145 results about "Gray level image" 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 mode recognition and data recognition, and discloses an intelligent visual detection method for non-standard precision part surface microdefects, which comprises the following steps: acquiring surface gray level image data of a to-be-detected part, physically abandoning low-frequency components through discrete wavelet transform, and reserving high-frequency detail components to construct a frequency domain input tensor; constructing a double-flow reconstruction model containing a space domain coding network and a frequency domain coding network, and minimizing the distribution difference of the same feature between double-domain characterization through potential feature space consistency constraint joint optimization; the method comprises the following steps of: calculating a spatial domain residual image and a frequency domain residual image, combining a texture topological residual image extracted by structural tensor characteristic decomposition, and generating a comprehensive abnormal response image through weighted fusion to judge the defect, and effectively inhibiting macroscopic geometric contour interference through frequency domain decoupling and a topological check mechanism on the premise of not needing a standard geometric template. And sensitive perception and accurate identification of weak texture defects on the surface of the non-standard part are realized.

The invention discloses a power equipment defect intelligent identification method, system and device and a medium, and the method comprises the steps: collecting an original image of power equipment, and screening the original image of the power equipment to obtain a channel image; carrying out enhancement processing on the channel image and then calculating a gray scale difference value to obtain a gray scale image; converting the gray level image into a frequency spectrum image by adopting fast Fourier transform, and constructing a Gaussian filtering function to carry out convolution and inverse transformation on the frequency spectrum image to obtain a spatial domain image; and performing adaptive threshold segmentation on the spatial domain image, dividing the image into a defect area and a non-defect area, obtaining a segmented image, and performing morphological processing on the segmented image to obtain an electrical equipment defect identification result. According to the method, the problem of aliasing in traditional spatial domain processing is solved, the defect area is accurately extracted, short-time interference and real defects can be effectively distinguished, and the segmentation accuracy is improved.

The invention discloses a mold quality detection method and system based on image processing, and relates to the technical field of mold quality detection.The method comprises the steps that a high-precision industrial camera is installed over a mold to obtain a mold surface gray level image, and graying and smoothing are conducted on the obtained image; constructing a distribution model according to the reference distance distribution condition of each pixel point in the smoothed image, calculating a period strength degree, preliminarily screening the pixel points according to the period strength degree, and distinguishing the possibility of texture pixel points and non-texture pixel points; by using the difference between normal texture periodicity and crack randomness, through calculating the periodicity intensity degree, screening texture pixel points and non-texture pixel points, screening suspected texture pixel points, calculating the periodicity similarity, screening out a texture region and then carrying out iterative threshold segmentation, the detection problem caused by mixing of cracks and normal textures is solved; and the detection accuracy is improved.

The invention discloses a motor current fault diagnosis method based on a de-noising diffusion probability model, and belongs to the technical field of mechanical equipment state monitoring and fault diagnosis, and the method comprises the steps: obtaining an original motor current signal sample, carrying out the wavelet transformation, obtaining a time-frequency grayscale image, and obtaining a time-frequency grayscale image; dividing the sample into a training sample used for diffusion model training and a test sample of a fault diagnosis model; constructing a diffusion model DDPM, carrying out training by adopting the training sample, and generating a pseudo sample based on the trained diffusion model; constructing a fault diagnosis model MAF-Cnet, and training the MAF-Cnet based on the training sample and the pseudo sample to obtain the trained MAF-Cnet; and inputting a test sample into the trained MAF-Cnet for diagnosis to obtain a motor current fault diagnosis result. The method solves the core problem that the generalization ability of the diagnosis model is insufficient due to scarcity of motor fault samples, improves the diagnosis accuracy, and is wider in application scene.

The invention relates to the technical field of video recognition and analysis, and discloses a video recognition and analysis method based on physical characteristics. Video frame pixels are mapped to a two-dimensional coordinate system with the upper left corner as an original point, and mirror image expansion and median filtering are carried out on a gray level image; constructing a binary image based on a gray threshold value, and analyzing and extracting a target region by using a four-neighborhood connected domain; using neighborhood search and polar angle sorting to close the tracking contour, and generating equidistant re-sampling points based on Euclidean distance and an interpolation method; the curvature of the re-sampling points is estimated through a three-point difference algorithm, and zero denominator is avoided through numerical protection; performing discrete Fourier transform on the curvature sequence to extract a frequency spectrum, and normalizing an amplitude to form a standardized feature vector; and finally, inter-frame similarity is calculated based on the feature vector, and the most similar frame is automatically retrieved. By processing unified data standards in stages, edge noise is suppressed, sampling uniformity is ensured, and feature stability and cross-frame comparability are improved.

