2311 results about "Encoder decoder" patented technology

The invention discloses a photovoltaic power station unmanned inspection method and system based on multi-mode fusion detection, and relates to the technical field of intelligent operation and maintenance of photovoltaic power stations. The method mainly comprises the following steps: (1) collecting multi-modal data of a photovoltaic module and carrying out preprocessing operation; (2) uniformly mapping the heterogeneous data into discrete marks by using each pre-trained modal data marker; (3) training a multi-modal fusion detection network based on a Transform encoder-decoder architecture by using the training sample, and adjusting network parameters to obtain a trained model; and (4) outputting a defect type, a position bounding box and a severity score by utilizing the trained model according to the obtained multi-modal data or single-modal data of the photovoltaic module. The problems that traditional single-mode detection is high in omission ratio and multi-mode fusion is low in efficiency are solved, the inspection collection module, the data processing module, the multi-mode fusion detection module and the fault decision module are provided in a targeted mode, and full-process automation from data collection to intelligent decision is achieved. The operation and maintenance cost of the photovoltaic power station is obviously reduced; and the fault response efficiency is improved.

The invention discloses a vehicle intelligent driving decision optimization method based on deep learning. The method belongs to a vehicle intelligent driving decision technology. Firstly, various sensors are installed on a vehicle to collect data, and data sets are divided according to a proportion after cleaning and marking. A model containing an encoder-decoder and an attention mechanism is constructed, the weight is initialized, and a hyper-parameter and anti-overfitting strategy is set. And training the model by using a specific algorithm, calculating a loss value and evaluating and adjusting according to a verification set. After the model is integrated to the system, a preliminary candidate decision is determined according to a probability threshold, the decision is optimized in combination with a vehicle surrounding environment information set and a risk assessment function, and decision probabilities are redistributed in real time according to environment changes by means of an environment perception feedback mechanism. The method overcomes the defects of the traditional technology, utilizes the advantages of deep learning, effectively improves the driving decision precision, enhances the adaptability of the system to complex road conditions and environments, and guarantees the safe and efficient operation of intelligent driving.

The invention discloses a person and post matching method, system and equipment based on Transform and a medium, belongs to the technical field of cross application of natural language processing and human resource management, and aims to solve the technical problem of how to reduce subjective errors in a person and post matching process, improve matching accuracy, fairness and objectivity and improve manual matching efficiency at the same time. According to the technical scheme, the method comprises the following steps: data collection: collecting post information data and job seeker resume data from a plurality of data sources, and cleaning and sorting the collected data to obtain a post description text and a resume text; text preprocessing: preprocessing the acquired and sorted post description text and resume text to obtain the preprocessed post description text and resume text; the method comprises the following steps of: fusing a Transform model and a BERT model; constructing an encoder-decoder structure based on the Transform model; optimizing the model; multi-label text classification: constructing a multi-label classification system; and outputting and evaluating a matching result.

The invention provides a microcrack precise segmentation method integrating feature fusion and convolution attention, and belongs to the field of image processing. According to the method, a crack segmentation network based on an encoder-decoder architecture is constructed, a convolution block attention module is introduced at an encoder end, background noise is adaptively suppressed and obvious characteristics of cracks are enhanced through a channel and space dual attention mechanism, and the method is suitable for the adaptive segmentation of the cracks on the premise of almost not increasing the calculation overhead. The sensitivity of the model to microcracks is improved; a feature fusion module is introduced at a decoder end, and cooperation of low-layer details and high-layer semantics is realized through cross-layer fusion, so that a semantic gap is effectively bridged, detail loss caused by traditional convolution stacking is avoided, and continuity and a complete topological structure of a long and narrow crack are ensured. According to the method, through collaborative optimization of multi-scale feature extraction and an attention mechanism, accurate capture of the saliency features of the crack and effective suppression of complex background interference are realized, and the detection sensitivity and overall segmentation consistency of the micro-crack are remarkably improved.

A method and apparatus for reconstructing a three-dimensional (3D) scene from a two-dimensional (2D) input image of the scene using a fully-differentiable transformer-based encoder-decode. A 2D input image encoded into a set of image features using a pre-trained vision transformer model, wherein the vision transformer model is pre-trained with multi-view RGB image supervision and point cloud supervision. The set of image features is projected onto a 3D triplane representation using a transformer decoder to obtain output triplane tokens. A triplane representation is created from the tokens and queried. 3D point features of color and density for volumetric rendering re predicted using a multi-layer perceptron. The geometry of the generated 3D asset is represented with a surface mesh including vertices and triangular faces. A texture map by is created with a multichannel image in UV space. Multiple views of the 3D scene are simultaneously generated based on the surface mesh.

