785 results about "Color difference" patented technology

The invention relates to the technical field of quality detection in material manufacturing and coating processes, in particular to an intelligent color difference analysis and detection method for paint, which comprises the following steps: S1, acquiring a multispectral dynamic compensation image; s2, texture-color coupling separation is carried out; s3, carrying out adaptive chromatic aberration modeling; s4, process correlation visualization is carried out, color difference data is mapped into a scalable thermodynamic diagram, and grid density is dynamically adjusted according to detection precision; and analyzing geometric feature parameters of the abnormal region, matching a process defect mode library to generate an optimization suggestion, and superposing the optimization suggestion on the thermodynamic diagram by a visual identifier. Through real-time data acquisition, texture interference elimination, model adaptive optimization and process defect analysis, the overall detection precision and efficiency are improved, the intelligent adaptive characteristic is achieved, and different production environments and base material types can be adapted, so that the coating production process is optimized, and the consistency of the coating quality is ensured.

The invention discloses an image processing method applied to printed matter surface color difference detection, which relates to the technical field of image processing, and comprises the following steps: acquiring a digital image of a printed matter to be detected, performing illumination non-uniformity correction, and converting the corrected digital image into a CIELAB color space; performing region segmentation on the digital image based on double constraint conditions of color gradient and texture boundary to generate a detection region graph; feature parameters are extracted based on the detection area graph, and a feature data set is generated; establishing a dynamic reference model by utilizing process parameters and material characteristics of the printed matter; and calculating the distance between the feature data set and the dynamic reference model, identifying color difference regions and generating color difference scores, and screening and grading the color difference regions according to the color difference scores. According to the method, a multi-layer image pyramid and homomorphic filtering combined illumination correction technology is adopted, and an adaptive weight fusion mechanism based on image features is introduced, so that the illumination nonuniformity is effectively eliminated while the definition of printing details is kept.

The invention discloses a product surface paint layer color difference detection method and system. The method comprises the following steps: converting an original image from an RGB color space to an LAB color space; then foreground segmentation is carried out to realize extraction of a to-be-detected object; lAB values of foreground pixels in the LAB image are extracted, a K-means algorithm is used for clustering the LAB values of the foreground pixels, and color grading of the foreground pixels is achieved; using a region merging denoising function to optimize the clustering labels, eliminating noise points with too small areas, and enabling the noise points to be automatically fused into regions with peripheral color levels; chromatic aberration between the clustering centers is calculated, and clustering center pairs with the distance larger than a threshold value serve as significant chromatic aberration pairs; and covering masks of different colors in each stage of color area, and generating a visual result of color grading and a visual result of an area with remarkable color difference. According to the invention, accurate and efficient image color difference detection is realized, and the method has a wide application prospect.

The invention relates to the technical field of display screen detection, and discloses a liquid crystal display screen color difference detection system and method.The system comprises a multispectral imaging module, a curved surface deformation modeling module, a coupling tensor decomposition module, a biological excitation feature extraction module, a dynamic compensation control module and a technological parameter interface module; the method comprises the following steps of synchronously collecting a multispectral image and screen deformation data, constructing and decomposing a multidimensional tensor containing geometric constraints, extracting biological excitation visual features, and generating and applying voltage compensation through model prediction control so as to dynamically correct the color difference of the flexible screen. According to the method, through multispectral and geometry synchronous perception, multidimensional tensor constraint decomposition, biological excitation and deformable convolution feature extraction, time-varying model prediction control and dynamic weight adjustment of fused manufacturing parameters, high-precision capture and personalized compensation of the dynamic chromatic aberration of the flexible screen are realized. The defects of dynamic perception, quick response and individual adaptability are overcome.

