77 results about "Brightness gradient" patented technology

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 an edge spraying compensation control method based on intelligent visual feedback. The method comprises the following steps: performing space scanning on a to-be-sprayed workpiece to obtain spraying image data, and establishing a space corresponding relation between a spray gun motion coordinate and a workpiece surface coordinate; registering the real-time spraying image by using the corresponding relation, identifying a geometric boundary line, brightness gradient change and a coating adhesion area of the edge of the workpiece, and generating edge feature data; according to the difference between the edge feature data and the target spraying image, the spraying coverage deviation and the boundary overlapping error are calculated, the coating thickness change trend, the optical density change rate and the boundary direction offset are extracted, and the spraying gun posture adjustment amount, the spraying distance correction amount, the spraying pressure correction amount and the path speed correction amount are generated; the angle, the spraying distance, the pressure and the movement speed of the spray gun are dynamically adjusted according to the parameters; according to the method, real-time visual feedback and self-adaptive compensation control in the spraying process are achieved, and the uniformity and consistency of the coating on the edge of the workpiece are effectively improved.

The invention relates to the field of defect detection, in particular to a wallpaper defect detection method and system based on machine vision. The method comprises the following steps: analyzing acquired to-be-detected wallpaper image data to obtain regional texture variance, edge density and brightness gradient indexes, and generating a scale image layer set; extracting salient edge points in each scale image and generating an edge anchor point set; analyzing the scale image layer set and the edge anchor point set to obtain a texture difference index, and generating a texture feature map set according to the edge anchor point set; processing the texture feature map set to generate a texture significance distribution map; based on the texture significance distribution diagram, main direction distribution is extracted, a direction residual error model is constructed, and a residual error diagram and a direction difference distribution diagram are generated; and establishing a region scoring matrix, and generating a wallpaper defect credibility distribution map and a defect classification image output set on the basis of the region scoring matrix. The wallpaper defect detection precision can be improved.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention relates to the technical field of visual control, in particular to a picture defect test equipment control system and method, and the system comprises a difference tensor construction module, a space-time constraint construction module, a strain response judgment module, a coupling focusing mapping module and a boundary stability evaluation module. According to the method, the multi-dimensional difference incidence relation is constructed by extracting the pixel gray difference value, the brightness gradient and the chrominance phase angle, and fine brightness fluctuation and color shift in a picture can be captured. The superposition of the time-space change rate enables the dynamic performance of image defects to be distinguished, and continuous and transient anomalies can be effectively recognized. And in combination with tensor strain ratio and difference amplitude analysis, an abnormal region can be adaptively recognized, and neglect of a traditional method on a complex scene is avoided. The weighted aggregation of the local energy response improves the stability of the defect boundary, ensures the accurate positioning and judgment consistency of the image defect, and significantly improves the reliability and accuracy of the detection result especially in dynamic change and complex illumination environments.

The invention relates to the technical field of computer vision, in particular to an image target detection system and method based on deep learning, and the system comprises a dynamic feature alignment unit, a motion blur compensation unit and a feature fusion control unit. A dynamic feature alignment unit generates spatial deformation parameters through a deformable convolutional layer and an offset prediction sub-network, resamples a shallow high-resolution feature map, and realizes deep and shallow feature space alignment, and a motion blur compensation unit generates a motion vector based on brightness gradient field difference, constructs a mask and weights a suppression blur region, so as to realize deep and shallow feature space alignment. The feature fusion control unit analyzes local entropy and target size distribution, dynamically distributes feature weights and feeds back and optimizes offset parameters, a closed-loop learning loop is formed by the method, and the problems of inaccurate feature alignment, fuzzy interference and poor scene adaptation are solved.

The invention discloses an automobile part stamping forming defect detection method based on machine vision, and relates to the technical field of automobile part stamping forming, and the method comprises the following steps: calculating the brightness gradient distribution and the structure direction difference of a corner region in an automobile part stamping forming image, generating a corner reflection index, and calculating the brightness gradient distribution and the structure direction difference of the corner region; according to the corner reflection index, determining an area containing the image ghosting generated by corner reflection in the image for identifying whether the image ghosting generated by corner reflection occurs or not; edge line segments are extracted in the area, containing the image ghosting generated by corner reflection, in the image, and edge repetition feature vectors are generated based on the direction consistency and the structure repetition degree of the edge line segments and used for determining edge repetition features under the condition that the image ghosting is generated by corner reflection. According to the method, the problem of image ghosting misjudgment cracks caused by corner light reflection in the automobile part stamping forming process is solved, and high-precision recognition and classification of real crack defects are achieved.

