1960 results about "Binary image" patented technology

The invention provides a bridge structure crack position identification method based on deep learning and computer vision, and the method comprises the steps: obtaining a multi-view visible light image and a depth image, fusing the visible light image and the depth image according to registration parameters, and generating a fusion image containing the texture and depth information of a bridge; for the fused image, extracting a crack region by adopting an adaptive threshold segmentation algorithm, and dynamically adjusting the threshold to obtain a crack binary image; according to the crack binary image, calculating three-dimensional coordinates of crack pixel points through a three-dimensional reconstruction algorithm, and performing reconstruction to obtain three-dimensional point cloud data containing crack space position and form information; on the basis of the three-dimensional point cloud data, three-dimensional local feature descriptors are adopted to describe geometric attributes of the surface of the crack, crack fragment parameters are calculated, and a three-dimensional crack model is constructed; based on a deep learning model, a transfer learning strategy is adopted, a self-adaptive transferable bridge crack three-dimensional detection method is formed, and model parameters are updated so that the model parameters can adapt to the characteristics of a target bridge.

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention relates to the technical field of industrial product surface quality detection, in particular to a flexible display module surface defect image recognition method, which comprises the following steps: acquiring a plurality of surface images of a flexible display module under different light sources and carrying out distortion removal processing on the surface images; reconstructing and generating three-dimensional reference point cloud data representing the current curved surface form of the module; re-projecting the distorted image to the ideal rigid plane according to the relationship, generating a plurality of corrected images, and generating a plurality of corrected images to eliminate geometric and luminosity distortion introduced by flexible deformation; obtaining a defect candidate area binary image; and extracting a multi-dimensional feature vector of the defect candidate region from the binary image of the defect candidate region, and classifying the feature vector by using a pre-trained defect classification model to obtain a defect identification result. Through the three-dimensional reference point cloud reconstruction and image re-projection technology, the problem of geometric distortion caused by surface deformation of the flexible display module is effectively solved, and misjudgment and missed judgment are avoided.

The invention relates to the technical field of image data processing, in particular to a forge piece surface defect detection method and system based on machine vision. The method comprises the steps of obtaining a gray image of a to-be-detected forging surface; determining a boundary significance weight; determining a path consistency weight; screening the boundary significance weight and the path consistency weight to obtain a final weight; and carrying out local adaptive threshold segmentation on the final feature map to obtain a binary image, and carrying out defect identification on the surface of the forge piece to be detected based on a connected region in the binary image. According to the method, the boundary significance weight and the path consistency weight are constructed and are respectively used for accurately positioning defect edges and verifying structure continuity, texture interference is effectively inhibited, and false alarms are reduced; through adaptive Gabor filtering, the problem of a response blind area of a traditional method is solved, finally two weights are fused to modulate filtering response, and the accuracy of forging surface defect detection is improved.

The invention belongs to the technical field of remote sensing image change detection, and particularly relates to an ultra-light remote sensing image change detection method and system, and the method comprises the steps: firstly obtaining a dual-time-phase remote sensing image of a target detection region, and carrying out the preprocessing of the dual-time-phase remote sensing image; inputting the preprocessed dual-temporal remote sensing image into an ultra-light twin encoder sharing weight parameters, and extracting multi-scale high-dimensional semantic features of the dual-temporal remote sensing image; performing cross-scale semantic fusion on the multi-scale high-dimensional semantic features of the dual-temporal remote sensing image by using a space-channel attention module to generate multi-scale semantic features of the dual-temporal remote sensing image; and carrying out differencing on the multi-scale semantic features of the dual-temporal remote sensing image, and inputting a generated multi-scale change feature map into an ultra-light decoder for decoding to obtain a binary image for change detection. According to the method, the parameter quantity and the calculation quantity are greatly reduced while the characteristic characterization capability is maintained, so that the detection speed is improved on the basis of ensuring the detection precision.

