1289 results about "Gray level" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention relates to the technical field of membrane material image processing, in particular to a membrane material surface treatment defect detection method and system based on image recognition, and the method comprises the following steps: collecting a membrane material surface image, carrying out the statistics of the frequency of gray levels in a gray level image, and obtaining the gray level distribution information of the membrane material surface image; and dividing the membrane material surface image into a plurality of local areas with the same size, calculating a gray level distribution entropy value in each local area, and generating a membrane material surface local area analysis result. According to the method, the membrane material surface image is converted into the gray level image, the gray level frequency is counted, fine gray level distribution information is obtained, preliminary region division of the surface image is achieved, and the positioning accuracy of potential defects is improved. The gray level distribution entropy value of the local area is used as a defect evaluation index, and a potential defect area is accurately marked by combining a preset distribution threshold value, so that the sensitivity of defect detection is effectively improved.

The invention relates to the field of road construction, and discloses a machine vision-based fabricated pavement precast slab flatness measurement method, which comprises the following steps of: positioning and marking an assembled fabricated precast slab area; generating point cloud data through three-dimensional scanning, and carrying out denoising processing and feature extraction; calculating the elevation of each point on the surface of the precast slab according to the feature data, identifying elevation change and generating flatness evaluation data; performing gray level detection on the three-dimensional point cloud data, and further analyzing a defect area in combination with elevation data; and utilizing the Gaussian process regression model to predict the elevation of the unscanned area or the boundary area, and finally outputting a flatness evaluation result. Through the scheme of the invention, efficient and accurate detection of the surface flatness of the prefabricated slab can be realized, feedback and adjustment suggestions are provided for construction, the detection precision is effectively improved, and the construction cost is reduced.

The invention belongs to the technical field of fire safety, and discloses a fire-fighting equipment detection early warning method and system based on a visual camera, and the method comprises the following steps: obtaining multi-focal-length image frames of a fixed spray head and a fire extinguisher edge region, judging the continuous change of gray scale in the continuous image frames, and carrying out the detection early warning of the fire-fighting equipment based on the visual camera. Comparing the gray level distribution difference under the long focus and the short focus, identifying the dynamic trajectory of the contour line angular point of the fire-fighting equipment association structure, analyzing the delay of the function action response, marking as an abnormal region, and generating a fire-fighting equipment collaborative early warning information set; according to the invention, by extracting and analyzing the multi-focal-length image frames of the edge areas of important facilities such as a fixed spray head and a fire extinguisher, fine changes of the fire-fighting facilities under differentiated focal lengths can be captured, working states and potential faults of the fire-fighting facilities can be judged, functional abnormalities caused by equipment aging or damage can be recognized in advance, early warning can be carried out in real time, and the safety of the fire-fighting facilities can be improved. And a higher guarantee level is brought to fire safety.

The invention discloses an intelligent positioning method and system for can making stamping, and relates to the technical field of image processing. Comprising the following steps: acquiring a tank stamping part image, and segmenting the tank stamping part from a background by adopting a self-adaptive threshold segmentation algorithm to obtain segmented tank region image data; utilizing a moment-based sub-pixel edge detection method to accurately position the edge coordinates of the contour of the tank body, and obtaining the key dimension parameters of the tank body; extracting texture features of the surface of the tank body by adopting a gray-level co-occurrence matrix and a local binary pattern algorithm, and judging whether the texture features belong to surface defects or not through a support vector machine classification model; the method solves the problem that edge information is difficult to accurately extract when a traditional image processing method faces tank surface reflection and a complex background, ensures the accuracy of a measurement result by establishing a tank coordinate system and performing attitude correction, overcomes the problem that the tank is easy to deviate or incline in the production process, and improves the production efficiency. And high-efficiency and accurate quality detection of the tank stamping part is realized.

