562 results about "Kernel (image processing)" patented technology

The invention discloses a glass lens surface scratch detection method and system, relates to the technical field of precision optical detection, and aims to solve the problems of scratch false detection, leak detection and poor algorithm adaptability caused by interference fringes, noise coupling and poor form adaptability in a high-reflection / complex coating process scene in the prior art. According to the scheme, an orthogonal polarization state composite light field is generated based on a multi-angle polarization light source array and a near-infrared compensation light source, and candidate regions are extracted through dynamic threshold segmentation and a direction gradient tensor matrix; gaussian pyramid multi-scale feature fusion and refraction angle consistency verification are utilized to eliminate artifact interference; constructing a direction constraint convolution kernel group to decompose scratches and background textures, and dynamically allocating computing resources in combination with a cascade network; feeding back closed-loop calibration light source wavelength and convolution kernel parameters in real time through coating parameters; according to the method, the precision and robustness of high-reflectivity surface scratch detection are remarkably improved, and meanwhile, the requirements for high-resolution image processing and real-time performance in a high-speed production line are balanced.

The invention relates to the technical field of image processing, in particular to a dual-branch coding desert segmentation model network structure based on structure state space duality, which adopts multi-dimensional dynamic convolution to replace traditional convolution in the initial stage of an encoder, introduces a mamba2 module based on the structure state space duality into the backbone design of the encoder, and improves the robustness of the encoder. The efficiency and adaptability of the model are remarkably improved, a double-branch parallel design is adopted, one branch uses cavity convolution to extract multi-scale context information, the other branch reinforces feature expression through a mamba2 module, the model is connected in series with a space attention module and a channel attention module between an encoder and a decoder, and the algorithm is more accurate. The method has the advantages that the method is simple and easy to implement, interference of irrelevant information on segmentation results is suppressed, a deformable large kernel attention module is introduced to the tail end of a decoder, and global and local modeling capability of the model in processing desert complex boundary regions is effectively improved by combining flexibility of deformable convolution and global receptive field characteristics of large kernel convolution.

The invention relates to the technical field of image processing, in particular to a high-voltage electrical equipment surface defect identification method based on image processing. The method comprises the following steps: analyzing the gradient of pixel points in a to-be-analyzed image of a to-be-detected area on the surface of the high-voltage electrical equipment to obtain the weight of each pixel point; weighting the gray value of each pixel point in the to-be-analyzed image subjected to Laplacian filtering by using the weight of each pixel point of the to-be-analyzed image to obtain a first feature map; calculating a local standard deviation of each pixel point in the to-be-analyzed image so as to obtain a first parameter and a second parameter; constructing two Gaussian kernels based on the first parameter and the second parameter to filter the to-be-analyzed image to obtain a second feature map; fusing the first feature map and the second feature map of the to-be-analyzed image to obtain a defect saliency map of the to-be-analyzed image; and recognizing a surface defect area of the high-voltage electrical equipment based on the defect saliency map of each to-be-analyzed image. According to the invention, the accuracy of high-voltage electrical equipment surface defect identification can be improved.

The invention belongs to the technical field of image processing, and particularly relates to a titanium cylinder inner wall defect detection method and system.The method comprises the steps that a titanium cylinder inner wall image is collected, and a highlight area and boundary pixel points thereof are extracted through threshold segmentation and corrosion operation; calculating a scale adjustment factor by combining a highlight area proportion and a boundary gradient amplitude, dynamically scaling the reference kernel according to the scale adjustment factor, and determining a standard deviation of a multi-scale Gaussian kernel; constructing a Gaussian kernel function of each scale, and calculating the sum of difference values of the titanium cylinder inner wall image and convolution results of all scales in a logarithm domain to obtain an enhanced titanium cylinder inner wall image; and performing threshold segmentation on the enhanced titanium cylinder inner wall image to identify a defect area. According to the method, the filtering scale is adjusted in a self-adaptive mode, mirror reflection interference is effectively restrained, halo artifacts are eliminated, and tiny defects covered by highlight are accurately recognized.

