feature mutation, and distributing fusion weights to establish a mapping relation. And executing image fusion and balancing the contrast to generate a fusion matrix result. According to the method, the fracture edge region is identified, interpolation compensation is executed, the structural similarity index of the local gray sequence in the image overlapping region and feature direction mutation detection are combined, accurate identification of the edge matching result is guided, and fusion weight factor mapping corresponding to signal-to-noise ratio distribution is introduced; according to the method, the distribution relation between the pixels in the region and the credible weight is effectively established, the edge transition among the multi-source images is more natural through Poisson constraint and contrast balance adjustment of the fusion region, and the structural fidelity and the judgment stability of the fusion image are remarkably enhanced.

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 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.

The invention discloses a light guide plate defect detection method and system based on a neural network, and particularly relates to the technical field of machine vision detection, and the method comprises the following steps: aiming at the problem of image instability of a light guide plate in a dynamic transmission or rotation process, continuously collecting an image sequence and extracting time domain features; and performing interference judgment in combination with the inter-frame consistency prediction coefficient and a first threshold to realize accurate identification of the abnormal image frame. For an abnormal image frame, further correcting the recognition credibility of the abnormal image frame by adopting a confidence adjustment and fusion mode, and meanwhile, introducing a frequency domain transformation and image enhancement strategy to compensate detail loss caused by motion blur; according to the method, inter-frame consistency analysis, confidence fusion regulation and control and frequency domain fuzzy recognition and compensation mechanisms are introduced, abnormal judgment and image quality restoration of the light guide plate image in the dynamic scene are realized, the recognition accuracy and stability of the neural network model on the defect type, position and confidence are improved, and the false detection and omission ratio is effectively reduced.

The invention relates to the technical field of industrial visual inspection, and particularly discloses a waste metal classification and recognition method and system based on image recognition, and the method comprises the steps: obtaining metal surface visual information through an image collection system, extracting multi-level depth features after preprocessing, and generating a preliminary classification result and confidence evaluation; when the confidence coefficient is insufficient, starting a multi-mode verification mechanism, acquiring element composition data by adopting a laser-induced breakdown spectroscopy technology, and acquiring surface topological characteristics by adopting a structured light three-dimensional scanning technology; matching the element data with a component database to generate a component verification result, and comparing the morphology features with a morphology database to generate a morphology verification result; and finally, three types of results are integrated based on a weighted fusion algorithm to generate a final classification decision, and a sorting mechanism is controlled to complete accurate sorting.

The invention relates to the technical field of stacking equipment, in particular to a movable robot stacking system and method. The incoming material conveying line is provided with a visual detection device, and each pushing station is provided with a limiting mechanism and a pushing assembly. The feeding ends of the marshalling conveying lines are connected with the pushing stations in a one-to-one correspondence mode. Each marshalling conveying line is provided with a cache area and a grabbing area. The empty tray conveying line is provided with stacking positions, and each stacking position is provided with a steering conveying assembly. The feeding ends of the full-stack tray conveying lines are connected with the stacking positions in a one-to-one correspondence mode. The movable robot assembly comprises a transverse ground rail assembly, a robot body and a clamp, and the robot body is configured to be capable of moving to the grabbing areas of all the marshalling conveying lines to grab products and moving to all the stacking positions to stack the products to the empty trays. The full-process automation of feeding, classifying, grouping, stacking and transferring of products is achieved, and the efficiency and accuracy of stacking operation are improved.

The invention provides an industrial visual anomaly recognition method, system and device based on multi-modal feedback and a medium, and belongs to the technical field of industrial visual detection.The method comprises the steps that a production line video stream is collected in real time, an ROI is automatically segmented, a picture sequence is generated, features of a set dimension are extracted, and the features of the set dimension are extracted; comparing a preset dynamic distance threshold with a feature library, and preliminarily identifying an abnormal image; a cue word of a detection requirement is constructed, the cue word and abnormal related information are input into the multi-modal large model, and a determined abnormal image is output; using the determined abnormal image to construct a data set to train a target detection model, and adjusting the learning rate in the training process; and inputting pictures captured in an industrial scene into the deep learning model, the multi-modal large model and the target detection model in sequence to obtain a recognition result, feeding back the recognition result to a user for confirmation, adding the recognition result to the data set, and optimizing training of the target detection model. According to the invention, accurate identification of abnormal images in industrial production is realized, and the identification efficiency is high.

