614 results about "Dual domain" patented technology

The invention relates to the technical field of intelligent manufacturing and production management, in particular to a self-adaptive production scheduling system based on artificial intelligence, which comprises a data acquisition and reference construction module for analyzing process data to construct a directed acyclic graph representing a non-interference state as a reference map; the theoretical disturbance simulation module is used for converting the interference rule into a graph change instruction, generating a theoretical damaged state graph and obtaining a theoretical difference feature vector; the theoretical difference feature vector comprises, but is not limited to, a vector form obtained after a difference matrix is expanded according to rows or columns in terms of mathematical representation; the real deviation extraction module is used for collecting real-time state data to construct a real-time operation state diagram and calculating a real difference feature vector; a double-domain coupling decision module; an adaptive scheduling execution module; according to the method, the causal relationship is verified by comparing the form of theoretical deduction and actual observation, non-systematic noise is effectively filtered, and accurate response to real faults is realized while the stability of the production rhythm is maintained.

The invention discloses an industrial part defect sample accurate generation method based on a conditional diffusion model, and belongs to the field of image processing and artificial intelligence. The method forms a closed-loop cooperative system by constructing four deep coupling modules of physical constraint noise scheduling, multi-scale feature coupling, double-domain feedback optimization and adaptive weight adjustment; a defect physical forming mechanism is converted into a dynamic noise scheduling strategy, deep interaction between condition information and a feature map is established at multiple levels of a diffusion network, quality closed-loop optimization is achieved through dual evaluation of a pixel domain and a frequency domain, and training weight is dynamically adjusted according to defect scarcity. And multi-scale accurate control is realized, a quality guarantee closed loop is established, the problem of data imbalance is effectively solved, and the performance of an industrial defect detection model is remarkably improved.

The invention discloses a complex scene traffic sign detection method and system based on dynamic frequency band focusing and double-domain attention screening. The method comprises the following steps: carrying out data preprocessing and data enhancement on a collected road traffic sign image; a CADPCM module and a CHAttention cross coordination attention mechanism are used to construct a CACHNet backbone network; designing a DWMSN neck network, and establishing a dynamic fusion mechanism of multi-scale features; a CACHNet and a DWMSN neck network are used to construct a CDWN model, and a traffic sign enhancement data set is used to train the CDWN model to determine the optimal model weight thereof. Compared with the prior art, the method has the advantages that the average detection precision is improved by 3.3% while the light weight of the model is maintained by constructing a three-level framework of the feature extraction unit, the attention feature expression enhancement and the dynamic feature fusion, the complex scenes such as illumination variation and shielding can be effectively dealt with, and the method is suitable for popularization and application. And high-precision traffic sign detection support is provided for a vehicle-mounted intelligent auxiliary driving system.

The invention discloses a power transmission line state simulation and prediction method based on digital twinning, and the method comprises the following steps: accessing meteorological data, electrical data, space and asset data and historical state data, carrying out the preprocessing, and generating a multi-source spatial-temporal feature sequence set; constructing a double-domain cross-coupling time convolution twinborn model, and obtaining corresponding feature representation through a quick response path and a slow response path; physical consistency state representation is generated through a physical constraint cross gating layer; outputting a prediction result set through a simulation engine; online observation data are obtained, and the model is corrected based on virtual and real residual errors; and generating a current prediction result set by using the corrected model, and converting the current prediction result set into a scheduling and operation and maintenance strategy set. According to the method, the double-domain cross-coupling time convolution twin model and a virtual-real closed-loop correction mechanism are adopted, multi-scale state simulation prediction of the power transmission line is achieved, and the method has the advantages of being high in precision, robustness and performability.

The invention discloses a multispectral image fusion model based on a double-branch self-attention-generative adversarial network and a fusion method thereof, and the model sequentially comprises an input preprocessing module which is used for carrying out the same-amplitude mapping, normalization and overlapping block embedding of visible light and infrared original images, and generating a to-be-fused feature block; the double-branch encoder module captures a cross-modal long-distance dependence and overall brightness structure through multi-head deep convolution transpose attention (MDTA) and a gated deep convolution feedforward network (GDFN), and extracts high-frequency texture and edge information by using a reversible residual gating layer and detail DetailNode iteration; the fusion decoder module is used for carrying out multi-level self-attention-convolution reconstruction on the two paths of features after channel dimension splicing, and outputting a single-frame high-resolution fusion image; and the double-domain discriminator module comprises a visible light domain discriminator and an infrared domain discriminator which are respectively used for carrying out adversarial evaluation on the fused image and the corresponding modal truth value image so as to improve the detail authenticity and the thermal target contrast ratio of the fusion result. The technical problems that an existing infrared-visible light image fusion method is insufficient in detail reservation, unbalanced in brightness and contrast, poor in unsupervised training stability and the like are solved, the method can be deployed on embedded platforms needing real-time and multi-modal information enhancement such as night monitoring, unmanned driving and edge security and protection, and high-contrast and high-information-amount fusion imaging is achieved.