The invention discloses a two-stage cascaded distributed optical fiber sound wave sensing vehicle trajectory reconstruction method and system, belongs to the technical field of intelligent traffic perception, and aims to solve the problem that geometric accuracy and topological integrity are difficult to consider under the conditions of low signal-to-noise ratio and complex road conditions in the conventional DAS vehicle trajectory reconstruction technology. The method comprises the following steps: in the first stage, carrying out nonlinear enhancement on original vibration data and converting the original vibration data into a two-dimensional space-time grayscale image, inputting the two-dimensional space-time grayscale image into a deep learning semantic segmentation network to generate a high-fidelity track mask, extracting a skeleton through morphological processing, and constructing an initial candidate topological graph; and in the second stage, multi-hop neighborhood kinematics constraint pruning is performed on the initial topological graph, false connection edges are eliminated, global optimization matching is performed by using a coupling cost function to repair fractures, then isolated points are recalled through local geometric scores, and finally a vehicle trajectory graph with complete topology is output. According to the method, geometric accuracy and topological integrity can be effectively considered in complex scenes such as speed change, lane change and multi-vehicle intersection, and the robustness of all-weather traffic flow monitoring is improved.

The invention relates to the field of melt state monitoring, in particular to an injection molding machine nozzle melt state monitoring method based on machine vision, and the method comprises the steps: obtaining an original image of melt at an injection molding machine nozzle, carrying out the preprocessing of the original image, obtaining a gray image and a melt region, and calculating the contour complexity representing the melt form based on the melt region; mapping the melt glue area to the gray level image, and calculating the energy and entropy value of the melt glue area; based on the gray level images of the current frame and the previous frame, the flow velocity fluctuation coefficient of the melt glue area is calculated, and the flow velocity fluctuation coefficient is in positive correlation with the fluctuation of the flow velocity die length of the pixel points; and constructing a feature vector by using the contour complexity, the energy, the entropy value and the flow velocity fluctuation coefficient, and inputting the feature vector into a preset prediction model to obtain a prediction result of the melt state. According to the invention, various types of defects such as caking, bubbles and flow marks can be monitored.

The invention relates to the image processing field, and especially relates to an airborne infrared image ship target segmentation method comprising the following steps: carrying out LBP characteristic value extraction to obtain an LBP characteristic image; obtaining a mode filtering smooth LBP feature image; performing adaptive threshold segmentation; filling a hole area; multiplying the image with the filled hole region by the airborne infrared gray level image pixel by pixel to obtain a region-of-interest image; obtaining an edge detection image; extracting all boundaries of the edge detection image to create a mask image; segmenting the interest region image by using an improved firefly algorithm to obtain a highlight target segmentation image; and multiplying the highlight target segmentation image and the mask image pixel by pixel to obtain a ship target segmentation result image. The ship texture difference is accurately captured through the LBP features, the improved firefly optimization algorithm is combined, different sea conditions and noise interference can be dealt with, the error segmentation rate is reduced, multi-threshold solution is combined, and the segmentation requirements of complex images can be met.

The invention relates to the technical field of elastography, in particular to an ultrasonic elastography liver fibrosis evaluation method based on artificial intelligence. The method comprises the following steps: analyzing the gray level and gradient distribution of pixel points in a gray level image of each image, and obtaining the fat deposition degree of the liver of a patient in each image; the fat display degree of the liver of the patient in each parameter combination is obtained, and a high-display parameter combination is screened out; according to the imaging depth of each high display parameter combination and the fat deposition degree of the corresponding shear wave elastic image, obtaining a corresponding correction parameter combination; according to gradient features of edge pixel points in different elastic modulus range areas, image resolution corresponding to the correction parameter combination is obtained; obtaining an optimal parameter combination, and obtaining the liver fibrosis risk degree of the patient according to the elastic modulus distribution in the shear wave elastic image under the optimal parameter combination. According to the method, the optimal parameter combination is obtained to carry out elastography, so that the accuracy of liver fibrosis risk assessment is improved.

