176 results about "Sobel operator" patented technology

The invention discloses a ginkgo leaf extracting solution state real-time monitoring method based on image processing, and relates to the technical field of ginkgo leaf extracting solutions. The method comprises the following steps: constructing a multi-light-source imaging environment to shoot a ginkgo leaf extracting solution image; obtaining an extracting solution mask through a U-net segmentation model, and obtaining an extracting solution foreground image in combination with the ginkgo leaf extracting solution image; segmenting the extracting solution foreground image through an adaptive threshold method to obtain an extracting solution block graph, and optimizing through a watershed boundary optimization method to obtain an optimized extracting solution block graph; a block boundary probability value is calculated through a boundary probability model with double distance changes, a block gradient magnitude is calculated through a Sobel operator, block boundary confidence is obtained by combining the block boundary probability value and the block gradient magnitude, and block boundary pixels are determined; and collecting a boundary gradient feature vector sequence of the block boundary pixels, inputting the boundary gradient feature vector sequence into the extracting solution evolution trend model, outputting to obtain an oxidation risk index, and performing early warning if the oxidation risk index is greater than a preset threshold value.

The invention relates to the technical field of three-dimensional modeling, in particular to a three-dimensional modeling system and method based on a laser radar, and the system comprises a point cloud boundary extraction module, a boundary error analysis module, a self-adaptive cutting repair module and a stitching structure optimization module. According to the method, three-dimensional coordinates, normal vectors, reflection intensity, distances and indexes of boundary points are called layer by layer, a Sobel operator and a Canny algorithm are combined to cooperatively recognize a boundary and an error region, then local region repair is completed based on least square plane fitting and spatial residual analysis, a tension vector is analyzed through dynamic planning, and stitching path adjustment is executed. On the cooperative basis of multi-type point cloud features, a spatial topological structure and boundary continuity dynamic modeling, the problems of local damage, connection errors and instable stitching paths of point cloud boundaries in complex scenes are solved, and the integrity of boundary structures, the coherence of stitching transition and the rationality of spatial geometric expression are remarkably improved.

The invention discloses a geotechnical engineering slope deformation monitoring method and system. The method comprises the following steps: acquiring multi-modal image data, enhancing the weak deformation area contrast of the multi-modal image data by using a CLAHE algorithm, extracting gradient edge information through a Sobel operator, and filtering laser radar point cloud noise based on VMD; capturing a bidirectional dependency relationship of a time sequence based on a BiLSTM bidirectional long short-term memory network, and dynamically allocating weights of different time steps and spatial positions through an attention mechanism; optimizing hyper-parameters of the hybrid prediction model by using a GJO Golden Litsea Optimization algorithm to obtain a target hybrid prediction model; and inputting the feature image data into the target hybrid prediction model for prediction, generating a risk probability thermodynamic diagram, and positioning the position of the potential slip crack surface of the slope according to the risk probability thermodynamic diagram. And reliability and accuracy of geotechnical engineering slope image data analysis are improved.

The invention relates to the technical field of medical image analysis, computer vision and deep learning, in particular to a texture perception and multi-scale feature adaptive fusion hip joint osteophyte detection method. According to the method, firstly, a bone texture feature extraction module is used for carrying out feature extraction and enhancement on a hip joint X-ray image, a parallel double-branch structure is adopted, gradient and space structure features are extracted through a Sobel operator and pooling operation, and feature maps are fused; then, the fused features are input into a double-backbone network, the first backbone network extracts local fine textures and long-range dependence features by combining convolution, self-attention and a gating mechanism, and the second backbone network extracts multi-scale semantic features through hierarchical grouping and depth separable convolution; and double-trunk output is dynamically weighted and fused through an adaptive fusion module, features are optimized through a multi-scale semantic fusion module, and finally the position and confidence of osteophyte are output.

The invention discloses a cascade multi-scale convolution and modal enhancement brain tumor segmentation method based on a Mama framework, and relates to the technical field of brain tumors. According to the method, an MCME-UNet model integrated with an MCMS module is provided, through a hierarchical feature extraction mechanism and a multi-modal feature fusion strategy optimized by an MEM module, the segmentation precision is remarkably improved while the calculation efficiency is kept, and particularly, an EEM module innovatively applies Sobel operator to be connected with residual errors, so that the tumor boundary continuity and the segmentation accuracy are synchronously improved; moreover, a novel training normal form of a focus Tversky loss function is introduced, so that the problem of class imbalance is effectively solved, the sensitivity of the model to a small tumor region is enhanced, a smoother segmentation boundary can be generated, and the stability and accuracy of a segmentation result are greatly improved.