The invention provides a high-resolution machine vision detection method and system for precise chromatic aberration detection and a medium, and the method comprises the steps: carrying out the calibration of a camera based on a camera calibration tool, and synchronously calibrating a light source parameter and a color parameter; setting shooting parameters based on the calibrated camera, obtaining a workpiece image in real time, preprocessing the workpiece image, mapping the preprocessed image to a standard color space from an original RGB space, and extracting color features of the preprocessed image; comparing the color feature with the color feature of the standard sample, calculating a color difference index, and comparing with the color difference index based on a set color difference threshold to obtain a detection result; by calibrating various parameters of the camera, the visual detection precision is ensured, and by performing color space mapping on the workpiece image and analyzing the difference between the color difference index of the color feature and the color difference threshold value, the color abnormal area is accurately analyzed, and the detection precision of the abnormal color is improved.

The invention relates to the technical field of image processing, in particular to a printing image-text defect detection method based on machine vision, and the method comprises the steps: obtaining an RGB image of a to-be-detected printed matter, and carrying out the Lab space conversion, and obtaining a target image; performing normalization processing on the L channel value of each pixel point in the target image to obtain a normalized brightness value so as to divide the pixel points in the target image into a preset number of illumination level intervals; obtaining a color fluctuation statistical parameter of any one illumination level interval, and obtaining an optimal color difference threshold value corresponding to any one illumination level interval; according to the optimal color difference threshold value corresponding to the illumination level interval to which each pixel point in the target image belongs, performing Lab color space template comparison on the target image to obtain an image-text color difference defect result of the to-be-detected printed matter, thereby improving the accuracy of image-text printing defect detection based on the color difference threshold values.

The invention discloses a water environment monitoring method based on image vision, and relates to the technical field of water environments, and the method comprises the steps: continuously shooting water body images of different depths at preset intervals in the vertical direction through an underwater camera; calculating the color difference of two adjacent pictures, and determining a water quality mutation layer; when it is detected that the turbidity in the water quality abrupt change layer interval exceeds a preset threshold value, infrared spectral imaging and ultrasonic ranging cooperative work is switched, the penetrability characteristic of an infrared image and ultrasonic depth calibration data are combined, the layering positioning error is corrected, and the water quality abrupt change layer interval is obtained. Through vertical interval shooting, color difference analysis, infrared and ultrasonic collaborative correction and a dynamic sampling strategy, the precision and efficiency of water quality layered monitoring are improved.

The invention discloses an LED liquid crystal display screen brightness and color difference detection and correction method and system, and belongs to the technical field of liquid crystal display. Initial brightness values and color values of all display units of a display screen are obtained; constructing a brightness and color difference reference model based on the convolutional neural network, and calculating a brightness deviation delta L and a color deviation delta C between the brightness and color difference reference model and a standard value; constructing a brightness-color difference error matrix W; according to the error matrix and in combination with a self-adaptive threshold algorithm, identifying a brightness-color difference abnormal region and corresponding coordinates of the brightness-color difference abnormal region; based on a deviation scoring result, introducing a gradient regression model to generate a brightness gain coefficient and a color adjustment coefficient, and constructing a correction parameter set P; the parameter set is issued to a control chip, dynamic image signal modulation and local frame level refreshing are executed, and point-by-point brightness and color difference correction is achieved; the method has the advantages of high precision, high automation, high adaptive capacity and the like, and is suitable for improving the brightness and color difference uniformity in various LED liquid crystal display screen scenes.

Provided in the present invention is a reconstruction and driving method based on a multi-view motion video of a clothed human body. The method comprises: using a two-layer three-dimensional Gaussian representation to model a human body and corresponding clothes; defining geometric attributes of three-dimensional Gaussian ellipsoids for any face on coarse three-dimensional meshes of the human body and the clothes; at a reconstruction stage, transforming coarse triangular meshes of the clothes and the body from a canonical space to an observation space, and updating Gaussian geometric attributes; and on the basis of a camera pose and parameters of an input video, rasterizing a three-dimensional Gaussian representation of the observation space to an image space, and rendering a picture on the basis of a Gaussian splatting formula. Self-supervised training can be performed on a model by means of calculating the color difference between a rendered picture and a real picture and using same as an optimization objective; when reconstruction is completed and driving is performed, a geometric result of a current frame is used to calculate the position of the coarse triangular mesh of the clothes in the next frame; and the reconstruction-stage method and related parameters are used to render a driving picture in a new posture.