The invention relates to the technical field of lithium battery production, and discloses a lithium battery binder production monitoring method and system based on the Internet of Things, and the method comprises the steps: collecting data in real time through multiple types of sensors, carrying out the classification according to the types of the sensors, generating time sequence data based on multi-time window dynamic segmentation, and converting the time sequence data into a grayscale image for feature extraction; a self-encoder is adopted to train an anomaly detection model, real-time anomaly is judged in combination with a dynamic threshold value, a correction proposal is generated by matching historical normal data, and a control point is optimized to generate a smooth correction curve; and displaying abnormal data and a correction scheme through a human-computer interface, dynamically marking a high-difference region, and incrementally optimizing a model parameter and a threshold value based on user feedback. According to the method, the anomaly detection precision can be improved, and the success rate of first anomaly repair can be improved.

The invention relates to a scratch detection method, device and equipment for a transparent film and a medium, and the method comprises the steps: firstly carrying out image multiplication processing and dynamic range mapping on a transparent film gray level image, and obtaining a target gray level image through threshold segmentation; calculating a pixel gradient magnitude based on the image, synthesizing an edge gradient image, and generating a first salient image through Gaussian filtering and gray linear transformation; meanwhile, logarithmic transformation is carried out on the original target image to obtain a second salient image; secondly, respectively calculating gray average values of the two salient images, determining a self-adaptive segmentation threshold by combining a preset threshold, extracting a defect region through double-image threshold segmentation, and obtaining an intersection to obtain an initial scratch region; and finally, screening according to a preset area condition to obtain a final scratch area. According to the method, through the multi-feature fusion and self-adaptive threshold technology, the problems of low contrast, uneven illumination and the like in scratch detection of the transparent film are effectively solved, the detection efficiency and accuracy are remarkably improved, and the method has high engineering application value.

The invention relates to the technical field of image processing, in particular to a flower disease and insect pest monitoring method based on machine vision, and the method comprises the steps: obtaining a candidate pseudo boundary set in a gray level image of a to-be-monitored flower leaf, and obtaining a gray level distribution feature of a pixel point in a local analysis region of each candidate pseudo boundary; obtaining a vein feature index of each candidate pseudo-boundary according to the linear structure feature of each candidate pseudo-boundary and the linear structure feature of each candidate pseudo-boundary; according to the vein feature index and the direction feature of each candidate pseudo boundary, screening in the candidate pseudo boundary set to obtain at least one pseudo boundary; in the process of carrying out region segmentation on the gray level image by using the watershed algorithm, the initial distance of each pixel point after distance transformation is obtained, adaptive distance compensation is carried out on the initial distance of each pixel point according to the position relation between each pixel point and each pseudo boundary, the compensation distance is obtained, complete segmentation of a scab region is realized, and the scab image segmentation efficiency is improved. And a reliable quantitative basis is provided for flower disease and pest monitoring and prevention decision making.

The invention discloses a low-altitude unmanned aerial vehicle autonomous cruise method and system based on cloud edge basic model collaboration, and the method comprises the following steps: S1, collecting an environment RGB image through an airborne monocular camera of an unmanned aerial vehicle, and carrying out the preprocessing of the image, and obtaining a preprocessed gray image; s2, a neural scheduler based on deep reinforcement learning generates a scheduling instruction according to the environment data and the network state reasoned by the navigation model at the previous moment; s3, generating a preliminary flight instruction; s4, generating an optimized flight instruction; s5, generating a structured flight instruction; and S6, the unmanned aerial vehicle executes the preliminary flight instruction or the optimized flight instruction or the structured flight instruction. According to the invention, through dynamic on-demand cooperation and intelligent scheduling of the end-edge-cloud three-level model, resource consumption and delay are substantially reduced, and high-robustness and high-safety autonomous cruise of the unmanned aerial vehicle in a complex open environment is realized.

The invention belongs to the technical field of image processing, and particularly relates to a pole adaptive positioning method and system based on image processing, and the method comprises the steps: obtaining edge pixel points in a pole gray level image and the gradient direction of the edge pixel points; estimating an illumination distortion main direction according to the gradient direction, and dividing edge pixel points into a positive edge set and a negative edge set; according to the forward edge set, calculating weighted votes of edge pixel points to gradient direction intersection points and accumulating the weighted votes to a forward accumulator, clustering accumulation results to obtain an initial forward candidate center, and finally performing iterative updating through corrected voting weights based on geometric consistency to obtain forward center estimation; obtaining negative center estimation according to the negative edge set; and determining a circle center positioning point of the pole hole by combining the positive center estimation and the negative center estimation. The method effectively overcomes the positioning error caused by uneven illumination and metal reflection, and improves the positioning precision and robustness.

