5397 results about "Visual detection" patented technology

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 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.

The invention discloses a target detection tracking method and device based on radar visual fusion perception, and a medium, and the method employs visual detection as a leading part to establish a radar visual fusion tracking module, and solves a problem that the detection precision, tracking stability and environment robustness are difficult to give consideration to the existing single-mode perception in a complex traffic environment at the same time. Through unified multi-modal fusion of distance and speed information of visual detection, visual tracking and millimeter wave radar, stable, continuous and reliable target identification and track output of targets such as pedestrians and vehicles under a low-computing-power platform are realized.

The invention relates to the technical field of mode recognition and data recognition, and discloses an intelligent visual detection method for non-standard precision part surface microdefects, which comprises the following steps: acquiring surface gray level image data of a to-be-detected part, physically abandoning low-frequency components through discrete wavelet transform, and reserving high-frequency detail components to construct a frequency domain input tensor; constructing a double-flow reconstruction model containing a space domain coding network and a frequency domain coding network, and minimizing the distribution difference of the same feature between double-domain characterization through potential feature space consistency constraint joint optimization; the method comprises the following steps of: calculating a spatial domain residual image and a frequency domain residual image, combining a texture topological residual image extracted by structural tensor characteristic decomposition, and generating a comprehensive abnormal response image through weighted fusion to judge the defect, and effectively inhibiting macroscopic geometric contour interference through frequency domain decoupling and a topological check mechanism on the premise of not needing a standard geometric template. And sensitive perception and accurate identification of weak texture defects on the surface of the non-standard part are realized.

The invention discloses a dexterous hand teleoperation method based on vision-touch fusion. The method comprises the following steps: 1) vision detection; the method comprises the following steps of (1) carrying out remote control, (2) carrying out redirection, (3) carrying out tactile grabbing, (4) carrying out vision-tactile interaction, (5) carrying out a communication process and (6) carrying out dexterous hand.The method can stably map human hand actions under the complex vision condition, safe and reliable grabbing control is achieved through tactile feedback, meanwhile, multiple dexterous hand configurations and cross-platform deployment are supported, and the precision, robustness and practicability of teleoperation can be improved.

The invention discloses a glass bottle grabbing method and system based on visual inspection, and the method comprises the steps: carrying out the real-time recognition of a bottle opening, a bottle body contour and defect features through an image recognition algorithm, and outputting a detection data set containing the precise three-dimensional pose and feature identification of a glass bottle; based on the detection data set, generating an action parameter set containing the expansion and contraction amount, the rotation angle and the grabbing time sequence; according to the action parameter set, the movable guide rail is controlled to slide to a target area along the fixed guide rail, and self-adaptive adjustment of grabbing force is achieved through an impedance control algorithm; and after grabbing is completed, the conveying path is dynamically corrected through a model prediction control algorithm in combination with the real-time movement speed of the conveying belt, the conveying belt is accurately placed on a preset detection station, and a closed-loop grabbing-conveying process is formed. By utilizing the embodiment of the invention, high-precision identification, dynamic task allocation and self-adaptive flexible grabbing of the glass bottle can be realized, and the grabbing success rate and the production efficiency are improved.

The invention discloses a contour detection method and system for aluminum bar quality inspection based on an image recognition technology, and belongs to the technical field of computer vision detection.According to the method, a high-speed optical scanner is used for scanning the surface of an aluminum bar at multiple incident angles, and reflection change curves at different incident angles are obtained; a two-dimensional grey-scale map is obtained, the two-dimensional grey-scale map is converted into a depth parameter matrix, meanwhile, the incident angle position of a light beam, the scanning speed and a scanning time sequence are synchronously recorded in the scanning process, gradient second-order differential transformation is conducted on the obtained depth parameter matrix in the contour direction, and a curvature amplitude spectrum is generated; a high-speed optical scanner is used for scanning the surface of the aluminum bar at multiple incident angles, a reflection change curve and a depth parameter matrix are synchronously obtained, key parameters such as the light beam incident angle and the scanning speed are recorded, the problem that traditional single-angle scanning contour information is incomplete is solved, the three-dimensional shape of the surface of the aluminum bar can be represented more comprehensively, and the accuracy is high. And initial feature extraction deviation caused by information loss is reduced.

