4056 results about "Grayscale" patented technology

The invention relates to the technical field of image data processing, and discloses a few-sample scene-oriented industrial product surface defect image analysis method, which comprises the following steps of: obtaining surface gray level image data of a product to be analyzed, calculating a structure tensor matrix and generating an anisotropy degree graph; searching similar blocks in a preset search neighborhood, and constructing a local texture data matrix; performing singular value decomposition on the local texture data matrix to extract a main subspace; constructing a projection operator and utilizing the projection operator to carry out orthogonal projection reconstruction on the local image block to generate a reconstructed background image block; according to the method, through an orthogonal subspace projection mechanism, good product textures and defect signals are separated, random noise is removed, meanwhile, high-frequency structural features are completely reserved, and the method is high in robustness, high in robustness and high in robustness. And the defect detection precision of a complex texture surface in a few-sample scene is improved.

The invention relates to the technical field of image data processing, in particular to a cable surface defect detection method and system based on machine vision. The method comprises the following steps: acquiring a surface image of a cable, and converting the surface image into a grayscale image; determining the local complexity of each pixel point; obtaining a plurality of areas of the grayscale image, performing complexity determination, and dividing each area to obtain a plurality of windows of each area; determining a contrast limit threshold value of each window; performing image enhancement by using a CLAHE algorithm to obtain an enhanced grayscale image; and carrying out cable surface defect detection on the enhanced grayscale image by using a defect detection algorithm. According to the method, the CLAHE parameters are adaptively adjusted based on local complexity, the window size and the contrast threshold are dynamically determined in combination with gray and gradient information, discontinuity is corrected and eliminated through boundary similarity, and the quality and reliability of a cable defect detection image are improved.

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

The invention relates to the technical field of image recognition, and further relates to a phenotypic character image recognition and extraction method for efficient corn breeding. The method comprises the following steps: step 1, acquiring an image of the same plant canopy in a test field by using an unmanned aerial vehicle carrying a multispectral sensor in a plurality of different growth periods from a corn jointing period to a mature period, so as to obtain a normalized grayscale image; 2, performing gradient operation on a target wave band based on the normalized grayscale image to obtain significant edge intensity, and generating a character sensitive probability field of a corresponding time phase; and 3, presetting an optimal observation period for each target character, selecting a pixel set which has a probability value not less than 0.5 and is judged to be the character type from an image of a time phase corresponding to the optimal observation period, and sequentially measuring the total projection area, the average connected domain area and the average leaf angle to obtain a comprehensive breeding sequence value of the character. According to the method, high-precision probability identification of the effective phenotype area of the grain-leaf is realized.

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 visual navigation, in particular to a robot navigation method based on vision, which comprises the following steps: acquiring a grayscale image to extract dynamic point locations, screening boundary features to reconstruct a feature set, calculating gradient change to generate candidate guide points, converting coordinates to construct a path track set, and outputting a navigation planning path. According to the method, rapid elimination of static backgrounds is realized through difference threshold selection, dynamic region extraction accuracy is effectively improved, combined screening of boundary line feature point frequency and average feature density is combined, key feature stability is enhanced, and the identification capability of image local structure mutation is enhanced by using a variance change rate of a gradient amplitude sequence. Robustness of path guide point extraction is improved through judgment of an abnormal section, dynamic construction of a path track is realized by means of continuous time integration of image coordinates, path identification precision and spatial positioning continuity are optimized, and continuity, precision and real-time performance of navigation path planning are improved.

The invention relates to the technical field of industrial machine vision detection and image processing, in particular to an automobile part production mold surface smoothness detection system based on image enhancement, which comprises a data acquisition module used for acquiring an original grayscale image of the surface of an automobile part mold; performing low-pass filtering processing on the original grayscale image to eliminate imaging thermal noise; the manifold reconstruction module is used for constructing a structure tensor field; reversely deducing a pseudo-curvature field of the mold surface; the adaptive enhancement module is used for generating a corrected image; constructing a texture orthotropic diffusion model; generating a texture reconstruction reference image; the surface metering module is used for calculating the difference between the corrected image and the texture reconstruction reference image and generating a defect saliency image; calculating the surface roughness value of the mold surface; according to the method, the problem that design textures and abnormal scratches are difficult to distinguish in the prior art is effectively solved, and the technical bottleneck that micro defects are easily missed in a complex geometric structure in traditional visual detection is overcome.

