125 results about "Linear complexity" patented technology

The invention discloses a physical information neural network-driven ocean island typhoon scene generation method and system. The method comprises the steps of collecting multi-source heterogeneous meteorological data and performing space-time alignment preprocessing; constructing a coarse-scale space-time probability prediction model, capturing space correlation of meteorological elements by using a graph topology learning network, efficiently processing long-time-sequence dependence of typhoon evolution by integrating a state space model with linear complexity, and generating a probabilistic typhoon scene with coarse resolution through a multivariable joint distribution probability model; further constructing a physical downscaling model, taking a coarse-scale prediction result as condition input, and performing physical consistency downscaling on a coarse-scale scene by embedding an atmospheric fluid mechanics equation in a loss function as a physical hard constraint; and finally, outputting a high-resolution typhoon scene with probability reliability and physical authenticity.

The invention discloses a medical image fuzzy boundary segmentation method based on an edge perception Mama network. The method aims at solving the problems that a camouflage pathological structure in a medical image is visually similar to surrounding healthy tissues, so that segmentation is difficult, and clinical deployment is difficult due to secondary calculation complexity of an attention mechanism in a traditional camouflage target detection (COD) method. According to the invention, a boundary guiding module inspired by COD is creatively combined with linear complexity state space modeling, and an E-Mama edge guiding framework is provided. The framework adopts an encoder-edge guide-decoder structure, and accurate boundary description is realized while the computational efficiency is kept. The system comprises five core components: a Mama encoder; an adaptive fusion processing module (AFM); an edge detection module (EDM); an edge guidance module (EGM); the invention relates to a Mama decoder. Experiments show that advanced segmentation precision is realized on a plurality of medical data sets, and meanwhile, the calculation complexity is reduced from O (n2) to O (n), so that clinical deployment becomes possible.

The invention discloses an optic disk atrophy arc image segmentation method based on full supervision, and particularly relates to the technical field of medical equipment, and the method comprises the following steps: S1, multi-scale feature extraction, S2, local-global feature enhancement, S3, adaptive feature fusion, S4, gradient flow optimization, S5, mixing of a loss function, and S6, data enhancement and generalization optimization. According to the method, the long-range dependence of the optic disk and the atrophic arc is globally modeled through the PVTv2 encoder, the detail segmentation precision of the optic disk atrophic arc at the blood vessel crossing and fuzzy boundary is remarkably improved in combination with the multi-scale attention and dilated convolution of the feature enhancement module, cross-level feature fusion is realized with linear complexity based on the Mama decoder, and the accuracy of feature fusion is improved. Efficiency and structure coherence are considered; the weight is balanced by the mixed loss function, and early lesion missing detection is reduced; a data enhancement and residual module enhances the generalization ability of the model, and a high-precision and efficient quantitative analysis tool is integrally provided for early screening of blind eye diseases such as pathological myopia and glaucoma.

The invention discloses an underwater image semantic segmentation method and device based on a lightweight double-flow Mama network, and a storage medium, relates to the technical field of underwater image processing, and solves the problems of poor environmental adaptability, low modal fusion efficiency, heavy model and high calculation overhead in the existing underwater image semantic segmentation technology. According to the method, a double-branch encoder is constructed, the double-branch encoder composed of an image encoder and a text encoder is adopted, and a visual feature map and a semantic feature vector in a preprocessed underwater image and text description information are extracted respectively; a cross-modal Mama module is adopted to carry out deep fusion on a flattened image feature sequence and text features in the module, the cross-modal Mama module adopts a Mama block with linear complexity, and continuous guidance and progressive enhancement of text semantics are realized in combination with a multi-level gating fusion mechanism and residual connection. And the recognition capability of underwater fuzzy and shielded targets is remarkably improved, and meanwhile, the calculation efficiency is remarkably improved.

