1786 results about "Gradation" patented technology

The invention provides an intelligent visual management method and system for enterprise big data. The method comprises the following steps: extracting a space-time association rule of an operation and maintenance report fault field and an equipment log error code, and generating a dynamic mapping data stream to drive a three-dimensional visual association topology; the method comprises the following steps: collecting cabinet vibration energy data, and synchronizing a highlight energy sudden increase area and error log entries according to a timestamp; dynamically distributing a vibration energy weight, and generating a risk probability matrix in combination with an error increment; fusing the time sequence characteristics and the physical topology path, constructing an abnormal event timestamp graph, and marking a fault propagation chain; and based on the map density gradient and the causal association strength, superimposing and rendering the penetrating thermodynamic diagram, associating topology, a fault chain and an equipment structure, and adaptively adjusting the color gradation highlighting abnormal region. According to the technical scheme provided by the invention, the multi-source fault correlation analysis efficiency is improved, and the abnormal risk is dynamically, visually and accurately positioned.

The invention discloses a cut workpiece defect detection method and system based on image feature feedback, and relates to the technical field of image processing.The method comprises the steps that cut workpiece technological characteristics are obtained, a preset defect type library is constructed, a hardware system is built, and parameters are initialized; synchronously acquiring a multi-view original image, and storing and associating annotation information; de-noising the original image, enhancing the contrast, and extracting a region of interest ROI; extracting texture, shape, edge and gray features from the ROI, and screening through a Relief-F algorithm to obtain an optimal feature subset; inputting into an SVM (Support Vector Machine) model for reasoning, and screening to obtain an effective defect detection result; and calculating an evaluation index and generating a feedback signal, and performing iterative optimization after adjusting parameters. The system comprises an acquisition module, a master control module, a data processing module and a display module. Through the precise design and closed-loop feedback of the whole process, the precision, efficiency and long-term adaptability of defect detection of the complex cutting workpiece are improved, and the industrial quality management and control requirements are met.

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

The invention relates to the technical field of image recognition, in particular to an image monitoring system for traditional village heritage risk assessment, and the system comprises a heritage image collection module which is used for obtaining an image data stream of a target traditional village building surface, carrying out the geometric correction of original heritage image data, carrying out the image brightness equalization processing, and obtaining an image data stream of the target traditional village building surface; and establishing a calibrated image set. According to the method, image distortion and local overexposure caused by shooting angle difference or uneven illumination on the surface of a traditional village building are eliminated through geometric correction and brightness equalization processing, and the input data quality of subsequent feature analysis is improved. The consistency degree of crack textures in a neighborhood range is quantified based on gray gradient direction field data, the gray contrast of continuous crack edges is enhanced in combination with a dynamic threshold adjustment mechanism, non-structural texture interference is inhibited, and separability of micro cracks and background materials is enhanced.

The invention relates to the technical field of image enhancement, in particular to a protector bimetallic strip detection method based on machine vision, and the method comprises the steps: obtaining a surface image of a bimetallic strip, and carrying out the preprocessing of the surface image, and obtaining a gray image; performing threshold segmentation on the grayscale image to obtain at least one segmentation region; obtaining an evaluation coefficient of each segmented region, obtaining an adaptive cutting parameter when contrast-limited adaptive histogram equalization is carried out on each segmented region according to the evaluation coefficient of each segmented region, and carrying out image enhancement on each segmented region in the grayscale image according to the adaptive cutting parameter to obtain an enhanced grayscale image; the corrosion detection result in the bimetallic strip is obtained by using the enhanced gray level image, so that the enhanced image can reflect more effective information, and the surface corrosion detection precision of the bimetallic strip according to the enhanced image is improved.