The invention provides a three-dimensional seismic fault identification method based on double-attention multi-scale fusion U-Net. The method comprises the following specific steps: constructing a U-shaped network architecture comprising an encoder, a decoder and jump connection; a constructed multi-scale feature fusion module is embedded in the first level of the encoder, and the extraction capability of fault features of different scales is enhanced through a multi-branch structure; introducing the constructed hole fusion modules into the second and third levels of the encoder, designing and expanding a receptive field by using multiple expansion rates, and capturing fault structures of different scales; a double-attention parallel mechanism is integrated in jump connection, and the sensitivity of channel attention and space attention to fault features is improved; constructing a combined loss function; and finally, performing three-dimensional seismic data training and reasoning based on the optimized model to realize high-precision fault identification. The method has high generalization and accuracy, and especially has good performance in the aspect of seismic image fault identification containing a large fault scale span.

Automatic recognition of non-verbal messages in speech, and in particular to detection or analysis of prosodic multilayered analysis of intonation units such as prosodic unit prototypes and their multi-labeled variations, may form a hierarchical classification for the analysis of non¬ verbal information or cues in speech. A speech captured by a microphone is fed to a weakly- supervised deep learning acoustic model for speech recognition and transcription, that may be based on encoder-decoder Transformer architecture, such as Whisper by OpenAI. The model is trained to output multiple words form the text in the captured speech, to identify Intonation Units (IUs) that include one or more words, and associate non-verbal labels to each of the IUs. The labels may indicate a prototype, a discourse function (such as a conversation action), an emotion, an emphasis, or an attitude, as well as a genre of a part of, or whole of, the entire captured speech.

Disclosed in the present invention is a non-autoregressive Transformer-based modeling method for a 4-level pulse amplitude modulation high-speed transmitter. The method involves establishing a deep learning model having an encoder-decoder architecture to predict the behavior of a 4-level pulse amplitude modulation transmitter. An encoder processes unordered non-sequential inputs, including an input signal parameter and link parameters, to generate a context vector and then transmit same to a decoder. The decoder uses both the context vector generated by the encoder and a transmitter output signal sequence to generate a categorical probability distribution for each point in the sequence one by one. The model is trained using a random masking strategy, and inference is performed by means of non-autoregressive decoding and filtering, so that the model can perform parallel prediction on an output sequence, and perform a filtering process to predict an output signal. Compared to traditional simulation methods, the present invention achieves a significant acceleration effect, particularly when processing multi-link systems.

The invention discloses an infrared small target detection method fusing local prior and a multi-scale global background, and the method comprises the steps: firstly obtaining image data containing an infrared image and a mask label corresponding to the infrared image, and carrying out the preprocessing; secondly, a target detection model of an encoder-decoder architecture is constructed, an encoder comprises a local detail prior mining branch and a multi-scale global background perception branch which are parallel, step-by-step feature extraction is performed on the preprocessed image data, and a decoder comprises a progressive feature fusion decoding branch; and inputting the features of each level of the encoder double branches into decoder branches for decoding step by step to obtain a detection result. And finally, a weighted depth supervision mechanism is introduced in training, auxiliary prediction output is set in a plurality of decoding layers, and weighting loss is calculated. According to the method, the problems of insufficient local detail modeling, insufficient multi-scale global background perception of Mamba, difficulty in global and local feature fusion and the like in the existing method are solved, and the detection precision of the infrared small target is improved.

The invention provides a medical image segmentation method based on wavelet transform and an attention mechanism, and belongs to the technical field of image segmentation of medical image processing. The technical problem that semantic differences between global features and features of coders and decoders at the same level cannot be captured due to limitation of a traditional CNN receptive field is solved. According to the technical scheme, the method comprises the following steps of 1, preprocessing an input polyp medical image; step 2, constructing an encoder decoder network based on wavelet transform and an attention mechanism; 3, inputting the preprocessed polyp image data set into an encoder-decoder network of a wavelet transform and attention mechanism for training, and reserving an optimal model; and step 4, after training is completed, inputting the polyp medical image verification set into the obtained optimal model. And detecting the focus segmentation effect of the model in the polyp medical image verification set. According to the method, the polyp lesion can be segmented more accurately, and the accuracy and robustness are improved.

