976 results about "Color difference" patented technology

Since defect areas in collected hydro turbine defect images account for a small proportion and there is a significant color difference between surrounding background and defects, which creates too much redundant information and greatly affects detection speed, a method for detecting defects in a hydro turbine top cover based on an improved YOLOv8 model involves first cropping collected defect images to obtain cropped images, then expanding the cropped images by image enhancement techniques such as image flipping and image blurring to obtain a training dataset. A defect detection network based on YOLOv8-CBAM is constructed and trained to generate a defect detection model for detecting the defects in the hydro turbine top cover. The method achieves high precision in defect detection for the hydro turbine top cover.

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 relates to a cloth color difference intelligent detection method based on an improved vision algorithm, and the method comprises the following steps: S1, obtaining the spectral image data of cloth, and carrying out the calibration; s2, setting of a high-definition camera is adjusted according to the real-time collected image features, and high-definition image data of the cloth is obtained based on the high-definition camera; s3, preprocessing the high-definition image data; s4, based on the preprocessed high-definition image data, performing cloth and background segmentation by using a deep network EfficentNet in combination with an Attention mechanism, extracting features, and establishing a comprehensive feature vector; s5, constructing a color difference detection model based on the improved SVM, and carrying out color difference detection on the cloth image based on the comprehensive feature vector; and S6, combining a fuzzy logic algorithm, and diagnosing the abnormal color difference condition of the cloth according to the color difference result. The cloth color difference detection accuracy and reliability are effectively improved.

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 a color calibration method and system based on a display driving chip, and the method comprises the following steps: carrying out the sub-pixel spectrum reconstruction of an original spectrum data stream based on a color gamut boundary vector group, and obtaining a sub-pixel spectrum matrix; performing white point chromaticity estimation on the sub-pixel spectrum matrix to obtain white point chromaticity coordinates; performing gamma curve correction on the sub-pixel spectrum matrix based on the white point chromaticity coordinates to obtain a gamma correction coefficient matrix; performing color space conversion on the gamma correction coefficient matrix through a preset three-dimensional lookup table to obtain color control parameters, and inputting the color control parameters into a preset display driving chip to drive sub-pixels of each pixel unit in the display panel to emit light to obtain color calibration display output, the defects of a traditional display driving chip in the aspect of color calibration are overcome, and the defects are mainly embodied in the technical problems that color differences between different display panels cannot be effectively processed and the color drift phenomenon occurs along with time lapse.

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 method for laser-induced color texture pattern on titanium surface, comprising: step 1: grinding, polishing and cleaning the titanium alloy surface, drawing a pattern on the titanium alloy surface by using a laser, and continuously adjusting the parameters of the laser until a processing database in which the parameters of the laser and the color of the titanium alloy surface pattern are mutually correlated is obtained; step 2: using a colorimeter to measure and calibrate the color of the pre-drawn pattern with Lab values, calculating the color difference between the color of the pre-drawn pattern and each color in the processing database, and when the color difference is less than or equal to a predetermined value, selecting the parameters of the laser corresponding to the corresponding color for processing; the invention induces the generation of oxide film layers of different thicknesses and types on the titanium surface by a laser heat source with adjustable pulse width, makes the film layer present different colors through the interference, reflection and absorption of light, adjusts the spot size, controls the scanning speed, and is equipped with a high-speed galvanometer scanning system to realize the preparation of various fine patterns.

The invention discloses an automatic pixel calibration method for a Micro LED display screen, and the method comprises the steps: carrying out the multi-dimensional detection of the real-time brightness, color and temperature of each pixel through a high-precision multispectral sensor, obtaining the actual optical characteristics of each pixel, including but not limited to the parameters of brightness, color temperature, color difference, spectral response and the like, and carrying out the detection of the pixels; and deviation analysis: analyzing the detection data of each pixel through a deep learning algorithm, identifying brightness, color and current deviation, determining the correction demand of each pixel, and performing quantitative analysis on the deviation of each pixel. A high-precision multispectral sensor and a deep learning algorithm are introduced, so that the detection and calibration process of each pixel is more accurate. By dynamically adjusting the current output, the problems of pixel aging, uneven brightness, chromatic aberration and the like can be solved, so that the stability and consistency of the display effect are remarkably improved, and the service life of the Micro LED display screen is prolonged.

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.

A decision unit decides whether or not a deblocking filter is applied to color difference components of pixels belonging to a line orthogonal to a block boundary in two adjacent blocks adjacent to each other sandwiching the block boundary of the decoded image, by using color difference components of pixels belonging to a line identical to a line used to decide whether or not the deblocking filter is applied to luminance components of the pixels belonging to the line orthogonal to the block boundary. The filtering unit applies the deblocking to the color difference components of the pixels for which it is decided that the deblocking filter is applied. The present technology can be applied to, for example, a case where encoding and decoding of an image are performed.