The invention relates to the technical field of image data processing, in particular to a forge piece surface defect detection method and system based on machine vision. The method comprises the steps of obtaining a gray image of a to-be-detected forging surface; determining a boundary significance weight; determining a path consistency weight; screening the boundary significance weight and the path consistency weight to obtain a final weight; and carrying out local adaptive threshold segmentation on the final feature map to obtain a binary image, and carrying out defect identification on the surface of the forge piece to be detected based on a connected region in the binary image. According to the method, the boundary significance weight and the path consistency weight are constructed and are respectively used for accurately positioning defect edges and verifying structure continuity, texture interference is effectively inhibited, and false alarms are reduced; through adaptive Gabor filtering, the problem of a response blind area of a traditional method is solved, finally two weights are fused to modulate filtering response, and the accuracy of forging surface defect detection is improved.

The invention relates to the technical field of metal defect detection, and discloses an aluminum alloy round aluminum rod surface defect image super-resolution method. The method comprises the following steps: acquiring a low-resolution original image sequence of surface defects of the aluminum alloy round aluminum rod, and synchronously acquiring gray value distribution at different illumination angles through a multi-channel optical sensor; constructing a dynamic degradation model according to pixel displacement of adjacent frames in the original image sequence, extracting cross-scale defect features in the original image sequence, and taking output parameters of the dynamic degradation model as spatial constraint conditions of a feature extraction network; and a high-resolution defect image is generated through the multi-stage residual error reconstruction network, the high-resolution image output by the reconstruction network is fed back to the dynamic degradation model, and the frequency domain response coefficient of the spatial fuzzy kernel function is updated to form closed-loop optimization. The identification degree of defect features is improved, and a reliable image data basis is provided for accurate detection of the surface defects of the aluminum alloy round aluminum rod.

The invention relates to the field of data processing, in particular to a part machining detection method and system, and the method comprises the steps: obtaining images of a to-be-detected part and a template part on a production line, and carrying out the preprocessing of the images, and obtaining corresponding gray images; using an edge detection algorithm to obtain a gradient magnitude and a gradient direction of each pixel point in the grayscale image, performing non-maximum suppression in a neighborhood of each pixel point, screening candidate feature points, and based on the candidate feature points and a plurality of attributes in the neighborhood, respectively obtaining significance feature values of the candidate feature points of the to-be-detected part and the template part; matching is carried out according to the saliency characteristic values of the to-be-detected part and the template part, the comprehensive matching degree is calculated, whether the image of the to-be-detected part is abnormal or not is judged based on the comprehensive matching degree, and part machining detection is completed; according to the method, the real feature points and the false feature points are effectively distinguished by introducing multi-dimensional features such as significance feature values and shape complexity, and the mismatching rate caused by similar texture structures is reduced.

The invention relates to the technical field of image processing, in particular to a bridge crack calibration method and device based on computer vision, and the method comprises the steps: collecting a grayscale image of a bridge, and extracting all edge contours in the grayscale image; obtaining each suspected edge contour; extracting a skeleton line of each edge contour, and determining a first evaluation value of each suspected edge contour; the irregularity and the edge collaboration degree of each suspected edge contour are calculated, and a second evaluation value of each suspected edge contour is determined; and determining a discrimination coefficient of each suspected edge contour, evaluating each suspected edge contour, and calibrating the crack of the bridge. According to the invention, the detection error of other interferences on the bridge crack can be reduced, the accuracy of detecting the crack on the bridge is improved, and the bridge crack is calibrated more accurately, so that the safe use of the bridge is ensured.

The invention relates to the technical field of computer vision, in particular to a defect segmentation positioning method and system for an inorganic mineral casting image, and the method comprises the following steps: calling an illumination image to analyze brightness, matching exposure parameters, splicing the image, analyzing a gradient, recognizing a defect, screening an effective region, calculating a gray variance, and constructing roughness weight recognition texture features. According to the method, the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined, the exposure interval can be dynamically adjusted when the casting image is processed, the defect type information is output in the direction, and the positioning information is generated by correcting the recognition position in combination with the actual coordinate of the target spot. The method has the advantages that the high-reflection area identification, the brightness gradient analysis, the pixel-level roughness weight and the structure tensor analysis are combined; the method has the advantages that the method is simple and easy to implement, detail loss of overexposure areas is reduced, the recognition precision of defect areas is improved, accurate area segmentation and classification processing are achieved, roughness weight calculation combining gray variance and pixel density is combined, the sensitivity to surface fine defects is enhanced, and the precision and reliability of defect positioning are improved.