The invention provides a gear keyway width measuring method applying machine vision, and relates to the technical field of gear detection. The method comprises the steps of image acquisition, preprocessing, boundary extraction, contour separation, central point calculation, pixel equivalent calculation, symmetric line slope calculation and keyway width calculation. The method specifically comprises the steps of extracting a boundary through a Sobel operator, separating a contour based on a connected domain area, calculating a central point through an arithmetic average method, determining a pixel equivalent in combination with the actual size of an inner hole, achieving symmetric line slope calculation and point classification through iterative fitting and Hough conversion, and finally completing width measurement. According to the invention, a non-contact machine vision technology is adopted to replace traditional contact measurement, so that the problems of large manual operation error, low automation level and insufficient online detection capability are solved, high-precision and automatic measurement of the width of the gear keyway is realized, the measurement efficiency and the result reliability are improved, and the production cost is reduced. The method is suitable for a rapid detection scene in gear batch production.

The invention discloses a wide-angle image acquisition and processing system for underwater target identification, which relates to the technical field of image processing and target detection, and comprises an image acquisition and preprocessing module for acquiring an underwater image, removing initial noise through a preset filter, processing an enhanced grayscale image by adopting image smooth combination, and obtaining an underwater image; the pixel density gradient calculation module is used for calculating a multi-direction pixel density gradient by adopting a horizontal direction convolution kernel and a vertical direction convolution kernel of a Sobel operator according to the enhanced grayscale image, and obtaining a gradient intensity graph through gradient amplitude calculation; according to the wide-angle image acquisition and processing system for underwater target recognition, through multi-level feature fusion and classification positioning, the detection precision and positioning accuracy of an abnormal region in an underwater complex environment are remarkably improved, and efficient technical support is provided for underwater target recognition.

The invention discloses an image classification method based on a Transform neural network, belongs to the field of image classification, relates to a Transform neural network technology, and comprises the following steps: obtaining an input image, and respectively calculating a gradient amplitude component of each pixel in the input image by using a Sobel operator; calculating a gradient variation value of the input image according to the gradient amplitude component, and performing dynamic self-adaptive partitioning on the input image according to the gradient variation value; flattening the blocks into a vector sequence, expanding a linear projection layer weight according to a pre-trained ViT model, and linearly projecting the vector sequence into a d-dimensional embedded vector through a linear projection layer; adding a position code to the embedded vector and generating a feature vector; according to the type of the dynamic adaptive block, processing the feature vector by using a multi-head attention mechanism or a cross-scale cross attention mechanism of Transform, and outputting category probability distribution of the input image; according to the invention, the efficiency and precision of image classification can be balanced.

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 invention discloses a dead pixel detection method and system combining spatial domain analysis and time domain analysis, and belongs to the technical field of image processing. The method comprises the following steps: an airspace analysis stage: extracting edge information through a Sobel operator; carrying out image binaryzation, filling potential holes by using morphological closed operation, and strengthening dead pixel areas; identifying all candidate dead pixel regions by adopting a connected domain labeling algorithm; in the time domain analysis stage, the spatial position and area characteristics of candidate dead pixel areas are combined, the candidate dead pixel area corresponding relation between cross frames is established, the continuity of the same candidate dead pixel area on a time axis is judged, and only two assumptions are met, the time domain analysis is completed. And finally judging the candidate dead pixel regions with the areas and the contrast ratios of the dead pixel regions exceeding the set threshold values as stable dead pixels and outputting the stable dead pixels. By analyzing the stability of the dead pixels in the time sequence, abnormal dead pixels continuously existing in multiple frames can be effectively recognized, and particularly, the method is more stable and accurate when detecting small-area and low-contrast dead pixels.

The invention relates to the technical field of pupil boundary detection, in particular to a multi-algorithm fusion pupil boundary detection method and system, and the method comprises the following steps: obtaining an eye image through an infrared camera, converting the eye image into a grey-scale map, optimizing the grey-scale map, carrying out the median filtering noise reduction, maintaining the edge information, and determining the pupil boundary through a Sobel operator and Hough transform. And the pupil center displacement is analyzed, the motion consistency is evaluated, the pupil position is corrected, and the pupil positioning accuracy is optimized. According to the method, the image preprocessing process is optimized by applying infrared camera shooting and image processing algorithms, accurate detection, median filtering and local contrast enhancement of pupil boundaries are achieved through multi-algorithm fusion, and particularly noise and edge information in the images are optimized; according to the method, the edge detection precision and the image definition are effectively improved, the pupil boundary is accurately mapped through combined use of the Sobel operator and Hough transform, and the misjudgment rate is greatly reduced.