The invention 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 discloses a radar echo extrapolation method and system based on frequency domain enhancement, and the method mainly comprises the following steps: obtaining and preprocessing a historical radar echo grayscale image sequence, generating a sequence sample through a sliding window, and dividing the sequence sample into a training set, a verification set and a test set; the method comprises the following steps: constructing a frequency domain enhanced U-Net network comprising an encoder-decoder structure, introducing a multi-scale deep convolution structure into an encoder and a decoder, and enhancing frequency domain features by using a frequency domain dynamic attention mechanism in jump connection; inputting the training set into the model for training by adopting a composite loss function comprising intensity weighted loss, frequency domain consistency loss and structural similarity loss; and inputting the test set into the trained model, and outputting a radar echo prediction result at a future moment. The method can be effectively applied to the fields of short-term and temporary weather forecast, severe convection monitoring and the like, and provides more accurate and reliable radar echo prediction support for meteorological disaster early warning.

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 a single cell image segmentation method based on a minimum circumcircle, which solves the technical problem of how to improve the precision, robustness and adaptability of an image-based single cell segmentation method, and comprises the following steps of: firstly, obtaining an original cell image, and secondly, converting the original cell image into a gray level image; preprocessing and binarization processing are carried out on the grayscale image to obtain a binarized image, a cell edge contour image is obtained through processing, a minimum circumcircle is drawn for each contour in the cell edge contour image to obtain a cell circumcircle image, and finally, the center of the minimum circumcircle is used as the center point of a cutting area to obtain a cell edge contour image. And determining the boundary of the cutting area by taking the background including the cells and within a certain range around the cells as a standard, cutting the cell circumcircle image, and finally obtaining a single cell image. The method is suitable for single cell identification and segmentation in a microscope image, and can be widely applied to the fields of cell biology research, medical diagnosis, drug screening and the like.

The invention discloses a smart mine safety production violation behavior identification method, system and device and a medium, belongs to the technical field of smart mine safety identification, and aims to solve the technical problem of how to improve the accuracy and efficiency of mine safety production violation behavior identification, realize real-time and accurate safety supervision of the whole process of mine operation and improve the safety of mine safety production violation behaviors. According to the technical scheme, the method comprises the following steps: data acquisition and preprocessing: installing a camera in a key operation area of a mine to acquire video image data, and carrying out denoising, graying and normalization preprocessing operation on the video image data to obtain preprocessed video image data; and constructing a deep learning model based on a convolutional neural network: introducing an attention module into the network structure of the deep learning model, and training the deep learning model by using the marked video image data including the safety production violation behavior and the normal operation behavior, and adopting a transfer learning method in the training process.

The invention relates to the technical field of electronic component detection, in particular to an IC (integrated circuit) carrier plate detection method based on surface state image extraction, which comprises the following steps: acquiring a gray image, analyzing structural parameters, extracting gradient features, detecting boundary disturbance, integrating the image, calculating an abnormal score, identifying a defect position area, extracting features and outputting an identification result. According to the invention, by analyzing the structure parameters of the bonding pad in the gray level image, calculating the edge line segment, the center coordinate and the spacing, and constructing the two-dimensional coordinate system, the regional positioning reference is enabled to have geometric consistency, the coordinate mapping is combined with the gradient direction change frequency and the continuous aggregation point, the boundary disturbance identification precision is improved, and the image division is executed based on the disturbance region. According to the method, non-functional region mixing is effectively avoided, a clustering and probability model is introduced after region gray level statistics, a deviation scoring mechanism is constructed, gray level feature abnormity is accurately recognized, the discrimination capability of small-amplitude and low-contrast defects is improved, and the selectivity and target focusing performance of feature detection are enhanced.