The invention discloses an image super-resolution edge reconstruction method based on morphology and coverage rate perception. The method comprises the following steps: dividing an image into an edge region and a non-edge region based on depth and brightness information; anti-aliasing edge detail recovery is realized through coverage rate perceived depth mode classification and a table look-up filter; generating direction features by using the brightness gradient and keeping details through a table look-up filter; and combining the reconstruction results of the current frame and the historical frame to ensure the image stability in a dynamic scene. The invention also provides an image super-resolution edge reconstruction system. According to the method provided by the invention, the quality of an object edge and a newly appearing pixel region can be remarkably improved, sawtooth artifacts are effectively inhibited, and relatively high time stability is kept in a dynamic scene, so that the requirements of real-time rendering and visual enhancement application are met.

The invention relates to the technical field of image analysis, and discloses a construction safety helmet wearing detection method and system based on computer vision, and the method comprises the steps: obtaining an image sequence of a construction scene; segmenting a moving object region of the construction scene based on the image sequence, and extracting a head structure contour of the moving object region; light-dark boundary distribution characteristics formed by projection of an environment light source on the head structure contour are identified, and according to the light-dark boundary distribution characteristics, a spatial position offset relation between a shielding object and the light source in the construction scene is inverted; detecting a regional deformation track of the head structure contour in the image sequence, and synchronously separating texture brightness gradient change of the regional deformation track; the method can solve the problems that the detection result is interfered by the change of the ambient light source and the dynamic deformation characteristic of the head structure in the image sequence is generally ignored in the existing detection method.

The invention discloses a multi-frame fusion fixed image noise dynamic compensation system and method, particularly relates to the technical field of image processing, and is used for solving the problem that fusion boundary noise has artifacts due to existing independent noise compensation. The method comprises the following steps of: establishing a mapping relation between pixel-level imaging parameter difference degree and fusion weight by acquiring a multi-frame image sequence of different imaging parameters and identifying a transition region of brightness gradient dramatic change; extracting a frequency domain energy skewness feature and a chroma phase change boundary feature of the transition region, and generating a noise visibility correction factor through nonlinear fusion; dynamically constraining the compensation intensity range to realize multi-frame noise compensation collaboration; and finally, performing cooperative compensation and fusion output within the constraint range. Fusion boundary noise discontinuity is effectively eliminated, and the visual quality of high-dynamic-range imaging is remarkably improved.

The invention relates to the technical field of road recognition, in particular to a road pavement abrasion state digital evaluation method and system, and the method comprises the following steps: extracting the brightness and texture features of pavement partitions, recognizing synchronous anomalies, generating a candidate set, screening connectivity and brightness anomaly partitions, analyzing the gray scale and the structure, screening out an abnormal region, and evaluating the abrasion state of a road pavement. And evaluating a stress deviation calibration abrasion grade, and outputting a pavement repair linkage adjustment data table. According to the method, the brightness change and the texture track fluctuation are synchronously analyzed on the time axis, dynamic capture of the abrasion area is achieved, the consistency of the brightness gradient direction and the texture fitting vector and boundary connectivity judgment are combined, the recognition accuracy and coherence are improved, non-abrasion interference is eliminated, and the recognition accuracy is improved. In combination with a stress distribution trend and an original flatness curve deviation condition, an abrasion grade is accurately calibrated, a coverage range is reasonably adjusted in a repair priority sequence, abrasion identification precision and grading reliability are improved, and repair response pertinence and resource allocation rationality are enhanced.