The invention discloses a pole-mounted transformer state evaluation and fault early warning method fusing multi-source data, and relates to the technical field of intelligent power grid monitoring, and the method comprises the steps: executing dual state judgment according to a multi-source fusion feature set, extracting a statistical deviation degree of operation fluctuation abnormity, and calculating a physical deviation degree of multi-physical field coupling imbalance; the statistical deviation degree and the physical deviation degree are fused in a double-domain cooperation mode, and a real-time health index is obtained; the degradation rate and multi-scale energy characteristics of the real-time health index are extracted, a potential degradation aggregation mode is identified by referring to a historical slow-varying baseline, and a hidden danger trend factor is generated; performing composite attribution matching on the hidden danger trend factor and the multi-source fusion feature set, executing grading risk criteria according to a multi-physical field anomaly mechanism chain, and outputting an early warning grade and an early warning reason; according to the method, diagnosis is carried out by embedding a multi-physical field anomaly mechanism chain, and the physical root and evolution stage of the fault can be accurately positioned.

The invention discloses an artificial intelligence abnormity early warning diagnosis method for industrial equipment operation state monitoring, and belongs to the technical field of industrial equipment intelligent monitoring, and the method comprises the steps: collecting a multi-source heterogeneous signal of industrial equipment, and carrying out the time-frequency dual-domain feature extraction; constructing a multi-scale time window based on the time-frequency features, and executing self-supervised contrast learning by injecting a preset abnormal mode to obtain cross-scale contrast feature representation; constructing a dynamic adjacency matrix according to the comparison features, extracting space-time correlation features through a graph attention network, and determining an abnormal score based on joint evaluation; according to the method, a deep coupling closed-loop cooperative system is formed, multi-dimensional state characterization, adaptive anomaly detection and root cause diagnosis are realized, and the problems of single data source and lack of fault analysis capability in the prior art are effectively solved.

The invention provides an image super-resolution reconstruction method based on double-domain feature fusion and implicit representation, and relates to the field of image processing and computers, and the method comprises the steps: obtaining a low-resolution remote sensing image, and carrying out the preprocessing of the low-resolution remote sensing image; performing feature extraction on the preprocessed remote sensing image through Haar discrete wavelet transform and a Transform-based pyramid structure to obtain frequency domain features and spatial domain features; performing double-domain cross attention fusion on the frequency domain features and the spatial domain features to obtain local detail features and global structure features; and through an implicit representation network, the fused local detail features and global structure features are mapped to any space coordinates, a final three-channel high-resolution image is obtained, and high-quality reconstruction of a remote sensing image of any scale is realized. According to the technical scheme of the invention, the implicit neural representation is guided to realize higher-precision image reconstruction through the cooperative expression of the frequency domain information and the spatial domain information.

The invention discloses a real-time back break control method of a heading machine based on visual association and digital twinning, which relates to the technical field of intelligent control of coal mining equipment, and comprises the following four steps: processing binocular visual data through a double-domain double-branch Transformer model, extracting coal rock and roadway structure characteristics and outputting cutting head offset; a GCN-LSTM model is adopted to fuse visual, laser radar and fiber-optic gyroscope data, and dynamic calibration and output of a three-dimensional pose are carried out; a roadway three-dimensional model is constructed based on the SLAM technology, and real-time updating of a digital twin model is achieved through edge-cloud collaboration; and in combination with a reinforcement learning strategy, generating a cutting parameter adjustment instruction according to the deviation of the digital twin model, and correcting model parameters through error feedback. The method solves the problems of low visual recognition precision, sensor data drift, digital twinning update lag and the like in an underground complex environment, improves the back break control precision, tunneling efficiency and equipment stability, and is suitable for tunneling operation under complex geological conditions.