The embodiment of the invention discloses a mobile phone glass cover plate defect detection method, system and equipment and a storage medium, which are used for improving the accuracy of detecting a mobile phone glass cover plate. The method provided by the embodiment of the invention comprises the following steps: acquiring a first grayscale image of a to-be-detected glass cover plate and a second grayscale image of a standard glass cover plate; aligning the second gray level image with the first gray level image pixel by pixel; calculating a gray value difference value of each pixel of the first gray image and the second gray image to obtain a residual image; calculating an optimal Otsu threshold value of the residual image; performing binarization processing on the residual image according to the optimal Otsu threshold to obtain a binary image; and determining the defect position of the to-be-detected glass cover plate according to the binary image.

The invention relates to the technical field of road pavement disease recognition, in particular to an intelligent road pavement disease recognition method and system based on a multispectral image, and the method comprises the steps: obtaining road pavement image data collected by a multispectral camera carried by an unmanned aerial vehicle; generating a registered multispectral fusion image; performing enhancement processing on the multispectral fusion image based on a dark channel algorithm to generate an enhanced image; performing shallow feature extraction and deep feature extraction on the enhanced image according to a convolutional neural network to generate an initial disease extraction image; performing segmentation processing on the initial disease extraction image through adaptive threshold segmentation to generate a binary image; performing morphological processing on the binary image to generate a candidate disease image; extracting edge information of a disease area from the candidate disease image based on an instance segmentation technology, and generating a segmented image; the segmented image is subjected to crack refining processing, a crack refined image is generated, and more comprehensive disease information support is provided for collection and fusion of multispectral data.

The invention discloses a steel-wood combined beam-column joint damage identification method and system applying machine vision, and the method comprises the steps: S1, obtaining a visible light, near-infrared and thermal imaging multi-mode image sequence of a steel-wood combined beam-column joint, and carrying out the denoising processing through adaptive anisotropic diffusion filtering, and obtaining a denoised multi-mode image sequence; s2, performing Gaussian pyramid decomposition and Laplacian pyramid feature fusion on the de-noised multi-modal image sequence tensor to obtain a fusion feature map; s3, performing adaptive threshold segmentation and morphological processing on the fused feature map to obtain a final binary image; s4, training the deep learning network to obtain a trained deep learning network; and S5, performing damage identification by using the trained deep learning network. According to the method, the steel-wood combined beam column joint damage can be accurately identified, the segmentation probability graph is obtained, the segmentation result is marked, and the visualization of the damage area is realized.

The invention discloses a highly dense broken ice image segmentation method based on an iteration MGAC and an SAM model, and the method comprises the steps: carrying out the sea ice instance segmentation of a preprocessing image based on the SAM model, and obtaining a sea ice mask image corresponding to the preprocessing image; according to the binary image and the sea ice mask image, obtaining an initial sea ice residual region image which is not identified by the SAM model; obtaining initial sea ice residual region images under different gray threshold values to obtain an initial seed mask graph; taking the initial sea ice residual region image and the initial seed mask image as inputs of a preset MGAC contour model, and obtaining an MGAC sea ice recognition result based on a multi-round iteration partitioning mechanism; and performing union operation on the MGAC sea ice identification result and the sea ice mask image to obtain a crushed ice segmentation mask result. The method solves the problem that the existing method is insufficient in structure extraction precision and boundary integrity of the dense broken ice area.

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

The invention discloses an improved filtering and adaptive edge detection steel plate gap identification method for a hull surface image, and the method comprises the steps: 1, carrying out the denoising of a hull surface image containing a steel plate gap through employing a local weighted Gaussian blur and dynamic bilateral filtering fusion algorithm, and obtaining a denoised hull surface image; 2, carrying out the edge detection of a local region of the denoised hull surface image through employing a self-adaptive dynamic threshold Canny edge detection method, and obtaining a binary image; and step 3, carrying out edge extraction on the binary image to obtain a steel plate gap image. According to the method, through a local weighted Gaussian blur and dynamic bilateral filtering fusion denoising algorithm, the noise in the image is efficiently removed in a proportional fusion mode, meanwhile, through self-adaptive adjustment of filtering parameters, the noise influence of a local area is improved in a targeted mode, loss of detail information is avoided, and the image quality and detail presentation are remarkably improved.

