153 results about "Image gradient" patented technology

The invention relates to the technical field of image processing, in particular to a super-resolution reconstruction system and method based on a pulse neural network, and the system comprises an image pulse encoder, a pulse feature enhancer and a differentiable pulse decoder. An image pulse encoder simulates a receptive field of a retina by using DoG response, performs adaptive pulse distribution on an input low-resolution image in combination with an image gradient, and converts the low-resolution image into a space-time pulse sequence reflecting a high-frequency region and a low-frequency region in the low-resolution image; a pulse feature intensifier performs coarse-grained and fine-grained structure reconstruction of a low-resolution image on the space-time pulse sequence by using a pulse time sequence dependent plasticity mechanism to obtain a granularity feature pulse; and the differentiable pulse decoder converts the granularity characteristic pulse to obtain a corresponding high-resolution image. According to the method, efficient and low-consumption image super-resolution reconstruction is realized through a bionic retina coding mechanism and a pulse time sequence optimization strategy.

The invention discloses a multi-modal remote sensing image matching method, particularly relates to the technical field of remote sensing image processing, and is used for solving the problem of multi-modal image matching. The method mainly comprises the following steps of: 1, improving a phase consistency model, and constructing a phase-moment weighted joint direction feature in combination with a maximum moment and a minimum moment to replace the feature expression of the traditional image gradient; 2, implementing a point product fusion strategy on the phase-amplitude characteristics extracted by the phase consistency model and the maximum moment, and constructing phase-moment weighted joint amplitude characteristics; and 3, on the basis of the steps 1 and 2, identifying the direction of the feature points and screening local peak values to determine the main direction. Three values adjacent to a peak value are selected, and the peak value position is interpolated through parabola fitting so as to improve the matching precision; and step 4, constructing a logarithm polar coordinate descriptor based on regularization non-uniform partition to generate a feature description vector. Through the mode, high-precision and high-efficiency matching of the multi-mode remote sensing image can be realized.

The invention relates to the technical field of image precision optimization, in particular to a surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, which comprises the following steps: acquiring and preprocessing image data, synthesizing an image gradient modulus length and a maximum principal curvature, and introducing a surface normal vector partial derivative to obtain a surface normal vector partial derivative; calculating a curvature-driven depth discretization step length, and generating an initial point cloud data set; constructing a point cloud complex network structure diagram and calculating the topology durability of a point cloud topology structure to obtain an optimized point cloud data set; based on the optimized point cloud data set, in combination with the target image information, the adaptive curvature gradient weight and the error diffusion control value, constructing an adaptive matching cost function, and calculating a point cloud matching cost matrix; and calculating an optimal point cloud transformation matrix based on the point cloud matching cost matrix. According to the surveying and mapping image mathematical precision evaluation method based on dense point cloud matching, low-texture region point cloud matching error suppression and structure optimization of high-curvature region point cloud on a complex curved surface are realized.

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention discloses an image quality evaluation method based on dynamic weight feature extraction and three-dimensional fusion under a meta-learning framework, and the method comprises the steps: designing a multi-receptive-field and dynamic parameter module in an image quality evaluation model, extracting feature information under different views, and enriching and weighing multi-receptive-field features; screening out a salient region by using image gradient information outside the image quality evaluation model, designing a distortion classification model based on the salient region, and providing shared features for the image quality evaluation model; using random cutting and three-dimensional convolution to deeply fuse the multi-receptive-field features and the shared features to obtain deep fusion features; shartcut Vision Transform is proposed to perform semantic analysis on the fusion features, the association between the overall features and quality fluctuation is summarized, and the quality score of the image is obtained through linear mapping. The multi-distortion image quality evaluation method can accurately evaluate the quality of the multi-distortion image, can effectively process a large amount of data, and has the advantages of being objective, rapid and high in usability.

The invention discloses a reversible information hiding method and system for enhancing image smoothness, and relates to the technical field of information security, and the system comprises a preprocessing module, an information embedding module and an optimization recovery module. According to the method, the Sobel operator is utilized to calculate the image gradient map, the region is divided based on the threshold T, the smooth region and the texture region can be accurately distinguished, an accurate basis is provided for differential processing of different regions, region characteristics are fitted, large-size blocks and sub-blocks of the smooth region are divided to facilitate subsequent refinement processing based on the gradient mean value, and the processing efficiency is improved. The small-size blocks of the texture area are beneficial to operation aiming at texture features, the rationality and effectiveness of overall processing are improved, meanwhile, the pixel mean value in the large-size blocks of the smooth area is calculated, the pixel and mean value difference value is recorded, reversible low-pass filtering is carried out, and the reversibility of the image in the whole information hiding and recovering process is ensured.