The invention discloses a double-domain artifact correction method based on multi-energy-level data and physical prior fusion, and the method achieves the efficient and precise removal of dispersive metal artifacts through the construction of a complete technical scheme of the combination of multi-energy-level data collection, double-domain cooperative correction and physical model constraint. The system has the beneficial effects that the system covers multiple fields of medical treatment, industry, security and protection, aerospace and the like, and has extremely high universality and adaptability. On the basis of a multi-energy-level slow switching scanning protocol, full-angle scanning of at least two energy levels is completed by dynamically adjusting radiation source parameters, and obtained complete multi-energy-level projection data is converted into a high-dimensional tensor through a channel dimension splicing image fusion method. The constructed multi-channel virtual image completely retains attenuation characteristics and structure information of a target object under each energy, provides a more comprehensive input source with discrimination for a deep learning network, builds a data basis for accurate correction in different fields fundamentally, and adapts to various imaging scenes containing metal targets.

The invention provides an unmanned aerial vehicle full-time perception image reconstruction method based on multi-mode cooperative reinforcement learning and degeneration decoupling. A frequency perception feature modulation model, a dual-mode dual-domain transformation module and a dynamic bidirectional guide mechanism are included. According to the system, firstly, feature information of different frequency bands is adaptively separated and modulated through a frequency sensing feature modulation model, and decoupling and compensation of composite unknown degradation are achieved; realizing cross-domain interaction and information fusion of visible light and infrared characteristics in a spatial domain and a channel domain by using a bimodal dual-domain transformation module; and finally, realizing collaborative enhancement of cross-modal degradation perception through a bidirectional dynamic guide mechanism, and generating an unmanned aerial vehicle visible light reconstruction image and an infrared super-resolution image with higher structural consistency and texture fidelity. According to the method, deep fusion and degeneration decoupling of multi-modal information can be realized in a complex degeneration environment, and the imaging quality and the environmental adaptability of an unmanned aerial vehicle full-time sensing system are remarkably improved.

The invention relates to a low-illumination underwater image enhancement method based on double-domain adaptive collaboration, and the method employs a multi-scale mixed attention mechanism to collaboratively integrate the spatial domain and frequency domain information of an image, and obtains the key feature information of the image. And on the basis of the obtained image key feature information, adaptive parametric convolution is used, convolution kernel parameters are dynamically adjusted by using a multi-branch convolution structure, and image dynamic key information is obtained. And a dynamic content perception Markov discriminator is used to cooperatively perceive space and frequency characteristics, and the authenticity of the enhanced image is discriminated, so that the fidelity of the texture is improved. And finally, optimizing the generator and the discriminator by using adversarial training, and finally outputting an enhanced underwater image by using the optimized generator. The method can be embedded into an underwater robot platform, image quality enhancement is achieved with fewer computing resources in a low-illumination complex water environment, image definition, contrast and color consistency are improved, the enhanced image is closer to human eye visual perception, and the visual perception capability and operation reliability of an underwater robot are 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 field of physiological parameter detection and medical diagnosis measurement, in particular to a multi-mode psychological state quantitative evaluation method and device and a medium thereof. The method comprises the following steps: acquiring multi-source physiological and behavior signals and situation metadata, performing clock synchronization and equipment identifier mapping processing, performing quality feature extraction, artifact detection and slice completion processing, and generating a quality passing fragment set; de-noising normalization, time windowing and feature stack construction, modal gating and attention weighting processing are executed, and unified time sequence embedding is generated; through domain alignment parameter estimation, double-domain drift correction and multi-task inference, a psychological state quantitative index and uncertainty thereof are output; and finally, generating a structured evaluation report and updating parameters through consistency comparison, online self-distillation updating and individual baseline refreshing. According to the method, the anti-interference performance, the individual suitability and the result credibility of psychological state assessment in a natural scene are effectively improved.

The invention discloses a camouflage target detection method and system based on a double-domain fusion enhanced network, and relates to the technical field of target detection. Through the nonlinear double-domain fusion module, in combination with nonlinear mapping of a spatial domain and a frequency domain, key difference characteristics of a frequency domain amplitude spectrum and a phase spectrum are captured, the problem that the detection performance is reduced in a scene of low contrast and the like depending on an RGB spatial domain is solved, and the target discrimination degree is improved; based on a lightweight scale perception modulation converter and a double-feature fusion module, multi-scale features are extracted, aligned and fused, a semantic relation is integrated by means of cross attention, and the problems of detail loss and boundary fuzziness caused by scale diversity are solved; the context feature enhancement module integrates cross attention and edge auxiliary injection, accumulates multi-layer feature integration, gives consideration to a global boundary and a local structure, effectively reduces false detection, missing detection and edge roughness, and further enhances robustness through multi-layer auxiliary supervision.

