11 results about "Machine vision image processing" patented technology

The invention relates to the technical field of precision manufacturing nondestructive testing and machine vision image processing, in particular to a titanium alloy ring piece surface microcrack defect detection system based on machine vision, which comprises an image acquisition module for acquiring a to-be-processed image data set; the manifold calibration module is used for acquiring a main direction field of background textures and converting the to-be-processed image data set into a standard space image with aligned texture flow; the sparse decomposition module is used for acquiring a shear wave coefficient and decomposing the shear wave coefficient into a low-rank component matrix and a sparse component matrix; the reconstruction judgment module is used for generating a microcrack defect distribution diagram, obtaining a residual image and generating a final defect distribution diagram; the self-adaptive feedback module is used for adjusting the sparsity constraint weight in the robust principal component analysis algorithm; according to the method, the problem of signal aliasing caused by frequency overlapping of cracks and background textures is effectively solved, and the detection sensitivity under the strong texture background is remarkably improved.

The embodiment of the invention provides a machine vision image processing system and method, a vision application card and electronic equipment, and relates to the technical field of machine vision, and the system comprises the electronic equipment and the vision application card which are connected through a PCIE channel; the electronic equipment is provided with a client of visual software, and an image processing scheme is established based on the client according to an image processing target and sent to the visual application card; the visual application card carries a server of visual software, and performs image processing on to-be-processed data according to the image processing scheme based on the server to obtain to-be-stored data under the condition of receiving the to-be-processed data; sending the to-be-stored data to the electronic equipment through the PCIE channel; and the electronic equipment receives and stores the to-be-stored data through the PCIE channel. According to the embodiment of the invention, the problem of insufficient performance during image processing is solved more simply.

This invention discloses an improved YOLOv8 strip steel burr detection model based on a fusion of a dynamic working condition perception module, belonging to the field of machine vision image processing technology. The specific steps are as follows: A feature extraction fusion network is constructed with CSPDarknet-53 as the backbone, combined with C2f and SPPF modules, employing multi-scale feature fusion and a decoupled detection head; a dynamic working condition perception module is constructed to collect real-time working condition parameters during strip steel production, which are then processed by Z-score standardization and dynamic weight allocation before being input into a dual-branch lightweight sub-network to generate adjustment signals for the detection threshold and anchor frame size; these adjustment signals are dynamically injected into the forward inference process of the YOLOv8 detection head through a hook function to achieve real-time adaptation of detection parameters to working conditions; the final model is obtained through multi-stage collaborative training. This invention significantly improves the robustness and generalization ability of burr detection under complex working conditions, achieving a detection accuracy of over 95% and a false negative rate of 3.1%, meeting the real-time detection needs of industry.

The invention discloses a method for processing a transparent melt based on a machine vision image and controlling crystal growth in real time. The method comprises the following steps: acquiring a video image of a crystal and a melt interface in a crystal growth process in real time through image acquisition equipment; preprocessing the acquired image and extracting effective features; based on a double-threshold segmentation image, determining an accurate identification range and identification accuracy, and in combination with a polar coordinate sector density analysis method, identifying the lower edge boundary of the crystal and calculating the radius of the crystal; recursive least square harmonic wave trapping is adopted to remove periodic interference signals caused by crystal rotation, and a balance diameter sequence reflecting the actual growth trend is obtained; according to the change of the balance diameter sequence along with time, calculating a diameter slope as a crystal growth rate prediction value; based on the growth rate and the crystal radius, a diameter-slope-power relation model is established, and real-time closed-loop regulation and control of the heating power are achieved; according to the invention, a function relationship is established between the crystal diameter change trend and the power correction, automatic correction of the temperature deviation is realized through a sine smooth power regulation function, and a closed-loop system of visual detection-trend analysis-power feedback is formed.

Embodiments of the invention provide a system and method for integrating HDR images in video processing in a time efficient process. In one embodiment, a method for machine vision image processing includes obtaining a plurality of images of a scene, each image having a different exposure value. The images are captured by a camera having a specific dynamic range. Pixel values of the plurality of images are compared to the dynamic range of the camera to determine, based on the comparison, a minimal number of optimal images. This minimal number of optimal images is combined, to obtain an HDR image of the scene. The scene may include an object on an inspection line.