The invention relates to the technical field of image processing, and discloses a fluorescent lifetime microscopic image large-view-field splicing method and system and electronic equipment, and the method comprises the steps: constructing a tissue region mask for a to-be-spliced image, and constructing a vignetting model in a tissue region to complete vignetting correction; counting brightness indexes in an organization area to determine a global brightness reference, and realizing image group brightness unification through global zooming and single image brightness adaptive correction; further realizing alignment of adjacent images through feature point matching, and finishing image block splicing in combination with a minimum color difference suture fusion method to obtain an image band; an overlapping area is extracted from an image belt to generate an effective content mask, relative displacement is estimated by using a phase correlation method after low-frequency suppression, image belt splicing is completed by multiplexing a minimum color difference suture fusion method, a large-view-field spliced image is output, the automation degree and robustness of splicing are improved, the spliced image is geometrically consistent and visually seamless, and the splicing efficiency is improved. And the requirements of medical research on high-resolution and large-field-of-view fluorescence lifetime microscopic images are met.

The invention relates to the technical field of lane edge detection, and provides a multi-source feature lane edge line identification method and system based on a scoring mechanism, and the method comprises the following steps: obtaining and preprocessing road point cloud data, and obtaining road region point cloud data; the color information and the laser intensity information are fused for scoring, and the road area point cloud is processed to obtain a lane line point cloud; for the lane line point cloud, extracting boundary points based on the central main axis and the normal feature jump score to obtain an edge line of each lane line; and performing smooth fitting on the edge line of the lane line to obtain a fitted lane edge line. According to the method, the color and laser intensity double-scoring function is constructed, the intensity threshold is adaptively determined through the OTSU algorithm, and the intensity threshold and RGB color difference are subjected to weighted fusion, so that the recognition accuracy and continuity of the lane marking points are effectively improved, and the problems of extraction fracture, misrecognition and the like in a traditional method are solved.

The invention discloses a method for dynamically monitoring the drying quality of fruits and vegetables by fusing chromatic aberration-shrinkage rate analysis. The method comprises the following steps: placing a fruit and vegetable sample on a rotatable objective table, drying according to a preset program, and carrying out multi-angle shooting by using a digital camera according to a timestamp to obtain an RGB color image; the method comprises the following steps: preprocessing by an image analysis system, converting an image into a Lab chromaticity space, calculating a chromatic aberration value delta E and a comprehensive shrinkage rate S, integrating data to generate a change curve, and constructing an appearance form model. In actual drying, real-time data and the model are compared, and early warning is carried out when the color difference value exceeds 3.5 or the shrinkage rate deviation exceeds 15% and image acquisition is abnormal. The drying equipment is provided with a high-precision electric rotary objective table, a specific annular light source array, a high-resolution camera and a standard calibration color card, and illumination and color measurement accuracy is ensured through calibration. Through multi-step operation, accurate and dynamic monitoring of fruit and vegetable drying quality is achieved, and drying process optimization is assisted.

The invention relates to the technical field of image processing, in particular to an optical lens surface defect detection method and system based on image processing, and the method comprises the following steps: obtaining an optical lens surface image, decomposing the optical lens surface image into three independent color channel intensity data of red, green and blue, and performing contrast adjustment on the color channel intensity data of each channel to generate a corrected color channel set. According to the invention, through performing detailed color channel decomposition and targeted contrast adjustment on the obtained surface image of the optical lens, the color difference degree between the defect area and the background area in the image of the optical lens is improved, and the detail expression effect of the defect area is enhanced; furthermore, through local calculation of intensity difference between color channels and spatial gradient amplitude analysis, color change and spatial features are effectively fused, and the visual expressive force of a defect area is enhanced.