The invention relates to the technical field of medical image processing, and discloses a human body abdominal fat analysis method based on a medical image, which comprises the following steps: acquiring multi-modal medical image data such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and ultrasonic images, constructing a fusion database through multi-scale affine transformation alignment, de-noising by using a model based on a residual self-encoder, and enhancing a boundary in combination with Canny edge detection. A segmentation model is constructed based on an improved U-Net architecture, multi-modal features are fused, and a dynamic convolution kernel and a channel attention mechanism are used for optimization. A quantitative analysis model is established by adopting a multi-task joint learning framework, gradient conflicts are solved, and a lightweight sub-network is searched and generated. Carrying out uncertainty modeling on the segmentation model, and carrying out active learning annotation to optimize the performance. A multi-granularity feature fusion framework is designed, anatomical priori knowledge is embedded to construct an association graph, a structured analysis report is generated, abdominal fat can be accurately analyzed, and diagnosis and treatment of obesity-related diseases are assisted.

The invention discloses a morphological gradient region replacement method based on SAM semantic segmentation and user guidance, and relates to the technical field of computer vision and image processing, and the method comprises the steps: carrying out the semantic segmentation of a to-be-processed image through an SAM model, extracting a multi-level semantic feature, carrying out the standardization and dimension reduction, extracting a causal factor based on independent component analysis, and carrying out the user guidance. A directed causal factor association graph is generated through Granger causal relationship test, and a causal attribution probability graph is generated through reverse mapping; constructing a structured causal graph, and generating a causal mask through a graph convolutional network; encoding the original interaction signal into a guide thermodynamic diagram; constructing a diffusion equation, forming a gradual change control equation by dynamically fusing and guiding the intensity distribution of the thermodynamic diagram and an image semantic diffusion item, and iteratively solving the gradual change control equation; generating an anisotropic morphological operation kernel according to the geometric curvature characteristics of each region in the replacement mask; and fusing the optimized replacement mask with the target content based on a gradient domain optimization algorithm to generate a gradient replacement image.

The invention discloses a lightweight super-resolution system and method of an adaptive wavelet attention network, and belongs to the technical field of image processing. The system is composed of a multistage wavelet attention module, a dynamic convolution kernel generation unit, a convolutional neural network and an output unit. The method comprises the following steps of: extracting features of a low-resolution image and performing Haar wavelet decomposition; calculating a cross-scale attention weight on each high-frequency sub-band and carrying out weighted fusion to highlight details; adaptively generating a dynamic convolution kernel of a Haar wavelet basis kernel weighted combination based on the fused features, and performing directional convolution enhancement on the features; high-resolution image reconstruction is realized through a lightweight residual network and pixel rearrangement; and during training, pixel domain mean square error and wavelet coefficient compensation loss joint optimization is adopted. The parameter quantity of the system model is smaller than 450KB, a 1080p video super-resolution task can be processed on mobile equipment in real time, and the texture recovery performance is improved by about 1.2 dB compared with that of an existing lightweight model.

The invention discloses a human body infrared image small target detection method based on an improved FGLCM feature, and relates to the technical field of image processing and target detection, and the method comprises the following steps: building a boundary singularity auditing baseline under a unified time baseline, carrying out the multi-scale energy mapping of a curved surface fitting residual error of a human body infrared image, and generating a traction residual error distribution diagram; and constructing a reflection pseudo peak discriminator based on the traction residual distribution diagram, and extracting a pseudo peak kernel position by combining polarization sensitivity estimation and view angle transformation consistency constraint. According to the method, a closed-loop self-adaptive regulation and control mechanism is constructed through residual distribution auditing, pseudo peak identification, gradient registration, differential entropy enhancement and time sequence threshold adjustment, fitting abnormity and false highlight spots in the infrared image are inhibited, the accuracy and stability of small target detection are improved, and the method is suitable for a complex photo-thermal environment.

The invention discloses a construction quality defect detection method and system based on image processing, and belongs to the technical field of material surface defect nondestructive testing, and the method comprises the steps: obtaining original image data of a construction area and preset building information model data, generating a material feature map and a reflection feature map, and carrying out the denoising and illumination compensation of the original image data, and acquiring enhanced image data, delimiting a key detection area to generate a dynamic mask, executing defect identification by adopting a preset high-precision convolution kernel to generate an initial defect distribution diagram, and outputting a final defect detection result by combining defect correlation correction parameters generated by matching the initial defect distribution diagram with the defect evolution database. According to the method, a double-flow convolutional network is adopted to decouple material attributes and illumination interference, a dynamic mask is combined to focus a high-risk area, and a defect evolution database is integrated to realize mechanical simulation analysis, so that the defects in the germination period can be accurately captured in a complex construction environment, the extension trend can be predicted, and the engineering safety management and control level can be remarkably improved.