The invention relates to the technical field of aircraft power supply systems, and discloses an intelligent power distribution and fault diagnosis method for an aircraft power supply system, which comprises the following steps: acquiring a real-time current signal and load working condition information of an aircraft power supply bus, a double-domain composite entropy feature is constructed by extracting a time domain entropy transition index representing the energy domain stability of a current signal and a phase synchronization index representing the time structural domain stability; according to the method, dual inspection of the energy domain and the time structure domain is carried out on the current signals, and cooperative judgment is carried out in combination with the load working condition, so that sensitive capture of real fault forebodes is realized; and meanwhile, tolerance is dynamically shown on normal loads in the airplane operation process, the restriction between safety and usability in the prior art is effectively avoided, and predictive health management is made possible.

The invention provides an unmanned aerial vehicle visible light-infrared image cooperative enhancement network and method based on dual-branch cooperation and frequency adaptive fusion, and relates to the technical field of unmanned aerial vehicle multi-modal image enhancement and super-resolution restoration. According to the method, visible light image enhancement and infrared image super-resolution reconstruction are respectively carried out by adopting a heterogeneous double-branch architecture; a frequency adaptive fusion module is embedded in a visible light branch, and spectrum decomposition and feature refining of degradation sensing are realized through a learnable frequency mask; in the infrared branch, a long-range dependency relationship is captured through a residual Transform module; bidirectional cross-modal guidance is realized through a multi-modal feature interaction module, the module integrates wavelet convolution transformation, frequency perception fusion and a cross-modal Transform mechanism, and feature alignment and semantic complementation of space-frequency double domains are realized. According to the method, the visual quality, the detail recovery capability and the cross-modal collaborative robustness of the visible light and infrared images of the unmanned aerial vehicle under complex illumination, weather and degradation conditions can be effectively improved.

The invention discloses an optical fiber and wireless fused power communication network fault intelligent switching and self-healing method, which comprises the following steps: constructing a multi-mode link health portrait, arranging four-in-one sensors at all nodes, and generating a health vector through edge calculation; mining a double-domain fault propagation chain, constructing a dynamic heterogeneous graph, and predicting fault propagation through memory attenuation GNN; pre-generating a hybrid redundant path, and selecting a main and standby path pair based on a game algorithm; establishing a programmable self-healing decision engine, and outputting an adaptive instruction; and zero-interruption service migration and closed-loop verification are realized. The method solves the problems of lack of physical constraints, insufficient dynamic adaptation and low efficiency of edge deployment in the prior art, and improves the reliability of the power communication network.

The invention discloses a panchromatic sharpening image fusion method and device based on a double-domain flexible converter. The method comprises the following steps: performing multilayer space-frequency joint attention operation on a low-resolution multispectral image and a high-resolution panchromatic image to generate a feature tensor; performing double-domain feature alignment operation, aligning structural semantic features based on an attention mechanism, fusing amplitude and phase frequency spectrum information through Fourier transform, and matching modal distribution differences by using instance normalization; multi-level fusion is carried out, and splicing, convolution and space-frequency joint attention operation are carried out on each level in sequence; carrying out residual error reconstruction on the fusion features; and adding the reconstructed features and the up-sampled low-resolution multispectral image element by element, and outputting a high-resolution multispectral image. According to the method, image structure distortion and detail blurring can be remarkably reduced, the image definition and the edge reduction capability are improved, the fusion consistency and the physical authenticity are improved, and the detail expressive force of the fused image is enhanced.

The invention discloses a sorting manipulator intelligent control system based on big data, and the system comprises a multi-source data collection module which is used for synchronously collecting the real-time operation parameters of a sorting manipulator, the multi-modal feature data of a to-be-sorted object, and the dynamic environment parameters; the cross-modal fusion module is used for performing space-time alignment on the multi-modal feature data through a dynamic weighting algorithm; the double-domain cooperative training module is used for constructing a mapping relation between a virtual simulation domain and a physical entity domain, migrating initial model parameters trained by the virtual domain to the entity domain based on transfer learning, minimizing double-domain data distribution difference through a domain adaptation loss function, and generating an intelligent decision model adaptive to an entity scene; and the adaptive control module is used for generating a real-time control sequence through a model prediction control algorithm according to the sorting path planning result output by the intelligent decision model. According to the invention, more intelligent sorting manipulator control is realized.