The invention provides an anti-interference serial port communication method and system for an industrial-grade wireless module, and the method comprises the steps: collecting interference data in the serial port communication of the industrial-grade wireless module in real time based on a multi-dimensional interference feature collection module, and forming a multi-scale interference feature vector; inputting the multi-scale interference feature vector into a Mamba communication state space model fused with a CRC (Cyclic Redundancy Check) mechanism, and predicting a potential interference mode through linear complexity time sequence modeling; based on an adaptive weight transmission optimization function, carrying out dynamic weighting adjustment on the transmission parameters in the potential interference mode; wherein the transmission parameters comprise a baud rate, a data bit length, a verification mode and a retransmission strategy. According to the invention, hardware cost does not need to be increased, and the cost of anti-interference serial port communication is reduced; meanwhile, based on an adaptive weight transmission optimization function, dynamic weighting adjustment is carried out on transmission parameters in a potential interference mode, and interference dynamic changes are tracked in real time to carry out anti-interference communication.

The invention relates to the technical field of protein prediction, and discloses an efficient protein stability prediction method for selective state space modeling. According to the method, the BiMama core module and the CoGNN core module are adopted, and limitation of an existing method on calculation efficiency and multi-scale information processing is broken through in a mode of combining selective state space modeling and the collaborative graph neural network. According to the invention, a local sampling strategy based on a k-hop sub-graph is provided, and efficient calculation is realized by focusing a key environment around a mutation site; a bidirectional selective state space model is adopted to model a long-range dependency relationship with linear complexity, and the calculation bottleneck of a traditional Transform architecture is effectively overcome. The gating fusion module developed by the invention can adaptively integrate long and short range features, so that the model can flexibly adjust a feature combination strategy for different types of mutations, and the accuracy and efficiency of protein delta delta G prediction are improved.

The invention relates to the technical field of sleep stage classification, and particularly provides a sleep quality monitoring method fusing multi-scale features. The method comprises the following steps: preprocessing input signals, and inputting the input signals into a multi-mode sleep signal analysis network after ensuring that the signal amplitudes are consistent; capturing features of a plurality of time scales through a multi-granularity feature learning module, and carrying out feature fusion; integrating spatial information by using a spatial-temporal feature enhancement module, and mapping the three-dimensional features into a two-dimensional time sequence feature sequence; on the basis of a time context module of Mama, long-distance time dependence is modeled with linear complexity, features are averagely aggregated along the time dimension through a classification module, a classification result is output through a full connection layer and an activation function Softmax, and the method improves the accuracy of sleep stage classification while ensuring the high efficiency of sleep stage classification.

The invention relates to the technical field of intelligent agriculture and computer vision, in particular to a small-sample high-density chicken counting framework based on a deep learning Mamba structure, which comprises a feature extraction network, a support-query enhancement module and a decoder. According to the method, a multi-scale feature extraction network based on a residual block (ResNet Block) is introduced, so that local detail information is effectively reserved; and then, a support-query enhancement module is constructed by introducing a Mamba structure, and context interaction between support features and query features is effectively enhanced by utilizing the long sequence modeling capability of linear complexity of the support-query enhancement module, so that the problems of individual overlapping and boundary fuzziness in a high-density chicken flock scene are solved. Experimental results on a PoultryCount real breeding data set show that the mean absolute error (MAE) and the root-mean-square error (RMSE) of the method are reduced compared with those of an existing method, and the chicken counting precision and generalization ability under the condition of a small number of labeled samples are remarkably improved.

The invention relates to the technical field of quantum computing, and provides a linear complexity quantum state preparation method based on tensor decomposition and a quantum circuit construction system.The high-dimensional tensor is decomposed into a series of low-rank core tensors through continuous singular value decomposition, each core tensor in a core tensor sequence is expanded into a unitary matrix, and the unitary matrix is used as a quantum circuit; the unitary matrix sequence is mapped to quantum lines coupled using adjacent qubits, and the quantum lines are run to prepare a target quantum state. Based on this, the line generated by the method can approximately or accurately prepare a target quantum state only by coupling adjacent quantum bits, and is perfectly adaptive to quantum chips of linear or grid topologies such as superconducting and semiconductor quantum dots and the like.