The invention provides a surface printing flaw detection method, system and device for a corrugated packaging box, and relates to the technical field of image processing. The method comprises the following steps: acquiring and aligning a to-be-detected corrugated paper image and a template image, and dividing the to-be-detected corrugated paper image and the template image into a corresponding detection area and a template area; identifying a suspected white leakage area based on the average reflectivity difference of the detection area and the template area; determining the edge connection fracture degree based on the color difference between the contour edge pixel points of the suspected white leakage area and the standard colors on the two sides; determining the filling defect rate of the closed area based on the fractal dimension of the suspected white leakage area and the distance between the areas; determining a white leakage unit based on the average color difference of a first repeated pattern unit in the template image and a second repeated pattern unit in the to-be-detected corrugated paper image, and determining a pattern repetition missing rate based on the white leakage density and the white leakage dispersion of the white leakage unit; and determining a defect type based on the edge connection fracture degree, the closed area filling defect rate and the pattern repetition missing rate, and associating the production process. According to the invention, the white leakage problem procedure can be positioned.

The invention relates to a dynamic color cast compensation Demura method and system and a display screen, and belongs to the technical field of display. Comprising the following steps: collecting a white light channel picture, obtaining relative brightness data, calibrating the relative brightness data, and constructing a mapping function; according to the function, converting the relative brightness data to obtain first Lab data; obtaining a Lab data initial value of a to-be-compensated screen; calculating a color difference value according to the Lab data initial value and the first Lab data; performing visual perception weighted deviation conversion on the difference value to obtain color deviation; calculating a dynamic compensation weight according to the deviation; obtaining a color deviation evaluation index according to the weight and the color deviation; and generating second Lab data according to the evaluation index and the gradient vector of the color difference value, and performing inverse operation on the second Lab data to obtain a Demura compensation value. According to the invention, through joint compensation of brightness and chromaticity, chromaticity compensation missing is avoided, and meanwhile, through a dynamic compensation weight mechanism, noise amplification in a low-brightness interval is avoided, and the color uniformity of a display screen is improved.

The invention discloses a display screen fault detection and repair method based on a Mini LED array, and the method comprises the steps: carrying out the real-time fault detection: carrying out the optical detection of each pixel of a Mini LED array display screen through a high-resolution multispectral sensor and a deep learning algorithm, obtaining the brightness, color temperature, color difference and spectrum information of the display screen in real time, carrying out the comprehensive fault monitoring of the display screen, and carrying out the real-time fault detection. Comprising defects of bright spots, dead points, uneven brightness and the like, and deep learning fault analysis: carrying out automatic processing on pixel data collected in real time by adopting deep learning algorithms such as a convolutional neural network (CNN) and the like. According to the display screen fault detection and repair method based on the Mini LED array, through adoption of a high-resolution multispectral sensor, sub-nanometer level detection can be realized, any tiny defect in the Mini LED array can be accurately captured, and the sensitivity and the accuracy of fault detection are remarkably improved. Through the assistance of the deep learning algorithm, the fault pixel type can be intelligently identified, the identification accuracy is improved, and false alarm and missing report are reduced.

The invention relates to the technical field of video special effect rendering, in particular to a video special effect high-quality rendering method, device and system in the film and television manufacturing process, and the method comprises the steps: carrying out the framing processing of a film and television video, and obtaining each frame of image; obtaining each angular point and each connected domain in each frame of image; obtaining the displacement fluctuation saliency of each angular point in each frame of image; acquiring a matched connected domain of each connected domain in each frame of image in the previous frame of image, and acquiring an adjacent frame abrupt change value of each connected domain by analyzing the contour difference and color difference between each connected domain and the matched connected domain thereof and combining the displacement fluctuation saliency of all angular points in each connected domain; clustering analysis is carried out through the adjacent frame abrupt change values of all the connected domains in all the frame images, and all suspected key frames are obtained; acquiring a key coefficient of each suspected key frame and a key change connected domain of each key frame; the invention aims to improve the quality of video special effect rendering by improving the recognition precision of the key frame.