The invention relates to a medical image segmentation method and system based on residual Mama and multi-scale boundary enhancement. The method comprises the following steps: acquiring and preprocessing a medical image; inputting the image into a segmentation model based on an encoder-decoder architecture; the encoder synchronously extracts local texture features and models long-range spatial dependence through residual error convolution blocks and residual error Mama blocks which are alternately connected; fusing and enhancing the jump connection features between the encoder and the decoder through a boundary enhancement module to optimize boundary characterization; integrating a multi-scale gating attention module in a decoding path, and adaptively selecting and fusing multi-scale context features; and finally outputting the high-precision segmentation mask. The method effectively solves the problems that in the prior art, long-range dependence and local details are difficult to consider, the multi-scale feature fusion capability is insufficient, boundary segmentation is fuzzy and the like, and the segmentation accuracy, the boundary continuity and the clinical practicability are remarkably improved.

The invention discloses an adaptive mask medical image segmentation method based on a self-supervised mask and deep reinforcement learning, and the method comprises the steps: employing a classic encoder-decoder architecture for a self-supervised mask reconstruction network, fusing a Swin Transform encoder, and carrying out the feature fusion of local image blocks through a self-attention mechanism; according to the self-adaptive mask model, a PPO deep reinforcement learning algorithm is adopted, a strategy network and a value network are constructed, mask actions are dynamically regulated and controlled, reconstruction errors are gradually reduced, a mask strategy is continuously optimized in multiple times of strategy updating for self-adaptive optimization, and high-quality reconstruction of a medical image influenced by missing information is achieved; according to the method, high-quality feature representation can be obtained in an unlabeled data environment, and relatively high precision and accuracy are presented on a public data set.

The invention belongs to the technical field of information technology and image recognition, and relates to a handwritten mathematical formula recognition method and system. The method comprises the following steps: constructing handwritten mathematical formula annotation data; constructing an initial handwritten mathematical formula recognition model, training the initial model by using the annotation data, and performing joint optimization on a sequence decoding task and a tree structure prediction task to obtain a handwritten mathematical formula recognition model; and carrying out handwritten mathematical formula identification by adopting the handwritten mathematical formula identification model obtained by training. According to the method, the advantages of a sequence decoding model and a tree decoding model are fused, the understanding and generalization ability of the model to a mathematical formula structure are effectively improved, and the accuracy of handwritten mathematical formula recognition is remarkably improved on the basis of keeping the efficient parallel training advantages of an existing encoder-decoder model-based method.

The invention discloses an image deblurring system and method based on Transform and multi-scale feature fusion, and relates to the technical field of image processing, the system comprises an input layer, a processing layer and an output layer, the processing layer comprises a multi-input multi-output encoder-decoder module, the multi-input multi-output encoder-decoder module is used for extracting features from blurred images of different scales through an encoder, and outputting the features to the output layer; a clear image is gradually restored based on the extracted features through a decoder; the multi-feature multi-scale fusion module is used for integrating local and global feature information and enhancing the learning ability of the system to global features; according to the improved Transform module, a self-adaptive window mechanism and a cross-channel attention mechanism are introduced, so that the system can better adapt to different types of blurred images, and the effects of feature extraction and image restoration are improved; and the attention fusion module dynamically learns and integrates useful feature information by introducing a channel attention mechanism. The image deblurring method is suitable for various image deblurring application scenes, and image detail features can be effectively recovered.

The invention discloses a vacuum preloading soil settlement prediction and control method based on deep learning, and aims to realize precise prediction and dynamic closed-loop control of soil settlement. According to the method, an encoder-decoder architecture is adopted to construct a model framework, and off-line generation of a proprietary model is completed through multi-measurement-point space-time joint training and a combinatorial optimization strategy; multi-source data of a target monitoring point is collected and processed to generate a sequence sample of an adaptive model; in order to adapt to the time-varying characteristics of the geological attributes, deploying an incremental online learning mechanism, and solidifying and finely tuning model parameters through an elastic weight; and based on a prediction result of the updated model, a PID control unit calculates a vacuum degree adjusting amount, a vacuum pump control system is driven to dynamically adjust the operation vacuum degree, and it is ensured that soil settlement is within a reasonable range. Finally, a cooperation mechanism of high-precision settlement prediction and dynamic vacuum degree regulation and control is constructed, and the settlement control precision, the response speed and the construction safety are remarkably improved.