The invention discloses a quality detection system and method for a glue injection laser contour, and belongs to the technical field of quality detection, and the method comprises the steps: obtaining a reflection point cloud signal of a glue injection finished product, constructing a point cloud data stream, setting an image reconstruction method, constructing a glue layer three-dimensional model, synchronously generating a pseudo-color grey-scale map, and achieving the visual representation of the surface texture of a glue layer. A three-layer feature extractor is constructed, a quality primary judgment method is set, features of different scales are fused in a cross-layer mode, the problem of missing detection of complex defects in traditional single-feature detection is solved, basic defects are automatically recognized, and a quality primary judgment result is output; and performing defect judgment on the glue-injected finished product which is unqualified in primary judgment, setting a defect fine judgment method, performing voxel-level segmentation on the three-dimensional image, calculating defect characteristics, and judging whether the defects affect the use performance of the product or not so as to generate a fine judgment result, so that accurate positioning of complex defects is realized, and the defect type identification accuracy is improved.

The invention belongs to the technical field of deep learning, and particularly discloses an ultra-precision full-field displacement measurement method and system based on a convolutional variational auto-encoder. Comprising the following steps: acquiring a video when a to-be-detected structure is subjected to vibration deformation, and selecting a picture of a deformation position to construct a data set; training a deep learning model of the convolutional variational auto-encoder based on the data set; reconstructing the gray value of the original image by using the trained deep learning model to obtain a gray value containing infinitesimal displacement information; and carrying out displacement calculation on the reconstructed image by utilizing an optical flow method, and realizing ultra-precision displacement calculation on the to-be-measured structure according to gray value conversion. According to the method, the problem that the infinitesimal displacement smaller than the sensitivity limit is difficult to measure is solved, the problem that the to-be-measured structure is blocked and cannot be measured is solved by utilizing the characteristics of the generative deep learning model, and the basic data precision of displacement measurement is remarkably improved.

The invention discloses an image multi-modal feature extraction method, a ground feature classification and recognition method and a GIS image generation method. Comprising the following steps: firstly, extracting texture features from a target image by adopting a multi-directional statistical method fusing rotation invariant coding of a local binary pattern and a gray-level co-occurrence matrix, and extracting color features from the target image by adopting an LAB-HSV dual-color space collaborative analysis method to obtain features of different modes of the target image; and then according to the information values of the extracted color features and texture features, adjusting the weight ratio corresponding to the color features and the texture features so as to optimize the recognition precision of the classification model on complex ground features. And finally, according to a weight ratio corresponding to the color feature and the texture feature, performing weighted fusion on the color feature and the texture feature to obtain a corresponding multi-modal feature vector.

The invention relates to a sub-pixel edge extraction method based on an interpolation method, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: obtaining a gray level image, and calculating the gradient of the gray level image in at least one direction based on a preset operator, so as to obtain the gradient amplitude of the gray level image; compressing the gradient magnitude to a preset threshold value, and calculating the gradient direction of each pixel in the gray level image; based on a preset interpolation model, in a preset period, repeatedly fitting the edge intensity in the gradient direction until a complete sub-pixel contour point set is obtained; and deploying the trained interpolation model as a sub-pixel edge extraction model so as to improve the image processing precision in edge detection. By means of the method, the problem that a traditional pixel-level edge detection method is insufficient in precision can be solved, and the detection precision and the measurement repeatability can be effectively improved in the field of high-precision measurement.