The invention discloses a casting riser image recognition method based on machine learning, and the method comprises the steps: carrying out the texture enhancement of a casting gray level image based on cross guide filtering, and forming a texture enhancement image data set; parallel straight line textures of the enhanced image are detected in a mode of combining a directional Gabor filter bank and direction consistency analysis; performing connected region analysis on a potential riser region, and performing triple screening through an area range, an aspect ratio range and a brightness contrast ratio; extracting an area feature, an edge feature, a texture feature and a shape feature of the riser candidate region, and marking the type of the riser candidate region to form a training sample set; a random forest classifier is introduced, the training sample set is input into the random forest classifier for training, a riser classifier is obtained, and the types of classified risers in the casting image are obtained; accurate positioning and classification of the casting risers are achieved, technical support is provided for automation and intelligentization of casting cleaning, and remarkable practical value is achieved.

The invention relates to the technical field of image data processing, in particular to a cord fabric production defect detection method based on machine vision, which comprises the following steps: acquiring a gray level image of a cord fabric and dividing the gray level image into image blocks; calculating a texture continuity score by fusing the local texture continuity and the periodic rule of the image block, and screening a defect area according to the texture continuity score; extracting the gray level, gradient and texture disorder degree of the defect area to calculate an adaptive weight, and adjusting a cutting limit value of a local contrast enhancement algorithm by using the adaptive weight; performing local contrast enhancement processing on each defect area by adopting the cutting limit value; and classifying the processed defect areas so as to output a defect detection result. According to the method, weak defects can be effectively highlighted, background noise is suppressed, high-precision defect recognition is completed in combination with a deep learning model, and the accuracy and robustness of detection are improved.

The invention provides an online defect detection method and device for a silk-screen product, and relates to the technical field of image processing.The online defect detection method comprises the steps that an online detection image of the silk-screen product is obtained and converted into a gray level image, and then silk-screen edge pixel points are extracted; analyzing the edge pixel points to obtain features reflecting the deinking defect, reconstructing the online detection image according to the features to obtain a feature image, taking the deinking defect condition as a label, and training a convolutional neural network by using a historical feature image; and using the trained convolutional neural network to identify whether the feature image has the deinking defect. According to the method, a sub-pixel-level edge retraction and groove structure which is originally difficult to recognize by a traditional algorithm is converted into learnable image depth features with numerical value mutability, the image is reconstructed on the basis, the convolutional neural network under the feature image is constructed, and the sensitivity and generalization ability of a model to structural slight defects are remarkably improved.

The invention discloses a printing defect detection method and device based on cross-modal alignment, and relates to the technical field of printing defect detection.The method comprises the steps that cross-modal feature alignment of a first printing target image (color image) and a second printing target image (gray level image) is achieved, and geometric transformation parameters of the two images are extracted to complete pixel-level registration; the registered image is converted into a grey-scale image, and channel dimension splicing is carried out to generate a spliced image; and detecting the spliced image by adopting an anomaly detection algorithm to realize the positioning of the printing defect. According to the method, the dependence of a traditional method on a reference image is broken through, model training can be completed only through a defect-free sample, the false detection problem caused by a complex texture background, anti-counterfeiting film interference and personalized features in a small sample scene is effectively solved, the time consumed by single-piece detection is less than 0.3 second, and the efficiency is improved by more than 10 times compared with that of traditional manual detection.

The invention discloses a biological information security authentication method based on deep fusion of fingerprint features and digital passwords. The method comprises the steps that a user customizes the digital passwords; extracting fingerprint features by using a transfer learning model based on a ResNet-50 architecture to obtain five high-dimensional digital feature vectors; a Sigmoid function is used to carry out normalization on all the feature vectors; obtaining a random sequence for encryption; performing scrambling operation on all the gray level images by using three-dimensional Class-Z transformation; diffusion operation based on a semi-tensor product is carried out on all gray level images; storing the ciphertext images in a database according to a sequence input by a user; when the user attempts to verify, the system executes a dynamic step. According to the security authentication framework based on deep fusion of the biological characteristics and the digital passwords, the permanent identity risk caused by inherent non-revocation of the biological characteristics is fundamentally solved, and a multi-protection mechanism with high efficiency and high security for original fingerprint characteristics is constructed with the assistance of a dynamic updating / immediate template generation mechanism.