The invention discloses an image deblurring method based on a rotation perception multi-dimensional attention and blurring sensitivity self-adaptive distribution mechanism, aiming at the defects of the prior art in the aspects of complex blurring processing and image reality sense improvement, and belongs to the technical field of image processing, and the method comprises the following steps: preprocessing a collected picture to obtain a training set; training an image deblurring model by using the training set; and performing image deblurring by using the trained image deblurring model. According to the invention, by designing a plurality of innovative modules and combining fuzzy degree adaptive selection, kernel estimation and dynamic weight distribution driven by a physical model, a multi-dimensional rotation perception attention mechanism, spectrum reconstruction anti-noise deblurring and detail enhancement of perception loss optimization, the image deblurring effect and processing efficiency are effectively improved; the method is especially suitable for processing blurred images with high complexity and high resolution.

The invention discloses a super-resolution image reconstruction method based on a multi-scale large-kernel convolution double-residual neural network, which is suitable for the field of image processing, and comprises the following steps: cutting a data set, inputting a cut original low-resolution image into a preprocessing module, carrying out image normalization and data enhancement operation, and carrying out image reconstruction; generating a preprocessed low-resolution image; the preprocessed low-resolution images form a distorted image block data set, and a training set, a verification set and a test set are formed; according to an existing distorted image block data set, a super-resolution image reconstruction method based on a multi-scale large-kernel convolution double-residual neural network is constructed; and inputting the data set into the constructed multi-scale large-kernel convolution double-residual neural network to extract semantic features, and amplifying a feature map by using an up-sampling module of the model to generate a super-resolution image. According to the method, a multi-scale large-kernel convolution and double-residual structure is introduced, a visual attention mechanism is used in the neural network, the extracted features better conform to human visual perception features, and super-resolution image reconstruction is more accurate.

The invention discloses an image video super-resolution enhancement method based on a degradation generative adversarial network, and relates to the field of image processing, and the method comprises the steps: carrying out the image collection and preprocessing; building and training a super-resolution enhancement model; and carrying out super-resolution enhancement on the image based on the degradation generative adversarial network model. According to the method, an image content self-adaptive dynamic degradation kernel generation mechanism is adopted, the degradation process of the image under different equipment and organization structures is truly simulated, a dynamic up-sampling and residual error correction network guided by the degradation kernel is adopted, the detail reduction capability and the structure fidelity of the super-resolution image are remarkably improved, and the super-resolution image quality is improved. The image texture authenticity and key organization density consistency are effectively enhanced, the balance of training games between a generator and a discriminator is realized, and the model stability and convergence quality are improved.

The invention discloses a seal area character recognition method and device and a nonvolatile storage medium. The method comprises the following steps: determining a candidate seal image area in an image according to color information of pixel points in the image; point-by-point sliding convolution processing is carried out on the candidate seal image area through a multi-scale annular convolution kernel group, so that a target seal image area is determined in the candidate seal image area, and the multi-scale annular convolution kernel group comprises a plurality of convolution kernels which are of concentric ring structures and have different radius lengths; mapping the target seal image area into a rectangular expanded image, and identifying and extracting a character image to be identified in the rectangular expanded image; and performing identification processing on the character image to be identified to obtain a seal text corresponding to the target seal image area. The technical problem that the text image processing efficiency is low due to the fact that the text information of the seal area cannot be effectively recognized in the related technology is solved.

The invention relates to a robust unmanned aerial vehicle detection method based on dynamic feature fusion and context attention, and belongs to the technical field of image processing. Aiming at the problems of small target feature loss, semantic gap, background noise interference and the like caused by a fixed convolution kernel scale, one-way feature fusion and a static attention mechanism in an existing unmanned aerial vehicle aerial image target detection method, the method comprises the following steps: constructing a detection model comprising a backbone network, a neck network and a detection head network; a feature rearrangement and extraction module is designed in the backbone network to enhance feature learning, an enhanced double-flow feature fusion pyramid is designed in the neck network to optimize multi-scale feature fusion, and a dynamic multi-scale context attention mechanism is designed in the detection head network to suppress irrelevant background noise. The method effectively improves the accuracy and robustness of small target detection, and achieves a clearer and more stable detection effect in a complex environment.