The disclosure deals with system and method for image gradient and derivative computation image processing. Noise, image sharpness, orientation, empirical parameters, and computational complexity are examples of image gradient and derivative computation challenges. Many traditional kernel-based operators excel at tackling one of these problems, but trade off their ability to handle others. Two new gradient detection kernels based on two-dimensional high order Taylor Series expansion tackle many such problems. The first kernel uses a wide range of the pixels in view to suppress noise, thereby improving the gradient intensities of edges. The second kernel builds on the first to leverage its noise suppression benefits while tackling an additional problem of degraded and low contrast edge boundaries. It can detect smooth lines in the presence of discontinuities and poor quality. The filter architecture allows for precise gradient calculation, edge detection, and orientation determination to less than one degree of the true value even when faced with signal to noise ratios that exceed 0.75.

The invention relates to the technical field of defect detection, in particular to a corrugated paper defect detection method and system based on machine vision. The method comprises the following steps: acquiring a corrugated paper image and converting the corrugated paper image into a frequency domain; identifying a periodic texture peak point based on amplitude spectrum angle energy distribution, generating a texture suppression filter, and combining with a multi-scale Gaussian high-pass filter to obtain a composite filter bank; the filter bank is used for filtering the frequency domain signals and performing energy weighted fusion; performing inverse transformation on the fused signal to obtain an enhanced image; and segmenting the enhanced image to obtain candidate defects, and verifying by combining the gradient information of the original image to determine a final defect. According to the scheme, the texture background can be adaptively inhibited, the multi-scale defect is enhanced, the false alarm rate is effectively reduced in combination with gradient check, and the detection accuracy and robustness are improved.

The invention discloses a visual calibration method, a terminal and a computer storage medium. The method comprises the following steps: generating theoretical grid coordinates; controlling a motion platform carrying a visual imaging unit to move to each target moving position in sequence, wherein the positions are obtained by superposing random two-dimensional offsets in a preset range; at each target moving position, a laser galvanometer and a visual imaging unit are synchronously triggered through the same hardware trigger source, and single-point mark engraving and image acquisition are executed; calculating a weighted centroid of the mark based on an image gradient magnitude, and extracting a sub-pixel center coordinate; combining the visual internal reference, the fixed coordinate transformation relation and the geometric constraint of the bearing plane, and resolving actually measured physical coordinates; carrying out space alignment, deviation calculation and invalid point interpolation on theoretical and actually measured coordinates, and generating a distortion data file which can be identified by a controller; the method further comprises a retry and fault-tolerant processing mechanism when coordinate calculation fails. According to the scheme, the calibration precision, the robustness and the automation level are effectively improved.

The invention relates to the technical field of image recognition control, in particular to a linear motion module accurate control method and system based on AI vision, and the method comprises the following steps: obtaining an interference fringe image and extracting a gray path, analyzing central point displacement to generate a coordinate sequence, repairing an interference feature position, and aligning the image and a control time sequence to generate a synchronization time table. The mapping structure response forms a set of control output configurations. According to the method, a peak path is constructed through interference fringe gray scale information in an image frame, central point pixel displacement is extracted, a coordinate migration sequence is constructed, fine-grained capture and tracking of an execution state are realized, an external interference area is identified according to image gradient change, vector repair is carried out, and image data stability and feature reliability are guaranteed. And aligning the repaired image response with a control instruction time sequence, constructing an impulse response lag statistical mechanism, realizing accurate mapping adjustment of the control frequency, and improving the control precision and response consistency in high-frequency displacement execution.