The invention relates to the technical field of industrial visual inspection, and discloses a leather surface defect identification method and system based on image processing. The method comprises the steps of performing preprocessing and multi-scale feature extraction on a leather image, generating a defect thermodynamic diagram through an attention mechanism, and dividing candidate regions; innovatively constructing a regional association graph, updating node embedding by using a graph attention network, and fusing global context information; and generating instance segmentation results through adaptive clustering and classifying the instance segmentation results. According to the method, region relevance is modeled through a graph structure, so that recognition of each region is benefited from global context information, and the problem that segmentation of adhesion and irregular defects is inaccurate in a traditional method is effectively solved; a clustering type instance generation mechanism can automatically aggregate discrete regions according to deep feature similarity, so that the defect contour segmentation precision and instance distinguishing capability under a complex texture background are improved, and accurate conversion from pixel-level prediction to instance-level segmentation is realized.

The invention relates to the technical field of mechanical arms, in particular to a man-machine interaction system of an intelligent mechanical arm with a body. The system decomposes a long time sequence task into sub-tasks through a large language model, and introduces a task acquisition module, a task queue management module, a task re-planning module and a control execution module, uses visual detection to identify gestures and environment events to trigger temporary tasks, and maintains interruptible marks and safety anchor points based on priorities and interruption risks. And during interruption, the mechanical arm with the timestamp and the environment state are collected, during recovery, the states are compared, local re-planning is carried out, transition sub-tasks are automatically generated for failure sub-tasks, and the mechanical arm is controlled to execute after constraint verification. According to the system, on the premise that the structure consistency, the space consistency and the time consistency are guaranteed, safe interruption and efficient recovery in a long-time-sequence task can be achieved, and the autonomy, the real-time performance and the operation safety of the mechanical arm in a man-machine cooperation scene are remarkably improved.

The invention relates to the technical field of image recognition, in particular to a construction site-oriented reinforcing steel bar binding normativity visual detection method, which comprises the following steps of: driving a camera lens to shoot a focus stack image sequence covering the depth of field before and after a single binding node, extracting a linear contour, and obtaining a multi-dimensional image set of a binding area. According to the method, the focus stack image sequence covering the depth of field before and after the single binding node is shot by the driving camera, and the mapping of the line segment coordinates and the depth of the reinforcing steel bar is established, so that the problems of mutual shielding and depth confusion caused by staggered reinforcing steel bars in the construction site are solved; on the basis, a steel bar binding topology network diagram describing the connection relation between steel bar intersection points is constructed, discrete steel bar line segments and intersection point information are integrated into an overall network with a global structure relation, detection is not limited to isolated nodes any more, and evaluation can be conducted from systematicness and continuity of a whole steel bar framework.

The invention relates to the technical field of unmanned aerial vehicle intelligent inspection path planning, and discloses an unmanned aerial vehicle intelligent inspection path planning method and system based on visual inspection, and the method comprises the steps: collecting an image, and carrying out the differential superposition processing, and obtaining environment visual data; feature correction is extracted to determine a candidate area, and a template is matched to determine the type and severity of an abnormal event; performing local path planning according to an event generation mode switching instruction; according to the trajectory parameters meeting the minimum turning radius constraint, a safe fly-around path is obtained; the method comprises the following steps of: acquiring a real-time image stream, adjusting a speed parameter to obtain an optimized trajectory, processing the real-time image stream and inertial measurement data generated in the optimized trajectory through a visual inertial odometer technology to obtain environment three-dimensional point cloud data, and performing cyclic verification according to the environment three-dimensional point cloud data to obtain an event processing verification result. According to the invention, hierarchical response and dynamic local path re-planning of abnormal events can be realized.

The invention provides an underground structure boundary identification method and system based on distributed optical fiber sensing, and relates to the technical field of underground structure detection and boundary identification. The method comprises the following steps: firstly, arranging a sensing array in a to-be-detected area, establishing a channel corresponding to a space coordinate, and collecting a non-excitation base line; medium and noise are estimated through low-energy probe, excitation frequency band, energy and repetition rate are optimized, and the signals are emitted by an adaptive seismic source; reflection and transmission responses are synchronously collected under the unified time reference; performing wavelet denoising, temperature and dispersion compensation and time alignment on the data, and extracting amplitude, phase and frequency characteristics; gradient is calculated on the feature field, non-maximum suppression is carried out, and stable boundary points are obtained by adopting self-adaptive double thresholds based on local statistics and combining time continuity and space connectivity constraints; a two-dimensional section is obtained through spline fitting, a three-dimensional model is reconstructed under the constraint of multi-section consistency, a section map and the three-dimensional model are output, and high-precision, real-time and visual detection of small-size boundaries is achieved.