The invention relates to the technical field of image processing, in particular to a craniocerebral disease area identification and detection method and system based on an MRI (Magnetic Resonance Imaging) image, and the method comprises the steps: obtaining a plurality of sub-images with different scales according to a gray level image of the craniocerebral MRI image; performing multi-scale analysis on the gradient value of any pixel point according to each sub-image to obtain a multi-scale gradient coefficient of any pixel point; obtaining a multi-scale local anomaly degree according to the gray values of the pixel points in different local ranges of any pixel point and the distribution in the gradient direction; optimizing the gradient value of any pixel point according to the multi-scale gradient coefficient and the multi-scale local anomaly degree to obtain a self-adaptive gradient value, and performing image enhancement on the grayscale image by using an anisotropic diffusion filtering algorithm according to the self-adaptive gradient value of each pixel point so as to identify a craniocerebral disease region. And the effect of performing image enhancement on the MRI image by using the anisotropic diffusion filtering algorithm is improved.

The present invention relates to the technical field of image detection, and in particular to a method for rapid detection of a fracture region in a precision-stamped part of a new energy vehicle. The method comprises: acquiring a complete stamped part grayscale image; acquiring each sub-region in the complete stamped part grayscale image; for each sub-region, taking a fitting curve of all edge pixels of the sub-region as an edge curve of the sub-region, and constructing a curvature evaluation factor between each pixel and the adjacent previous pixel on the edge curve; constructing the degree of curvature change of the edge curve; constructing a curvature sequence and a distance sequence of the edge curve; acquiring each subsequence of the curvature sequence of the edge curve; constructing the fracture edge matching degree, a local stress discontinuity index and a fracture region confidence level of the sub-region; and on the basis of the fracture region confidence levels of all sub-regions, using a clustering algorithm to cluster and delineate a fracture region. According to the present invention, the accuracy of detection of the fracture region in the precision-stamped part of the new energy vehicle can be improved.

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

The invention relates to the technical field of image recognition, in particular to a mechanical casting detection method and system based on image data analysis processing, and the method comprises the following steps: obtaining a casting image gray value, classifying according to a gray scale standard, generating a multi-layer image structure and a layering graph, setting a grid, counting pixels to form a density sequence, extracting a trend region, and building an index table; and extracting multi-angle image boundary point matching intersection points to form a coordinate matrix, screening a stable intersection point set, searching an original image comparison trend coordinate to extract an overlapping region, and generating a defect annotation image result. According to the method, an image is subjected to gray scale division to form a multi-layer structure, texture differential expression is enhanced, a pattern layer grid statistics density sequence is used for extracting a trend region, abnormal distribution positioning is achieved, boundary mutation points in a multi-view image are uniformly mapped, boundary recognition consistency and robustness are improved, and a stable boundary and the trend region are compared and marked; defect expression intuition and credibility are enhanced, and detection precision and stability are 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 provides a ship target segmentation method and system based on an improved Harris eagle optimization algorithm, and relates to the technical field of image processing and mode recognition. According to the technical key points, the method comprises the following steps: processing an airborne grayscale image including a sky domain, a sea area, a marine ship and a coastline, and obtaining a region-of-interest image; obtaining an optimal segmentation threshold value by using an improved Harris eagle optimization algorithm, and segmenting the airborne grayscale image by using the optimal segmentation threshold value to obtain a segmented image based on the optimal segmentation threshold value; and fusing the region-of-interest image and the segmented image based on the optimal segmentation threshold to obtain a final segmented image. According to the method, the high response area of the ship target is accurately locked through the SIFT features, and adaptive threshold optimization is realized in the target gray band in combination with the improved Harris eagle optimization algorithm, so that the ship target segmentation precision and robustness under the complex sea condition are remarkably improved, and high-reliability technical support is provided for maritime affair monitoring and other scenes.