The invention discloses a desulfurization optimization control method and system based on a multi-modal model, and relates to the technical field of industrial intelligent control, and the method comprises the steps: constructing a gas-liquid interface boundary point set, and generating a control feature vector; calculating fractal dimensions of control feature vectors by using a box counting method, constructing a disturbance point set, multiplying disturbance intensity by an expansion trend to obtain an unstable diffusion grade, matching key propagation points with nearest disturbance points by using nearest neighbor matching, and generating a control priority sequence; and defining a control unstable diffusion level, defining an optimization objective function, and solving by using constraint optimization to obtain a final control variable. According to the method, a composite strength factor is constructed through an image gradient field and a signal response ratio, a gas-liquid interface boundary point set and a control feature vector are constructed, the robustness and information density of features are improved, a main resonance frequency is recognized by combining a fractal interference field and short-time Fourier transform, key propagation points are screened by using wavelet transform and information entropy analysis, and a high-precision gas-liquid interface is obtained. The desulfurization efficiency and the stability of the reaction tower are improved.

The invention relates to a double-branch gradient information guided dark light image enhancement method, which comprises the following steps of: firstly, extracting low-illumination image gradient information by using a Sobel operator; a shallow network feature used for optimizing global brightness balance, a middle network feature used for correcting local area brightness of the image and a deep network feature used for carrying out contrast adjustment of scene perception in high-level semantic features are sequentially obtained through a depth feature enhancement branch; according to the image features after gradient information extraction, gradient features of a plurality of stages are obtained in sequence in combination with a gating gradient optimization module; and obtaining an enhanced image through an attention fusion module. According to the method, the problems of high-frequency detail loss and insufficient real scene generalization ability in a low-light environment in an existing method can be solved. Meanwhile, the enhanced image processed by the image enhancement deep network guided by the double-branch gradient information is not easy to have an overexposure phenomenon, and the feature description force of the network on a dark light image is effectively improved.

The invention relates to the field of subway fault analysis, in particular to a subway fault data analysis method based on a cloud platform, and the method comprises the steps: obtaining train operation difference parameters and noise characteristic difference parameters of all key monitoring points, so as to determine a monitoring difference state; determining a difference analysis strategy of each key monitoring point position according to the monitoring difference state; when abnormal defect analysis is carried out, a defect analysis mode is determined according to the noise period stability coefficient and the image gradient difference coefficient of the key monitoring point position; under the condition that abnormal defect analysis is completed, a monitoring compensation mode is determined according to the defect interference radiation coefficient of the key monitoring point position; when parameter optimization analysis is carried out, the setting mode of the point location anomaly coefficient is determined according to the noise anomaly frequency of the key monitoring point location and the operation interference proportion, whether the point location anomaly coefficient is adjusted or not is determined according to the coincident anomaly parameters, and the accuracy of the operation parameter optimization result is improved.

The invention relates to the technical field of Internet of Things and remote monitoring, in particular to an unattended platform monitoring and alarming system and method based on an Internet platform. The method comprises the following steps of: acquiring space parameters of a platform, and performing sound field signal acquisition to obtain a sound pressure level numerical value group; sound source space positioning is carried out according to the sound pressure level numerical value group, and sound source space coordinate data and a sound pressure level space-time distribution diagram are obtained; deploying a camera array according to the platform space parameters, and performing platform scene image analysis to obtain an image frame sequence, enhanced image data, a multi-scale image pyramid and image gradient data; performing optical flow equation solving according to the multi-scale image pyramid and the image gradient data to obtain sparse optical flow data; and generating a motion vector field according to the sparse optical flow data. According to the invention, the accuracy and reliability of platform monitoring are greatly improved through multi-modal fusion perception, refined anomaly detection and intelligent analysis and decision.

The invention relates to the technical field of medical image processing, in particular to an acupuncture guide system based on image registration. According to the method, the height parameter field of the human body surface three-dimensional model is established and the contour line density distribution is extracted, so that hierarchical division and density aggregation identification of morphological characteristics of a target area can be realized, and local geometric characteristic identification is carried out by carrying out tangential analysis on direction changes of continuous contour line segments and combining Gaussian curvature. According to the method, geometric abnormal point locations of the surface structure can be accurately calibrated, and an area with direction stability and geometric continuity can be discriminated in multi-point data in combination with a space consistency judgment mechanism in the normal direction, so that the space identification process is more robust, and the accuracy of the space identification is improved. Multi-feature re-check judgment of a fitting end point can be realized through density peak value and normal consistency dual screening, the spatial accuracy of a positioning point is guaranteed, and the consistency of structural continuity and visual guidance information can be considered through a path construction mode generated by combining an image registration result and image gradient information.