The invention provides a wafer back surface and edge detection device which comprises a base, and a rotary lifting module, a clamping lifting module, a motion direction conversion module and a visual detection module which are mounted on the base, the rotary lifting module comprises a rotary mechanism and a lifting mechanism; a supporting part for placing a wafer is arranged in the lifting mechanism, and the lifting mechanism is configured to jack the wafer up / down to a working position; the rotating mechanism is configured to provide a circumferential direction movement trend for the movement direction conversion module; the motion direction conversion module is arranged above the rotary lifting module and the clamping lifting module, and is driven by the clamping lifting module to realize displacement in the vertical direction, and the lifting motion of the clamping lifting module is converted into radial displacement of the motion direction conversion module; and the visual detection module comprises a camera unit, the camera unit faces the wafer, and the visual detection module is configured to perform image acquisition and detection on the wafer.

The invention discloses a high-reflective surface defect detection method based on structured light and phase deflection, which belongs to the technical field of visual inspection, and comprises the following steps: generating a multi-frequency sinusoidal grating pattern by using a structured light module, and projecting the multi-frequency sinusoidal grating pattern to the surface of a detected object; multi-frequency phase shift coding is introduced, an absolute phase is calculated through a pilot frequency unwrapping method, a depth map obtained by the structured light is calculated, and a structured light point cloud is reconstructed; a unit normal vector is calculated, a Poisson reconstruction model is adopted to obtain a depth map obtained by phase deflection, and deflection point cloud is obtained; the structured light point cloud data and the deflection point cloud data are registered through an iterative nearest point algorithm and then fused to form a fused depth map; and determining a defect area by adopting the maximum principal curvature and the minimum principal curvature combined mask. By means of the mode, the limitation of a traditional three-dimensional measurement means in high-reflection and complex-curvature surface defect recognition is solved, and submicron-level three-dimensional deformation detection precision is achieved.

The invention discloses an auto spare part deformation detection device based on visual analysis, relates to the field of auto spare part detection, and solves the problem that an existing detection device reduces the accuracy of a lens, the auto spare part deformation detection device comprises a workbench and a positioning column fixedly mounted at the top of the workbench, and a top plate is fixedly mounted at the top of the positioning column; a display host is fixedly mounted at the top of the top plate; a detection probe is fixedly mounted at the bottom of the display host; the feeding mechanism is used for cleaning the surface of the detection probe and conveying accessories to be detected, and the feeding mechanism is mounted at the top of the workbench; a plurality of accessories are conveyed to the outer side of the detection probe in sequence through the feeding mechanism, the detection probe can conveniently conduct visual detection on the accessories, the detection condition is transmitted to the display host to be observed by workers, the surface of the detection probe is brushed in the accessory conveying process, and therefore the effect of conveniently cleaning the detection probe is achieved; and the visual detection accuracy of the accessory is improved.

The invention discloses an unmanned aerial vehicle infrared thermal imaging method and device for cold leakage detection of a low-temperature storage tank, and relates to the field of automatic inspection and detection of storage tanks, and the method comprises the steps: determining the inspection range and detection distance of an unmanned aerial vehicle based on geometric parameters, a cold leakage frequency region and environment parameters of a site; calculating the distance between the shooting points and the vertical coverage height of single-circle flight, further dividing flight elevation layering, and calculating the number of the shooting points of each circle of flight; establishing a three-dimensional model; collecting a visible light image and an infrared thermal imaging image of each shooting point; the method comprises the following steps: performing multi-dimensional correction and temperature image conversion on an acquired infrared thermal imaging image, performing image registration on a temperature image with a temperature scale and a visible light image, performing cold leakage area identification to obtain a cold leakage area, performing quantitative calculation to obtain three-dimensional positioning of a cold leakage position and a cold leakage area, and performing cold leakage area identification on the cold leakage area. And generating a visual detection result, a detection report and a maintenance suggestion. According to the invention, automatic detection and accurate analysis of the cold leakage area of the low-temperature storage tank can be realized.