The invention discloses a robot full-coverage path planning method based on cattle farming movement. The method aims at improving the path coverage efficiency of a robot in a complex environment. According to the method, a two-dimensional plane map of a real environment is obtained and converted into a binary image, and a grid map suitable for the size of a current robot is generated through rasterization processing. And dynamically adjusting a path planning direction according to a main direction and regional characteristics of a map / map region by adopting a motion direction priority judgment method of cattle farming type motion. And when the robot encounters an obstacle and falls into a dead zone, replanning a path by using a backtracking point set construction rule in combination with an A * algorithm, optimizing path selection of the robot for escaping from the dead zone, and ensuring that the robot can cover all reachable areas. The method effectively reduces path overlapping and calculation amount, improves efficiency and coverage rate of path planning, and is especially suitable for environments with complex boundaries and obstacles. The invention further discloses a system and application for implementing the path planning method, and the system and the application have wide application scenes.

The invention discloses a chest CT (Computed Tomography) image processing method and system, and relates to the technical field of medical image processing, and the main scheme is as follows: acquiring image data of two groups of X-rays with different energies and attenuation measurement values of pixel points by adopting dual-energy CT scanning, respectively calculating concentration values of soft tissues and blood vessels, and generating concentration maps; generating an initial image based on the soft tissue and blood vessel concentration map, calculating a pixel value, obtaining a target image pixel value through contrast stretching, segmenting the target image pixel value to obtain a binary image, and distinguishing a blood vessel region and a soft tissue region in the binary image by identifying and analyzing a connected region in the binary image; carrying out center line extraction on the blood vessel region, calculating a local diameter, and respectively comparing the local diameter with a blood vessel dilation threshold value and a blood vessel constriction threshold value to judge abnormal blood vessel dilation or constriction; extracting features from the soft tissue region to form a first feature vector, and comparing the first feature vector with a preset second feature vector to judge abnormality; abnormal conditions are automatically labeled or highlighted, so that the diagnosis accuracy and efficiency are improved.

The invention discloses a thermal printing image processing method, device and equipment and a medium, and relates to the field of thermal printing. The method comprises the following steps: acquiring an original image to be printed, and converting the original image into a grayscale image; calculating a gradient difference value of the grayscale image, and generating a threshold distribution map; generating a heat accumulation risk map based on an average gray value in a preset neighborhood window taking each pixel point of the gray image as a center; generating a preliminary binary image according to the threshold distribution map; extracting image features of the preliminary binarization image, and classifying the original image to obtain an image classification result; calling a target processing parameter set corresponding to the image classification result from a plurality of preset processing parameter sets according to the image classification result; performing optimization processing on the preliminary binary image according to the target processing parameter set and the heat accumulation risk map to obtain an optimized binary image; and the final printing image adaptive to the resolution of the target printer is generated, so that the printing definition is improved.

The invention provides a core permeability prediction method based on time sequence decomposition and multi-dimensional feature interaction, and belongs to the technical field of core permeability prediction. The method comprises the following steps: performing CT scanning on multiple sandstone samples to obtain high-resolution three-dimensional rock core original data; the method comprises the following steps: performing image preprocessing on original data, performing adaptive threshold segmentation, and dividing pores and matrixes to obtain three-dimensional binary image data; performing small window sliding sampling on the binary image data to obtain three-dimensional core small cubes, and calculating the permeability of each core cube as a label to obtain a final experimental data set; a core image sequence is decomposed from an experimental data set, fusion features are extracted, information is extracted from the fusion features from three dimensions of channel, height and width, features of different dimensions are combined, and the permeability is predicted through a full connection layer. According to the method, lightweight design is adopted, multi-dimensional information of the core image is fully utilized, prediction efficiency and precision are remarkably improved, and computing resource consumption is reduced.

The invention discloses an unmanned aerial vehicle bridge crack beam disease identification method based on a progressive domain adaptation strategy. A bridge image is acquired through an unmanned aerial vehicle, the bridge image acquired by the unmanned aerial vehicle is subjected to overlapping type blocking, the segmented bridge image is marked, and bridge body image blocks are classified by adopting a CNN model; performing small-scale marking on cracks in the obtained bridge body image blocks, mixing the cracks with a public data set to obtain a mixed data set, and training a crack segmentation model based on the mixed data set; and carrying out type classification on the binary image after crack segmentation, and measuring a crack index at the same time. The bridge crack disease detection problem is decomposed into three stages of bridge body image block classification, bridge body crack segmentation, crack classification and geometric morphology estimation. The domain adaptation problem is dispersed to three different stages, so that the labeling cost is reduced, and the pressure of data acquisition is reduced. And an effective solution is provided for the bridge crack disease problem.