The invention provides a medical image segmentation method, which comprises the following steps: acquiring PET indirect development original image data, and preprocessing the image, including denoising, contrast enhancement and gray level normalization, to obtain a preprocessed PET image data set; designing segmentation middleware, integrating a plurality of segmentation algorithms including threshold segmentation, region growth and level set methods, and selecting proper segmentation algorithms for different types of lesion regions according to feature vector analysis results; pET image data with labels are generated through the simulation technology, a training data set is constructed in combination with real case data, the sample size is expanded through the data enhancement technology, and a large-scale data set used for algorithm training is generated; and the segmentation result of the U-Net is refined by using the segmentation middleware, the lesion area edge is focused on, and the lesion boundary is accurately positioned through an edge detection algorithm to obtain a fine segmentation result.

The invention relates to the field of image processing, in particular to a seamless steel tube defect detection method based on image processing, which comprises the following steps: acquiring an image of a sized steel tube through a high-resolution industrial camera, and preprocessing to enhance the image quality; designing an adaptive filter based on image gradient direction and spatial frequency characteristics, and enhancing defect characteristics; extracting a defect region by adopting adaptive threshold segmentation and morphological operation, and constructing a comprehensive feature vector through a Hu moment and a gray-level co-occurrence matrix; and finally, defect identification is realized by using a support vector machine classifier, and technical support is provided for steel pipe production quality control. According to the method, the adaptive filter is constructed based on the gradient direction and the spatial frequency, and efficient detection and classification of seamless steel pipe surface defects are realized by combining the comprehensive feature vector and the support vector machine classifier.

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 analysis, in particular to an ETFE film cutting control method and system based on image recognition, and the method comprises the following steps: enhancing an edge through multi-scale Gaussian filtering, extracting a gray level change rate pixel by a Sobel operator, generating a path segment, outputting a coordinate, calculating a gradient direction, constructing a direction continuous sequence, and recording an angle deviation; a dynamic window judges abnormal fluctuation to trigger calibration, a fluctuation segment vector is replaced to update a motion track, and a B spline is fitted to generate a track output cutting instruction. According to the method, multi-scale Gaussian filtering is combined with Sobel operator enhanced edge extraction continuous pixels, the fracture problem caused by a fixed threshold value is solved, vector space projection constructs a direction sequence to quantify angle deviation, dynamic window range judges fluctuation to trigger calibration, and a vector replacement strategy corrects an abnormal section control point direction. A smooth track is generated through B spline interpolation, mechanical vibration is reduced, and the flexible material cutting precision and the equipment cooperation efficiency are improved.

The invention provides a display panel brightness compensation model construction method, compensation method and device, and the method comprises the steps: determining a training sample set which comprises display parameters obtained through photographing an actual display image of a preset gray scale displayed by a display panel through an image collection device, and corresponding target display parameters; and inputting the training sample set into a pre-constructed neural network model to train the neural network model to obtain a trained compensation model, wherein the compensation model is used for predicting the brightness compensation value of each pixel on the display panel based on the display parameters of the display panel. Compared with the existing Demura technology, the brightness compensation value is predicted by considering the influence of each pixel, the surrounding pixels and the image acquisition equipment, the accuracy of brightness compensation value prediction is improved, and the compensation precision and the compensation effect can be further improved.

The invention provides a diffractive optical element design and manufacturing integration method based on gray level laser direct writing photoetching, which comprises the following steps that: a manufacturing simulator and a wave optical simulator are established, and the manufacturing simulator represents the spatial frequency response of an optical system through a low-pass filtering model; a mapping relation between exposure dose and a developing result is established by combining nonlinear response of photoresist, and the wave optical simulator predicts optical performance by utilizing output of the manufacturing simulator and combining a wave optical theory; by setting initial manufacturing parameters and performing iterative optimization, the surface appearance of the photoresist is dynamically adjusted to be close to the target optical performance, the optimization process is combined with an automatic differential technology, a gradient optimization algorithm and a block optimization strategy, and the calculation efficiency is improved; and the optimized parameters are used for processing gray laser direct writing equipment to manufacture the diffractive optical element. The problem of manufacturing deviation caused by the optical proximity effect in the prior art can be solved, and meanwhile the overall performance of the diffraction optical element is remarkably improved.