The invention relates to the technical field of medical examination, in particular to a method for calculating the definition of a leukocyte microscopic image based on deep learning, which comprises the following steps: preparing and expanding a data set of the microscopic leukocyte image, outputting a clear image and a blurred image, and enabling each image to have a corresponding definition evaluation index; performing multi-feature fusion definition evaluation on the microscopic leukocyte image based on comprehensive evaluation calculation of a Sobel operator, a Laplacian operator and information entropy; accessing multi-feature fusion definition evaluation into a deep learning model, and building a definition evaluation model; the definition evaluation model is trained, and in the training process, parameters of the definition evaluation model are adjusted by optimizing a loss function; the trained definition evaluation model is deployed in an automatic microscope system, and the problems that definition evaluation is carried out on the whole view, the definition evaluation algorithm is limited, and cell nucleus and cytoplasm characteristics are not distinguished in the prior art are solved.

The invention relates to the technical field of image processing, in particular to a lens edge detection method and system based on image generation, and the method comprises the following steps: collecting a lens image through an industrial camera, carrying out the weighted graying, extracting an edge coordinate through a Laplace operator, constructing an edge pixel coordinate set, and carrying out the image processing; screening an edge validity marking result by combining a gray difference threshold, calculating a phase difference between a gradient direction and light source incidence based on a Sobel operator, extracting pixels in a consistent direction, carrying out spatial clustering, generating a lens edge pixel cluster, and fitting a continuous curve to construct a complete lens edge contour. Noise and reflection interference are restrained through pixel neighborhood gray difference, effective pixels are screened in combination with a gradient direction and light source incident phase relation, edge direction consistency is enhanced, pixels are clustered according to spatial continuity and gradient intensity, contours are continuously fitted based on pixel cluster distribution, and lens edge integrity and geometric consistency under complex illumination are improved.

The invention discloses a corn seed environment adaptability cultivation method based on edge calculation, which comprises the following steps: step 1, acquiring environment factor data of a corn seed sowing area, and preprocessing the environment factor data; 2, extracting the edge contour of the corn seedling by adopting Canny edge detection and a Sobel operator based on the RGB image; 3, constructing a multi-source heterogeneous data sample set according to time and space coordinate alignment; 4, constructing a causal structure diagram by adopting an improved NOTEARS algorithm; 5, carrying out comprehensive sorting to obtain a key factor path; step 6, inputting the multi-dimensional environment factor vector of the target area to the starting node of the key factor path to obtain a predicted phenotypic feature vector; and 7, comparing the actual phenotypic feature vector with the predicted phenotypic feature vector, and counting a prediction error. According to the method, causal modeling and image analysis are fused, and the breeding efficiency and the accuracy of regional suitability judgment are improved.

The invention provides a photovoltaic module shadow recognition and power generation optimization method based on deep learning, and the method comprises the steps: carrying out the shadow edge detection through employing an improved Sobel operator in combination with the illumination intensity and temperature data of the surface of a photovoltaic module, and optimizing the shadow feature extraction through a local weighting correction factor; combining the extracted shadow feature data with electrical parameters, temperature and other information to form a photovoltaic feature data set, training a deep learning model to predict the theoretical maximum output power and actual output power of the photovoltaic module, and optimizing an MPPT control strategy; in addition, the mode of combining static voltage correction and dynamic voltage adjustment is adopted, and it is ensured that the photovoltaic system can still keep the optimal power generation performance under the complex shadow condition. According to the method, the shadow recognition precision is improved, and meanwhile, the power generation efficiency and reliability of a photovoltaic system are effectively improved.