The invention discloses a capillary pressure and contact angle synchronous characterization method and device of a microfluidic fluid interface, and relates to the field of interface analysis and multiphase seepage in pore media, and the method comprises the following steps: based on a pre-configured pore, particle and fluid binary image, distinguishing and extracting a fluid-particle contact interface contour and an attack / defense fluid interface contour; fitting and determining the curvature radius of each fluid interface contour through a watershed region division algorithm and reordering smoothing of Fourier descriptors; generating a multi-level size pore space based on the pore binary image, and obtaining a pore overlook radius; calculating a necking angle in combination with the curvature radius and the multi-stage size pore space to obtain an apparent contact angle and an intrinsic contact angle; and calculating the capillary pressure of the attack / defense fluid interface by combining the curvature radius, the pore overlook radius, the chip depth and the interfacial tension. According to the invention, high-precision technical support is provided for micro-fluidic multiphase seepage research.

The invention designs a road crack detection method based on thermo-optical matrix fusion, and the method comprises the steps: obtaining a temperature matrix and a visible light image based on a visible light-temperature matrix collection device, extracting crack edge features through temperature gradient analysis, and carrying out the binarization processing, thereby obtaining a temperature matrix binary image; a thermal-optical matrix image fusion module is provided, and a temperature matrix and a visible light image are effectively fused; a thermal-optical matrix fusion crack image obtained based on the fusion module is used as the input of an improved YOLOv8-AMPN network model, the semantic enhancement effect of a temperature matrix on a visible light image is further improved through a feature weight adaptive weighted fusion and region block level dynamic fusion method in the network, and crack features are highlighted. The method has the advantages that a temperature matrix branch is introduced, the limitation of a visible light image under environment change is effectively overcome, the problem that multi-modal image information is redundant and complex is solved, and the sensitivity of a model to crack characteristics and the detection response speed are improved through temperature information.

The embodiment of the invention provides a pose estimation method and device and electronic equipment, and belongs to the technical field of visual inspection and positioning. The method comprises the following steps: acquiring a target image in real time, wherein the target image is an image of a target object containing a similar two-dimensional code ARuco; performing image preprocessing according to the target image to obtain binary image data; performing mark detection processing according to the binarized image data and a pre-established multi-scale image pyramid so as to identify and position a similar two-dimensional code ARuco mark; performing camera calibration processing according to preset camera parameters to obtain camera calibration data; performing corner detection processing according to the ARuco mark to obtain positioning corner position information; and according to the positioning corner point position information, the preset target three-dimensional space position information and the camera calibration data, carrying out mark attitude calculation processing to obtain target pose estimation information. The method is used for achieving the effect of improving the accuracy of the pose estimation result.

According to various embodiments, a system for detecting information about a road relating to digital geographical map data is provided. The system comprises an input device configured to obtain remotely captured geographical image data; and a processor configured to generate ground truth image data from the digital geographical map data, and generate binary image data of the road segments from the remotely captured geographical image data using a semantic segmentation task. The processor is further configured to: skeletonize the binary image data to generate skeletonized binary image data including a center line of each road segment of the road segments, detect a road segment missing from the digital geographical map data using the skeletonized binary image data, detect a road width from the binary image data and the center line of each road segment of the road segments; and detect number of lanes from the detected road width.

The invention discloses a light guide plate dot density detection method, system and device and a storage medium, and relates to the technical field of light guide plate dot density detection, and the method comprises the following steps: converting a historical light guide plate image into a historical light guide plate binary image based on a graying processing method and a binary processing method; obtaining a standard dot area and a dot area range based on the historical light guide plate binary image; obtaining a detection normal fitting area and a detection incomplete fitting area based on the standard dot area, the dot area range and the detection light guide plate binary image; calculating and obtaining the dot density of the detection light guide plate based on the detection normal fitting area and the detection incomplete fitting area; the method is used for solving the problem that errors are generated in dot density calculation due to the fact that edge incomplete dots are not analyzed and calculated in an existing light guide plate dot density calculation technology.