The invention relates to the technical field of mineral processing visual detection, and discloses a visual analysis system for detecting the grade of a phosphorite flotation froth layer. The system comprises an image acquisition and decomposition module, a parallel feature extraction module, a dynamic feature fusion module, a foam evolution analysis module and a grade decision output module. The system performs multi-scale decomposition on a foam image, extracts physical and semantic features in parallel, constructs a dynamic fusion network based on bidirectional mapping to perform iterative interaction, and generates a multi-modal feature descriptor. Therefore, self-organizing growth of a foam evolution graph is driven, an evolution track of a key foam primitive is positioned and tracked, a foam grade state vector is formed, and finally, a regulation and control decision is output in combination with external control parameters. According to the system, deep fusion of physical and semantic features and deep analysis of the foam dynamic evolution process are achieved, the accuracy and predictability of foam grade state sensing are improved, and an effective means is provided for accurate control over the flotation process.

The invention discloses a visual defect detection method based on multilevel Transform. The method comprises the following steps: firstly, extracting multi-scale features of an input image through a pre-trained convolutional neural network, and performing hierarchical fusion; then, a multi-level Transform coding and decoding structure is used for carrying out deep reconstruction on the fusion features, and long-distance dependency relationships of different granularities are captured through hierarchical patch segmentation and a self-attention mechanism; meanwhile, introducing a standard deviation to estimate branch learning reconstruction uncertainty; and finally, a pixel-level anomaly score graph is generated by calculating a normalized residual error and adopting a multi-scale anomaly score aggregation strategy, so that accurate anomaly positioning and discrimination are realized. According to the method, multi-scale feature representation and global semantic modeling capability are fused, the accuracy, robustness and generalization capability of anomaly detection are effectively improved, only normal sample training is needed, and the method is suitable for complex scenes such as industrial visual detection and has a good application prospect.

The invention relates to the technical field of workpiece detection, and discloses an intelligent visual detection method and system for the surface of a workpiece, and the method comprises the steps: projecting a polarization structure light pattern to the surface of the workpiece, and synchronously collecting an image of the polarization structure light pattern after deformation on the surface of the workpiece; separating a structured light component and a polarized light component from the image, reconstructing a surface three-dimensional shape according to the structured light component, and generating surface polarization degree distribution according to the polarized light component; performing multi-scale gradient scanning on the surface three-dimensional morphology, extracting edge change features, performing texture consistency analysis on the surface polarization degree distribution, and identifying an abnormal texture mode; identifying a mirror reflection area based on the surface polarization degree distribution, performing height compensation on a corresponding area in the surface three-dimensional morphology, generating a corrected surface three-dimensional morphology, and combining edge change features and an abnormal texture mode; according to the invention, the accuracy and robustness of defect identification can be improved, and the reliability and efficiency of the system in industrial field application are enhanced.

The invention relates to cloth printing and dyeing defect visual detection equipment, and belongs to the technical field of cloth detection. The invention aims to solve the problems of poor shooting effect and low detection precision in a detection box caused by difficulty in detecting the head end and the tail end of cloth and easiness in loosening, wrinkling or deviation during transmission in the conventional equipment. The equipment comprises a rack, a detection box, a winding roller, a driving roller, a motor II, a driving mechanism and a pulling mechanism. A plurality of guide rollers are arranged on the rack and are used for guiding cloth to move; the detection box is fixed at the top of the rack and is internally provided with a camera and a light supplementing lamp which are arranged at an angle of 45 degrees and can clearly shoot the front and back surfaces of the cloth; the winding roller is matched with the driving roller through the driving mechanism, cloth winding is achieved under driving of the motor II, the driving mechanism can adjust the position of the winding roller according to the winding diameter, the equipment stably pulls the cloth through the pulling mechanism and flattens the cloth in cooperation with a pressing wheel of the leveling assembly, the detection precision is effectively improved, and the device is suitable for flaw detection work after printing and dyeing of various kinds of cloth.

The invention relates to the technical field of industrial automation and machine vision, in particular to a peripheral signal control method for a visual detection system, and the method comprises the steps: creating a virtual signal node for each peripheral in an upper computer, and configuring a time sequence parameter for a device function node; dragging the link nodes through a graphical interface to form an executable signal sequence triggered by a product in-place signal; in response to an initial event, the signal control unit records the position P0 of the reference encoder, and plans and generates an absolute execution position point of a complete sequence control instruction at one time so as to trigger a peripheral action; and the upper computer graphically monitors the node state and records a sequence log, and supports triggering of a standby sequence and pose offset self-adaptive compensation based on image feedback in case of abnormality. The system is suitable for quality detection of industrial products such as glass bottle mouths, solves the high-precision synchronization problem of multi-equipment cooperative control, and improves the reliability, detection precision and deployment flexibility of a visual detection system.