The invention provides a panel lamp light spot uniformity detection method and system and electronic equipment, and the method comprises the steps: analyzing the non-uniform characteristics of brightness gradient distribution according to the intensity and range information of a light spot region, carrying out the image processing of a projection image region, carrying out the quantitative evaluation of the brightness uniformity of a screen, and determining the specific region and degree data of the non-uniform brightness; if the dynamic influence range of the light interference exceeds a preset threshold value, meeting room layout adjustment and equipment position change are determined according to a light interference distribution diagram and equipment configuration parameters, the adjusted light distribution mode change is identified, and the detection priority is determined; according to the detection priority and the brightness deviation index, a brightness compensation method is adopted to carry out local correction on the specific area with the uneven brightness, and corrected brightness distribution data are obtained; and identifying the image definition index and the color rendition degree index after the brightness distribution is corrected, obtaining an area coverage proportion for clearly presenting the information, and judging whether the projection display effect reaches a preset definition standard value or not.

The invention discloses a lens smudginess monitoring method, computer equipment and a storage medium, and relates to the technical field of image processing, and the method comprises the steps: controlling an image collection device to shoot a plurality of images with different exposure degrees, and forming an image sequence from overexposure to underexposure; processing the image sequence, and respectively extracting a smudginess feature based on pixel brightness variance, a smudginess feature based on block brightness gradient change and a smudginess feature based on frequency domain enhancement; and fusing the extracted multiple smudginess features to generate smudginess area information. By collecting a multi-exposure image sequence and respectively extracting pixel brightness variance, block brightness gradient change and frequency domain enhancement features, light transmission abnormity, local response abnormity and edge texture features of a dirty area in exposure change are effectively captured, multi-dimensional features are fused, the accuracy, sensitivity and robustness of recognition of various pollutants are improved, and the recognition accuracy, sensitivity and robustness of various pollutants are improved. The problem of misjudgment of single-frame detection in a complex environment is solved, and reliable and comprehensive monitoring of lens smudginess is realized.

The invention discloses a display visual effect optimization method and system of a display screen, and belongs to the technical field of display methods of electronic displays. The method comprises the steps of dynamic brightness gradient mapping, dynamic refresh rate cooperative adjustment and flicker suppression variable-frequency driving, an ambient light sensor is used for collecting ambient light change frequency and intensity interval data in real time, the dynamic change of ambient light can be accurately captured, a brightness adjustment area is divided, a gain coefficient is dynamically adjusted, the change of the ambient light is matched, and the flicker suppression variable-frequency driving is achieved. Smooth brightness transition, display effect and energy consumption balance, moving object feature recognition, dynamic and static region division, differential refresh rate adjustment, motion blur reduction and power consumption reduction are realized, frequent switching of region division is avoided by setting a region stability threshold and predicting a moving object track, and the accuracy of the moving object is improved. The driving signal parameters are accurately adjusted according to the picture brightness change, so that the signal frequency fluctuates randomly, the flicker rule is broken, real-time monitoring and fine adjustment are realized, and the display quality of the display screen and the user experience are comprehensively improved.

The invention discloses a slag block contour recognition method and device, a storage medium and electronic equipment. An initial image of a slag block on a grid plate at the bottom of a slag well is obtained through at least two cameras, brightness gradient analysis is conducted through the brightness difference between the slag block and the low-temperature environment, and the contour is preliminarily recognized. The camera can cover the whole area of the slag block, is not limited by a fixed point, can capture irregular edge details, and avoids the loss of contour information. Contours recognized by multiple cameras are fused to obtain a three-dimensional contour, a three-dimensional form is constructed, the space size is completely reflected, the limitation of a single view angle is broken through, and the overall form is comprehensively captured. And continuously acquiring image capture brightness dynamic change, correcting the contour based on the relevance of the image capture brightness dynamic change and the contour, tracking state change in real time, and ensuring that the contour is consistent with an actual form. The corrected contour is used for the hydraulic shutoff door to adjust the extrusion depth, accurate slag extrusion is achieved, the problem of inaccurate operation caused by incomplete contour recognition and large size estimation deviation is solved, and a reliable size basis is provided.

The invention discloses a multi-view camera splicing monitoring system based on a sky target, and relates to the technical field of visual intelligence. The multi-view camera splicing monitoring system based on the sky target comprises the following steps: a multi-view data acquisition module used for acquiring multiple types of data sets and carrying out time alignment and dimensionless normalization; the target detection and luminosity reference construction module is used for carrying out sky target detection and standard color block analysis and constructing a multi-view luminosity reference library; the deviation estimation and mapping construction module is used for constructing a cross-camera global luminosity mapping model; the adaptive control and real-time correction module is used for regional luminosity correction and illumination change detection; and the panoramic stitching and evaluation feedback module is used for constructing a joint area luminosity consistency evaluation index. According to the method, the multi-view splicing brightness color consistency and the target detection robustness under complex illumination are effectively improved, and the problems of seam brightness gradient and difficult color cast elimination caused by independent automatic exposure and white balance are solved.