A scene text detection method, comprising: acquiring an image to be subjected to detection, and performing feature extraction on said image, wherein said image contains scene text; processing features obtained by means of the feature extraction; and on the basis of a processing result, performing core-region probability prediction and boundary threshold prediction on the scene text, so as to obtain a binary image of said image, wherein the processing at least comprises one of the following: attention feature fusion and first fusion processing, and joint attention processing, or the processing at least comprises one of the following: feature screening and second fusion processing, and segmentation processing, edge enhancement and third fusion processing, and the feature extraction comprises: performing feature extraction on said image by means of a convolutional neural network, and adjusting a feature dimension of a plurality of extracted branches to a preset dimension, so as to obtain a shallow feature of the plurality of branches.

The invention discloses an IMU (Inertial Measurement Unit)-assisted moving target detection method under a dynamic condition, which comprises the following steps of: firstly, extracting feature points of a target image for matching to obtain feature point pairs; then, relative coordinate conversion is carried out on IMU sensor data, a corresponding basic matrix or homography matrix is solved, and then corresponding polar lines or mapping projection feature points are obtained; classifying the extracted feature points into two parts, namely static background feature points and dynamic target feature points; an affine transformation matrix is solved according to the corresponding relation of the static background feature points of the two adjacent frames of images and the motion parameter model, an inter-frame difference method is used for carrying out difference on the two frames of images and carrying out image binarization and morphological processing, and a corresponding motion target frame is obtained; and finally, traversing the moving target frame to carry out overlap ratio matching, and screening out a detection object of which the overlap ratio is greater than a set threshold value, namely an actual moving target in the current image. The method provided by the invention can effectively improve the precision and accuracy of moving target identification in a dynamic background.

The invention discloses a metallographic detection method and system based on image recognition, and belongs to the technical field of metallographic detection. The detection method comprises the following steps: image acquisition and preprocessing: acquiring a metal metallographic image by an optical microscope, performing median filtering and denoising, and extracting a grain boundary by adaptive binarization; and field-of-view and scale calibration: selecting a corresponding scale according to the shooting magnification. The multi-parameter automatic measurement comprises grain size analysis, grain size rating, second phase analysis and intelligent report generation, a report is automatically generated based on a preset template and comprises an original image, a binary image, measurement data and a histogram, and report fonts and layout can be adjusted in real time through a visual control. According to the detection method disclosed by the invention, the efficiency, the precision and the reliability of metallographic structure analysis of the metal material are remarkably improved through the full-process design of image acquisition standardization, preprocessing intellectualization, data analysis automation and report generation templating.

The invention discloses a PDF engineering drawing structured recognition method and device based on multi-dimensional feature fusion and a storage medium, and belongs to the technical field of engineering drawing intelligent processing. The method comprises the following steps: PDF analysis and preprocessing: analyzing a PDF engineering drawing file, extracting a vector graph drawing instruction, text block contents and respective original coordinate information, and carrying out image rendering and standardized preprocessing on a drawing page to generate a binary image with a standard resolution; multi-dimensional feature extraction: extracting the following four types of features from the binary image: a visual depth feature, a text semantic feature, an industry specific symbol feature and a spatial topology feature between elements; feature fusion and relation reasoning: inputting the visual depth features, the text semantic features, the industry specific symbol features and the spatial topological features into a graph neural network fusion module, and constructing a graph structure taking each identified element as a node and taking a spatial topological relation between the elements as an edge, performing feature interaction and fusion among nodes through an attention mechanism, and reasoning a semantic association relationship among drawing elements; and generating structured data: classifying and grouping the drawing elements according to the semantic association relationship, and outputting a structured data file according to a predefined structured format. Therefore, the technical problems of low recognition efficiency and accuracy in the prior art are solved, and the technical effect of efficient and accurate engineering drawing structured recognition is achieved.