The invention relates to the technical field of optical lens defect detection, and particularly discloses an optical lens laser-induced damage on-line detection method and system, a linear polarizer and a narrow-band filter are connected in series in a detection laser light path, non-target polarized light is filtered through polarization direction matching, meanwhile, interference of the environment and scattered light is inhibited through the narrow-band filter, and the laser-induced damage on-line detection system is obtained. According to the method, external source noise such as ambient light and non-target polarized light is reduced through polarization matching and narrow-band filtering, randomness of speckle noise is offset through multi-angle collection and signal fusion in a visual detection mode, exposure is dynamically adjusted, an image is fused to improve the signal-to-noise ratio, noise and real signals are decoupled through self-supervised learning, and the real-time performance of the system is improved. Dust interference is removed in combination with morphological operation; a deep learning reasoning model enhances the damage identification capability in a noise environment, defocusing blur caused by mechanical vibration is eliminated through phase conjugate correction, and the influence of photoelectric conversion noise, dust interference and the like on the detection efficiency and accuracy is remarkably reduced through multi-link linkage.

The invention discloses an industrial image anomaly detection method based on deep learning, and particularly relates to the technical field of industrial visual detection. The problems of high false alarm rate, fuzzy fine defect positioning, insufficient real-time response capability, difficulty in model increment updating and the like caused by data distribution drift in an industrial scene are solved. According to the method, robust features are extracted through a multi-scale feature fusion auto-encoder, and a dynamic memory bank is constructed to update a normal sample prototype online; a dual-path detection mechanism is adopted to cooperate with a pixel-level reconstruction error and attention weighted feature matching deviation; efficient edge reasoning is realized in combination with block parallel processing and model compiling optimization; and designing an elastic incremental learning framework to prevent disastrous forgetting. And finally, false alarms caused by environmental changes are reduced, accurate positioning of pixel-level defects is realized, millisecond-level detection requirements of high-resolution images are met, safe and efficient model online evolution is supported, and adaptability and reliability of an industrial quality inspection system are comprehensively improved.

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 invention relates to an axial magnetic field motor rotor assembly fault detection method and system, and the method comprises the steps: collecting vibration waveform data, temperature gradient distribution data and magnetic field intensity distribution data of a rotor assembly in an operation state, and constructing a dynamic monitoring data set; performing dynamic feature extraction on the data in the dynamic monitoring data set to generate a dynamic operation feature set comprising a vibration coupling feature, a temperature abnormal fluctuation feature and a magnetic field offset feature, and performing multi-dimensional correlation mapping processing based on the dynamic operation feature set to generate a comprehensive abnormal index set; according to a matching result between the comprehensive abnormal index set and a preset fault judgment threshold value, determining a fault type set of the target rotor assembly and a corresponding fault confidence score; according to the fault type set of the rotor assembly and the corresponding fault confidence score, a visual detection report is generated, the visual detection report is used for displaying a fault result at a terminal, and accurate detection and visual presentation of the fault of the rotor assembly are achieved.

The invention relates to the technical field of digital oil painting optimization, in particular to a multi-level visual texture feature optimization method and system for a digital oil painting. The method comprises the following steps: acquiring an original digital oil painting image, performing regional image semantic deep mining, performing illusion perception noise abstract injection, and constructing an illusion perception texture feature layer; performing local continuity texture visual detection on the illusion perception texture feature map layer, performing stroke tension transmission analysis, and constructing a stroke tension perception texture map; performing multi-scale semantic region division and inter-region cross shielding mining on the stroke tension sensing texture map, and constructing a multi-layer texture detail expression map; and carrying out layer-by-layer texture time sequence drawing speculation on the multi-layer texture detail expression map, and carrying out time sequence texture visual rendering optimization to generate a time sequence layered texture rendering effect. According to the method, through multi-layer texture reconstruction, oil painting textures of different time sequence levels are improved, and the stereoscopic visual effect is embodied.

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.

The invention relates to an intelligent building damage detection method based on machine vision. The method comprises the following steps: multi-modal data acquisition; preprocessing and feature enhancement: processing the visual data and the acoustic data; damage detection: designing a material constitutive driven GAN architecture, calculating and generating stress distribution of a crack by a generator through a differentiable finite element analysis layer, performing L2 loss constraint on the stress distribution and an ABAQUS simulation result, evaluating image authenticity and physical consistency by a discriminator by adopting a double-branch structure, and outputting damage parameters in cooperation with a lightweight YOLOv8-Nano detection head; acoustics-vision depth fusion detection: aligning a potential space through a double-flow cross-modal encoder in combination with cross-modal contrast learning, and preferentially processing hidden damage by using a gating fusion unit for dynamically calculating a fusion weight through material acoustic impedance; when the visual detection confidence coefficient is smaller than a set threshold value, triggering frequency domain mutation detection and spatial positioning of the acoustic mode; and edge-cloud cooperative processing.