The invention discloses a road underground defect AI intelligent identification method based on a three-dimensional radar image, and the method comprises the steps: setting electromagnetic wave parameters of ground penetrating radar equipment, and carrying out the multi-view image collection; the gray gradient change rate of the C-Scan image is calculated through a Sobel operator, the road state is identified, and a road disease area corresponding to the abnormal color plaque is extracted; constructing an advancing direction road defect type identification model, and identifying the road defect type of the road disease area; constructing a depth direction road defect type identification model, and identifying the road defect type of the road disease area; and according to the road defect type identification accuracy of the model, performing model optimization in combination with a preset accuracy threshold. According to the method, the problems of single parameter setting, high image interference misjudgment rate, insufficient defect identification precision and the like in traditional ground penetrating radar detection are effectively solved, and high-precision, multi-dimensional and intelligent identification of road underground structure diseases is realized.

The invention discloses a part machining surface defect visual detection method and system based on image enhancement and a binarization algorithm, and belongs to the field of machine manufacturing. The problem of how to realize high-precision and low-false-detection defect detection by optimizing an image processing flow without relying on big data training is solved, and meanwhile, the requirements of an industrial scene on real-time performance and deployment cost are met. The method comprises the following steps: inputting a part surface image and a mask image, and carrying out corrosion operation to generate a safety detection area; carrying out bottom cap transformation by adopting longitudinal and transverse morphological nucleuses, and separating scratches and noise; extracting transverse and longitudinal gradients by using a Sobel operator, and generating a multidirectional gradient feature map; performing dynamic threshold segmentation to generate a binary image, and connecting fracture scratches through closed operation; and screening longitudinal and transverse scratch contours according to the length-width ratio, carrying out distance transformation and minimum inscribed circle radius calculation, and judging the region with the radius smaller than a threshold value as a real defect. The method is mainly applied to the defect detection field.

The invention discloses a long-distance binocular camera calibration optimization method based on multistage feature enhancement, and the method comprises the steps: carrying out the processing of a condition that a long-distance calibration plate has a highlight region and is fuzzy in angular points, employing a bilateral filter to suppress the reflection of a highlight mirror surface, reducing the reflection of light, and maintaining the definition of an image; the checkerboard texture is enhanced by adopting a CLAHE algorithm in combination with blocking processing and a contrast gain threshold value; a Sobel operator and a Harris corner response function are fused, gradient direction distribution characteristics of a pixel neighborhood are extracted through the Sobel operator, weighted fusion is carried out on the gradient direction distribution characteristics and the Harris corner response function, and the response intensity and specificity of a corner area are remarkably enhanced. A three-level image preprocessing framework including reflection suppression, contrast enhancement and corner enhancement is constructed, the problem of feature extraction of a traditional method under a complex illumination condition is solved, the corner area detection capability is enhanced, the problems of specular reflection noise, low contrast and corner blur are effectively solved, and the calibration precision is improved.

The embodiment of the invention provides a steel rail corrugation recognition method and system and a storage medium, and belongs to the technical field of railway track detection. The steel rail corrugation identification method comprises the following steps: sending preprocessed steel rail corrugation true value image data into an image segmentation identification model to extract global semantic features and local texture features; performing channel averaging on the output of the intermediate feature extraction stage, generating edge features through a Sobel operator, and fusing the global semantic features, the local texture features and the edge features to obtain a segmented mask graph; and inputting the ROI image segments cut based on the segmentation mask image and the features extracted in the intermediate feature extraction stage and used for target detection into a target detection network together, executing regression calculation based on bounding box overlapping degree optimization loss, and outputting a rail corrugation detection result. According to the scheme of the invention, through multi-scale feature extraction and channel space attention fusion, the positioning precision and segmentation robustness of the rail corrugation region are effectively improved.

The invention discloses a crack sub-pixel precision edge detection method based on an improved Canny operator. The crack sub-pixel precision edge detection method comprises the steps of obtaining a crack image corresponding to a structural body surface crack; the crack image is preprocessed; carrying out gradient calculation on the preprocessed crack image through an improved Sobel operator to obtain a gradient image containing a gradient magnitude and a gradient direction; performing non-maximum suppression on the gradient image, adaptively obtaining an optimal threshold by adopting an improved maximum between-class variance method, judging a crack edge according to the optimal threshold, and further obtaining a coarse edge pixel-level image; processing edge information in the coarse edge pixel-level image by using a Zernike orthogonal moment to obtain coordinates of sub-pixel edge points; and connecting all the sub-pixel edge points, and outputting a crack detection result image. On the basis, the crack edge detection precision is improved to a sub-pixel level.