The invention belongs to the technical field of printing, and particularly relates to a printing ink color real-time calibration method and system based on dynamic color gamut mapping, high-frequency online monitoring is achieved by constructing a local color gamut contour and arranging a multispectral sensing array, and the change trend can be captured at the initial stage of color shift; a stable color block recognition and local weighted mapping updating mechanism is combined, an updated lookup table is dynamically generated, and correction is prevented from being triggered by dependence on significant offset; further, according to the response relationship between the color offset direction and the printing parameters, the adjustment of the ink key opening and the printing pressure is implemented in stages, the offset source is inhibited from the physical level, the mapping adjustment is started when the color difference does not exceed the limit, the equipment parameter fine adjustment is synchronously driven, the response time is remarkably shortened, the generation of out-of-tolerance products is reduced, and the product quality is improved. And the real-time performance and the stability of color control are improved.

The invention relates to the technical field of printing image detection, in particular to a printing quality evaluation method, system and equipment based on image recognition and a medium. The method comprises the following steps: firstly, preprocessing a to-be-detected image, and digging a wine box printing image based on edge contour information; then, according to different unfolded surfaces of the wine box, the wine box printing image is divided into a plurality of printing sub-images, the positions, the number and the area of all chromatic aberration, stain, blurring and through printing areas in all the printing sub-images are counted, and a printing defect recognition result is obtained; then, based on a printing defect identification result, whether key defects exist in a key area of the wine box or not is judged, fatal defects in the key area are screened firstly, and unqualified products are quickly positioned and eliminated; and finally, based on the printing defect identification result, whether the important area has defects is judged, if yes, the printing quality evaluation value is refined through position compensation, an accurate quality classification result is obtained, the method is used for wine box printing detection, the detection effect is good, the efficiency is high, and the quality is controlled in place.

The invention relates to a printing control method and computer equipment. According to the method and the device, the basic contour information of the printed image during full-ink-quantity ink dots is identified, the edge compensation width is determined according to the database information, the basic contour information and the ink-medium-environment-chromatography multivariate model, and the halftone-processed database information is subjected to edge compensation in the edge compensation width; the phenomena of sawteeth, blurring and blurring of the edges of printed characters, graphs and images can be avoided, the edge compensation gradient is set according to the size and the tone type of the printed image, and gradient compensation of a compensation point from the outer edge of the printed image to the center position in the edge compensation width is achieved. The color difference, caused by edge compensation, of the outer edge and the center position is reduced, and low cost and high printing speed are guaranteed while the amount of printing ink is saved.

The invention discloses a chromaticity automatic gain control method and device for a composite video signal, and belongs to the technical field of signal processing, and the method comprises the steps: obtaining a chromaticity signal adjusted by a historical chromaticity gain parameter, and demodulating the chromaticity signal from the chromaticity signal to obtain a chromaticity component reference value of a color synchronization signal; calculating an actual color synchronization level and a phase deviation between an input color subcarrier and a local color subcarrier according to the color difference component reference value; in response to the chroma phase deviation meeting a preset chroma phase locking condition, generating a chroma phase locking identifier, and calculating a chroma gain error according to the actual color synchronization level; and determining a chrominance gain control stable identifier according to the chrominance gain error, and determining an output chrominance gain parameter based on the chrominance gain control stable identifier, the chrominance phase locking identifier and the chrominance gain error so as to dynamically adjust the next line of gain through closed-loop control. According to the invention, the influence of long-distance cable transmission on the composite video chrominance signal can be reduced, and the chrominance difference of different transmission distances is reduced.