The invention discloses an LED screen surface damage detection method based on machine vision, and belongs to the technical field of machine vision recognized.The LED screen surface damage detection method based on machine vision comprises the following specific steps that firstly, a light source device is arranged, a flexible OLED light source module with the adjustable angle is used and installed around an LED screen, and the flexible OLED light source module with the adjustable angle is arranged around the LED screen; it is ensured that a light source can be evenly adjusted within the range of 0-180 degrees, the angle of the light source is based on the normal of a screen, and five angles including 0 degree, 45 degrees, 90 degrees, 135 degrees and 180 degrees are preset through an embedded controller; and 2, image acquisition control. Through a multi-angle illumination strategy (0-180-degree dynamic adjustment), the problem of missing detection caused by reflection angle difference of defects under a traditional single light source is solved, dynamic gray anomaly analysis is combined with DBSCAN clustering, real defects and environmental noise are effectively distinguished, simultaneous processing and fusion of multi-light-source angle images are supported, and the method is suitable for large-scale popularization and application. And the time accumulation problem of traditional serial detection is avoided.

The invention relates to the technical field of computer vision, in particular to a jujube tree disease and insect pest recognition method and system combined with a vision technology, and the method comprises the steps: extracting closed edge contours of a disease leaf gray image, and obtaining a contour skeleton of each closed edge contour; based on the graphic features of each contour skeleton, obtaining a to-be-recognized region, analyzing the distribution of gradient amplitudes of the skeleton contour edge and inner pixel points and the gradient amplitude distribution of the skeleton contour outer pixel points, and obtaining an edge transition value of each to-be-recognized region; based on the overall distribution characteristics of the gradient amplitudes of all edge pixel points of the skeleton contour of each to-be-identified area, obtaining an edge energy value of each to-be-identified area; and determining an edge distinguishing value of each to-be-identified area, and obtaining a disease identification result of each to-be-identified area. The invention aims to improve the identification capability of the jujube brown spot and the jujube gray leaf spot and improve the identification precision of disease detection.

The invention discloses a large-scale bearing state identification method based on multi-sensor multi-domain feature dynamic interactive fusion. The method comprises the steps that S1, sensors are installed at multiple positions of a large-scale bearing to collect vibration signals; s2, generating a time-frequency map, a time-frequency grey-scale map, a Gramer angle field map and a recurrence map; s3, inputting the multi-domain image data into a ResNet50 model for feature extraction; s4, by taking the multi-domain features as nodes, constructing a graph convolutional network to perform dynamic interaction of the multi-domain features; s5, generating a fused compact feature vector by using low-rank tensor fusion; s6, constructing a graph attention network, and realizing multi-sensor feature fusion through a multi-head attention mechanism; and S7, fault classification is carried out through manifold space similarity measurement, and a final diagnosis result is obtained. According to the method, dynamic interaction and fusion of multi-sensor multi-domain features are realized through the multi-stage improved graph neural network, the accuracy and credibility of the result are improved, and the technical blank of health state recognition of the large bearing is filled.

The invention discloses an image encryption method and device based on a chaotic system and electronic equipment, and relates to privacy computing or other related fields, and the method comprises the steps: converting a target image into a three-channel grayscale image, and converting the three-channel grayscale image into a one-dimensional array; obtaining a first random key stream generated by the new four-dimensional chaotic system, and performing pixel value diffusion on the one-dimensional array; merging the diffused image arrays, generating a scrambling function based on chaotic tent mapping, and scrambling pixel positions of the merged arrays; obtaining a second random key stream, selecting a DNA coding rule, and coding the scrambling array into a DNA sequence; obtaining a third random key stream and selecting a DNA operation rule to perform encryption operation on the DNA sequence; and decoding the encrypted sequence into a decimal system, and combining channels to obtain a target encrypted image. Through the method and the device, the technical problem that the encryption information is easily attacked and cracked due to the fact that the sensitive data encryption method in the related technology is limited to the encryption dimension and the key space is solved.

The invention relates to the technical field of metal material detection, in particular to a titanium alloy bar machining quality detection method and system, and the method specifically comprises the steps: carrying out the superpixel segmentation of the section of a titanium alloy bar, constructing a mirror image edge texture saliency value of a superpixel block based on the proportion of edge points in the superpixel block and the edge gradient, and carrying out the detection of the machining quality of the titanium alloy bar. Constructing the mirror image texture intensity of the titanium alloy bar by combining the gray level distribution concentration characteristics of the titanium alloy bar, and performing image segmentation on the section of the titanium alloy bar; and in each segmented region, based on the edge line fuzzy degree difference and the pixel gray level difference of each super-pixel block and the rest of super-pixel blocks, combining a mirror image edge texture saliency value to construct a segregation evaluation value of each super-pixel block, and based on the segregation evaluation value, carrying out titanium alloy bar segregation quality defect detection. The titanium alloy bar segregation defect identification capability is enhanced, the titanium alloy bar segregation defect detection accuracy under the titanium alloy bar mirror image scene is improved, potential problems can be found earlier, missing detection is avoided, and the defective rate is reduced.