The application discloses a kind of nematode body bending behavior identification and counting method and related equipment, the method comprises: obtaining nematode video, the original gray image is obtained by frame processing to nematode video, and binary image is obtained according to the original gray image of nematode;According to the original gray image of nematode, the numerical coordinate regression algorithm based on convolutional neural network obtains head coordinate and tail coordinate;According to the binary image of the nematode, the feature point extraction algorithm based on curvature calculates the peak point on the center line of nematode;The maximum distance of the line between the pharynx and tail of nematode is calculated, and the number of nematode body bending is calculated according to the change of maximum distance of each frame.The numerical coordinate regression algorithm based on convolutional neural network is used for the coordinate identification of nematode head and tail, the accuracy is improved, and the number of nematode body bending is calculated more simply and quickly by extracting feature points.

The present application belongs to the field of medical image segmentation, and particularly relates to a retinal blood vessel image segmentation method. In view of the problems of low segmentation accuracy, insufficient segmentation ability of small blood vessels at the edge of eyeball, fracture at the blood vessel branch, and excessive interference of image noise in the existing retinal blood vessel image segmentation, the method comprises the steps of retinal image preprocessing and establishment of a retinal blood vessel segmentation model, wherein the preprocessing comprises converting a color retinal image into a gray image by giving different weights to the RGB three channels of the color retinal image; using a normalized and contrast-limited adaptive histogram equalization method to improve the image; using a local adaptive gamma change algorithm to adjust the retinal image; using translation, rotation, and noise increase to expand the data set; and the model establishment comprises feature extraction, feature fusion, and retinal blood vessel image segmentation.

The application belongs to the technical field of industrial automation and computer vision, and discloses a grasping point pose calculation method based on a target detection model and application thereof. FPFH The grasping point pose calculation method comprises the following steps: step 100, extracting a corresponding sub-region point cloud from a three-dimensional point cloud of the object to be grasped according to a target center region obtained by processing a gray image of the object to be grasped according to the target detection model; step 200, performing coarse matching on the sub-region point cloud and a template point cloud to obtain a coarse matching result; step 300, performing fine matching on the sub-region point cloud and the template point cloud according to the coarse matching result to obtain a fine matching result; step 400, judging whether the fine matching result meets a preset requirement according to an inlier root mean square error and a coincidence degree, if yes, proceeding to the next step, otherwise terminating the process; and step 500, calculating a grasping point pose of the object to be grasped according to the fine matching result and coordinate transformation. RANSAC GICP The application has simple process steps and is easy to implement and control.​

The invention relates to the technical field of image processing, in particular to a silicon carbide tray defect detection method and system based on image recognition, and the method comprises the steps: obtaining a gray image of a silicon carbide tray to construct an observation matrix, and initializing a low-rank matrix, a sparse matrix and a Lagrange multiplier matrix for representing error accumulation; iterative optimization is executed; and repeating the iteration steps until a termination condition is met, and taking the final sparse matrix as a defect detection result. Through the technical scheme of the invention, the problems of false detection and missing detection caused by misjudging the background texture as the defect can be reduced, and the accuracy and robustness of the defect detection result of the silicon carbide tray are improved.

The application discloses a printing defect detection method and device based on cross-modal alignment, relates to the technical field of printing defect detection, and realizes cross-modal feature alignment of a first printing target image (a color image) and a second printing target image (a gray-scale image), extracts geometric transformation parameters of the two images to complete pixel-level registration; converts the registered image into a gray-scale image and performs channel dimension splicing to generate a spliced image; an anomaly detection algorithm is used to detect the spliced image, and the positioning of a printing defect is realized. The application breaks through the dependence of a traditional method on a reference image, and only needs a non-defect sample to complete model training, effectively solves the false detection problem caused by a complex texture background, an anti-counterfeiting film interference and personalized features in a small sample scene, and the time consumption of single detection is less than 0.3 seconds, which is more than 10 times higher than the efficiency of traditional manual detection.