The invention discloses an industrial whole disk multi-target intelligent code scanning method and system based on deep learning, and the method comprises the steps: obtaining an original image containing a plurality of two-dimensional codes; performing multi-exposure simulation processing on the original image to generate a plurality of enhanced images with different brightness levels; inputting the enhanced image into a YOLOv8-SSD lightweight neural network model fused with a significance modulation layer and a residual feature fusion path, and extracting a two-dimensional code candidate box; the saliency modulation layer adjusts the channel attention weight according to the local brightness gradient and the standard deviation in the image so as to suppress reflection interference; applying a Soft-NMS algorithm with a reflective area adaptive weight strategy to a candidate box output by the model, and reserving an effective detection result of a shielded or reflective area; performing contrast enhancement and texture detail restoration processing on the two-dimensional code image in each detection frame; and the processed two-dimensional code image is sent to a decoding module. The technical scheme of the invention aims to improve the accuracy and reliability of two-dimensional code recognition.

The present application relates to computer vision and three-dimensional reconstruction technical field, disclose a kind of based on image recognition's building decoration component three-dimensional modeling method, method includes the following steps: receiving near-axis active illumination image sequence, solving camera pose and equivalent point light source trajectory;Inverse projection two-dimensional foreground mask generates maximum base envelope grid;Combining brightness gradient and texture high-frequency edge tensor field removes false shadow, extract effective sweep shadow mask;Tracking shadow residual optical flow displacement and combining light source trajectory analysis absolute depth, generate local three-dimensional point cloud;Surface reconstruction is executed to point cloud and opening plugging constructs negative space primitive entity grid;Extract the Laplace curvature energy of interface to generate normal shrinkage constraint, control entity grid after shrinkage and envelope grid execute Boolean difference set operation output model.The present application extracts absolute depth in featureless recessed area, and eliminates the boundary interference and distortion of grid Boolean operation.

The utility model discloses a projection device, a projection module and a vehicle. The projection device comprises a plurality of local projection single channels arranged in an array; each local projection single channel is provided with a mask unit, the mask unit of each local projection single channel is provided with a local pattern block for light to pass through, each local pattern block is a part of a preset overall pattern, two or more local pattern blocks form the overall pattern, and the projection device is configured to respond to a light source component arranged on the light incident side. And an integral image corresponding to the integral pattern is formed on the light emitting side. Local blocks in different local projection single channels are respectively a part of a preset overall pattern, that is, the overall pattern is composed of at least two local blocks, and the intensity of light energy which can pass through different local blocks can be freely and flexibly regulated and controlled. Therefore, the overall image which is finally projected and superposed on the ground can easily present the image effects of partition brightness change and brightness gradual change, and the effects of emphasizing display information, increasing interactivity or increasing the sense of science and technology and advanced sense of projection are achieved.

The application discloses a heat-shrinkable tube detection method based on image recognition and relates to the technical field of power material defect detection, and is used for solving the problems of defect profile distortion, positioning deviation and insufficient classification accuracy caused by optical ghost interference in heat-shrinkable tube detection; the mapping relationship of light refraction paths is established based on the brightness gradient difference and the refractive index difference, the theoretical ghost interference fringes are generated by dynamically adjusting the optical path difference of the interferometer, and the abnormal areas deviating from the theoretical distribution are screened; meanwhile, the residual ghost interference is eliminated by combining light source incidence angle optimization and defect displacement amount calculation, and finally, the precise classification and positioning of defects are realized through multi-dimensional matching of geometric parameters and historical defect data, the detection accuracy is improved, the dependence on complex algorithms is reduced, and the real-time detection demand of industrial production lines is adapted.