The invention relates to the technical field of medical data processing, in particular to a data analysis and processing system based on clinical hearing detection, and aims to solve the problems that dynamic characteristics of auditory nerve response cannot be systematically described, high-dimensional multi-modal feature vectors cannot be constructed, and the data analysis and processing efficiency is low in the prior art. A stable and reliable input basis cannot be provided for intelligent classification, anomaly detection and individualized analysis, and the sensitivity and specificity of hearing state recognition are reduced; the multi-dimensional hearing feature extraction module fuses time domain, frequency domain and non-linear complexity analysis methods, systematically depicts dynamic features of auditory nerve response, constructs a high-dimensional multi-modal feature vector by integrating three-dimensional features, remarkably improves sensitivity and specificity of hearing state recognition, and improves the accuracy of hearing state recognition. The feature representation method provides a stable and reliable input basis for subsequent intelligent classification, anomaly detection and individualized analysis.

The invention discloses a trajectory planning method based on a multi-stage optimization strategy and a mixed diffusion model. The method comprises the following steps: fusing multi-source sensor data and extracting features; screening and fusing intention anchor point tracks; and optimizing the fine-grained trajectory based on the Transform-Mamba mixed diffusion model. According to the method, an intention anchor point track screening mechanism is provided, the anchor point track matched with the scene is selected from the compact vocabulary through the dynamic screening module and fused with the static prior anchor points, the calculation complexity is reduced, the diversity of the initial track and the scene consistency are guaranteed, and the problem that a traditional fixed anchor point set is insufficient in flexibility is solved. According to the method, a mixed diffusion decoder is designed, space environment dependence is efficiently modeled through a cross attention mechanism, a bidirectional Mama module captures time sequence motion dependence with linear complexity, global perception ability and long sequence modeling efficiency are both considered, and safety and dynamics feasibility of generated tracks are ensured.

The invention belongs to the technical field of machine state prediction, and particularly relates to a fault diagnosis method and system based on a coding and decoding attention mechanism enhanced pulse neural network, and the method comprises the steps: collecting a device vibration signal, and segmenting the device vibration signal into time sequence segments; converting each fragment into a two-dimensional time-frequency image by using S transformation to construct a data set; the image is input into a lightweight hybrid network model to extract deep features, the model is composed of a pulse convolution encoder and a pulse efficient additive attention mechanism encoder which are alternately cascaded, and the pulse convolution encoder extracts local features through pulse neurons and depth separable convolution; the global context dependency is modeled by adopting an efficient additive attention mechanism with low linear complexity; and finally, fault classification is completed based on deep features. Through fusion of pulse calculation and an efficient attention mechanism, the feature extraction capability and diagnosis precision are improved while the parameter quantity of the model is remarkably reduced, and the method is particularly suitable for being deployed on edge equipment with limited resources to realize fault diagnosis.

The invention relates to the technical field of computer vision and document understanding, and discloses a table recognition method based on structure perception coding and state space decoding. The method comprises the steps of firstly extracting table image features; then, features are enhanced through structure perception coding, two-dimensional position coding and row and column embedding are superposed to form grid prior, and serialized visual representation is constructed to improve modeling of a global structure and a row and column alignment relation and reduce redundant calculation. In the decoding stage, state space decoding with linear complexity is adopted, and a table structure mark is generated through autoregression; when a cell start mark is detected, synchronous prediction of cell positioning and content decoding is triggered, and joint optimization is carried out in training. And finally, carrying out fusion and consistency verification on the structure and content results, and outputting a standardized structure and content alignment grid. The method has high recognition precision, robustness and reasoning efficiency in complex scenes of merging cells, invisible boundaries, irregular layout, super-long sequences and the like.