The invention relates to the technical field of oral decayed tooth detection, and discloses an oral decayed tooth detection system and detection method. Through quadric surface fitting and smoothing processing, image noise is reduced and local details are reserved. By calculating an image gradient and constructing a gradient field, the gray level change direction and amplitude are effectively extracted. The divergence calculation and the weighted aggregation degree improve the accuracy of abnormal region identification, and especially in early-stage decayed tooth detection, the abnormal region can be highlighted and the normal region can be ignored. The self-adaptive threshold setting is combined with the local mean value and the standard deviation, the threshold is dynamically adjusted, and false detection and missing detection are reduced. And 8, gathering the scattered candidate points into a complete area according to an adjacency principle and connectivity analysis, and ensuring that a decayed tooth area with an irregular shape is not missed. The method ensures that the final detection area has clinical significance through the screening of features such as physical area and boundary pixel number, and enhances the operability of the detection result through red contour labeling.

The invention discloses an image binarization method based on a dynamic window. The method comprises the steps of 1, target image noise suppression and gray level conversion; 2, carrying out compression processing on the dynamic range of the bright part of the image through a logarithmic function; 3, calculating a global threshold value of the grayscale image by utilizing a maximum between-class variance method; 4, dividing the grayscale image into image blocks according to the image gradient, and calculating a local threshold value for each block by using a Sauvola algorithm; 5, according to the global threshold value and the local threshold value of each image block, the threshold value of each image block is obtained through calculation according to the weight proportion; and 6, segmenting each image block by using a corresponding threshold value to obtain a final segmented image. According to the invention, the size of a local window is adaptively changed when any pixel point is binarized; and the image binarization capability under the condition of non-uniform illumination in a complex illumination environment is remarkably improved.

The present invention relates to the field of image processing technology, and more specifically to a cross-view heterogeneous image matching method for airborne sensors. The method comprises obtaining a registration image and a reference image to be matched; extracting edge features of the registration image and the reference image respectively; coarsely aligning the registration image and the reference image; matching based on the coarsely aligned positions to obtain feature pairs, and organizing them into a multi-scale feature pair set; and estimating the transformation matrix between the images using a RANSAC algorithm based on the multi-scale feature pair set, ultimately completing precise image matching. The present invention effectively achieves a balance between matching accuracy and computational speed by combining a mechanism of fast coarse registration based on image gradient edge features with precise multi-scale feature registration based on image phase information. Furthermore, the method exhibits good robustness for airborne heterogeneous images.

The present application belongs to the technical field of mobile robot navigation, and particularly relates to a SLAM navigation method and system for a mobile intelligent cabinet, which comprises the following steps: acquiring a laser point cloud sequence and a grayscale image of the mobile intelligent cabinet and performing data cleaning; obtaining a geometric feature index based on the spatial jump distribution of the laser point cloud sequence; obtaining a visual texture index based on the local dispersion of the image gradient; calculating laser dynamic weight and visual dynamic weight by using the geometric feature index and the visual texture index, weighting and fusing the pose change quantity calculated by the single-line laser radar odometry and the visual odometry to obtain a fused pose quantity; and updating the global state based on the fused pose quantity and driving autonomous navigation. The present application can adjust the sensor weight in real time according to the environmental characteristics, solves the problem of positioning divergence in the long corridor of a shopping mall or a high-reflectivity environment, and improves the robustness of navigation.

The invention discloses a multi-angle template matching method based on gradient topology sparse coding. The multi-angle template matching method comprises the following steps of S1, extracting a gradient topology skeleton of a template image; s2, constructing and coding a skeleton sparse dictionary; s3, coefficient domain rotation mapping construction is carried out; s4, performing multi-angle matching and similarity calculation; and S5, result fusion and optimization. The invention relates to the technical field of computer vision and image processing, and has the beneficial effects that the features based on the gradient topology skeleton are insensitive to illumination variation, gray shift and noise interference can be effectively overcome, and the matching stability in a complex environment is improved. By constructing the coefficient domain rotation mapping relation, physical rotation reconstruction does not need to be carried out on the image, the calculation overhead of multi-angle matching is remarkably reduced, the search efficiency is improved, sparse coding is used for carrying out compact representation on the image structure, and the adaptive capacity to local deformation and partial shielding is enhanced.