The application discloses a complex image multi-threshold segmentation method based on an improved grey wolf optimization algorithm, and establishes an optimization model of maximum entropy threshold segmentation; the improved grey wolf optimization algorithm is used to solve the best segmentation threshold of image segmentation, wherein the improvement of the grey wolf optimization algorithm comprises the following steps: firstly, the non-linear factor is improved to balance the searching and mining capabilities of the algorithm; then, a reverse learning strategy is introduced to improve the population quality, a sine function is introduced again and the weight of the head wolf is adjusted to improve the grey wolf updating formula and enhance the mining capability of the algorithm; next, the head wolf closing strategy and the population mutation strategy are alternately introduced to update the position, the former improves the convergence performance of the algorithm, and the latter enhances the ability of the algorithm to jump out of the local optimum; further, the best segmentation threshold matrix of the grey image is obtained; finally, the image is segmented by using the best segmentation threshold matrix; and the stability of the complex image segmentation and the precision of the segmentation result are effectively improved.

The application discloses a kind of power environment monitoring systems applied to UPS power supply in computer room, it is related to environmental monitoring technical field, the application includes: UPS equipment environment inspection module, UPS equipment fault analysis module, UPS equipment fault analysis module and local database, by through dust concentration, wind speed, wind direction, line width and temperature five-dimensional data fusion, dynamic wear model is established, the impact of sand and dust to connecting line is simulated, to assess the harm of sand and dust to connecting line, can timely and accurately style connecting line hazard, guarantee the safety of the connecting line of UPS power supply equipment, simultaneously, by gray image analysis vertical direction jitter offset coefficient, vibration-soundprint composite diagnostic model is constructed in combination with operating decibel value, to reduce the failure rate of UPS power supply equipment.

The invention discloses an image encryption method based on phase-shift digital holography and a multi-mode biological characteristic key, which comprises the following encryption steps of: firstly, encrypting a grayscale image into a DNA compilation result by using DNA coding and an iris chaos mask; secondly, encrypting a DNA compiling result into three ciphertext holograms by using phase-shift digital holography and fingerprint and finger vein chaos masks; the decryption step comprises the following steps of: firstly, authenticating three biological characteristics of decrypted iris, fingerprint and finger vein; if the authentication is successful, the iris chaos mask and the fingerprint and finger vein conjugate chaos mask for subsequent decryption are generated by the system. And finally, obtaining a final decrypted image by using a digital holographic reconstruction technology and a DNA decoding technology. The method provided by the invention has the advantages of high security level, high biological key robustness and high digital key sensitivity.

The present application belongs to the technical field of image processing, and particularly relates to a high-voltage switch shell stain detection method and system based on machine vision, which comprises the following steps: acquiring a multi-view gray-scale image of a high-voltage switch shell, calculating a stain sensitivity gradient index of a pixel point by fusing multi-scale gradient intensity and direction consistency information, generating a local reflection non-ideal weight in combination with a color correlation index, constructing a dynamic prior gate diffuse reflection conversion network, performing pixel-level weighted fusion on a standard diffuse reflection prediction branch and an adaptive diffuse reflection prediction branch based on the local reflection non-ideal weight to obtain a diffuse reflection image, and performing stain detection based on the diffuse reflection image. The present application effectively solves the problem that the traditional method misjudges the stain texture on the surface of the high-voltage switch shell as highlight to be removed, and improves the accuracy of high-voltage switch shell stain detection.

The application belongs to the technical field of video encryption transmission, and particularly relates to a video transmission method and system for multimedia video, which comprises the following steps: identity authentication of a receiving end and a sending end; a gray image and an information sequence are randomly generated by the to-be-authenticated end, the information sequence is a binary sequence, and the number of digital 0 and 1 is equal; according to the MAC address of the to-be-authenticated end, the information sequence is hidden in the gray image through a steganography algorithm to obtain an authentication image and send the authentication image to the authentication end; the authentication end extracts an authentication sequence from the received authentication image through the steganography algorithm according to the stored MAC address of the trusted terminal, and the to-be-authenticated end passes the authentication when the authentication sequence satisfies that the number of digital 0 and 1 is equal; after the receiving end and the sending end both pass the authentication, an information key is created through a DH key exchange protocol, and the information key is used for encrypted transmission of the video. The application improves the security of video encryption transmission.