The invention discloses a dam break flow rapid prediction method and a dam break flow rapid prediction system adopting a Transform-ResUNet model. Comprising the steps of obtaining actual dam break data or obtaining a data set for model training through numerical simulation; a dam break flow space-time coupling prediction model fusing Transform and ResUNet is constructed, the dam break flow space-time coupling prediction model is an encoder-decoder architecture, an encoder extracts spatial features of a water flow field through a plurality of residual convolutional layers, then feature maps are partitioned and flattened into time sequence features, and the time sequence features are input into a parallel stacked Transform module for time dynamic modeling; the decoder fuses the spatial features and Transform enhanced time sequence features through cross-layer connection, and reconstructs a predicted water flow field at a future moment through bottleneck residual blocks which are up-sampled and stacked step by step; and training the constructed dam break flow space-time coupling prediction model, and realizing rapid prediction of the dam break flow by using the trained prediction model. According to the method, high-fidelity flow field detail reconstruction is realized in flow field prediction based on an improved ResUNet encoder-decoder structure.

The invention belongs to the technical field of artificial intelligence and data science, and discloses a causal discovery method and related device based on a large model and structural constraint, comprising the following steps: according to meta-information of each causal variable, adopting a large language model and combining multiple rounds of dialogues, multi-role play and auxiliary cue word modes, generating an initial potential causal graph of each causal variable; repeating the following steps until a preset condition is met, and outputting a candidate causal graph with the highest value score: generating the candidate causal graph by adopting a policy network based on an encoder-decoder architecture, and carrying out iterative optimization on the candidate causal graph by adopting a reinforcement learning algorithm based on an act-critic framework; and calculating a value score of the candidate causal graph after iterative optimization according to the value network, and updating parameters of the policy network based on the encoder-decoder architecture according to the value score. The efficiency and accuracy of causal relationship recognition are remarkably improved, and a causal relationship graph with high confidence can be generated in a complex data environment.

The invention discloses a radar echo extrapolation method and system based on frequency domain enhancement, and the method mainly comprises the following steps: obtaining and preprocessing a historical radar echo grayscale image sequence, generating a sequence sample through a sliding window, and dividing the sequence sample into a training set, a verification set and a test set; the method comprises the following steps: constructing a frequency domain enhanced U-Net network comprising an encoder-decoder structure, introducing a multi-scale deep convolution structure into an encoder and a decoder, and enhancing frequency domain features by using a frequency domain dynamic attention mechanism in jump connection; inputting the training set into the model for training by adopting a composite loss function comprising intensity weighted loss, frequency domain consistency loss and structural similarity loss; and inputting the test set into the trained model, and outputting a radar echo prediction result at a future moment. The method can be effectively applied to the fields of short-term and temporary weather forecast, severe convection monitoring and the like, and provides more accurate and reliable radar echo prediction support for meteorological disaster early warning.

The invention relates to the technical field of virtual power plant fault early warning, in particular to a virtual power plant fault early warning method based on a hierarchical interactive causal graph Transform, which comprises the following steps: establishing a three-level hierarchical interactive causal graph; according to a multi-resolution causal distillation mechanism, global causal association modeling is carried out on an encoder-decoder architecture of the Transform; a multi-level contrast causal attention mechanism is introduced into a Transform model for automatic quantitative analysis; performing false correlation elimination on the inter-layer complex causal relationship according to a high-order Markov random field framework; constructing an interlayer information transfer mechanism model based on the conditional variational graph diffusion model and the actual interlayer complex causal relationship; optimizing the interlayer information transmission mechanism model; and identifying a potential cascade fault path of the virtual power plant according to the optimized interlayer information transfer mechanism model. According to the method, the potential cascade fault path can be accurately identified, and the early warning accuracy of the virtual power plant in a complex fault scene is remarkably improved.

The invention relates to a spatio-temporal prediction method and system based on meta-pattern mining and prompt guidance, belongs to the technical field of spatio-temporal data mining, solves the problem of inaccurate prediction of a complex scene with high spatio-temporal heterogeneity in the prior art, and comprises the following steps: S1, collecting historical spatio-temporal data in a to-be-predicted spatial region, performing standardization processing on the historical spatio-temporal data to obtain a standard data set; s2, constructing a prediction model based on meta-mode mining and prompt guidance, wherein the prediction model comprises an encoder, a decoder, a self-supervision meta-mode mining module, a space-time prompt generation module and a space-time prompt guidance module; s3, training the constructed prediction model based on meta-pattern mining and prompt guidance by using a standard data set to obtain a trained prediction model; and S4, applying the trained prediction model to prediction of future spatio-temporal data to obtain a spatio-temporal prediction result of the future traffic road speed.