The invention relates to an intelligent ship repair quotation method and system based on multi-modal analysis and a large language model, and the method comprises the steps: firstly receiving a ship repair inquiry sheet file uploaded by a user, and carrying out the feature enhancement of image data through a DocEnTR model, and obtaining a binary image; automatically extracting unstructured text data, semi-structured table data and text information in the binarized image by adopting an OCR (Optical Character Recognition) technology, and performing multi-modal analysis on image features of the binarized image and the extracted text information by adopting a large language model to generate an initial data stream; synchronously generating a standard semantic tag and a non-standard content tag by adopting semantic association of the large language model, automatically generating a dynamic cue word according to the standard semantic tag, inputting the dynamic cue word into the large language model, and outputting a standardized engineering description; performing manual auditing on the non-standard content mark to correct the engineering item field so as to generate structured engineering data; and finally, calculating the total price based on the structured engineering data and the material quantity parameters, and further generating a final quotation list to complete ship repair quotation.

The invention discloses a semi-rigid asphalt pavement crack depth estimation method, equipment and a medium, and relates to the technical field of road detection. The method comprises the steps of collecting a high-resolution image of a road surface through a mobile detection platform, segmenting a crack region in the high-resolution image by adopting an image segmentation model, and obtaining a binary image of a crack; pixel accumulation, edge detection, skeletonization, skeleton trimming and orthogonal projection are carried out on the crack binary image to extract surface parameters of the crack, and the surface parameters comprise the type, the length, the width and the area; inputting the fracture surface parameters into the fracture depth prediction model, and outputting a fracture depth prediction value; and generating a crack depth grade label according to the depth predicted value, and outputting a corresponding maintenance strategy. According to the method, the crack surface parameters and the pavement structure parameters are combined, the prediction result has good accuracy and consistency through the coupling relation with the depth index, and the method adapts to different crack types and working conditions.

The invention discloses a coal rock fracture image recognition method and system based on deep learning and post-processing, a coal rock fracture image recognition model is adopted to recognize and classify spliced coal rock fracture images, the model comprises an improved YOLOv5 target detection network, an image segmentation network and a classifier which are connected in sequence, after the image segmentation network outputs the binary image, parallax depth calculation is carried out on cracks and non-cracks in the crack images at different angles, and the cracks are determined based on the parallax depth; after the fracture is determined, carrying out image feature numeralization on the binarized image, and extracting fracture image feature parameters; the fractal dimension of the fracture is calculated, whether the fracture can be represented or not is preliminarily judged based on the fractal dimension, if yes, the fracture image feature parameters are input into a classifier, the type of the coal rock fracture is output, and if not, the specific type of the fracture is output. And the parallax depth, the fractal dimension and the image features are integrated for recognition and classification, so that the problems of misrecognition and missed recognition are effectively reduced.

The invention provides an IVE-based regional image texture complexity calculation method and device and a storage medium, and the method comprises the steps: obtaining a to-be-processed RGB image, determining a region of interest according to the RGB image, and obtaining the coordinate information of the region of interest; carrying and storing the region of interest by using a dma operator of an IVE hardware acceleration module according to the coordinate information of the region of interest to obtain a region image; calculating a structure tensor of the regional image through an image processing operator in the IVE hardware acceleration module to obtain a class structure tensor image; performing binarization processing on the class structure tensor image to obtain a binarized image of the class structure tensor image; and carrying out quantity statistics on the maximum value according to the binary image of the class structure tensor image, and calculating to obtain the texture complexity. According to the calculation method, on the premise that the performance and the effect are guaranteed, the calculation amount is greatly reduced, and the calculation of the texture complexity of the regional image is accelerated.

The invention discloses a monitoring method and system for identifying crop drought based on leaf morphology, and relates to the field of agricultural drought monitoring, and the method comprises the following steps: selecting a completely unfolded healthy leaf, cleaning with ultrapure water, blow-drying with nitrogen, and fixing on a standard objective table to obtain a standardized leaf sample; based on the standardized leaf sample, a high-resolution scanner is used for obtaining a front gray image and a back gray image of the leaf, and a binary image with a clear vein network is obtained through image splicing and binarization processing; skeletonizing the binary image with the clear vein network, and calculating the vein fractal dimension by adopting a box counting method to obtain a fractal dimension change rate; the method is in a fixed detection area of a standardized leaf sample. Through innovative leaf vein fractal dimension analysis and contact angle dynamic measurement, a crop drought monitoring system is constructed, and in the aspect of leaf vein analysis, skeletonization processing and a box counting method are adopted to realize accurate quantification of a leaf vein network topological structure.