The invention relates to a medical image segmentation system and method based on context and frequency guidance and a storage medium, and the system employs an encoder-decoder architecture, and integrates three core modules: a visual state space module which captures global context information with linear complexity based on a selective state space model; the frequency-guided representation module is used for explicitly separating and enhancing structure and boundary characteristics through frequency domain transformation and complex weight modulation; and the multi-scale adaptive context aggregation module is used for integrating multi-scale semantics through parallel multi-branch convolution and dual Top-K sparse attention and focusing a key region. According to the method, the technical problems that global modeling and boundary details are difficult to consider, the calculation complexity is high and the adaptability to multi-scale targets is poor in the prior art are solved, the segmentation precision and the boundary description accuracy are remarkably improved in various medical image segmentation tasks such as heart MRI, polyp, skin lesion and pathological sections, and the medical image segmentation efficiency is improved. And meanwhile, the calculation efficiency is ensured.

The invention discloses a double-flow fault diagnosis method based on state space modeling and dynamic edge context awareness, the method constructs a time domain state flow and a time frequency edge flow, and the specific implementation process is as follows: the time domain state flow is subjected to state space modeling under a Transform framework to form a MambaT encoder, long and short term dependence is efficiently captured with linear complexity, and the time domain state flow is subjected to dynamic edge context awareness; time domain sensitive features are accurately represented; the time-frequency edge stream converts vibration data into a two-dimensional time-frequency image through wavelet transformation, a differentiable edge sensitive mask is designed to adaptively separate time-frequency image impact characteristics and background noise, boundary content perception adaptive filling is designed to maintain image boundary topology integrity, and the two parts cooperate to form dynamic edge context perception convolution, so that the image boundary topology integrity is improved. Time-frequency edge features are effectively extracted; and a channel enhancement and channel gating module is designed to establish a dynamic channel interaction gating mechanism, so that time domain-time frequency domain double-current cascade characterization is realized, and the fault discrimination capability is remarkably improved. Experiments prove that the method has higher fault diagnosis performance.

The invention belongs to the technical field of medical image segmentation, and relates to a dual-path fusion neural network for prostate precise segmentation and a segmentation method, the neural network constructs a dual-path decoupling encoder architecture based on an nnU-Net framework, captures fine anatomical structure and local texture information through a context sensing residual encoder of a local path, and obtains a dual-path fusion neural network for prostate precise segmentation. A long-range dependency relationship is modeled with linear complexity through a visual state space module of a global path, a double-flow alignment gating module is designed to realize self-adaptive alignment and fusion of cross-path features, and model training is optimized in combination with a mixed loss function and a depth supervision strategy; according to the method, the limitation of an existing segmentation model in the aspect of local structure and global semantic integration is solved, and the segmentation precision, the boundary goodness of fit and the generalization performance of the focus and gland region in the prostate MRI image are improved.

The invention discloses a question and answer method and system based on large model causal graph discovery and causal graph enhanced reasoning, and belongs to the technical field of natural language processing and artificial intelligence. The method comprises the following steps: firstly, carrying out hierarchical expansion from an initial root node by adopting a breadth-first search strategy, and efficiently constructing a reliable causal graph conforming to directed acyclic graph constraints with linear complexity in combination with real-time loop detection; in the question and answer stage, key entities in user questions are analyzed, and causal paths connecting the key entities are retrieved in a causal graph; and taking the retrieved structured causal path as a constraint condition to be injected into a decoding process of a large-scale language model, and generating a natural language answer which is strict in logic and can trace reasoning steps. According to the method, the resource consumption of large-scale causal discovery is remarkably reduced, the accuracy and interpretability of the answers of the questions and answers are effectively improved, and technical support is provided for medical treatment, finance and other scenes needing high-reliability reasoning.