The invention relates to the technical field of electric power detection, in particular to an electric power inspection robot detection method and system based on machine vision. According to the method, the three-dimensional model of the target area is constructed by pre-collecting the basic data of the area, the historical defect data is obtained to mark the detection key area, the initial path and the key detection point are optimized by using the genetic algorithm, redundant collection points are removed, and the accuracy of the visual detection path is improved; meanwhile, visual data are analyzed in real time through a path acquisition collaborative analysis module, an optimization instruction is generated to dynamically adjust a path, and the problems of low efficiency and missing detection caused by independent operation of vision and navigation are avoided; the visual perception module collects detection dynamic data of definition, vibration acceleration, environment wind speed and environment light, the environment adaptability module analyzes and outputs a comprehensive fluctuation value, a corresponding fluctuation value interval is obtained, a corresponding environment adaptability regulation and control instruction is generated, and influences caused by motion blur and environment sudden change are avoided.

The invention discloses an assemblage multi-robot collaborative welding system in ship sections, and relates to the technical field of industrial robot collaborative control, the assemblage multi-robot collaborative welding system comprises an industrial internet platform, the industrial internet platform is in communication connection with the following modules: a visual inspection modeling module, which is used for scanning the surface of a workpiece through a 3D camera, constructing a point cloud model, and establishing a point cloud model; matching the point cloud model with a design drawing, and identifying a deviation between an actual workpiece and the drawing; and the path planning and correcting module is used for planning a collision-free welding path in combination with the visual detection data and the motion range of the robot. Through high-precision visual inspection scanning and an SLAM matching algorithm, a workpiece point cloud model is constructed in real time and is accurately aligned with a design drawing, the welding seam position and the workpiece geometric deviation can be accurately recognized, a collision-free welding path is generated in combination with robot kinematics constraint and a safety boundary, it is ensured that the pose of the tail end of a welding gun is attached to the surface of the workpiece, and the welding quality is improved. Welding deviation is reduced, and process stability is ensured.

The invention relates to the technical field of blank visual inspection, in particular to a blank defect visual inspection method and system for a plastic extruder, and the method comprises the steps: carrying out the region segmentation of a real-time image, extracting a first-stage defect region, completing the preprocessing, and selecting a specific analysis path according to the defect type. Extracting multi-level defect features and forming a structured feature vector; the reliability degree of each feature is judged through feature integrity judgment and feature quality evaluation, real defects are recognized by adopting multi-stage judgment logic, defect type classification is carried out, and quality influence level multi-stage judgment logic and a branch processing strategy are given, so that accurate detection, classification and analysis of the defects of the plastic extrusion blank are realized. The detection accuracy and adaptability are remarkably improved, the optimal processing path can be automatically selected according to different production conditions and defect types, meanwhile, potential systematic problems are identified through defect correlation analysis, and comprehensive and in-depth information support is provided for quality control.

The invention discloses an image data management method and system for high-precision size visual inspection, and relates to the technical field of image data, the image data management method for high-precision size visual inspection comprises the following steps: S1, collecting a data set, and carrying out region segmentation processing; s2, correcting and enhancing the data set, and extracting contour feature parameters; s3, mapping association is carried out, and screening classification is carried out; s4, parameters are optimized and adjusted, and a standardized size detection feature set is generated; s5, constructing a size detection model to obtain a target size quantification detection result; and S6, performing accuracy verification, and establishing an image feature size parameter association database. According to the invention, through synchronous acquisition of a multi-view image data set and a physical size calibration data set, and in combination with calibration reference of a geometric quantity standard appliance, the problems of incomplete information and large physical size mapping deviation of a traditional single-view image are solved.