The invention relates to the field of underground multi-physics field monitoring, in particular to an intelligent well completion underground multi-parameter fusion monitoring system, which comprises a functional casing acquisition module, an acoustic-electric-magnetic excitation module, an acoustic-electric-magnetic excitation module and a multi-parameter fusion monitoring module, wherein the functional casing acquisition module is integrated with a strain sensing element, a temperature sensing element, a stress sensing element and a resistivity sensing element; a double-domain inversion fusion module based on a multi-physics field equation and a graph time sequence causal network; the edge closed-loop regulation and control module adopts entropy modulation dual strategy reinforcement learning; and the communication interface has time slot encryption and version verification functions. According to the method, second-level data alignment, minute-level adaptive regulation and control and hour-level global optimization are realized, the crack conductivity is improved, the energy consumption is reduced, and yield increase and well completion integrity are considered.

The invention discloses a parallel U-Net-based dual-domain collaborative infrared and visible light image fusion method. The method comprises the following steps: firstly, extracting initial features of a source image by using dense connection blocks; then, parallel frequency domain branches and spatial domain branches are constructed, the frequency domain branches are combined with discrete wavelet transform and fast Fourier convolution to decompose and enhance multi-scale global frequency domain features, and the spatial domain branches capture long-distance spatial dependence with linear calculation complexity by using a convolutional layer and Mama based on a selective state space model; dynamic interaction and weighted fusion of double-domain information are realized through an adaptive feature fusion module; and finally, generating a fused image through an image reconstruction module. According to the method, the problems of high calculation overhead and video domain information negligence in the prior art are solved, and infrared heat radiation maintenance and visible light texture enhancement are effectively considered.

The invention provides a self-encoding hyperspectral anomaly detection method based on double-domain feature learning, and mainly solves the problem of non-ideal detection performance caused by insufficient frequency domain feature mining, missing detection and poor fusion in the prior art. Comprising the following steps: 1) acquiring an original hyperspectral image, and dividing the original hyperspectral image; 2) constructing a self-encoding hyperspectral anomaly detection model based on double-domain feature learning, wherein the self-encoding hyperspectral anomaly detection model sequentially comprises an encoder, a mask-based frequency domain interactive attention MFIA module, an attention-aware visual state space AVSS model, a frequency domain spectrum interactive fusion FSIF module and a decoder; 3) using an L1 norm as a loss function of the detection model, and guiding the model to be trained to converge; and 4) inputting the original hyperspectral image into the trained final detection model to obtain a reconstructed hyperspectral image, and calculating to obtain an anomaly detection result. According to the method, the background reconstruction effect can be improved, abnormal feature expression can be remarkably inhibited, and the hyperspectral anomaly detection performance is effectively improved.

The invention discloses a medical image segmentation method based on boundary constraint. The method comprises the following steps: S1, obtaining a medical image amplitude spectrum and a medical image phase spectrum; s2, forming a structure sensing frequency domain disturbance result; s3, obtaining a frequency domain enhanced medical image; s4, calculating structure perception medical image boundary enhanced attention output features; s5, performing global relation modeling and local texture modeling on the frequency domain enhanced medical image sequence; s6, generating an edge heat map; and S7, performing bidirectional gating interaction on the edge heat map and the output of the linear self-attention encoder by adopting a double-domain interaction fusion strategy to generate a double-domain interaction fusion feature map, performing up-sampling and convolution processing on the double-domain interaction fusion feature map in a decoder, and outputting a boundary enhancement segmentation feature map. According to the invention, the structure perception capability of the model in a medical image scene with fuzzy anatomical structure boundary and low texture contrast is effectively improved.

The invention discloses an automobile electronic wire harness insulation sheath crack detection system based on computer vision, and the system comprises a multi-channel input module which is used for obtaining and preprocessing multi-channel original data frames; the double-domain feature extraction module is used for extracting a double-domain feature pyramid; the double-domain fusion module is used for executing cross-domain attention and mutual information consistent weighting and inhibiting mirror surface highlight pseudo response; the topological structure output module is used for outputting a preliminary crack structuring result through the multi-head crack structure prediction head set; the communication repair module is used for carrying out breakpoint bridging and conflict rollback on the preliminary crack structuring result; and the geometric measurement module is used for measuring crack data and forming a structured detection report. According to the method, polarization reflection suppression and flattening geometric modeling are fused, a double-domain crack detection network is constructed, accurate identification and quantitative measurement of sheath cracks are achieved, and the method has the advantages of being high in reflection resistance, stable in topology and traceable in result.

