87 results about "Max pooling" patented technology

The invention discloses a medical image generation method and device based on bimodal fusion, equipment and a medium, and the method comprises the steps: respectively extracting the visual features of a medical image and the semantic features of text description through an image encoder and a text encoder; mapping the two types of features to a shared semantic space by adopting comparative learning to realize cross-modal alignment; a first path captures hierarchical semantic information through a convolutional neural network, a second path retains local significant features through maximum pooling, and two paths of outputs are fused layer by layer to construct spatial context features; projecting the cross-modal features into a spatial feature map through a multi-layer perceptron, splicing the spatial feature map with coding features, and inputting the spliced spatial feature map into a decoder for up-sampling reconstruction; and performing joint optimization on the comparison loss and the structural similarity loss to realize end-to-end training. And finally, the SSIM index of the generated medical image is improved.

The invention provides a lightweight bearing surface defect small target detection method based on improved YOLOv8. According to the method, based on a YOLOv8n network architecture, a dynamic detection head is constructed, an ADown module is introduced, a small target detection layer is additionally arranged, and finally an improved YOLOv8n-DAS model is established; the method comprises the following steps: firstly, constructing a dynamic detection head, fusing scale, space and task perception attention mechanisms, intensifying feature expression ability in all directions, and accurately capturing complex tiny defect features on the surface of a bearing; secondly, the ADown module highlights edge defect information, simplifies the model parameter scale and reduces consumption of computing resources by combining traditional convolution, average pooling and maximum pooling operations; finally, small target detection layers are arranged at the neck and the head of the network, the small target detection layers are added, an extra small target detection head is arranged, shallow details and deep semantic information are deeply integrated, key details are reserved to the maximum extent, and the recognition capacity of small targets is improved. According to the method, the detection performance of the model on irregular and tiny defects and the detection capability of the model on small target defects are remarkably improved, the parameter quantity and the calculation complexity of the model are reduced, and deployment and application on lightweight equipment with limited resources are facilitated. And the method can be expanded to defect detection of industrial parts such as gears and blades by replacing training data, has the characteristics of light weight and high universality, and meets multi-scene deployment requirements.

The invention relates to the technical field of artificial intelligence prediction, in particular to an artificial intelligence-based teenager psychological health development trajectory prediction system, which comprises a data acquisition module, an emotion recognition module, a trajectory prediction module, a risk assessment module and an intelligent feedback module, and provides a feature extraction method based on a point cloud structure. Time sequence feature modeling is carried out by combining K nearest neighbor with Transform, and global feature extraction is carried out by using position sensing optimization and Max Pooling, so that key features of the mental health state of the teenagers are extracted more comprehensively; according to the method, a clustering-based bidirectional LSTM is combined with a GRU prediction model, firstly, DBSCAN is used for clustering, different mental health state modes are defined, Bayesian optimization and Bayesian filtering are adopted for improving the prediction precision, and finally more accurate mental health development trajectory prediction is provided.

The invention relates to a mechanical fault diagnosis method and system based on deep learning. The method comprises the following steps: converting a multi-source time domain signal into a time frequency image through continuous wavelet transform, extracting features by using a primary feature encoder, and extracting cross-source common features through adversarial training of a shared feature discriminator; therefore, a gated multi-scale encoder is guided to enhance common feature expression, and deep fusion of multi-source features is realized through a cross multi-head attention network. Global average pooling and maximum pooling are synchronously carried out on the fused features to give consideration to overall and local information, and a comprehensive feature vector is formed; and finally, by means of a double-branch diagnosis network, the training loss of the multi-class network is dynamically weighted according to the prior probability output by the binary network, so that multi-source information is effectively fused under the condition of data imbalance, and the accuracy and robustness of fault classification are remarkably improved.