The invention relates to the technical field of medical image processing and computer vision, in particular to a parasitic ovum microscopic image detection method and system based on polymorphic prior, and the method comprises the steps: obtaining a to-be-detected microscopic image, and carrying out the feature extraction of the to-be-detected microscopic image through a convolutional neural network, and obtaining an initial feature map; constructing a polymorphic convolution kernel library based on preset biological morphological characteristics of the parasitic ova; performing deep convolution and feature fusion operation on the initial feature map by using a polymorphic convolution kernel library to generate a space attention map; performing feature enhancement processing on the initial feature map by using the spatial attention map to obtain an enhanced feature map; and performing bounding box regression and category prediction on the enhanced feature map to obtain a parasitic ovum detection result. According to the method, morphological priori and attention mechanisms are introduced, so that the problems of egg form similarity, background interference and the like are solved, accurate and robust automatic detection is realized, and the clinical diagnosis efficiency is remarkably improved.

The invention belongs to the technical field of image processing and deep learning, and particularly relates to an image defogging method based on dynamic wavelet prior and double-domain learning. Aiming at the requirements of all-weather clear imaging in the fields of intelligent traffic systems, safety monitoring and the like, and in order to overcome the defect that a static convolution kernel adopted by a traditional defogging method is difficult to adapt to different haze degradation, the invention provides a method for dynamically generating a convolution kernel by using haze priori contained in a multi-scale wavelet LL sub-band; and an efficient, robust and accurate image defogging model is constructed. According to the invention, based on a multi-scale U-shaped coding-decoding architecture, a dynamic wavelet depth separable convolution module DyWConv is embedded in front of each level of a coder to realize content adaptive feature extraction, and a double-domain feature learning module SPAFormer Block cooperatively utilizing Fourier domain global modulation and wavelet domain multi-scale decomposition is designed. And double-domain features are fully fused through an adaptive gating fusion mechanism, and finally a clear image is reconstructed and output step by step. According to the method, a method for explicitly encoding frequency domain degradation prior into dynamic convolution kernel parameters is innovatively provided, the complementary advantages of Fourier transform and wavelet transform are cooperatively utilized, spatial non-uniform haze can be effectively removed, image details can be recovered, leading performance is achieved in a synthetic data set and a real scene, and the method has a wide application prospect.

The invention relates to the technical field of image processing, in particular to a picking ingot coating defect online detection method and system based on machine vision, and the method comprises the steps: obtaining a multi-view image, carrying out the feature extraction of an image data stream after preprocessing, and carrying out the matching or clustering with a preset defect dictionary; the method comprises the following steps: preliminarily identifying a potential defect area, triggering refined multi-angle image acquisition, applying a multi-task CNN model fused with a polarization perception convolution kernel, jointly optimizing pixel-level segmentation loss and boundary prediction loss, outputting pixel-level semantic segmentation and an accurate boundary of the defect area, and calculating a reconstruction error to carry out defect classification. And finally, the system also has the functions of defect root cause analysis and process flow adjustment, so that defect tracing and production optimization are realized. According to the system, advanced visual technology, deep learning and multi-modal information fusion are integrated, and online, accurate and automatic detection of picking ingot coating defects is realized.

The invention discloses a motion blur removing method based on dynamic image interpolation, and the method comprises the steps: firstly correcting a blur track according to the dynamic change of time and direction; then, considering the stage speed change of the target, and carrying out weight updating on the initialized fuzzy kernel; then, under the condition that the target speed cannot be estimated, feature points are detected by using an SIFT algorithm, and the feature points are tracked by using an L-K optical flow method, so that a more accurate dynamic fuzzy kernel is constructed, and then deconvolution operation is performed by combining a neighbor interpolation technology, so that the definition of an original image is recovered; in order to further optimize the image quality, three image enhancement technologies, including histogram equalization, contrast enhancement and image sharpening, are combined to cope with visual interference in different environments, enhance the contrast and detail expressive force of the image, and further improve the image processing effect. Therefore, the unmanned aerial vehicle identification system can capture and identify the hostile target more accurately.