The invention discloses a concrete crack detection method based on a hybrid Mama attention segmentation model, and the method comprises the steps: 1), obtaining an original image, carrying out the preprocessing of the image, and dividing a data set; 2) constructing an MA-UNet model, adopting an encoder-decoder symmetric structure, extracting features by an encoder through convolution and pooling, recovering resolution by the decoder through up-sampling of transposed convolution, and fusing high-resolution detail features of the encoder with deep semantic features of the decoder through jump connection; (3) a visual state space module VSS is adopted to suppress background noise and reinforce crack features through a dynamic focusing mechanism; 4) performing grouping channel attention weighting on the feature map through a multi-head depth separable channel attention module MDCA; according to the method, the calculation complexity and the parameter quantity are reduced through the lightweight module design, the continuous segmentation capability of the model on the micro cracks is enhanced, and the anti-interference performance under the complex background is improved.

The invention relates to a wind power equipment blade fault detection technology, discloses an unsupervised wind power equipment blade fault detection method based on a phase perception parallel attention mechanism, and solves the problems that an existing wind power equipment blade fault detection method is high in dependence on labeled data, insufficient in generalization ability under strong noise and variable working conditions and high in fault detection efficiency. And a weak transient fault signal and a dynamic change characteristic are difficult to capture robustly. According to the scheme of the invention, the method comprises the steps: collecting a blade operation audio signal, and extracting a dual-channel time-frequency feature containing an amplitude spectrum and a phase spectrum through improved short-time Fourier transform; a deep adversarial auto-encoder is constructed by using an encoder containing a phase perception parallel attention module, a decoder and an auxiliary encoder, and normal working condition feature distribution is learned by reconstructing an error loss, potential representation consistency loss, adversarial loss and phase consistency loss optimization model during off-line training; in the reasoning stage, the fault is judged based on the feature distance score and the reconstruction error score.

The invention belongs to the technical field of AI vision, and particularly relates to a universal six-degree-of-freedom grabbing pose detection method and system for a robot. According to the detection method, the point cloud feature extraction process is optimized by introducing an attention mechanism, so that the prediction precision of the detection head on the six-degree-of-freedom grabbing pose is improved. The method comprises the following steps: firstly, carrying out serialization processing on unstructured, sparse and non-uniform point cloud data generated from an RGB-D image, and then designing a multi-encoder-decoder cascaded backbone network based on a Transform architecture so as to effectively extract rich features of the point cloud. On the basis, a network module special for six-degree-of-freedom grabbing pose detection is designed, and the predicted six-degree-of-freedom grabbing pose is optimized in combination with antipodal grabbing constraints, so that it is ensured that the six-degree-of-freedom grabbing pose conforms to actual physical rules. Through verification of a grabbing experiment on an actual Franka robot, the general grabbing pose detection performance of the method on any object in actual application is further proved.

The invention provides a marine engine lubricating oil system fault diagnosis method and system based on transfer learning from a simulation domain to a real domain, and the method comprises the steps: carrying out the fault simulation of different working conditions based on a built marine engine lubricating oil system simulation model, and obtaining a simulation fault signal response; acquiring real domain fault data of different working conditions of an engine lubricating oil system based on an engine experiment platform; a feature fusion structure based on an encoder-decoder is adopted to obtain simulation domain fault data; an engine lubricating oil system fault diagnosis model based on data migration and algorithm migration dual migration learning is obtained; and based on the engine lubricating oil fault diagnosis model, performing real-time fault diagnosis on the marine engine lubricating oil system to obtain a diagnosis result. According to the method, transfer learning from simulation domain fault features to real domain fault features is realized, so that the problems of high construction cost and insufficient generalization performance of a diagnosis model are solved.

The invention provides an evidence obtaining method and system based on image processing, and the method comprises the following steps: S1, generating a pixel-level depth-of-field distribution diagram of an input image through a multi-scale encoder-decoder network, employing an edge perception optimization layer in a decoding stage, and improving the depth-of-field boundary precision through minimizing a local gradient consistency loss function; s2, performing depth-of-field rationality verification based on an optical imaging physical model, and triggering a first-level tampering alarm by calculating a defocusing fuzzy radius and a gradient direction of a selected region when a difference between the defocusing gradient directions of a target region and a background region exceeds a preset threshold value; and S3, dynamically positioning a key pixel region, identifying a depth-of-field mutation boundary by using an edge detector, calculating by combining local texture complexity, screening a pixel set of which the entropy value is higher than a threshold value and which is located at the mutation boundary, correlating metadata to verify the rationality of the physical size and the spatial position of an object, and eliminating false detection caused by perspective transformation.