The invention belongs to the technical field of abnormal driving recognition, and particularly relates to an abnormal driving behavior recognition method based on multi-modal sensor data fusion, and the method comprises the steps: obtaining a road image in front of a vehicle and vehicle speed data; the method comprises the following steps: respectively converting vehicle speed data by applying short-time Fourier transform (STFT), a Markov transition field (MTF), a Gramb angle sum field (GASF) and a Gramb angle difference field (GADF), and splicing conversion results to obtain a speed spectrogram; constructing a feature extraction sub-network based on an asymmetric space attention convolution block, an asymmetric channel attention convolution block, a stride convolution layer, a maximum pooling layer and a full connection layer; respectively carrying out feature extraction on the road picture and the speed spectrogram; and carrying out double-flow feature fusion on the features extracted from the road image and the speed spectrogram, carrying out identification by using a classifier based on the obtained fusion features, and outputting a classification prediction result of the driving behavior. According to the invention, the accuracy and robustness of abnormal driving behavior recognition are improved.

The invention discloses a mature rapeseed oil content prediction method and system based on a deep learning model, and the method comprises the following steps: deleting a decoding layer in an image segmentation network U-Net, and replacing a convolution module in a U-Net coding layer with a residual connection module, the residual connection module is used for performing a series of convolution and nonlinear transformation on the mature rapeseed samples to obtain depth feature representation of the samples; the input of the residual connection module and the depth feature representation are added through jump connection to serve as the output of the residual connection module; a time sequence feature extraction module and an average pooling module are introduced into the U-Net, the time sequence feature extraction module is used for extracting time sequence features in mature-period rapeseed samples, the detail features and the time sequence features are spliced and then input into the average pooling module and a maximum pooling module, and local features and global features in the samples are further extracted; the oil content of the rapeseed sample in the mature period is output through the full connection layer.

The invention belongs to an image processing technology, and particularly relates to an underwater image enhancement method and system based on combination of multiple convolution models and an attention mechanism, and the technology is formed by combination of a DIAM model and a Shallow-UWnet improved model. The DIAM model is mainly used for carrying out color restoration in a mode of complementing hue weakening and non-uniformity of the underwater image, and can solve the problems of underwater noise, atomization and the like. According to the Shell-UWnet improved model, on the basis of Shell-UWnet, a highest pooling layer and an Inc multi-convolution module are added, the highest pooling layer effectively reduces network parameters and calculation overhead through dimension reduction operation, the operation efficiency is improved, the key feature extraction capacity is enhanced, detail loss is avoided, Inc can extract features from different scales, and the algorithm is simple and convenient to operate. And the feature capture capability of the model is enhanced.

The invention discloses a pseudo label generation method, system and device based on pathological image segmentation and a medium, and the method comprises the steps: segmenting a pathological full-slice image into a plurality of square small-block images, and carrying out the multi-label labeling of the square small-block images; inputting the marked square small block images into a pre-trained segmentation model, and extracting features of the marked square small block images to obtain a feature map; based on the feature map, information spreading is carried out by using an attention mechanism, and feature representation of the nodes is updated; according to the feature representation of the updated node and the spatial information of the node, generating probability distribution of each node belonging to different categories, and obtaining a pseudo label; refining the pseudo labels; and optimizing the refined pseudo tag to obtain a final pseudo tag. The image attention network and the global classification maximum pooling technology are introduced, and the accuracy and stability of pathological image segmentation are improved by establishing the context relation between pixels, enhancing transmission of weak supervision signals and optimizing the pseudo label generation process.

The invention relates to the technical field of ship detection, in particular to a high-precision lightweight method for ship target detection of a synthetic aperture radar. The high-precision lightweight method for synthetic aperture radar ship target detection comprises the following steps: constructing a lightweight backbone network: based on a YOLOv8 algorithm, introducing a GCADDown module and an ERC2f module, and the GCADDown module reducing the calculation amount through the combination of average pooling, maximum pooling and Ghost convolution. According to the invention, by reducing detection heads, an existing YOLOv8 algorithm is enabled to better fit a radar ship image target detection task, but precision loss caused by reduction of the detection heads is made up by providing an EDWR module, the loss is made up, a light-weight and high-precision model is constructed, and the detection efficiency is improved. The complexity of the algorithm is further reduced, and meanwhile the high precision of the algorithm can be kept.