The invention discloses an unmanned aerial vehicle low-altitude photography image enhancement method and system. The method comprises the steps of data preparation, motion joint deblurring optimization, multi-source data registration, scene adaptive enhancement and image comprehensive enhancement. The invention relates to the technical field of unmanned aerial vehicle image enhancement, and realizes high-precision deblurring, precise registration and adaptive visual enhancement of an unmanned aerial vehicle image by fusing inertial measurement unit guided blurring kernel prediction, cross-modal graph structure matching and elastic transformation optimization and an enhancement strategy dynamic adjustment mechanism based on scene analysis. The image definition, the structure consistency and the scene readability are remarkably improved, and the method is suitable for image processing tasks in a complex low-altitude flight environment.

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 relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The invention relates to the technical field of machine vision and food quality detection, in particular to a food defect real-time detection method and system based on image processing. The method specifically comprises the following steps: integrating multi-modal data, and adaptively adjusting a weight coefficient to improve data quality; multi-stage noise reduction is carried out, key frames are extracted, and a key frame verification mechanism is enhanced through a 2FA control variable; constructing a YOLO-BioNet model, and optimizing the detection efficiency and precision in a complex scene through dynamic convolution kernel allocation and a spiking neural network; integrating Kalman filtering, LSTM (Long Short Term Memory) time sequence modeling and a deep reinforcement learning model, analyzing a food surface change track and predicting a defect state; real-time data and historical information are fused, and a dynamic threshold value is calculated through a Bayesian network; performing linkage response between an automatic sorting instruction and a production line; a distributed storage and graph neural network optimization model is adopted, and defect tracing is supported. The method has remarkable advantages in the aspects of real-time performance, detection accuracy and self-adaptive capability.

The invention discloses a three-dimensional blood vessel image segmentation method and system. Belongs to the technical field of medical image processing and particularly relates to the technical field of three-dimensional blood vessel image segmentation. The method solves the following problems existing in a blood vessel segmentation task in an existing method: small blood vessel features are difficult to accurately extract in a CTA image which is low in contrast and contains noise and artifacts; global context modeling is difficult to consider and local correlation is difficult to guarantee, so that long-distance dependent modeling is insufficient or a local structure is fractured; limited by a fixed geometrical shape of a traditional convolution kernel, the traditional convolution kernel is difficult to adapt to deformation characteristics of a complex topological structure of a blood vessel, resulting in discontinuous segmentation or fuzzy boundary of a branch region. Channel dynamic grouping and energy-driven attention generation are achieved through a grouping self-adaptive attention module, and self-adaptive modeling of a blood vessel complex branch structure and a geometrical shape is achieved through a multi-scale space structure aggregation module in combination with a strip-shaped deformable convolution and cross-scale guiding mechanism.

The invention discloses an underwater image enhancement method based on relation-driven state space modeling, belongs to the technical field of computer vision and image processing, and aims to solve the problems of color distortion, detail blurring and the like of an underwater image caused by water attenuation and scattering. Carrying out structure perception enhancement modeling; and image reconstruction and decoding output. Wherein the structure sensing module extracts spatial continuity information through an offset generation network, adaptively rearranges scanning paths, preferentially focuses on a semantic rich region and executes dynamic state propagation, so that the accuracy and interpretability of global modeling are improved; and meanwhile, a local convolution kernel is dynamically generated according to global channel statistical characteristics by inputting a dependent convolution branch, so that the adaptability to a background region is enhanced. In order to further improve the feature fusion effect, a cross-feature fusion bridge module is provided, multi-level features are guided and fused through bidirectional attention of a structural path and a semantic path, and detail information and context semantics are effectively integrated.

The invention relates to the technical field of remote sensing image processing, in particular to an intelligent landslide identification method based on deep learning of a multi-source remote sensing image, which comprises the following steps of: firstly, acquiring high-resolution optical and radar data of the multi-source remote sensing image by adopting a spatio-temporal data expansion method; the problem that an existing landslide identification method is insufficient in data type and data quantity is effectively solved, more comprehensive and accurate landslide data are provided, the generalization ability of the model is enhanced, then a deep learning model with semi-supervised learning and adaptive convolution multi-scale feature fusion is adopted, the size of a convolution kernel can be dynamically adjusted, and the robustness of the landslide identification method is improved. Landslide features of different scales are accurately captured, understanding of landslide morphology and structure is enhanced, robustness of the model in a complex terrain is improved, finally, a landslide boundary and morphology are corrected through a positioning correction method, inaccurate or discontinuous boundaries caused by prediction errors are eliminated, and stability of the model in a complex environment is further improved.