This invention discloses an automatic detection method and system for magnetic strip appearance defects based on image recognition, belonging to the field of image recognition technology. The method includes: acquiring geometrically disturbed images of the target magnetic strip to generate first image data; acquiring line-scan images of the target magnetic strip to generate second image data; performing image recognition based on geometric distortion on the first image data to generate a first defect detection result; performing image recognition based on spatial brightness gradient calculation on multiple consecutive frames of the second image data to generate a second defect detection result; analyzing the first and second defect detection results for defect cross-verification; and based on the defect cross-verification results, calling a pre-trained generative restoration model to perform residual detection under defect repair to generate a third defect detection result. This solves the technical problem of low accuracy in magnetic strip appearance defect detection in existing technologies, achieving the technical effect of improving the accuracy and reliability of magnetic strip appearance defect detection.

The invention provides a heavy calcium carbonate packaging quality detection method based on visual inspection, and relates to the technical field of heavy calcium carbonate packaging quality detection.The method comprises the steps that a low-dust detection angle and a high-dust detection angle are calculated according to the radian angle range of a packaging bag; the method comprises the following steps: acquiring pixel difference matrixes of in-bag areas of two images with high and low dust detection angles, performing shadow and brightness gradient identification on the in-bag areas according to the pixel difference matrixes, dividing brightness areas, and calculating the identification thickness of dust in each area according to the brightness of each brightness area, the method comprises the following steps: acquiring environmental parameters of a package detection area, calculating the thickness, position and quantity of different brightness areas of a bag-in-bag area, calculating a quality evaluation value of the bag-in-bag area, establishing a visual detection model, calculating a quality evaluation value of a bag end area, calculating a package quality score according to the quality evaluation value of the bag-in-bag area, and identifying the package quality.

The application discloses an aluminum veneer engraving path automatic correction method and system based on positioning image feedback, and particularly relates to the technical field of digital program control path correction, and is used for solving the problem that the path correction method includes the protective film pseudo edge into the space transformation calculation, resulting in the deviation of the corrected path from the real contour of the aluminum veneer; the positioning image containing the protective film state of the aluminum veneer surface is acquired, and the boundary features are extracted; the plate material contour in the original tool path data is called to match each boundary feature with the predetermined cutting contour to determine the candidate pseudo features; the brightness gradient asymmetry is calculated along the normal direction of the boundary feature to identify the protective film tearing pseudo edge; the process feature point coordinates are called to review the protective film tearing pseudo edge; after the protective film tearing pseudo edge determined in the pseudo edge judgment and the candidate pseudo features are removed, the remaining boundary features form a trusted boundary feature set; and only the trusted boundary feature set is used as a constraint to calculate the space transformation relationship and to perform overall transformation on the original tool path data.

The present application relates to lightning arrester intelligent monitoring technical field, specifically to a kind of lightning arrester lightning fracture risk intelligent monitoring system, system includes collecting lightning arrester tension electric field displacement disturbance data and rearranging section according to time, screening multi-source disturbance node and locating paragraph position, according to node number matching unmanned aerial vehicle continuous navigation section, extract corresponding image analysis brightness gradient and texture change, combine image and paragraph number cross relationship output lightning fracture risk section number.The present application is through the time series reorganization of tension, electric field, displacement disturbance data and paragraph number, constructs continuous disturbance structure, selects trigger node by the relationship of multiple physical quantity change, matches planning path according to node paragraph, compresses the range of inspection, identifies fracture suspect point by image gradient and texture change, combines the cross relationship of coordinate and outputs risk number section, enhances the coverage and traceability of risk identification.

The invention discloses a digital ink-jet printing color analysis method and system based on image processing, and relates to the technical field of printing color analysis. According to the method, a multi-angle fabric image is obtained, the brightness gradient is calculated, the reflectivity is measured, and surface scattering distribution is deconstructed by combining an initial absorption baseline; then, according to the law of conservation of energy, utilizing reflectivity and scattering distribution to iteratively update an absorption baseline, and dynamically stripping the absorption and scattering coefficients of the fabric; and finally, inputting the dynamic coefficient as a bottom layer parameter into a Coubella-Miscang model to reversely solve the target spectral reflectivity, and matching an optimal ratio in an ink ratio database, so that high-precision color prediction which does not need manual intervention and adapts to various complex fabrics is achieved, the matching degree of printing colors and design intentions is improved, and the printing quality is improved. The problems that in the prior art, correction coefficients and simple linear hypotheses are set depending on artificial experience, color prediction adaptability is poor in the face of complex fabric, and automatic high-precision real color matching cannot be achieved are solved.