The invention discloses a welding quality visual detection method, and relates to the technical field of weld defect detection, and the method comprises the steps: obtaining a pixel neighborhood block corresponding to each pixel point in a to-be-detected welding gray image, and obtaining the illumination influence degree in each pixel neighborhood block according to the gray feature and gradient feature of each pixel point in each pixel neighborhood block; according to the position distribution characteristics between the edge end points of the adjacent edges in the welding gray level image to be detected, the same kind of edges are judged; obtaining a gamma value adjustment coefficient of a pixel point at the central position in each pixel neighborhood block by obtaining the confidence coefficient that each similar edge is the inner edge of the welding seam and the defect degree of each similar edge; the gamma value of each pixel point is adjusted, the gray level output value of each pixel point in each pixel neighborhood block is obtained, and an optimized welding gray level image is obtained; performing quality detection on the to-be-optimized welding grayscale image; the accuracy of welding quality detection can be improved.

The invention relates to the technical field of optical detection, in particular to an AOI automatic optical detector calibration method and system, and the method comprises the following steps: obtaining a standard color palette RGB reflection reference, collecting a measured value, calculating an offset contrast, extracting image brightness, generating a correction ratio, converting gray level, extracting abrupt change, screening anomalies, and analyzing a brightness trend to generate a finishing record. And formulating a rule matching correction output comparison table. According to the method, the accuracy and reliability of image detection are enhanced through detailed analysis and accurate correction of color offset and brightness difference, the scheme combines real-time light source data and image information, fine changes are effectively identified, the misjudgment rate is reduced, the accuracy of gray and color correction is improved through comprehensive analysis of light intensity offset and regional brightness trend, and the accuracy of image detection is improved. The detection efficiency and quality of a high-density circuit are ensured, the detailed calibration mechanism remarkably reduces detection errors especially in a high-precision environment, and product quality control and the stability of a production process are optimized.

The invention discloses a hydraulic engineering dam body crack detection method and system based on machine vision, and relates to the technical field of computer vision. A high-definition camera is used for shooting a dam body image, obtaining a sample data set, extracting feature parameters of different noise types, and constructing a feature database; filtering the current dam body image to obtain a first dam body image, enhancing the crack gray scale difference of the first dam body image, obtaining a second dam body image, segmenting the second dam body image, screening candidate cracks in the crack image, and identifying real cracks according to the edge features of the candidate cracks. According to the method, a feature database is constructed, a targeted dam body image processing flow is combined, a high-definition camera collection and automatic processing flow is used, filtering parameters are dynamically adjusted by constructing the feature database, gray level enhancement and precise segmentation are combined, real cracks are precisely recognized through edge feature analysis and comparison, and quantitative parameters are output; and a reliable basis is provided for dam body safety assessment.

The invention relates to the technical field of image recognition, in particular to a bearing surface defect automatic detection system based on image recognition, and the system comprises an image collection and preprocessing module which obtains a bearing surface image, and carries out the cutting and filtering of the image, so as to remove the noise and adjust the contrast, and obtain a processed image; and carrying out gray level conversion on the processed image to generate a preprocessing result. According to the method, environmental noise interference is reduced through image cutting and filtering, the contrast ratio is adjusted, the image quality is optimized, and the distinction degree of the defect area and the background is improved. Image channel information is standardized through gray level conversion, so that the calculation precision of feature extraction is kept consistent, and the analysis result is prevented from being interfered by multi-channel data. According to the method, image hierarchies with different scales are constructed by an image pyramid method, so that the adaptability to fine cracks and large-area spalling defects is improved in the local contrast and texture feature extraction process, and the stable recognition capability to defects with different sizes is enhanced.

The invention relates to the technical field of image state recognition, in particular to an unhooking and rehooking AI accurate recognition grabbing system and method based on pattern recognition, in the system, node construction is conducted through contour changes, boundary difference values and gray level dynamic states of a hook assembly in an image sequence frame, edge displacement accumulation analysis is combined, meanwhile, through a graph neural network, an image sequence frame is obtained, and the image sequence frame is obtained. Cosine values and coordinate difference values between nodes are subjected to combined comparison, and a path hopping sequence is constructed, so that the response sensitivity to state abrupt change is enhanced, a key path of morphological evolution can still be stably extracted under the condition of complex background interference or local shielding, the anti-interference performance and fault tolerance of space path identification are effectively improved, and the space path identification accuracy is improved. Statistical modeling is further carried out on state rate sudden change points through a hidden Markov model, paragraph merging and invalid fragment removing operation are carried out on abnormal point segments by matching a standard state mode, a state label sequence is constructed, and accurate division of high-confidence and multi-segment continuous states is achieved.