The invention discloses an LED lamp strip dynamic image quality intelligent optimization method and system based on deep learning driving, and relates to the technical field of LED intelligent control. The LED lamp strip dynamic image quality intelligent optimization method and system based on deep learning driving comprises the steps of S1, preprocessing real-time LED working condition data, memory reference data and historical fluctuation abnormal data; s2, adaptively dividing the LED lamp strip into different independent partitions according to spatial and physical attributes through multi-dimensional feature analysis and clustering; s3, based on a time sequence derivative sensitivity theory, judging thermal drift instability and color temperature abnormity, and entering a sudden change criterion process; s4, executing a sudden change criterion process according to a trend sudden change composite criterion result, and entering an offset compensation process; and S5, based on a criterion compensation theory, executing an offset compensation process. The problems of image quality degradation and color difference accumulation after long-term operation due to the fact that an existing LED lamp strip image quality intelligent optimization system is difficult to achieve real-time self-adaptive compensation of color temperature drifting and color cast phenomena caused by chip junction temperature rise are solved.

The invention discloses a nozzle voltage adjustment method and device based on camera automatic identification printing color blocks and a storage medium, and relates to the technical field of printing device calibration, and the method comprises the steps: controlling a printing device to output a pattern containing each nozzle independent test area and color blocks; acquiring chromaticity distribution data through an image acquisition device, and extracting characteristic parameters such as brightness and saturation; determining a reference characteristic parameter based on a reference selection rule, and calculating a deviation value between each test area and the reference; an adjustment instruction is generated according to the mapping relation between the deviation value and the voltage adjustment amount, verification printing and iterative optimization are carried out after implementation until the precision is met, and through dynamic selection of a reference color block, LAB color space chromatic aberration calculation, voltage sensitivity coefficient matrix mapping and aging compensation, the adjustment precision and efficiency are improved, the dependence on artificial experience is reduced, and the adjustment precision and efficiency are improved. The method is suitable for nozzle calibration of high-precision printing equipment.

The invention discloses an ink-jet printing defect identification method and system, and relates to the technical field of image processing, and the method comprises the steps: collecting a to-be-detected printing image, and carrying out the template matching with a standard image of a corresponding printing template; calculating a color difference value between each pair of matched pixel points, obtaining a color difference matrix, dividing a color difference range, and obtaining a high color difference region and a low color difference region; based on the high-color-difference area, identifying an obvious ink dot defect area; calculating the defect probability of each pixel point based on the low-color-difference region; obtaining a corrected Euclidean distance between the pixel points based on the defect probability, and clustering and identifying an unobvious ink dot defect area; the detection result and the distribution condition of the ink dot defects are counted, and feedback regulation and control of the ink jet system are achieved; through the pixel point template matching, clustering and color difference correction technologies, the problem of unobvious ink dot defects caused by missing detection is solved, and accurate identification of the ink dot defects is realized, so that the accuracy and reliability of product quality detection are enhanced, and the printing quality of products is improved.

The invention discloses a machine vision-based micro-plastic intelligent identification and measurement method, which comprises the following steps: filtering a water sample containing micro-plastic or not containing micro-plastic, and acquiring images under a uniform amplification factor by using a microscope to obtain a micro-plastic image to be detected and a contrast image; calculating the absolute value of the color centroid value of the contrast image and the color difference value of the micro-plastic image, generating a difference image, and carrying out denoising, graying and binarization processing on the difference image to obtain a binary image with highlighted micro-plastic; extracting pixel coordinates of the micro-plastics, setting a threshold value to carry out connected clustering, and distinguishing the micro-plastics and image impurities in combination with a preset micro-plastic recognition threshold value to realize micro-plastic recognition and quantity statistics; and further shooting a standard objective lens micrometer image, calculating the actual physical size represented by a single pixel, and automatically calculating the area and the length of the micro-plastic by combining each clustering enclosing rectangle and the number of the pixels. The method is accurate in recognition and high in operation automation degree, and can be widely applied to intelligent recognition and analysis of water body micro-plastics.