The invention provides a novel spatial micro-motion target identification method based on a radar network system, and the method comprises the following steps: building a target micro-motion model and a signal model, and constructing a data set of an RCS sequence, a time Doppler image and an HRRP sequence for the spatial micro-motion target identification of a networking radar system; constructing an adaptive feature fusion sub-network; xCA and LA-Att modules with linear complexity are introduced, fine-grained features are effectively extracted, self-adaptive weighted fusion of multi-domain features is realized by adopting a CAFM module, and information useful for recognition is effectively enhanced; and designing a time-space modeling sub-network based on BiGRU and double-graph fusion. According to the method, multi-band and multi-view target features can be obtained, and more accurate information is provided for identification of a spatial micro-motion target; according to the method, the feature information of each radar in the heterogeneous radar network is fully utilized, the recognition precision of 96% or above can still be obtained under the condition that the signal-to-noise ratio is 0dB, and the effectiveness and robustness of the method are obvious.

The invention provides a remote sensing image target detection method using a scale-sensitive Mama network, which comprises the step of preparing a training set and a test set of a DIOR data set, and specifically comprises the following steps: carrying out SSMNet network training by using images in the training set to generate a training model; storing the training model in a local folder, testing the effect of the training model by using the images in the test set, and if the training model is satisfied, storing the training model as a satisfied training model; and storing the satisfactory training model in a local folder, and testing an unlabeled remote sensing image by using the satisfactory training model. The invention provides a scale sensitive Mama network which comprises an area intersection-to-parallel ratio (AIoU) loss function, a remote sensing Mama block (RSMB) and a sparse feature fusion module (SFFM). The AIoU improves the scale sensitivity and the small target convergence speed; the RSMB models a global context with linear complexity; and SFFM is fused with deep and shallow features to enhance small target detection. The model significantly improves the remote sensing image target detection effect.

The invention provides an efficient single-stage super-resolution method and a remote sensing image super-resolution system for RGB remote sensing images, and the method integrates Fourier position coding guided geometric node modeling, hypergraph high-order relation modeling and a Mama long-range dependence modeling mechanism based on a state space model. And consistent modeling of a multi-scale structure is realized while the linear calculation complexity is kept. In the method, FOPE is introduced as explicit geometric priori to guide adaptive screening of key nodes on global, regional and local scales; then, an FOPE enhanced hypergraph network is constructed, and high-order space association among multiple nodes is modeled through hyperedge; according to the method, a two-stage Mama aggregation module is further designed, and intra-node feature consistency modeling and inter-node long-range spatial dependency modeling are completed with linear complexity; and finally, a high-resolution RGB remote sensing image is generated through a lightweight direct up-sampling module.

The invention relates to an RGB-T target tracking method and system based on spatio-temporal state evolution, and the method comprises the steps: constructing a double-branch RGB-T target tracking model, enabling RGB and TIR branches to share the weight of a ViT encoder, employing an iterative processing frame, and transmitting spatio-temporal context information between frames through an updatable context memory Tokens. In each iteration, after RGB and TIR modal features are extracted respectively, cross-modal time sequence context modeling is carried out through a modal perception time sequence Mamba module, and the module realizes long-time target perception representation learning through a cross-modal coupling state transition mechanism and a prompt guide strategy; and carrying out intra-modal and inter-modal feature fusion in a spatial dimension through a cross-modal Mama aggregation module, and finally outputting a target position through a prediction head. According to the method, lasting cross-modal state evolution can be realized with linear complexity, the spatial-temporal characteristics of visible light and infrared light are effectively fused, and the tracking robustness and accuracy are improved in complex scenes such as rapid target movement, shielding or modal degradation.