The invention discloses an endoscope video frame specular reflection highlight detection method and system, and belongs to the field of endoscope optical detection. The method comprises the following steps: firstly, converting an endoscope video frame into a grey-scale map; a Sobel operator is utilized to extract edge gradient to generate a binary image gradient mask, and meanwhile, a binary image highlight mask is generated by obtaining an area with the brightness obviously higher than the average level of a grey-scale map; dividing the binary image gradient mask and the binary image highlight mask into image blocks, adaptively adjusting the sizes of the blocks according to the highlight ratio of the image blocks so as to fuse the binary image gradient mask and the binary image highlight mask, and performing morphological processing and region screening to obtain a final binary image highlight mask of the grey-scale image; and carrying out time domain compensation on the final binary image highlight mask to obtain a specular reflection highlight detection result of the endoscope video frame. The accuracy, integrity and robustness of specular reflection highlight detection of the endoscope video frame are effectively improved.

The application relates to the technical field of image recognition, and discloses a hydraulic plunger pump intelligent fault diagnosis method and system based on pressure signals, which comprises the following steps: arranging pressure signals of a hydraulic plunger pump according to a spiral structure, and performing interpolation processing on the arranged signals to obtain an optimized pressure image; performing visual enhancement processing on the optimized pressure image to obtain an enhanced pressure image; analyzing multi-scale features in the enhanced pressure image to obtain global feature parameters of image brightness statistical features; performing local texture feature analysis on the enhanced pressure image to obtain local feature parameters of image gradient changes; taking the global feature parameters as an upper layer framework and taking the local feature parameters as a bottom layer benchmark to construct overall feature parameters of a running state; comparing the overall feature parameters with preset standard feature threshold values in multiple levels, and determining a fault state according to a comparison result; and the application can improve the efficiency of the hydraulic plunger pump intelligent fault diagnosis based on the pressure signals.

The application discloses a kind of intelligent image compression fast code rate control methods based on block level.The method includes the following steps: S1, the input picture is divided into non-overlapping same size block;S2, sample part image block;S3, the image block of twice coding and decoding is decoded to the sample, according to the relationship between the coding and decoding result and the quality factor λ of image block rate R, and the relationship between the quality factor λ of image block distortion D;S4, the average image gradient of each image block is calculated;S5, using linear relationship fitting each sampled image block average image gradient and the coefficient of R-λ relationship and D-λ relationship;S6, the R-λ relationship and D-λ relationship of not being sampled block are predicted;S7, according to the R-λ relationship and D-λ relationship of each block, the code rate control of image is carried out.The application compared with other code rate control methods, has more accurate code rate control precision, faster speed and lower memory consumption.

Images are reconstructed from data acquired using an ultra-fast-pitch acquisition with a CT system. As an example, an ultra-fast-pitch acquisition mode in single-source helical CT (≥1.5) can be used to acquire data. A trained machine learning algorithm, such as a neural network, is used to reconstruct images in which artifacts associated with insufficient data acquired in the ultra-fast-pitch mode are reduced. An example neural network can include customized functional modules using both local and non-local operators, as well as the z-coordinate of each image, to effectively suppress the location- and structure-dependent artifacts induced by the ultra-fast-pitch mode. The machine learning algorithm can be trained using a customized loss function that involves image-gradient-correlation loss and feature reconstruction loss.

The present application relates to the technical field of water conservancy, in particular to a water conservancy facility sluice intelligent control method, the method comprises the following steps: step 1: the water body sparse spectrum data of target water area is collected; the water body sparse spectrum data is pretreated, and noise and spectrum distortion are eliminated; step 2: the spatial distribution image of target water area is obtained by multispectral imaging technology, and image decomposition is carried out, and the image gradient is obtained; the Fourier transform result is obtained by Fourier transform to key features; according to the image gradient and the Fourier transform result, the water level feature of target water area is extracted; step 3: a water level conversion model is established according to the water level feature of target water area extracted; water level prediction is carried out using the water level conversion model, and the water level prediction result is obtained; step 4: according to the water level prediction result, the operation of the water conservancy facility sluice is controlled. The water level measurement with wider range and more accurate precision is provided, the sluice control is carried out based on this, and the control accuracy is improved.