The invention discloses a printing color closed-loop control system based on spectral analysis, and relates to the technical field of computer processing, and the system comprises the following steps: S01, collecting the spectral reflectivity data of a plurality of key color blocks on a printed matter measurement and control strip in real time through an online high-speed spectrometer at the frequency of not less than 100 color blocks per second; s02, constructing a color prediction model, wherein the model receives the spectral reflectivity data and outputs absorption scattering characteristics, the spectral transmittance of ink and dot expansion, trapping and overprinting effects in the printing process; s03, setting a dynamic tolerance threshold value delta Et based on a chromatic aberration formula according to the color precision requirement of the printed matter, and judging the absorption and scattering characteristics, the spectral transmittance of the ink and a grading tolerance interval in which the dot expansion, trapping and overprinting effects in the printing process are consistent with visual perception; and S04, converting the spectral reflectivity data acquired in real time into a CIELAB color space irrelevant to equipment, and comparing the spectral reflectivity data with a preset standard color target value. According to the method, the problems of different standards and color drift caused by artificial experience difference and visual fatigue are solved.

The invention relates to the technical field of image data processing, in particular to a high-voltage switch shell surface coating uniformity evaluation method and system, and the method comprises the steps: collecting a shell surface coating image; dividing the image into a plurality of super-pixel areas by using a super-pixel segmentation algorithm introducing an adaptive distance measurement mechanism; extracting geometric structure features, texture features and adjacent color difference features of the target area; and carrying out nonlinear fusion on the adjacent chromatic aberration and geometric structure characteristics to construct inter-class characteristics, fusing the inter-class characteristics with texture characteristics serving as intra-class characteristics to obtain a comprehensive score, and judging whether the coating is uniform or not according to the comprehensive score. According to the method, the segmentation weight is adjusted in a self-adaptive manner, and the weak chromatic aberration is amplified in a nonlinear manner, so that accurate segmentation of a tiny defect region is realized; geometric irregularity, color mutation and texture anomaly features are integrated, detection of various tiny coating defects is achieved, and the evaluation accuracy is improved.

The invention relates to the technical field of pesticide detection, in particular to a pesticide residue detection method and system based on artificial intelligence, and the method comprises the following steps: obtaining an image and normalizing brightness difference, constructing a gradient trend labeling region, extracting features to generate a label weight sequence, matching categories to calculate residue intensity, and outputting an identification result. According to the method, local brightness comparison is enhanced through difference value normalization of image region blocks, region attributes are labeled in combination with a multi-direction gradient change trend, the dynamic perception capability of deposition features is improved, a multi-dimensional feature system is constructed by fusing brightness, edge and density indexes, the screening precision of pesticide residue salient regions is enhanced, and the screening precision of the pesticide residue salient regions is improved. A label dependency sequence is constructed based on color difference path sorting, structural optimization of category judgment is achieved, the micro-difference recognition capacity is improved through texture and gray matrix double alignment, primary and secondary screening output is conducted according to the relation between pesticide residue intensity and a threshold value, the classification guidance and risk screening functions are achieved, and the capacity of adapting to supervision requirements is improved.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

The invention relates to the technical field of pressure-sensitive paint, in particular to a structural stress monitoring system based on color difference identification of pressure-sensitive paint, which comprises an image analysis module, a frequency identification module, a rhythm extraction module, a path comparison module and an early warning judgment module. According to the method, dynamic tracking and response feature extraction of a color difference abnormal area in a time sequence are achieved through combination of image color difference gradient density analysis and physical response frequency information, and the color difference abnormal area is obtained through continuous extraction and comparison of the color evolution trend and the gravity center track direction. The method improves the recognition precision of the path change of the abnormal region and the judgment capability of the structure deviation trend, enhances the recognition sensitivity of the accelerated evolution state of the region through the synchronous analysis of the frequency change and the sudden change time point, and optimizes the matching relation between the color difference response and the stress evolution. And by using trend sequence and direction consistency analysis, crack early warning has time continuity and space positioning capability, and the abnormal behavior identification effect in a complex environment is improved.