The invention discloses a human body posture estimation method based on millimeter wave radar point clouds, which comprises the following steps of: processing each frame of input millimeter wave radar sparse point clouds by utilizing a built posture estimation model, and finally predicting and outputting a three-dimensional coordinate sequence of corresponding human body key joint points; wherein the attitude estimation model is composed of a point cloud completion network based on an encoder-decoder architecture, a global-local double-branch network and a feature fusion and prediction mechanism, and the method comprises the following steps: firstly, enhancing the density and integrity of an original sparse point cloud by using the point cloud completion network; the method comprises the following steps: complementing point clouds and original point clouds, respectively processing the complemented point clouds and original point clouds through a global-local double-branch network, extracting global structure features and local detail features, finally fusing the two features through a feature fusion and prediction mechanism, predicting three-dimensional coordinates of human body joint points through a regression layer, and realizing accurate attitude estimation. The method improves the estimation precision and robustness, maintains the privacy protection advantage, and is suitable for complex application scenes.

The invention discloses an extreme sea condition parameter identification system based on deep learning, and relates to the technical field of ship navigation auxiliary equipment, in particular to a self-adaptive sea condition identification device which is used for acquiring image data and inertial measurement data of a current sea condition; the wave field visual depth estimation module is used for extracting visible light image features and infrared image features of a wave area from image data of the current sea condition, fusing the extracted visible light image features and infrared image features, using an encoder-decoder architecture and fusing an energy function to obtain a pixel-level wave height field, and outputting the pixel-level wave height field. The three-dimensional reconstruction of the wave surface is realized; the multi-modal data fusion module uses a filter dynamic model and a cost function to eliminate space-time asynchronous errors between inertial measurement data and visual perception data, performs multi-modal data fusion, and outputs wave field real-time parameterization information. According to the invention, the sea condition parameter real-time high-precision identification capability of the autonomous unmanned ship or the offshore carrying platform can be improved.

The invention relates to the technical field of image compression, in particular to a hierarchical multi-context space adaptive image compression method. The method comprises the steps of obtaining a training set and a test set; constructing a spatial adaptive feature modulation network, wherein the spatial adaptive feature modulation network comprises an encoder, a decoder, a super-prior encoder and a super-prior decoder; constructing a multi-context joint entropy estimation model, wherein the multi-context joint entropy estimation model comprises a local context model, a channel context model, an in-layer anchor point context model and a cross-layer global context model; obtaining an image compression network in combination with the spatial adaptive feature modulation network and the multi-context joint entropy estimation model; respectively training and testing an image compression network by using the training set and the test set to obtain a trained image compression network; and inputting a to-be-processed image into the trained image compression network to obtain a reconstructed image. According to the invention, the image compression effect is improved.

The invention belongs to the technical field of image processing, and particularly relates to a wood surface defect detection method and device based on multi-view anomaly detection, and the method comprises the steps: obtaining a target image based on an original wood image; constructing a wood surface defect detection model comprising an encoder, a decoder and a defect marking module, constructing positive and negative samples based on the target image, and training the encoder by using the positive and negative samples through contrast learning; the output of the encoder is used as the input of a decoder, a loss function is constructed through a mean square error and a structural similarity index to train the decoder, and the decoder outputs a reconstructed image; obtaining a residual image between the reconstructed image and the target image through a defect marking module to obtain a fused residual image, and marking a pixel region exceeding a dynamic threshold as a defect; and detecting the preprocessed to-be-detected wood image by using the trained wood surface defect detection model to realize wood defect detection and pixel-level positioning.

The invention discloses a 2D medical image segmentation method and system based on Mama and UNet, and the method comprises the steps: collecting and preprocessing a medical image segmentation data set, and obtaining a training set; constructing a 2D medical image segmentation model based on Mama and UNet, wherein the 2D medical image segmentation model comprises a block embedding layer, an encoder, a decoder and a prediction generation layer; designing an adaptive hierarchical loss function based on gradient statistics, and training the 2D medical image segmentation model on the training set; and inputting the medical image with segmentation into the trained model to complete image segmentation. According to the invention, the method can achieve the automatic and intelligent segmentation of the medical image through the innovative construction of the 2D medical image segmentation model based on Mamba and UNet, and is higher in segmentation accuracy and efficiency.