The invention discloses a medical image segmentation system and method combining position and channel double attention, the system comprises a feature encoder and a feature decoder, the ResNet50 part of the feature encoder comprises a standard convolution layer, a group normalization layer, a ReLU activation function, a maximum pooling layer and three different stage blocks; the ReLU activation function and the output features of the first two stage blocks are respectively connected to cascade up-sampling units of different levels of a feature decoder through a double attention block so as to reconstruct a feature map; the output feature of the third stage block is connected to a Transform encoder part of the feature encoder through a double attention block; the double attention block extracts features of sparse codes from the angles of positions and channels; a coding block of the Transform encoder adopts a space reduction attention layer to replace a multi-head self-attention layer. According to the method, the robustness of the model is enhanced, the sensitivity of the model to overfitting is effectively reduced, and the overall performance and generalization ability of the model are improved.

The invention relates to a high-fidelity point cloud completion method and system based on a double-path attention and fractal structure. The method comprises the following steps: processing an input point cloud by an iterative farthest point sampling algorithm; extracting and constructing a joint feature vector by combining an extended combined multi-layer perceptron with a double-path attention mechanism; reconstructing missing region point clouds in a layered manner by using a pyramid point generator; designing a composite loss function and a dynamic weight scheduling strategy to optimize a reconstruction target; designing an adversarial training framework of a discriminator based on a built-in spectrum normalization and gradient penalty mechanism; and reasoning to generate a point cloud reconstruction result. According to the method, the problem that a traditional point cloud completion method depends on geometric prior and a matching template is solved, the limitation that calibration on semantic information of different dimensions is lacked, excessive dependence on maximum pooling is achieved, and the uniformity of discriminator gradient anomaly and a loss function is difficult to guarantee is relieved; and the geometric detail recovery capability and the visual authenticity in a complex structure scene are obviously improved.

The present invention relates to a method for thermal video surveillance based on an Encoder-Decoder-induced feature pooling module. The method is explained as follows: An input thermal image that is to be processed, by a pre-trained ResNet-152 deep learning network, wherein the network comprises several convolutional layers, batch normalization layers, and a rectified linear unit (ReLU) function to extract features at low, mid and high levels, and the in-depth target features; receiving, by a feature pooling module (FPM), from the deep learning network, wherein the feature pooling module comprises of a max pooling layer, a convolutional layers, and various atrous convolutional layers for extracting the target features in higher dimensional features space at multi-scales; obtaining higher dimensional features space by a decoder network, wherein the decoder network is configured to project the higher dimensional features into image space for the generation of a probability mask.

The invention discloses a semantic and structure preserving-based point cloud adaptive downsampling method and device, and belongs to the field of computer point cloud analysis and feature learning. Firstly, point cloud features are extracted, a local neighborhood is constructed, and semantic features and space coordinates of all points are obtained; counting the number of times of selection of the feature channels in the neighborhood based on maximum pooling, and obtaining a local importance score through normalization; through cross-neighborhood aggregation and in combination with spatial distance attenuation weight, geometric consistency is enhanced, and a global importance score is generated; a lightweight multi-layer perceptron is used for fusing semantic and spatial features to predict a comprehensive importance score, and key points are selected according to the score to form a down-sampling subset; and finally, a teacher-student self-supervised training framework is adopted, and a high-confidence-coefficient pseudo tag is generated through a teacher branch to guide student branch parameter optimization, so that efficient reasoning is realized. According to the method, the semantic information and the geometric structure of the point cloud can be effectively kept while the data volume is remarkably reduced.

The application discloses a ground three-dimensional laser scanning point cloud collection system and relates to the field of three-dimensional point cloud data processing of the ground; the size of data input is 10 square meters; a KPConv algorithm is improved; the characteristics of each input point are expanded; Max Pooling is used to aggregate the relative position and the Euclidean distance of the field points and the center point; Average Pooling is used to aggregate the field characteristics of each point extracted through the multi-layer KPConv into global characteristics; and a set of point cloud visualization websites is deployed by using WebGL.