The invention discloses a shield segment dislocation detection method and system fusing visual and geometric features, and the method comprises the steps: employing a mobile track three-dimensional laser scanning system, and rapidly obtaining the three-dimensional point cloud data of a tunnel segment; projecting the three-dimensional point cloud data of the tunnel segment to a two-dimensional image according to the scanning parameters and the image preset resolution, and mapping the laser point reflection intensity into a pixel gray value; shield segment inter-ring joints and shield segment in-ring joints are extracted respectively, and segment joint positioning is completed; and according to the seam positioning information, extracting local point clouds at the two sides of the seam, respectively carrying out circular model fitting, calculating the height difference of circular models at the two sides at the seam, and obtaining an in-ring slab staggering value. According to the method, the visual information of the point cloud reflection intensity and the geometric information of the spatial position are fused, rapid and accurate shield segment in-ring slab staggering detection is realized, and the efficiency and the accuracy are high.

To implement image synthesis without loss of surface information of SE images and shadow information of BSE images, the present disclosure proposes image processing techniques include applying data of a first quality image (low quality image) to a trained model, estimating a structural feature and a material feature of a second quality image (high quality image) corresponding to the first quality image, calculating at least one shadow datum based on the structural feature and a synthesis parameter and calculating at least one gradation datum based on the material feature and a synthesis parameter, generating a synthesized image from the at least one shadow datum and the at least one gradation datum, and outputting the synthesized image as a prediction result of the second quality image (see FIG. 8).

The invention discloses an improved filtering and local direction adaptive rust recognition method for a hull surface rust image, which comprises the following steps of: 1, performing gray processing on the hull surface rust image to obtain a gray image; 2, performing global denoising on the image after gray scale by adopting a wavelet transform and total variation fusion denoising algorithm to obtain a globally denoised image; 3, performing local denoising on the globally denoised image by adopting a self-adaptive Gaussian kernel denoising algorithm to obtain a final denoised image; 4, carrying out the edge extraction of the final denoised image through a direction self-adaptive edge detection algorithm, and obtaining a rust image; according to the method, the improved wavelet transform and total variation fusion denoising algorithm is combined, efficient noise removal is achieved, the size and the standard deviation of the filtering kernel are adjusted in a self-adaptive mode, noise of a local area is effectively processed, detail loss is avoided, and the image quality is improved.

The invention relates to the technical field of image processing, in particular to a capsule production quality detection method and system based on machine vision, and the method comprises the steps: collecting a grayscale image of any capsule, and obtaining edge pixel points in the grayscale image according to the gradient value of each pixel point in the grayscale image; for any edge pixel point, obtaining neighborhood pixel points of the edge pixel point, obtaining a crack edge degree of the edge pixel point according to the gray value difference and the gradient value difference between the neighborhood pixel points, and obtaining a crack edge pixel point according to the crack edge degree of each edge pixel point; according to the method, the suspected crack area is obtained according to each crack edge pixel point, the confusion degree of each suspected crack area is obtained according to the gray value and the gradient direction of each pixel point in each suspected crack area, the quality of the capsule is detected according to the confusion degree of all the suspected crack areas, and the accuracy of a capsule production quality detection result is improved.

The invention discloses a space target point cloud three-dimensional reconstruction and rendering method, belongs to the field of space target scene reconstruction and rendering, solves the problem of low accuracy of space target scene reconstruction and rendering, and comprises the following steps: generating an initial sparse point cloud and a camera pose of a space target by using an SFM; performing denoising and outlier processing on the initial sparse point cloud by using a bilateral filtering algorithm to obtain an initial point cloud; distributing an initial gray Gaussian volume for each point in the initial point cloud, and projecting the initial gray Gaussian volume to a two-dimensional plane by using a projection formula in combination with a camera pose; rendering the two-dimensional projection image by using a micro-rasterization rendering technology; fusing the luminosity loss function, the structural similarity loss function and the depth loss function to construct a total loss function, and optimizing an initial gray Gaussian body attribute in the rendered image by using a stochastic gradient descent method; a self-adaptive density control mechanism is introduced to adjust the density of the initial point cloud, and a space target point cloud three-dimensional reconstruction structure is generated; according to the invention, the reconstruction and rendering accuracy of the space target scene is improved.