The invention discloses a thermal printing image processing method, device and equipment and a medium, and relates to the field of thermal printing. The method comprises the following steps: acquiring an original image to be printed, and converting the original image into a grayscale image; calculating a gradient difference value of the grayscale image, and generating a threshold distribution map; generating a heat accumulation risk map based on an average gray value in a preset neighborhood window taking each pixel point of the gray image as a center; generating a preliminary binary image according to the threshold distribution map; extracting image features of the preliminary binarization image, and classifying the original image to obtain an image classification result; calling a target processing parameter set corresponding to the image classification result from a plurality of preset processing parameter sets according to the image classification result; performing optimization processing on the preliminary binary image according to the target processing parameter set and the heat accumulation risk map to obtain an optimized binary image; and the final printing image adaptive to the resolution of the target printer is generated, so that the printing definition is improved.

The invention discloses a BGA welding spot bubble detection method and system based on a self-adaptive threshold value and multi-scale analysis. The method comprises the following steps: taking an obtained grey-scale map of a PCB device as an original image; the method comprises the following steps: carrying out linear gray transformation and binarization on an original image, carrying out morphological optimization on the binarized image, and carrying out connected domain analysis and area screening on the optimized image to obtain a candidate welding spot image; performing edge detection and contour screening on the candidate welding spot image to obtain a welding spot extraction image; traversing the contour in the welding spot extraction image, and intercepting a welding spot sub-image from the original image according to the circle center coordinate and the radius of the contour; dividing regions of the welding spot sub-graph, dividing each region into groups, and calculating an Otsu threshold value of a pixel gray value in each group; according to an Otsu threshold value, carrying out bubble judgment on the pixels in the group; taking the pixels meeting the conditions as bubble detection results; and splicing the bubble detection results of the welding spot sub-images according to the circle center coordinates of the contours on the original image to generate a bubble defect image. According to the invention, the detection accuracy and efficiency are improved.

The invention relates to the field of image processing, in particular to a fire-fighting equipment fault automatic detection method and system based on image processing, and the method comprises the steps: collecting a plurality of images in a plurality of regions at the same time interval, and carrying out the graying of the images; pixel points are classified according to gradient distribution of gray level images in the area, and corrosion probability parameters of the area are obtained; obtaining a water leakage probability parameter according to the time sequence change of pixel points in the suspected corrosion area; and comparing the change conditions of the pixel points of the suspected corrosion area and the non-suspected corrosion area to obtain a water leakage correction parameter, obtaining a fault early warning parameter by combining the parameters, setting a threshold value, comparing the threshold value with the fault early warning parameter, judging whether a water leakage condition exists or not, and completing automatic fault detection of the fire-fighting equipment. The method can effectively distinguish environment condensate water interference and real leakage, and has anti-noise capability.

The invention belongs to the technical field of image processing, and particularly relates to a flour impurity screening method and system based on image processing, and the method comprises the steps: obtaining a surface grayscale image; obtaining a texture response value according to the gray scale standard deviation in the pixel point neighborhood and the local information entropy; adjusting the basic gradient amplitude by using the texture response value to obtain an enhanced gradient value; obtaining an impurity probability value according to the difference of the structure tensor characteristic values and the enhanced gradient value; constructing a background substrate by using morphological reconstruction, and obtaining a significance index according to a difference value between the impurity probability value and the background substrate; and performing segmentation and connected domain screening according to the significance index. While impurity edge signals are reserved, dust noise and natural fluctuation interference of flour are effectively filtered out, and the accuracy of detecting near-color impurities on the surface of the flour is improved.