The invention relates to a lightweight traffic road semantic segmentation method based on multi-scale feature fusion, and belongs to the field of intelligent driving. The method solves the problems that precision and speed are difficult to balance and small target recognition accuracy is low in real-time application of a traditional semantic segmentation method. According to the technical scheme, context information is extracted by adopting a Vision Transform branch, space information is extracted by adopting a CNN branch, a model is optimized through feature fusion and alignment loss during training, and only the CNN branch is used during reasoning; sobel operator edge detection, a multi-scale feature fusion module and a feature distribution simulation module are integrated. The method has the technical effects that high-precision semantic segmentation is realized, the small target recognition capability is remarkably improved, and meanwhile, the real-time processing speed is ensured.

The invention relates to a reconstructed image anti-aliasing processing method based on an adaptive sampling rate, belongs to the technical field of CT (Computed Tomography) reconstructed image processing, and solves the problem that the existing anti-aliasing processing method for a CT reconstructed image is poor in effect. The anti-aliasing processing method for the reconstructed image comprises the following steps: preprocessing the reconstructed image to obtain a to-be-processed image; performing edge extraction on the to-be-processed image based on a preset gradient threshold and a preset Sobel operator, and segmenting the to-be-processed image into a to-be-replaced image region and a reserved image region; determining an adaptive sampling rate based on the horizontal direction edge pixel connection number and the vertical direction edge pixel connection number of all sawtooth vertex pixels in the to-be-processed image; determining a pixel value of each pixel in the to-be-replaced image region based on the adaptive sampling rate and the to-be-processed image, and obtaining a replaced image region; and combining the replaced image area and the reserved image area to obtain an image after anti-aliasing processing. And the anti-aliasing effect of the CT reconstructed image is improved.

The invention belongs to the technical field of building boundary segmentation, and provides a remote sensing image building boundary segmentation method based on a U-net network model, and the method comprises the steps: extracting a gradient magnitude image through a Sobel operator, generating a binary boundary mask, dynamically adjusting the weight of a convolution kernel in combination with a gradient direction, and enhancing the feature extraction capability of a boundary region; boundary artifacts output by cavity convolution are suppressed through a high-frequency residual boundary mask, an artifact area is restored in combination with gradient direction constraint, and original detail features are reserved; calculating complexity scores based on boundary density and tortuosity, adaptively selecting voidage, and balancing global context and local detail capture capability; extracting a high-frequency component through a Laplace operator, marking an artifact point by combining a dynamic threshold value, and repairing an abnormal region by utilizing a neighborhood gradient direction; the method solves the performance bottleneck of a traditional method in a boundary fuzzy, artifact interference and receptive field fixed scene, and is suitable for remote sensing image analysis tasks such as urban planning and disaster assessment.

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 discloses an MRI (Magnetic Resonance Imaging) accelerated reconstruction method and system based on a K-space enhanced reversible diffusion model, and the method comprises the steps: firstly collecting a brain MRI scanning image, carrying out the undersampling processing, normalizing a gray value, and randomly cutting an image block with a specified size; inputting the image blocks into an end-to-end adaptive K-space enhanced reversible diffusion model to generate a reconstructed image; a progressive mask strategy and cross-domain feature injection are adopted, image edges and detail textures are recovered in a targeted mode, adaptive optimization of different anatomical structures is achieved through a learnable mechanism, and reconstruction quality and robustness are remarkably improved. According to the method, the performance is outstanding on the core indexes such as the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM), the precise recovery capability of the image edge structure is verified through Canny and Sobel operators, key breakthrough is achieved on the reconstruction quality, the training efficiency and the clinical applicability, and a solution with theoretical innovation and engineering application value is provided for the MRI accelerated imaging technology.

The invention discloses an MFSE-UNet-based rail extraction method in unmanned aerial vehicle aerial images, relates to the technical field of unmanned aerial vehicle remote sensing image semantic segmentation, and solves the problems of fuzzy edges, poor multi-scale adaptability and weak generalization ability in complex scenes of rail extraction in unmanned aerial vehicle aerial images in the prior art. According to the method, a multi-scale feature fusion and low-resolution self-attention module is provided, multi-dimensional features are efficiently extracted, key information is enhanced, and segmentation definition and structural integrity are improved; a bidirectional feature fusion module is designed, detail and semantic information of an encoder, a decoder and adjacent layers are subjected to weighted fusion, semantic differences are bridged, and the multi-scale detection performance is improved; a learnable Sobel + EFR edge guiding mechanism is innovated, learnable characteristics of a Sobel operator and an EFR module are fused, complex scene edge characteristics are dynamically adapted, and track boundary positioning precision and segmentation definition are improved. According to the method, the rail extraction precision in a complex scene is effectively improved, and technical support is provided for intelligent detection and maintenance of railway tracks.