The invention discloses an automobile production line gluing system based on visual inspection, and relates to the technical field of industrial automation and intelligent control, and the automobile production line gluing system comprises a gluing data acquisition module, a gluing feature extraction module, a compensation correction module, a gluing decision module and a closed-loop management and control module; the gluing data acquisition module preprocesses assembly line gluing track images and glue gun nozzle three-dimensional coordinate data, the gluing feature extraction module constructs a gluing feature sequence table, the compensation correction module outputs glue outlet opening degree compensation amount and nozzle track correction amount, and the gluing decision-making module outputs glue gun pressure adjusting amount and nozzle movement speed. And the closed-loop management and control module detects pressure abnormity and speed stability in real time, optimizes control parameters and generates a process quality map. Through real-time visual detection and dynamic closed-loop control, the glue line width precision is improved, the track compensation response is shortened, the glue gun speed fluctuation coefficient and glue waste are reduced, the gluing qualification rate is improved, the production cost is reduced, and the automobile manufacturing and production requirements are met.

The invention provides a high-speed lamination production line product quality detection system based on visual identification, and belongs to the technical field of visual detection. Comprising a multispectral image acquisition module (visible light, near infrared and ultraviolet band synchronous imaging), a motion compensation module (encoder synchronous correction displacement deviation), a preprocessing module (denoising, enhancement and ROI coarse positioning), a multi-scale feature fusion module (geometric, texture and spectral feature fusion) and a multi-task deep learning model. And a production line is linked to realize hierarchical control (sudden stop, early warning and parameter adjustment). The system eliminates image dislocation and blurring under high-speed motion through a multispectral information complementation and motion compensation technology; the recognition capability of tiny and complex defects is improved by utilizing multi-scale feature fusion; detection and decision-making integration is realized through a multi-task model, a high-precision and low-delay quality detection effect in a high-speed scene is finally achieved, and the quality control level of a lamination production line is effectively improved.

The invention relates to the technical field of computer vision detection, and discloses a height feature information generation method, device and equipment based on point cloud and a medium, and the method comprises the steps: obtaining three-dimensional point cloud data of the surface of an object to be detected, carrying out the preprocessing of the data to obtain a target point cloud, and carrying out the reference plane fitting of the target point cloud, and calculating the vertical distance of each point in the target point cloud based on the reference plane obtained by fitting and generating height difference data, and finally extracting and outputting surface height feature information according to the height difference data. According to the method, the three-dimensional point cloud data is subjected to preprocessing noise removal, datum plane accurate fitting and height difference data separation, high-precision extraction of tiny height changes of a complex surface is achieved, the detection efficiency is improved, dependence on high-cost optical equipment is reduced, and the method is compatible with various workpiece shapes.

The invention relates to the technical field of railway engineering, and provides a cantilever hoop attitude detection method and system based on visual inspection, and the method comprises the steps: calculating the dynamic shrinkage rate and stress distribution gradient of a metal material at a preset low temperature, and generating a deformation compensation coefficient; acquiring surface geometric deformation data of the cantilever hoop at a preset low temperature and non-uniform distribution data of a surface temperature field; and obtaining corrected deformation data, obtaining aligned data based on the corrected deformation data and the non-uniform distribution data, and performing low-temperature deformation field reconstruction on the aligned data to obtain a low-temperature deformation field reconstruction result so as to extract an overlapping region of a surface deformation path and a temperature abnormal boundary of the cantilever hoop. According to the surface deformation path and the geometric center offset of the overlapping region, calculating a posture offset parameter of the cantilever hoop, and generating a low-temperature deformation early warning signal; according to the method, the problem of insufficient detection precision caused by data collaboration loss is solved, and high precision, high robustness and scene adaptability of high-iron cantilever hoop low-temperature deformation detection are realized.

A visual defect detection method, device, medium and product relate to the field of visual detection, and the method comprises the following steps: firstly, recording brightness changes of pixel points in different illumination directions by constructing an illumination response matrix, and establishing a complete mapping of surface reflection characteristics; secondly, performing joint calculation by using the light source direction matrix and the illumination response matrix to obtain normal vector data and reflectivity data, and representing spatial geometric features and material optical features of the surface respectively; thirdly, calculating a gradient feature map, a curvature feature map and a deformation feature map based on the normal vector data, generating a reflectivity map by using the reflectivity data, and forming a multi-dimensional feature representation system; and finally, in a defect detection stage, a strategy of combining gradient feature pre-screening with multi-feature verification is adopted, and final judgment is carried out by calculating a comprehensive defect feature value, so that the technical problem of insufficient micro defect detection precision of a visual detection method in the prior art is solved.