The invention discloses a geographic information collection method and system based on a remote sensing image, and relates to the technical field of geographic information extraction, and the method comprises the steps: firstly obtaining a remote sensing image sequence, carrying out the time sequence registration, and extracting pixel gray level change features to generate building edge region pixels; delimiting an edge area, and identifying disturbance points; road node positions are extracted, and a path steering angle is corrected; establishing a disturbance chain communication density value; and finally generating a structured image data unit. According to the method, pixel gray level change features are extracted through time sequence registration, a building edge area is positioned, disturbance points are recognized by combining the disturbance amplitude and gray level direction consistency, path steering deviation is corrected, and the stability of a path communication structure is enhanced; a disturbance chain communication density value is established through an extension path, a continuous response boundary is extracted by fusing a gray scale trend and structural integrity, an image data unit with a gray scale trend, spatial connectivity and disturbance characteristics is generated, and the accuracy and continuity of boundary recognition in a complex region are improved.

The invention discloses a crack identification method and device based on multi-scale infrared thermal feature fusion, and the method comprises the steps: obtaining the gray images of cracks and noise for a single-frame infrared thermal image; histogram equalization is adopted to adjust image gray level distribution, and a gray level enhanced image is obtained; setting a larger local feature sensitivity weight coefficient for the background dominant window and the noise dominant window, and setting a smaller local feature sensitivity weight coefficient for the target composite window; counting the number of foreground pixels in each window, and calculating the ratio of the number of foreground pixels to the total number of pixels; determining a background dominant window, a noise dominant window and a target composite window; setting local feature sensitivity weight coefficients of a background dominant window, a noise dominant window and a target composite window; segmenting the grayscale enhanced image to obtain a binary image, and extracting a connected domain of foreground pixels; for each foreground pixel connected domain, calculating the total number of pixels, randomly selecting a plurality of seed points, determining corresponding maximum inscribed circles, taking the average value of the diameters of all the maximum inscribed circles as the width, taking the ratio of the total number of pixels to the width as the length, and screening out connected domains with the length-width ratio smaller than 10 as noise; and optimizing the foreground pixel connected domain, removing fine noise, smoothing the contour, and regarding the finally obtained foreground pixel connected domain as a crack.

The invention discloses a virus infection positioning detection method and system based on diffractive optics. The method comprises the following steps: acquiring diffraction fingerprints corresponding to all to-be-detected cells in an imaging area by utilizing a diffraction imaging principle; respectively extracting texture features of each diffraction fingerprint in the imaging area by using a gray level co-occurrence matrix method; the extracted texture features are compared with standard texture features pre-stored in a relation model of cell morphological change and diffraction fingerprints, so that the'cell state 'of each cell to be detected in the imaging area can be obtained, and the virus infection state of each cell in the imaging area can be positioned and identified. The efficient light diffraction spectrum system is used for virus infection detection, and the requirement for professionals and expensive and complex professional field detection equipment is eliminated. The continuous and high-throughput evaluation on the cell condition after virus infection is met, and a foundation is laid for popularization and application of a virus infection instant detection platform.

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 relates to the technical field of image analysis, in particular to a mobile phone screen uniformity detection method based on image analysis, which comprises the following steps: extracting a central region gray sequence, calculating a slope to generate a trend chart, positioning a change starting point to adjust coordinates, extracting gray difference to enhance comparison, and analyzing a bright-dark span to generate a map. And extracting a gravity center gray level judgment difference and outputting a detection scheme. According to the method, through multi-direction gray scale sequence slope extraction and fluctuation aggregation feature construction, detail capture of a brightness trend is enhanced, reverse gray scale change starting point positioning and coordinate offset are combined, abnormal region positioning precision is improved, a local transition zone and a jump section are extracted, and gray scale fluctuation partition enhancement is realized. Through high-gray pixel set gravity center and local fluctuation difference analysis, brightness abnormal points are accurately recognized, the whole process is fused with the links of gray trend extraction, change track adjustment, contrast enhancement, gravity center analysis and the like, the detection sensitivity and the judgment precision are enhanced, and the screen brightness differential detection requirement is met.