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, in particular to an OLED display screen quality detection method based on machine vision, and the method comprises the steps: obtaining a gray image and a color image of a surface image of an OLED display screen; the difference between the gray value of each pixel and the overall gray average value is calculated, the compensation degree of the pixel points is calculated, the larger the difference is, the more obvious the brightness nonuniformity is, and the larger the compensation degree of the pixel points is. And then different weights are given to each pixel point according to the brightness non-uniformity. According to the invention, the final gray value of the pixel point is calculated through the compensation degree and the correction coefficient of the pixel point, the compensation degree is determined according to the neighborhood gradient value difference and the overall gray difference in the gray-scale image, and the correction coefficient is determined according to the color difference of the pixel point in the color image. According to the invention, the Mura defect area with non-significant brightness difference but obvious color distortion can be identified, so that the accuracy of quality detection of the OLED display screen is improved.

An information processing apparatus includes an obtaining unit which obtains first color information from an image that includes a pixel representing color information of a first color and a pixel representing color information of a second color. A determination unit determines whether the obtained first color information includes second color information. A conversion unit executes first color conversion processing on the image. A correction unit, in a case where a color difference between the third color and the fourth color is less than a predetermined threshold, corrects a conversion parameter in the first color conversion processing such that a color obtained by converting the first color is a seventh color which is different from the third color and whose color difference from the fourth color is greater than the color difference between the third color and the fourth color.

The invention relates to the technical field of textile detection, and provides a fabric sticky hair identification method and identification system based on fluorescence enhancement and machine vision, and the fabric sticky hair identification method comprises the following steps: carrying out fluorescent dyeing on a fleece when the fleece is dyed; rubbing the fuzzing cloth with a to-be-detected fabric so as to enable the to-be-detected fabric to be fuzzed; placing the to-be-detected fabric in a preset environment so as to enable the sticky hair on the to-be-detected fabric to emit fluorescence; acquiring a surface image of the to-be-detected fabric after hair sticking and fluorescent color development; analyzing the area proportion of a fluorescent region in the surface image based on image color identification; and according to the area proportion of the fluorescent region, determining the hair sticking grade of the fabric to be detected. According to the invention, the problem that the precision and reliability of sticky hair detection are seriously influenced by material and color differences of a machine vision detection system in the prior art can be solved.

The invention relates to the field of shadow removal, in particular to a single-image shadow removal method based on global context perception and a computer device. According to the scheme, an original data set is preprocessed, then a shadow model based on the Retinex theory is introduced, and the model can describe illumination and color differences between a shadow area and a non-shadow area in an image; an encoder and a decoder which are constructed by using a multi-scale channel attention mechanism are used for efficiently capturing hierarchical global information; constructing a shadow removal network based on context awareness based on an encoder and a decoder, and adding a shadow interaction module and a shadow interaction attention mechanism in a bottleneck stage of the shadow removal network based on a shadow model; training a shadow removal network by using a loss function; testing the trained shadow removal network by using the public data set to evaluate the performance of the shadow removal network; and finally, performing image shadow removal according to the evaluated shadow removal network. The method is suitable for single image shadow removal.

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 discloses a multi-scene SAR image uniform color processing method and device considering terrain radiation, and belongs to the technical field of remote sensing image processing, and the method comprises the steps: obtaining an SAR image of a to-be-detected region, carrying out the preprocessing of the obtained SAR image, and extracting a sampling point; the sampling points are substituted into a sinc function model, global optimization is carried out by fitting the overall SAR image of the to-be-detected area, and a global sinc function compensation coefficient is obtained; constructing polynomial fitting for each original SAR image in the to-be-measured region by using the idea of block adjustment, and integrally calculating polynomial model correction parameters of all SAR images; and performing uniform color correction on the original SAR image in combination with the global sinc function compensation coefficient and the polynomial correction coefficient. According to the method, the problems of path error propagation and accumulation, color difference residue in an overlapping region and the like generally existing in an existing color equalization processing method are effectively avoided, and an effective way is provided for uniform color processing of the SAR image.

There is provided with an information processing apparatus. A first obtaining unit obtains first color information from a first image, which includes a pixel representing color information of a first color defined in a first color gamut and a pixel representing color information of a second color defined in the first color gamut. A first correction unit, in a case where a color difference between a third color defined in a second color gamut and a fourth color defined in the second color gamut is less than a predetermined threshold, corrects a first conversion parameter for the color conversion processing such that a color obtained by converting the first color is a fifth color. A conversion unit performs color conversion processing in which the corrected first conversion parameter is used on a second image different from the first image.

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 image processing, and particularly relates to a honey quality detection method based on machine vision, which comprises the following steps: extracting bubble features from a honey sample defoaming video; determining bubble uniqueness and color uniqueness of each honey according to bubble feature difference and color difference of all honey samples; all the honey is divided into unique honey and non-unique honey based on bubble uniqueness and color uniqueness; establishing a unique honey detection model; establishing a bubble model of the non-unique honey according to the bubble change in the standing process; on the basis of the difference of the bubble models of all the non-unique honey, feature video frames of all the non-unique honey are obtained, and a detection model of all the non-unique honey is established; and comparing the to-be-detected honey sample with the detection models of the unique honey and the non-unique honey to complete quality detection. According to the method, the detection accuracy is improved while the honey quality detection convenience is ensured.