The invention relates to the technical field of machine vision image processing, and discloses a part surface defect detection method based on image processing. The method comprises the following steps: acquiring a part surface image through image acquisition, extracting defect sensitive characteristics by using a multi-scale Gaussian difference operator, and filtering through a threshold value to obtain a candidate defect point set; and inputting the candidate point set into a serialized network containing an attention mechanism and a memory unit to generate an enhanced feature sequence. And carrying out space alignment on the sequence and an original topological association graph, and generating an enhanced feature graph containing node attributes and edge weights through graph convolution. And on the basis, performing region growing and merging operation to generate a connected candidate defect region. And extracting geometric and texture descriptors of each region to form a feature vector, matching the feature vector with a standard vector in a defect feature library so as to identify the defect category and position, and finally outputting a defect detection map. According to the method, the detection rate and the segmentation precision of the complex distribution defects are improved.

This invention provides a method and system for reconstructing the outer contour function of a projectile's head shape based on machine vision, belonging to the field of machine vision image processing and automatic parameter calculation technology. First, the invention performs semantic segmentation on the frontal view image of the projectile's head shape to obtain a binary mask of the projectile target, and extracts and crops the outer contour of the head shape segment. Second, through coordinate axis self-calibration and attitude alignment, the standard attitude coordinates of the projectile's head shape outer contour are obtained. Then, in the local coordinate system, geometric constraint feature points are extracted to obtain the endpoints, pixel areas, and single-point coordinates of the projectile's head shape outer contour. Third, a two-parameter head shape outer contour shape function satisfying endpoint constraints is constructed, and the shape parameters are solved using area constraints and single-point constraints to obtain the analytical expression of the head shape outer contour. Finally, the outer contour function and its derivative are substituted into the head shape factor integral formula to achieve automatic calculation of the head shape factor.

A computer architecture for artificial intelligence model training allows for dual model machine-vision image processing that can reduce loss and computational load by acquiring training data including a training target image, a training reference image, and ground truth information for processing the training target image so that a training target pose of a training target object coincides with a training reference pose of a training reference object. A model calculates a first training target feature and a first training reference feature, and outputs first training processing information. A second model calculates a second training target feature and a second training reference feature, and outputs second training processing information based on the first training processing information, the second training target feature, and the second training reference feature.

The present application relates to the technical fields of intelligent control of building decoration construction, machine vision image processing, computer vision detection, artificial intelligence construction quality analysis and green building energy-saving construction, and particularly relates to a building decoration construction intelligent gluing method and system based on machine vision. The method applies a trace amount of diagnostic glue point to the starting concealed section or the preset test coating section of the gap to be coated, collects diagnostic glue point images and diagnostic glue point point clouds according to a first rephotographing time and a second rephotographing time, generates glue absorption decay rate, spreading rate and surface drying remaining time; divides the glue path center line into a capacity supplement section, an anti-collapse compaction section and a time-limited reinspection section, and generates glue output, nozzle speed, nozzle spacing and reinspection time of each section; collects a real-time glue line cross section after gluing, performs glue supplement, compaction or edge trimming according to the cross section void volume and the corresponding section type within the surface drying remaining time, and outputs the repair position if the surface drying remaining time is exceeded. The present application can improve the gluing quality stability and reduce the repair.

The present application relates to the technical fields of precision manufacturing nondestructive testing and machine vision image processing, in particular to a titanium alloy ring surface micro-crack defect detection system based on machine vision, which comprises: an image acquisition module for obtaining image data sets to be processed; a manifold calibration module for obtaining the main direction field of background texture and transforming the image data sets to be processed into a standard space image aligned with the texture flow; a sparse decomposition module for obtaining shear wave coefficients and decomposing into a low-rank component matrix and a sparse component matrix; a reconstruction and discrimination module for generating a micro-crack defect distribution map, obtaining a residual image and generating a final defect distribution map; and an adaptive feedback module for adjusting the sparse constraint weight in the robust principal component analysis algorithm; the present application effectively overcomes the signal aliasing problem caused by the frequency overlap of cracks and background texture, and significantly improves the detection sensitivity under strong texture background.

The application discloses a red fruit ginseng sorting device and method based on machine vision, wherein the device comprises a feeding hopper, a conveying table, a conveying belt, a camera, a sorting steering engine, a distributing hopper and a control unit; the red fruit ginseng sorting method based on machine vision comprises the following steps: S1, collecting the image of the red fruit ginseng on the conveying roller by the camera and transmitting the image to the MCU of the control unit; S2, identifying and extracting the image contour features of the red fruit ginseng; S3, extracting the color features of the red fruit ginseng image; S4, establishing a machine learning model; and S5, identifying the levels of different fruits by using the machine learning model; according to the characteristic that the red fruit ginseng peel is easy to break, the smooth conveying belt designed by the application can separate the red fruit ginseng and avoid stacking and peel breakage; the machine vision image processing method provided by the application has high fruit recognition accuracy and high sorting accuracy.