The invention relates to the technical field of oral X-ray image recognition, in particular to an intelligent oral X-ray film lesion area recognition system based on artificial intelligence. The method comprises the following steps: obtaining gray level change characteristics of each tooth region in an oral X-ray image according to gray level and gradient distribution in each tooth region; obtaining an optimal factor according to the gray change feature similarity between different regions, and further selecting a standard tooth region; obtaining a ray absorption coefficient according to the gray difference of different areas and the difference of the optimal factors; obtaining caries area possibility of the pixel points according to the positions of the pixel points in the tooth area; and the caries area is identified. According to the method, the caries area in the oral X-ray image is accurately segmented, and finally accurate treatment can be carried out on a patient.

The invention relates to the field of infrared image processing, in particular to an infrared blind pixel detection method based on temperature self-adaption and time-frequency domain synthesis, which comprises the following steps of: traversing an infrared image by using a sliding window to obtain gray values of pixel points around a current pixel point; carrying out image edge judgment by using a Sobel-Otsu algorithm based on working temperature dynamic adjustment, and reserving a non-image edge window to participate in blind pixel judgment; grouping pixel points in the window according to horizontal and vertical directions; calculating the alpha filtering value of the gray scale of each group of pixels; calculating the deviation between the gray value of each pixel in the window and the alpha filtering value of the corresponding row coordinate and column coordinate group; if the deviation is greater than an adaptive threshold, setting the blind pixel as a potential blind pixel, and constructing a potential blind pixel distribution matrix; a time domain accumulation method for pre-screening periodic noise based on FFT is adopted, periodic noise interference is eliminated through frequency domain analysis, and then high-probability blind elements are screened through time domain accumulation; and obtaining a final blind pixel detection result. The method can effectively cope with the working temperature change of the sensor and the noise interference in a complex environment, and remarkably improves the accuracy and anti-interference capability of blind pixel detection.

The invention discloses an image generation method and device, equipment, a medium and a product, and relates to the field of image processing. The method comprises the following steps: acquiring a first image and a gray level change graph, and acquiring a noise image; in the process of carrying out iterative noise reduction on the noise image by taking modification of the first image as a noise reduction condition, obtaining the ith noise image corresponding to the ith iteration noise reduction stage; when i = 1, performing noise reduction on the first noise image to obtain a first noise reduction stage image output in a first iteration noise reduction stage; when i is greater than 1, based on a fusion proportion indicated by a second pixel value in the gray level change graph, fusing the i-th noise image and the (i-1)-th noise reduction stage image output in the (i-1)-th iteration noise reduction stage to obtain an i-th fused image; and carrying out noise reduction on the ith fusion image to obtain an ith noise reduction stage image output in the ith iteration noise reduction stage until the iteration noise reduction requirement is met, and obtaining a generated image as a modification result of the first image. The method can improve the generation effect of the image modification result.

The invention discloses an SAR satellite remote sensing surface water extraction method for an African climate complex region, and the method comprises the steps: carrying out the preprocessing of satellite image data, and making sample point label data; making a shadow mask file, and removing the mountain shadow on the satellite image data; calculating a gray level co-occurrence matrix to generate texture features; establishing a multi-dimensional feature space, and screening the feature space; building a semi-supervised cooperative training model by using two random forest classifiers, and introducing label-free data to assist in model training; and predicting the global surface water distribution of the research area. According to the method, the SAR remote sensing image is combined with the semi-supervised collaborative random forest model, the manpower and time cost of actual sample labeling is greatly reduced, the surface water monitoring efficiency is improved, the problem of visible light remote sensing data missing in regions with complex climates can be solved by using microwave remote sensing data, and the method is suitable for popularization and application. And timely and accurate information is provided for water resource management in the African region.