The application discloses a micro-vision threshold segmentation algorithm based on OTSU improvement for micro-nano object images, and comprises the following steps: S1, combining image gradient information, calculating a new gray weight factor based on an exponential function and a logarithmic function, and performing bilateral filtering processing on the image; S2, calculating a traditional gray threshold of the filtered image by using a traditional inter-class variance formula, and calculating an improved gray threshold by using an improved inter-class variance formula based on a natural constant function; and S3, performing adaptive iterative operation on pixels of the image by using the traditional gray threshold and the improved gray threshold, so that three-region threshold segmentation is completed. The micro-vision threshold segmentation algorithm based on OTSU improvement disclosed by the application overcomes the problem that the calculation result is inaccurate due to the imbalance of the pixel quantity ratio during threshold segmentation, and improves the limitation of poor local analysis capability of the global threshold segmentation algorithm.

This invention belongs to the field of image generation, specifically relating to a method and system for generating 360° images of subway vehicles based on image stitching. The method includes: acquiring source images collected along the subway vehicle; obtaining effective matching point pairs through feature extraction matching and spatial topological similarity filtering; selecting the camera pair with the strongest geometric constraints as a benchmark; using incremental triangulation to recover the camera pose and 3D point cloud; in global bundle adjustment, optimization is performed by combining reprojection error and linear regularization terms based on the geometric principal axes of principal component analysis, with the regularization term weights adjusted according to the average distance from the point cloud to the principal axes; after optimization, the source images are projected onto a cylindrical canvas, and the optimal image gradient is selected based on gradient information entropy to construct a gradient field; a seamless 360° panoramic image is generated by solving the Poisson equation. This invention can effectively eliminate mismatches, suppress 3D reconstruction offset, and generate high-quality seamless panoramic images.

The invention belongs to the technical field of unmanned aerial vehicle visual guidance, and particularly relates to a visual guidance autonomous docking method and system for an intelligent cabinet by an unmanned aerial vehicle, and the method comprises the steps: obtaining a motion blur index according to the distribution characteristics of image gradient amplitudes; determining an adaptive sharpening coefficient based on the motion blur index, extracting a high-frequency detail layer and performing weighted enhancement to obtain an enhanced docking image; extracting a contour feature and a texture feature in the enhanced docking image, and obtaining a confidence coefficient weight of the texture feature according to a scale proportion factor of the contour feature; and calculating poses according to the contour features and the texture features, performing weighted fusion on translation vectors and rotation vectors of the two groups of poses by using a confidence coefficient weight, and performing unmanned aerial vehicle docking according to the fused poses. Through adaptive sharpening and feature fusion, the problem of inaccurate positioning caused by motion blur and scale change is effectively solved, and smooth docking of the unmanned aerial vehicle is realized.

This invention relates to the field of thread inspection technology, and discloses a thread inspection method, apparatus, system, and device based on image recognition and deep learning (DL). The method includes: acquiring thread images from multiple angles; filtering the thread images by combining image gradients and information entropy; measuring the thread's inner and outer diameters, thread pitch, and thread angle using image recognition methods based on the filtered thread images; detecting whether there are damage or folding defects in the thread teeth; detecting whether there are crack defects in the thread as a whole; and using a deep learning model combining classification and segmentation networks to detect whether there are crack defects at the top, waist, and bottom of the thread; comparing the measured thread inner and outer diameters, thread pitch, and thread angle with standard specifications; and determining whether the thread is qualified based on the defect detection results of the thread teeth, the thread as a whole, and the top, waist, and bottom of the thread. This invention can reduce inspection costs and improve inspection efficiency and accuracy while achieving overall thread inspection.

The present application relates to the technical field of medical image analysis, in particular to a burn area automatic evaluation method based on AI image recognition, comprising the following steps: analyzing image gradient sequence, screening for inflection points and encoding texture order, analyzing brightness difference to obtain attenuation trend to generate degradation structure, comparing tension mutation and convergence direction, screening for bridging points, identifying color difference density inversion to construct boundary point set, analyzing curvature difference to perform area expansion correction, in the present application, the texture order identifier is constructed by analyzing the pixel gradient sequence in the original image, the regional damage intensity judgment mechanism is established by combining the brightness change and the thermal damage signal difference, the wound edge point structure is derived by the tension direction trend, the continuous boundary range is identified by combining the color difference density, the area expansion comparison and correction are carried out by fusing the curvature difference, the systematic evaluation logic from boundary recognition to area mapping is established, the damage range is accurately delimited and the body surface characteristics are matched, and the precision evaluation and regional expansion of the burn area are realized.