The present application relates to the field of artificial intelligence. Provided in the present application are a method and system for implementing a self-attention mechanism having a linear complexity, and a device and a medium. The method comprises: performing positional encoding on sequence data to be processed, and performing feature mapping to apply the positional encoding to a query matrix and a key matrix; on the basis of the query matrix and the key matrix, performing calculation to obtain a low-rank query matrix, a low-rank key matrix and a low-rank generalized inverse matrix; augmenting a value matrix to obtain an augmented matrix, and on the basis of the augmented matrix, the low-rank key matrix, the low-rank generalized inverse matrix and the low-rank query matrix, obtaining a sequence self-attention matrix; and transposing the sequence self-attention matrix, using the transposed matrix as sequence data to repeat the foregoing steps, using the newly obtained sequence self-attention matrix as a feature self-attention matrix, and on the basis of the sequence self-attention matrix and the feature self-attention matrix, obtaining a self-attention matrix having a linear complexity. The present application implements linear self-attention, better maintains the model performance and has relatively good expansibility.

The invention discloses a traffic flow prediction method and system based on CT-SSM and two-way dynamic graph convolution, and the method comprises the steps: constructing an urban road network topological graph, and constructing a multi-scale periodic historical traffic flow sequence; inputting the multi-cycle fusion feature tensor into a time sequence feature extraction module based on CT-SSM, and based on a selective parameter driven by data, performing state recursion after discretization of a zero-order retainer, and extracting time sequence features; based on a space-time attention mechanism and static mask constraints, generating a global implicit semantic graph and a local physical evolution graph in parallel; performing spatial depth feature aggregation through double-path collaborative dynamic graph convolution; and the dynamic gating unit carries out weighted fusion and then sends the fused data to a prediction result output module to give a prediction result, and model training is completed based on back propagation. According to the method, long-range time sequence dependence is extracted through linear complexity, time-space dependence is modeled through time-space attention depth, topology solidification is broken through construction of a two-way dynamic graph, and the precision and robustness of traffic flow prediction are remarkably improved.

The invention discloses a power system moving target defense method based on power flow betweenness and power flow disturbance, and relates to the field of power systems. Calculating to obtain a power flow betweenness and a power flow disturbance index of the normalized power system line; constructing a comprehensive index based on the power flow betweenness and the power flow disturbance index; and determining a to-be-deployed line set according to a preset device deployment number and the comprehensive index. And changing the line impedance value corresponding to the line set to obtain a target measurement matrix, calculating a measurement value residual error after the line impedance value is changed according to the original measurement matrix and the target measurement matrix, and determining an attack success rate based on a size relationship between the measurement value residual error and a detection threshold value. And deploying power system moving target defense according to a result of the attack success rate. According to the invention, the detection effectiveness and the MTD concealment are balanced. A traditional global combination search problem is converted into a linear complexity sorting problem, rapid solving is achieved, calculation efficiency is improved, and rapid generation of a hidden MTD strategy is achieved.

The application discloses a seismic data processing method, and relates to the technical field of seismic data processing. The method comprises the following steps: acquiring a low-resolution seismic image; performing attention convolution operation on the low-resolution seismic image to perform weighting, and performing nonlinear transformation on the weighted key features to obtain initial features; sequentially performing twice attention convolution operation on the initial features to perform weighting; performing nonlinear transformation on each weighted initial feature and performing a cavity convolution operation to obtain intermediate features; performing an up-sampling operation on the intermediate features, and performing attention convolution operation on the up-sampled intermediate features to perform weighting; and performing nonlinear mapping on the weighted up-sampled intermediate features to obtain a high-resolution seismic image. The method can reduce the complexity to close to linear complexity while maintaining the performance of super-resolution reconstruction, and realizes the dual optimization of efficiency and precision.