The invention discloses a wiring terminal full appearance detection system and method based on an annular track platform, and the system comprises a track which is provided with a transparent material conveying chain plate segment and is used for the uniform motion transmission of a wiring terminal; the multi-station visual detection device comprises a plurality of visual camera groups and an object distance adjusting assembly, the plurality of visual camera groups are respectively arranged at different stations of the track and are used for carrying out image acquisition on six front-view main surfaces and a set surface of the wiring terminal, and the object distance adjusting assembly is connected with the visual camera groups and is used for carrying out image acquisition on the set surface of the wiring terminal. The object distance adjusting assembly is used for adjusting the object distance or / and the position of the linear scanning camera; the AI visual inspection module comprises an image marking unit used for carrying out defect classification and marking on the collected images; the model training unit is used for carrying out iterative training on the marked image based on a small sample learning algorithm to generate a multi-station detection model; and the real-time detection unit is used for deploying a multi-station detection model to execute component statistics, defect detection and defective product rejection.

The invention discloses a part visual inspection method and system for powder metallurgy machining, and belongs to the field of powder metallurgy manufacturing. The method comprises the steps that a gray image of the surface of a part is acquired; obtaining a window scale coefficient of each pixel point according to the gray level change of other pixel points in the neighborhood range of each pixel point in the gray level image; obtaining a global gray center point according to the gray distribution of the gray image; obtaining a central point offset coefficient of the pixel point according to the window scale coefficient, the gray scale difference and the global gray scale central point of the pixel point; according to the center point offset coefficient of the pixel point, obtaining the center importance degree of the pixel point to the grayscale image, including obtaining the overall importance degree and the local correction coefficient; the pixel points are divided into interested areas or non-interested areas according to the center point offset coefficient, scale transformation is carried out in combination with the center importance degree, visual detection is completed, and the detection adaptability to defects of different scales and forms is improved through self-adaptive window analysis and an overall-local double correction mechanism.

The invention discloses a lens production defect detection method and equipment thereof, and relates to the technical field of visual inspection.The equipment comprises an information acquisition module, a central processing unit, a process regulation and control module and an interactive communication module.The information acquisition module acquires multi-source sensing information at regular time, so that both comprehensiveness and accuracy are improved; high accuracy of visual detection is guaranteed; through an information processing model, quantification is accurate and traceable, defect degree visualization and defect accurate classification are realized, then process defects are positioned, the interference degree of the environment on a detection result is determined, and misjudgment of the defects due to environmental factors is avoided; through linkage of defect data, process precision and environmental interference, a process optimization scheme is generated in a targeted manner, closed-loop response from defect generation to process adjustment is realized, and the production line response speed is improved; and finally, the lens production yield is remarkably improved, and the production cost of long-term detection is reduced.

The invention discloses an intelligent manufacturing system and method based on an industrial robot, and relates to the technical field of industrial robot control, and the method comprises the steps: constructing a workpiece posture-space structure topological graph according to a cross-scale visual recognition result, carrying out the space comparison with an original trajectory planning graph, outputting a space deviation mapping relation, and carrying out the calculation of a spatial deviation mapping relation; performing spatial correlation analysis and path reachability evaluation on the workpiece attitude-spatial structure topological graph by using a graph neural network to obtain an end execution path instruction; driving the industrial robot to perform intelligent manufacturing operation through the tail end execution path instruction, and obtaining an actual operation result image; and performing feature alignment and difference comparison processing on the actual operation result image and the process reference image to obtain an operation deviation feature mapping relation, performing operation quality evaluation, and outputting an operation result visual detection label. According to the method, the adaptability and the manufacturing precision of the operation path of the industrial robot are effectively improved.

The invention discloses an automobile part quality detection method and system based on artificial intelligence visual inspection, and belongs to the field of artificial intelligence machine visual inspection, and the method comprises the steps: firstly, carrying out the registration of a collected RGB image and a depth image, and extracting a part region through a saliency detection network; two-dimensional key points are extracted based on an RGB region image and are matched with key points of a three-dimensional model, an initial three-dimensional attitude is obtained by adopting a PnP algorithm, iterative registration is performed with the three-dimensional model in combination with a point cloud generated by a depth image, and a fine three-dimensional attitude is obtained. And calculating a geometric transformation matrix from the part to a standard front view attitude according to the attitude, and performing attitude correction on the RGB and depth region image. And then matching the corrected image with a standard template image by using a feature detection and matching network so as to correct the position of the detection window. And finally, the three-dimensional size of the part is calculated in the corrected detection window in combination with the depth value, and tolerance judgment is carried out. And the precision, the robustness and the automation level of online detection of the automobile parts can be obviously improved.