The invention relates to the technical field of semiconductor device manufacturing, solves the technical problem that in the prior art, automatic grain digging treatment cannot be carried out on detected LED support defective products, and provides a grain digging control method, device and equipment for LED support defective products. Acquiring a real-time image of the to-be-detected LED bracket under a preset target illumination condition; performing visual detection processing on the real-time image, and judging whether the to-be-detected LED bracket is a defective bracket product or not according to a visual detection processing result; when the to-be-detected LED bracket is judged to be a defective bracket product, defect position detection is carried out on the defective bracket product to obtain defect position information; according to the defect position information, defective stent particles in the defective stent products are removed, and particle digging control over the defective stent products is achieved. According to the automatic grain digging device, automatic grain digging treatment can be accurately carried out on the LED support defective products.

The invention discloses a hardware defect detection method and system based on image processing, particularly relates to the technical field of industrial visual inspection, and is used for solving the problem that real three-dimensional defects and axial micro-motion type pseudo defects are difficult to distinguish in a vibration environment in the prior art. The method comprises the following steps: extracting a suspected defect area by collecting a multi-frame image sequence in a vibration environment, calculating an information entropy change waveform of the suspected defect area, carrying out phase cluster division based on vibration phase information, calculating inter-cluster dispersion and intra-cluster dispersion, extracting a symbolized dynamic entropy, establishing a fusion decision rule to generate a dynamic behavior description factor, and carrying out dynamic behavior analysis on the dynamic behavior description factor. Accurate distinguishing between a real three-dimensional defect and an axial micro-motion type pseudo defect is realized; by fully utilizing the dynamic characteristic change rule of the defect area in the vibration environment and analyzing the incidence relation between the information entropy fluctuation characteristic and the vibration phase in the multi-frame image sequence, the discrimination model capable of effectively representing the essential attribute of the defect is constructed, and the accuracy and the reliability of defect detection in the vibration environment are remarkably improved.

The invention relates to a laser galvanometer detection system with temperature drift self-detection and correction functions. The laser galvanometer detection system comprises an error judgment module and an error calibration module, the error judgment module comprises a CMOS image sensing module, a visual detection module and a storage module; the error calibration module comprises a calibration module and a laser marking module; the CMOS image sensing module is used for shooting a high-definition image, and the visual detection module can generate a prediction image and perform deviation identification on the image shot by the CMOS image sensing module. According to the invention, the temperature drift error is automatically detected through real-time detection of the position drift error of each visual single detection area in the galvanometer processing breadth, and each visual single detectable area is detected one by one to form an adjacent array, so that the temperature drift error detection of the whole processing breadth is realized; and comparing the difference between'theoretical due 'and'actual existing' through an intelligent algorithm so as to detect, compensate and correct the error in real time.

The invention relates to the technical field of industrial automatic detection, in particular to a collaborative visual detection method and system for an SMT production line, and the method comprises the steps: obtaining a beat reference signal which is synchronous with the periodic motion beat of a mounting head in the SMT production line; determining a stable acquisition window of which the vibration intensity is lower than a preset vibration threshold value; in the stable acquisition window, sending a synchronous trigger instruction to a plurality of visual inspection cameras deployed on the SMT production line in a unified manner; performing space-time alignment processing on the multi-path image data; performing motion blur correction processing on the image data subjected to the space-time alignment processing; and carrying out validity verification on the result of the space-time alignment processing, and executing a preset exception processing strategy when the verification result indicates that the verification fails. According to the invention, the beat reference signal synchronized with the motion beat of the mounting head is directly obtained, and the opportunity decision of image acquisition and the real-time vibration monitoring result are deeply bound, so that the double strategies of beat synchronization and vibration avoidance are realized.

The invention belongs to the technical field of intelligent manufacturing, and particularly relates to a paperboard sorting and self-adaptive indentation intelligent control system based on machine vision, which comprises an indentation operation component, a sorting visual identification component, a finished product transportation line and a defective product transportation line, and further comprises a self-adaptive indentation control system, an integrated thin film pressure sensor is mounted in the indentation cutter in the indentation machine; an image acquisition and processing unit, a time-illumination association unit, an adaptive shooting adjustment unit, an optimal frame selection strategy unit, a motion control and cooperation unit, a regular verification unit and a pressure adjustment unit are arranged in the adaptive indentation control system. Visual inspection is introduced, reflection interference can be eliminated, the problem of false detection caused by reflection of the film-coated paperboard in traditional single-light-source shooting is solved, the image recognition capacity is excellent, the screening efficiency is high, technological parameter backtracking and optimization are supported, and the yield only has small deviation due to seasonal fluctuation.