The invention discloses an unmanned aerial vehicle image target detection method based on collaborative feature fusion, and the method comprises the steps: carrying out the detection of a to-be-detected unmanned aerial vehicle image through employing a trained unmanned aerial vehicle image target detection model, and obtaining a detection result; the model comprises a backbone network, a neck network and a detection head network; the backbone network is integrated with a multi-scale dynamic double-domain coupling module, a multi-scale feature map is extracted through frequency domain-space domain combined processing, and the multi-scale feature map serves as input of a neck network after target edge features are enhanced; the neck network adopts a collaborative feature pyramid network to fuse features layer by layer, linear deformable convolution is used in a P2 layer to enhance details, a wide-area sensing module is combined with large kernel convolution to capture a long-range context in a P3 layer, and finally, the detection head network outputs a target detection result. The method solves the technical problems that the extraction precision of small target features in the aerial image of the unmanned aerial vehicle is not high, background noise interference is serious, and multi-scale target fusion is insufficient.

The invention provides an underwater image enhancement method based on a U-Net backbone network and fusing a position sensing module, a double-branch attention bridge and a DCT frequency domain enhancement module, and aims to solve the problems of low contrast ratio, color distortion, detail loss and the like caused by absorption and scattering effects in the light propagation process of an underwater image, improve the image quality and improve the image quality. And the visual perception effect is improved. The method specifically comprises the following four main steps: firstly, acquiring and preprocessing initial data sets of different underwater scenes; secondly, extracting spatial relation information of the image through a position sensing convolution module, and enhancing image details; thirdly, a double-branch attention bridge module is used for further integrating local details and a global structure, and the response capability of a key area is enhanced; and finally, in combination with RGB and NIR modal features, weighted fusion of frequency-space features is realized through a DCT fusion enhancement module, so that detail information of the image is recovered, color deviation is corrected, and the definition and visual effect of the underwater image are remarkably improved. According to the method, the enhancement performance of the underwater image in a complex environment can be improved, and powerful support is provided for applications such as underwater detection, target detection, object recognition and image segmentation.

The invention relates to the technical field of image processing, and discloses an image deblurring model and method based on double-domain feature fusion, and the method comprises the following steps: collecting data, and building a blurred image data set; building a DAF-UNet neural network based on spatial domain and frequency domain feature fusion; the DAF-UNet neural network is composed of n UNet sub-networks with different scales, and n is a positive integer; based on the blurred image data set, training the DAF-UNet neural network to obtain a trained image deblurring model; collecting a to-be-processed image; and processing a to-be-processed image by using the trained image deblurring model to obtain a clear image result. According to the method, the problems of low information mobility and key feature loss in the prior art are solved, and the method has the characteristics of good image recovery effect and model miniaturization.

The invention discloses an umbilical cable anomaly detection method, and the method comprises the steps: constructing an image data set containing winding, damage and bending, employing a target detection model (including WTConv convolution, a double-domain selection mechanism and a mixed expansion residual attention module) based on deep learning, and combining fine-grained distribution refinement and a global optimal positioning self-distillation algorithm to optimize a prediction result. After the model is trained, the abnormity of the umbilical cable can be detected in real time, online learning is supported, the detection precision is improved, the method is suitable for monitoring the cable state of the underwater robot, and the operation safety and efficiency are remarkably improved.

The invention relates to the field of computer vision, and discloses a defect detection method, system, medium and equipment based on adaptive dual-domain filtering and Gaussian mixture prior constraint, and the method comprises the steps: carrying out the multi-scale feature extraction of an ultrasonic C-scan image through a Vision Transform network after the ultrasonic C-scan image is preprocessed; respectively inputting the shallow fusion features and the deep fusion features into a frequency-space double-domain adaptive feature filtering module for filtering, inputting the filtered features into a Gaussian mixture modeling module Ada-GMM, and modeling normal feature distribution; carrying out Ada-GMM-Guided decoding, and carrying out interactive fusion on the corresponding deep semantic features and shallow texture features by adopting a deep and shallow multi-scale feature interaction mechanism; performing optimization by adopting cosine reconstruction loss, filtering consistency, entropy regularization loss and distribution alignment loss, and adaptively learning normal distribution characteristics according to an optimization process to obtain a model weight; and reasoning the input ultrasonic C-scan image by using the trained network weight to realize anomaly detection and positioning, and outputting an interpretable anomaly thermodynamic diagram.