The invention provides a brightness adjusting method, electronic equipment, a storage medium and a computer program product. The method comprises the following steps: for each image frame in an HDR video, determining a brightness gradient value corresponding to each pixel of the image frame; the brightness gradient value is used for reflecting the brightness change of the pixel value of the pixel and the adjacent pixel value; obtaining a battery SOC coefficient of a device used for displaying the image frame; determining a brightness adjustment coefficient of the image frame based on the brightness gradient value corresponding to each pixel of the image frame and the battery SOC coefficient; and adjusting the brightness of the image frame based on the brightness adjustment coefficient. According to the invention, the display quality of the image frame after brightness adjustment can be improved.

A device for optically scanning surfaces and capturing surface profiles is proposed. For error correction (color edge effect), the evaluation unit is designed to compensate for local brightness gradients and color differences of a surface that is not homogeneous with respect to its optical scattering and reflection properties, so that these are not evaluated as changes in distance or surface profile. The evaluation unit is designed to capture the cross-sectional profile of the line and to detect any asymmetrical deviation of the cross-sectional profile from the Gaussian shape, interpreting it as a color difference and / or brightness gradient, and / or disregarding it for calculating distances or the surface profile, and / or replacing and correcting it with a substitute cross-sectional profile.

The invention discloses a driving mode judgment method and system based on computer vision, and relates to the technical field of driving mode judgment, and the method comprises the following steps: extracting a static image frame, and dynamically adjusting the frame extraction frequency; carrying out local contrast self-adaptive adjustment on the image frame to obtain an enhanced image; extracting and fusing a brightness gradient and texture distribution based on multi-level visual feature analysis to generate a unified feature vector; inputting the unified feature vector into an intelligent discrimination model for navigation picture recognition and outputting a result with confidence; and carrying out dynamic weighted updating on the confidence of the continuous frames based on time sequence attenuation and a sliding window mechanism. Through computer vision identification and time sequence regulation and control, intelligent identification and dynamic release of the navigation picture by the vehicle-mounted terminal are realized, interruption caused by error limit is avoided, navigation continuity and driving safety are guaranteed, and identification accuracy and response sensitivity are kept in stable and sudden change scenes through an adaptive adjustment mechanism, so that the navigation effect is improved. And the intelligence and user experience of the vehicle-mounted system are improved.

The present application relates to the technical field of defect detection, in particular to an electronic component defect visual detection system and method, the system comprising an image illumination control module, a defect boundary identification module, a confidence level calculation module, a result level classification module and a structure repetition elimination module.In the present application, the edge gray value and the inside pixel gray value of the packaging pin and the soldering point area are extracted, and the brightness gradient change trend is calculated, to realize dynamic adaptation of image illumination control, improve the clear identification of defect contour, build a defect intensity index sequence, and combine historical fluctuation interval difference to complete dynamic adjustment of the confidence interval, so that the defect level evaluation has stable distinguishing ability, compare the defect intensity value with the upper and lower limits of the confidence interval, combine the label attribution logic to form a clear distribution layer, match the boundary contour with the gray texture feature to realize structure repetition elimination between defects of the same level, and improve the independence and annotation accuracy of the detection layer.

The invention discloses a mobile robot pallet identification method based on computer vision, and relates to the technical field of robot visual perception, and the method comprises the steps: obtaining external parameters from a camera to a robot base, collecting a color image and a depth image, carrying out the distortion removal and alignment processing, fitting a ground plane based on the depth image to generate a near-earth mask, and carrying out the recognition of a mobile robot pallet. Constructing a structure dimension candidate region by combining a depth gradient and a brightness gradient, extracting an effective structure, weighting and solving a pallet center pose and a pose covariance, further calculating a docking deviation, and constructing an error uncertainty domain to complete docking judgment; structural recognition accuracy is improved through spatial constraint and multi-source information fusion, pose estimation stability is enhanced through observation quality weighting and uncertainty quantification, conservative evaluation of docking risks is achieved, and docking safety and reliability are improved.