The invention discloses a method and a system for reconstructing a cavity shielded by a shallow reinforcing mesh. The method comprises the following steps: constructing a layered medium scene containing the shallow reinforcing mesh and the cavity; acquiring image data information with different shielding states and preprocessing the image data information to construct a reconstruction data set; constructing a reconstruction initial model of the cavity shielded by the shallow reinforcing mesh, and training to obtain a reconstruction model of the cavity shielded by the shallow reinforcing mesh; and adopting the obtained reconstruction model of the cavity shielded by the shallow reinforcing mesh to reconstruct the actual cavity shielded by the shallow reinforcing mesh. According to the method, multiple types of training data are acquired through a constructed layered medium scene containing a shallow reinforcing mesh and a cavity, and a reconstruction model of the cavity shielded by the shallow reinforcing mesh, which comprises a U-shaped network, a Sobel operator, an attention mechanism, a residual structure, an average mechanism and a jump connection scheme, is trained; therefore, the reconstruction of the cavity under the shielding of the shallow reinforcing mesh can be realized, the reliability is higher, the accuracy is better, and the reconstruction efficiency is higher.

The invention discloses an intelligent segmentation and feature analysis method for a medical image, and relates to the technical field of image analysis, and the method comprises the steps: reading a multi-modal medical image, and carrying out the preprocessing and size standardization; performing medical image segmentation based on a U-Net structure, performing edge detection by adopting a Sobel operator, and optimizing the edge of the segmented image in combination with morphological operation to obtain an edge-optimized focus segmentation map; feature point matching is carried out on the multi-modal focus segmentation image based on self-organizing mapping, and non-rigid registration is carried out through TPS transformation based on matched feature points; carrying out foreground region enhancement operation on the registered image, introducing a channel attention module based on a DenseNet-201 model to carry out feature extraction on the registered image, and optimizing features by using a whale optimization algorithm to generate an optimal feature subset; and carrying out illness state classification on the feature subsets by an adaptive neural fuzzy inference system optimized based on a genetic algorithm.

The invention provides an image multi-feature matching, tracking and positioning method, system and device for a rear axle assembly line and a medium, and belongs to the technical field of augmented reality processing of automobile rear axle assembly lines, and the method specifically comprises the steps: carrying out the image collection of a target at the automobile rear axle assembly line; calculating gradients of the image in the horizontal and vertical directions by using a Sobel operator to obtain gradient amplitudes and directions; matching the video frame with an offline collected template, and determining the initial position of the target in the video frame according to the similarity of the gradient descriptors; matching the ORB feature vector of the current image with an ORB feature vector of a pre-stored template image, and when the Hamming distance is smaller than a set threshold value, determining that the current image is a matched feature point pair; and the position of the target object is updated by minimizing the error of the feature points in the adjacent video frames. According to the method, the target can be accurately identified and tracked, and high-precision target position information is provided for quality detection, assembly guidance, process monitoring and other links in industrial automatic production.

The invention relates to an automatic duck egg grading and sorting system and method based on image processing, and the system comprises a multispectral imaging module which comprises a visible light CCD camera and a near-infrared hyperspectral camera, and the spectral range is 900-1700 nm; the mechanical sensing module is composed of an array type piezoelectric sensor and a weighing sensor; and the embedded processing unit carries an FPGA (Field Programmable Gate Array) and an ARM dual-core processor. According to the method, the comprehensive accuracy rate reaches 98.7% through multi-modal data fusion, the accuracy rate is improved to 99.2% through near-infrared differential imaging and an improved Sobel operator, the height error of the air chamber is detected to be + / -0.15 mm through transmission-type NIR, the mAP on an EgNet2023 test set by adopting a deep learning model reaches 94.3%, the detection precision is remarkably improved, a DenseNet-GRU hybrid neural network architecture is proposed through a grading model, and the detection accuracy is improved. The time sequence spectral features and the spatial texture features are fused, a multi-dimensional quality database of the first duck eggs is established, the sorting speed is increased to 1500 duck eggs per hour, the comprehensive accuracy rate reaches 98.7%, the breakage rate is reduced to 0.05% or below, and the energy consumption is reduced by 40%.