The invention relates to the technical field of image segmentation, in particular to a medical image processing method for tumor partition recognition, which comprises the following steps: acquiring a multi-modal medical image, extracting gray features and adjusting image gray, recognizing tissue boundary and metabolism features, grouping and generating tumor boundary response partitions, extracting heterogeneous nodes and classifying and labeling. Dividing a core area, an infiltration area and a necrosis area, and generating a tumor functional structure chart. According to the method, the gray frequency and the edge trend of the multi-modal image are unified, the structure contrast is enhanced, the consistency of each modal image in space and gray level is ensured, the information fusion integrity is improved, the gray, texture and metabolism characteristics are combined, the density mutation and metabolism enhancement region is identified, and the boundary clear contour is constructed; boundary partitions are established through response direction consistency, the cross-modal discrimination ability is enhanced, texture and metabolic characteristics are fused to classify heterogeneous nodes, a core area, an infiltration area and a necrosis area are divided, and functional partitions with clear expression and clear structures are generated.

The invention relates to the technical field of image processing, in particular to an intelligent welding defect positioning and detecting system based on image processing. Obtaining a suspected noise degree according to gradient features and neighborhood gray level distribution features of pixel points in the welding image; obtaining a gray confidence coefficient according to the gray features of the pixel points and the gray difference features of the pixel points and the neighborhood; obtaining a structure confidence coefficient according to the area feature of the connected domain where the pixel point is located, the contour change feature of the edge line of the connected domain and the gradient distribution feature of the edge line in the normal direction; obtaining a denoising coefficient of the pixel point according to the suspected noise degree, the gray level confidence coefficient and the structure confidence coefficient; and adjusting the standard deviation in the Gaussian filtering algorithm according to the de-noising coefficient. The method comprises the following steps: denoising a welding image according to an adaptive standard deviation, and performing image enhancement on the denoised welding image to obtain a to-be-detected image; welding flaw detection is carried out on the to-be-detected image, and the detection accuracy is improved.

The invention relates to the technical field of defect detection, in particular to a die-casting die defect detection method and system based on image recognition, and the method comprises the following steps: irradiating the surface of a die-casting die by using a target wavelength light source, capturing a die surface image by using a camera, and carrying out the color correction and illumination compensation of the captured die surface image, and storing the output high-definition original image. According to the method, the color depth and the contrast ratio of the high-definition original image are adjusted, so that the highlighted display of defect features is greatly improved, the recognizability of surface defects such as cracks and air holes is enhanced, the detection accuracy is improved, detail revelation and defect contour recognition are carried out, and the separation of a defect area and a background is further accurate; the accuracy and efficiency of analysis are optimized, accurate quantification of the shape and size of the defect is achieved through gray level analysis, quantitative data are provided for defect management and die maintenance, the service life of the die-casting die is comprehensively prolonged, and the production problem caused by the defect is relieved.

The invention discloses a high-speed strip steel defect detection method and system based on anomaly enhancement and image reconstruction, the problem that part of defects are difficult to recognize is solved by adopting a pseudo anomaly enhancement method, the defects difficult to detect are divided into a scratch type and a stain type, a local average gray level floating method is designed to generate scratch type pseudo anomalies, and the defect detection accuracy is improved. And a contour random disturbance method is used to generate stain type pseudo anomalies, and local difference features are provided for the model. And a lightweight image reconstruction model of an encoder-decoder structure is designed to achieve high detection speed and meet the requirement of synchronous detection. According to the method, performance testing is carried out on a strip steel surface image data set, advanced performance evaluation is obtained, and the effectiveness of the method is proved.

The invention relates to the technical field of conductive powder mixing uniformity detection, and discloses a conductive powder mixing uniformity detection method, which comprises the following steps: acquiring a conductive powder image, and converting the conductive powder image into a grayscale image; setting a gray level according to the gray value of each powder before mixing, and carrying out gray level conversion on the gray image; gray level co-occurrence matrixes of the converted gray level images under different interval parameters and differences between the gray level co-occurrence matrixes under different intervals are calculated; and judging whether the conductive powder is uniformly mixed or not according to the difference between the gray-level co-occurrence matrixes. By adopting the scheme of the invention, the mixing uniformity of the powder can be accurately judged according to the difference between the conductive powder textures obtained under different parameters.