The application discloses a kind of based on double-domain calibration image segmentation method and system, method uses double-flow feature extraction network, first branch extracts local space feature to obtain first local feature;Second branch is converted to frequency domain after extracting second basic feature, and noise filtering is carried out to obtain frequency domain calibration feature by spectrum texture calibration module, spatial boundary calibration module is extracted in spatial domain Structure boundary response obtains spatial domain calibration feature, then linear attention mechanism is aggregated with second basic feature to obtain long-range global feature;First local feature and long-range global feature are adaptively fused to output segmentation result.The application is filtered out by double-domain collaborative calibration of frequency domain and spatial domain, and weak boundary response is enhanced, linear complexity global context modeling is realized by combining linear attention, and the robustness and accuracy of image segmentation in strong noise and weak boundary scene are significantly improved.

The invention discloses a cross-modal image fusion method based on deep learning, relates to a computer vision and image processing technology, and aims to solve the problems of structural distortion, texture loss and overhigh calculation overhead in an existing image fusion method. According to the method, the state space model is introduced firstly, linear complexity is achieved during modeling long-distance dependence, the calculation overhead is greatly reduced, the system operation efficiency is improved while the fusion quality is guaranteed, and the method is suitable for deployment and application of resource-limited scenes. Through dynamic feature enhancement and a cross-modal fusion mechanism, difference and complementary information can be automatically extracted, and the problem of detail omission or information bias caused by artificial feature selection in a traditional method is effectively avoided. Based on the scheme of the invention, image information from different modalities can be fully fused, and a fused image with stronger detail retention and structural consistency is automatically generated, so that the development of tasks such as subsequent target detection and medical diagnosis is facilitated, and the image use efficiency and the intelligent level are improved.

The application provides a method and device for graph data processing, and relates to the technical field of artificial intelligence, and the method comprises: a method for graph data processing, which comprises the following steps: acquiring graph data, extracting the basic structure features of the graph data through a static topology path, adaptively screening the key neighbor features in the graph data based on an attention mechanism and in combination with the basic structure features through a dynamic path, establishing long-distance semantic association between nodes through topology-aware coding based on the basic structure features and the key neighbor features through a linear graph transformer with O ( n ) of linear complexity, and establishing long-distance semantic association between nodes through topology-aware coding; performing frequency domain projection on the node features to generate frequency domain features, explicitly extracting and enhancing the periodic discriminant components in the frequency domain features through a Fourier analysis network to generate graph neural network data. Thus, the graph representation learning capability of the graph neural network is effectively improved.

The invention provides a feature extraction method and system for a parallel state space model based on dynamic aggregation, and the method comprises the steps: carrying out the spatial sampling of an input image at a current moment, and obtaining sampling points belonging to an omega set; performing spatial alignment on the plurality of parameter matrixes and the input image; based on the sampling points, carrying out vectorization definition on one parameter matrix after space alignment; carrying out additive dynamic aggregation on the sampling points by using a parameter matrix after vectorization definition, and calculating hidden state variables of the sampling points at the current moment; constructing a parallel state space model based on the input image at the current moment, the hidden state variable and the plurality of spatially aligned parameter matrixes; and inputting an input image at the current moment into the dynamically aggregated parallel state space model to obtain extraction features. According to the method, the limitation of a serialization model is broken through on the premise of keeping the linear complexity, so that the two-dimensional image can be efficiently modeled.

The invention discloses an instance segmentation method in a low-contrast environment and a readable storage medium, and belongs to the technical field of image processing, the instance segmentation method in the low-contrast environment introduces an adaptive state space module, performs long-range dependence modeling based on a down-sampled input image to obtain a state space feature, and obtains a state space feature; and performing multi-scale edge feature extraction based on the state space features by adopting a multi-scale adaptive edge enhancement module to obtain multi-scale edge enhancement features, screening a final prediction frame of the target object through an instance segmentation network based on the multi-scale edge enhancement features, generating a final mask of the target object, and obtaining the final mask of the target object. And the target object is subjected to instance segmentation according to the final mask, so that sequence modeling of linear complexity is realized, excellent long-range dependence modeling capability is obtained while efficient calculation is kept, and the edge features of the object to be subjected to instance segmentation can be well adaptively enhanced in a low-contrast situation.