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 visual defect detection method for precise and complex parts, and particularly relates to the technical field of part detection. The method comprises the following steps: dividing the surface of a precise complex part into a plurality of to-be-detected areas, calculating a gray level co-occurrence matrix characteristic value, texture characteristic distribution and a spectrum analysis result by combining image data under multi-angle and multi-light-source conditions, further comparing surface bulges and gray level difference characteristics of a normal area and the to-be-detected areas, and calculating the surface defect similarity. On the basis of surface texture complexity and defect similarity, risk misjudgment is performed through fuzzy logic analysis, a detection area is divided into high, medium and low risk areas, a detection algorithm is optimized to improve accuracy, finally, algorithm performance is verified through test data, a parameter adjustment range is limited when the misjudgment rate is increased, systematic deviation and performance reduction are avoided, and the detection accuracy is improved. Therefore, the detection stability and the overall quality control reliability are improved.

The invention discloses a film material surface defect detection method and system based on machine vision, and relates to the technical field of machine vision. Joint modeling is carried out by utilizing a local reflectivity difference value Ploc (x, y) of a pixel coordinate (x, y), a local stacking disturbance index S (x, y) of the pixel coordinate (x, y) and a local gray variance Wvar (x, y) of the pixel coordinate (x, y) in a film material image irradiated by a dual-angle light source, and the detection sensitivity of asymmetric optical disturbance caused by trace salt mist deposition is enhanced. Compared with the current situation that a traditional visual detection method is insufficient in response to early corrosion spots, the method has the advantages that a light reflection subsurface corrosion map can be effectively formed through quantitative combination of two factors of reflection anomaly and structural disturbance in the image, and rapid positioning and early warning of the membrane material salt spray corrosion initial stage are achieved.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

The invention discloses a large-size grinding wheel concentricity error measurement method and system based on machine vision, and relates to the technical field of grinding wheel concentricity error measurement, and the method comprises the steps: calibrating an industrial CCD camera through PyCharm software, collecting a part of images of a grinding wheel, and carrying out the RGB three-channel extraction, gray conversion, filtering and binary segmentation processing of the collected images; secondly, a Canny edge operator is adopted to carry out coarse positioning on the edge of the image, and then a novel edge judgment criterion is established to construct an edge detection model with sub-pixel-level precision; and finally, carrying out feature fusion on sub-pixel contours of RGB channels, clustering extracted discrete point sets by using an improved DBSCAN algorithm, and then carrying out ring fitting by using improved RANSAC to realize accurate measurement of the inner and outer diameters and concentricity of the grinding wheel.

The invention discloses an unstructured file identification method based on primitive identification, and the method comprises the following steps: (1) preprocessing: cutting and zooming an electrical wiring drawing to a required size, and carrying out the gray processing; (2) primitive recognition: constructing a feature extraction network of an electrical primitive detection model by adopting a YOLO electrical primitive detection algorithm fused with an attention mechanism; (3) character recognition, which comprises the following steps: removing primitives, detecting an annotated text area, constructing a text candidate box, segmenting and adjusting an annotated text boundary, recognizing annotated text content, and filtering an electrical text recognition result; and (4) electrical element association relationship analysis, which comprises the following steps: frame element extraction, primitive group region division, template matching and electrical primitive-annotation text association. According to the method, the primitives and characters of the electrical wiring diagram can be obtained, the problem that different fonts and styles appear in marked characters can be solved, and semantic information can be understood.

The invention discloses a projection single attitude calibration method, system and device and a medium, and relates to the technical field of computer vision and projection interaction.The method comprises the steps that a plurality of calibration patterns are generated and projected to a calibration plane, gray level images are collected, corner point detection and sub-pixel refinement processing are carried out, and a projection single attitude calibration result is obtained. The method comprises the following steps: acquiring an angular point corresponding relation between a projector image and a camera image, carrying out iterative solution on a projection matrix of a projector and a projection matrix of a camera, separating an internal parameter matrix and an external parameter matrix, calculating a basic matrix and an essential matrix, and decomposing to obtain a rotation matrix and a translation vector of the camera relative to the projector; camera imaging distortion is estimated, a distortion correction coefficient is obtained, and distortion correction is carried out; and counting a re-projection error generated in the correction process, and adjusting parameters of the projector and the camera until the error meets a preset precision requirement. Projector parameter calculation is completed through projection single posture calibration, posture adjustment is not needed in the calibration process, the automation degree is improved, and meanwhile manual intervention is reduced.