The invention relates to the technical field of image processing, and particularly discloses a mobile terminal real-time image style migration method based on a lightweight convolutional network, and the method comprises the steps: obtaining an original image inputted by a user, and converting the original image into standardized image tensor data through a pixel normalization and dynamic partitioning strategy; performing feature coding on the image tensor data by using a lightweight convolutional neural network, and extracting a multi-scale feature map layer by layer through a deep separable convolutional layer and a bottleneck layer structure; based on dual-channel feature coding, dynamically performing weighted fusion of style features and content features through multi-scale convolution kernel parallel deployment and learnable parameters, and generating a fused feature map; according to the method, the pixel normalization and dynamic partitioning strategy is introduced, the image partitioning size is adaptively adjusted, the calculation efficiency and the feature retention degree are balanced, the calculation burden is reduced while the high image quality is ensured, and the model calculation amount is reduced by adopting the lightweight convolutional neural network and the depth separable convolution in combination with the bottleneck layer structure.

The invention provides a thoracic surgery pathological section image super-resolution reconstruction method, and relates to the technical field of image processing, and the method specifically comprises the steps: collecting a high-resolution thoracic surgery pathological section image, and generating a low-resolution image; constructing a local directional dot matrix tensor and a response adjustment factor based on the low-resolution image, calculating an angle response aggregation kernel tensor, and performing segmented mapping in combination with a nonlinear response reconstruction function to complete directional fusion normalization so as to obtain a directional aggregation enhanced image; a robust local reference value and local texture energy are calculated in a neighborhood range, a structural strength weight is generated in combination with a variance balance parameter and a contrast balance parameter, a difference amplification item is constructed, image fusion with direction aggregation is enhanced through a residual mode, and a structure guide enhanced image is obtained; carrying out weighted mean and variance calculation to generate a difference regulation factor, and combining convolution smoothing and nonlinear amplitude limiting processing to obtain a structure contrast mapping image; and finally, inputting the image into an image reconstruction module to realize resolution improvement.

The invention provides a stone gradation automatic detection method based on image processing, belongs to the technical field of image processing, and designs a two-dimensional convolution kernel capable of enhancing features according to the features that small-particle stones are bright in center, dark in edge and approximate to a circle by acquiring a gray image and a background image of a stone field through a video stream. Convolution operation is carried out on the image to improve the contrast ratio of the stone and the background, then an accurate binarized image is obtained through double-threshold segmentation and background difference processing, in order to further separate the adhered particles, the particle center is positioned by adopting distance transformation, and effective segmentation is carried out by combining a watershed algorithm. And according to the extracted particle contour, geometric parameters are calculated and the mass is estimated, so that a stone grading curve for evaluating the filling quality is automatically generated. According to the invention, rapid and non-contact automatic analysis of rockfill material grading is realized.

The invention belongs to the field of image processing, discloses an improved remote sensing image tiny target detection method based on a large kernel convolution selection protocol and a loss function, and is used for solving the problem that a traditional remote sensing tiny target detection method is difficult to realize robustness detection under the conditions of tiny target size, rare features, low signal-to-noise ratio and the like. The model comprises a feature extraction network based on large kernel convolution, re-parameterization and feature fusion and a detection head; according to the method, the feature extraction network is designed by using large kernel convolution, re-parameterization and feature fusion, the target characterization capability of the network is enhanced, and the model reasoning speed is not reduced while the precision is improved; meanwhile, a novel segment loss function is designed, weight factors are dynamically adjusted in the training process, weight distribution of small targets is optimized according to the number of times of training, and therefore the model is guided to pay more attention to the small targets; the method can significantly improve the precision of remote sensing image tiny target detection, reduce the model parameter quantity, and maintain the reasoning speed.

The present disclosure relates to a brain image processing method and apparatus. One specific implementation of the method comprises: using a spatiotemporal feature extraction unit to perform feature extraction on a brain magnetic resonance image of a first modality to obtain spatiotemporal features of a brain region; using a specific convolutional layer to perform feature extraction on a brain magnetic resonance image of a second modality to obtain structural features of the brain region, the specific convolutional layer having a convolution kernel of a set size and a convolution kernel stride; and performing feature fusion on the spatiotemporal features of the brain region and the structural features of the brain region to obtain fused features, and performing feature classification on the basis of the fused features to obtain an image processing result.