The invention discloses a metal processing detection system and method based on computer vision, and relates to the technical field of defect detection.Rotatable polarization filters are installed in front of a lens of a vision sensor, the polarization filters are arranged at four angles respectively, and four sets of polarization images are collected; calculating the polarization degree of each pixel point on the surface during metal processing by using the gray values of the four groups of polarization images, setting a polarization region interval, and dividing a metal surface reflection region into a specular reflection region and a diffuse reflection region; acquiring images of different wavebands by using a multispectral vision sensor, and constructing a multispectral image set; for the same wave band, the images of different exposure times are synthesized to obtain an HDR high dynamic range image; setting a weight for the HDR image of each wave band, and combining the weighting of the HDR image and the suppression of the polarization degree to carry out multispectral image fusion so as to obtain a final image; and a gray threshold value and an area threshold value are respectively calculated through the average value and the standard deviation, and the metal processing defects are judged by using the two threshold values.

The invention discloses a method for identifying small target features in a ray detection image, and relates to the field of image processing, and the method comprises the steps: carrying out the size unification, pixel standardization and normalization preprocessing of an original ray image; constructing a training sample set through image region cutting, and introducing a dynamic sampling strategy to realize positive and negative sample proportion adaptive control; enhancing the number and diversity of small target samples in a training set by using point-shaped and linear artificial defect generation strategies; constructing an image segmentation model and introducing feature jump connection to fuse shallow space and deep semantic information; combining Dice loss and Focal loss to form a composite loss function, and guiding the model to pay attention to a target region with a small area and weak gray level; and finally, a segmentation result is optimized through morphological processing and connected domain analysis, and structured target detection information is output. According to the invention, the recognition accuracy and integrity of the tiny target in the ray image can be effectively improved, and the adaptive capacity of the detection method to the change of the imaging quality is enhanced.

The invention belongs to the technical field of inspection robot systems, and particularly relates to an inspection robot path planning method and system.The visual semantic perception module collects an industrial environment image through a top industrial camera, after graying and Gaussian filtering preprocessing, feature points are detected and matched through an ORB algorithm, and a path planning result is obtained; in combination with an illumination self-adaptive threshold screening mechanism, mismatching points are eliminated, semantics are marked, and a semantic feature map is constructed; the initial path planning module generates an initial path through a semantic cost-containing A * algorithm based on the map; the dynamic obstacle avoidance module captures a moving obstacle by using a visual sensor, and predicts a trajectory through Kalman filtering; the path optimization module combines an initial path and an obstacle track, optimizes the path by using quadratic programming of a fusion curvature constraint and an energy consumption model, and corrects positioning by fusing vision and IMU data through a dynamic weight fusion algorithm; and the execution feedback module generates an instruction according to the optimized path, re-triggers path optimization, forms a closed loop, and ensures the inspection stability.

The invention discloses a color printed matter defect automatic detection system and method based on machine vision, and relates to the technical field of computer vision, and the method comprises the steps: converting a color image into a gray level image, generating a boundary enhanced gray level image through a Canny edge detection algorithm, calculating the offset of edge pixels through sub-pixel recovery, and obtaining a color printed matter defect detection result. Extracting an edge pixel proportion based on the sub-region division table, performing amplification tracking on edge dense sub-regions, and analyzing RGB color deviation; through a multi-scale Gaussian pyramid decomposition and edge refinement technology, analyzing multi-scale features of the boundary enhanced grayscale image and optimizing edge precision; and classifying edge defects and color defects of each sub-region by utilizing a defect threshold value, calculating a comprehensive risk value, increasing a risk level according to a boundary condition, and generating a detection report. According to the method, the Canny edge detection algorithm is combined with sub-pixel recovery, so that the edge extraction precision and the positioning capability of color printed matter defect detection are improved.