The invention relates to the technical field of image processing, in particular to a 3D point cloud classification segmentation method based on dynamic edge convolution and residual double attention, which comprises the following steps: acquiring an image to be processed; a 3D-AGCN network is constructed; feature alignment is carried out by using a Transform encoder, feature groups are obtained through a plurality of EADEC, RDA and AMFE modules, the feature groups are connected in series, and then convolution compression is carried out to obtain shared features; the classification branch performs global maximum pooling and average pooling on the shared features, and outputs classification scores; and the segmentation branches repeat the shared features and the one-hot category vectors to each point and then splice the shared features and the one-hot category vectors, and a segmentation score is output. The method solves the problem that an existing method still needs to be improved in the aspects of effectively fusing local details and global contexts and optimizing model performance.

The invention provides a distracted driving detection method based on depth separable convolution and multi-spectrum attention, and the method comprises the following steps: firstly, carrying out the efficient feature extraction of an input driver image through a feature extraction module based on depth separable convolution, so as to reduce the calculation complexity of a model and the number of parameters; secondly, a multi-spectrum attention mechanism is designed and integrated, and the capturing capability of the model on key features is enhanced through self-adaptive attention on different spectrum information, so that the accuracy of distraction driving detection is improved; secondly, carrying out diversified processing on training data by adopting a data enhancement technology so as to improve the generalization ability of the model; and finally, constructing a deep neural network architecture containing a maximum pooling layer and a classifier, and realizing real-time detection and classification of distraction driving behaviors. Compared with the prior art, the method has the following advantages: 1) by introducing the depth separable convolution, the calculation amount and the parameter scale of the model are significantly reduced, and the training and reasoning efficiency is improved; 2) a multi-spectrum attention mechanism enhances the attention capability of the model on key features, and improves the accuracy of distraction driving detection; 3) an optimized data enhancement strategy improves the generalization performance of the model and reduces the risk of overfitting; and 4) the whole model is simple in architecture and convenient to deploy and expand in practical application.

This invention discloses a UAV detection method based on multi-view feature fusion using residual networks, comprising: constructing multi-view data: obtaining the time-domain plot, short-time Fourier transform plot, continuous wavelet transform plot, and Wegener-Will distribution plot of the measured signal; constructing a ResNet34, including an input structure, an intermediate structure, and an output structure; the input structure processes the input data through convolution and max pooling operations; the intermediate structure consists of four similar structural layers, each consisting of multiple residual blocks, each residual block containing three convolutional layers and a shortcut connection; starting from the second structural layer, the initial residual block of each structural layer also contains an up-dimensional sampling structure; constructing a multi-view feature fusion network model based on residual networks, and outputting the UAV detection accuracy after multi-view feature fusion at the output end. This invention improves the UAV detection efficiency by fusing features from the multi-view data of the signal.

The present invention discloses an ultra-low complexity max pooling backpropagation architecture for CNN. The main objective of the present invention is to solve the problem that in the max pooling backpropagation of convolutional neural networks, when using the traditional recovery matrix algorithm, most of the values in the recovery matrix are zero, and directly storing the recovery matrix C will cause a large waste of storage resources. According to the sparse data characteristics in the input matrix, the present invention is implemented using an improved algorithm. It designs a Matrix mapping logical operation to directly obtain the mapping address of the input matrix in the convolutional kernel window and determines whether it is located in the current window, without restoring it to the original matrix. It optimizes the data storage scheme, selects non-zero values for convolution hardware implementation, thereby improving the hardware implementation frequency in the max pooling backpropagation, reducing the consumption of storage resources, lowering the hardware implementation complexity, and reducing the consumption of computing resources by selecting non-zero values to complete the convolution operation.

This invention discloses a method for reconstructing building point clouds based on a DGCNN model with an improved KNN algorithm. The method includes: normalizing the original building point cloud to be reconstructed to obtain normalized point cloud data; constructing a DGCNN network based on the improved KNN algorithm and training the DGCNN network to obtain a trained DGCNN model. The DGCNN network based on the improved KNN algorithm includes a spatial transformation layer, four graph convolutional layers, a max pooling layer, a first multilayer perceptron, and a second multilayer perceptron connected sequentially; and inputting the normalized point cloud data into the trained DGCNN model to obtain the corresponding prediction results. This invention utilizes the local update mechanism of the KD tree to efficiently and dynamically adjust the adjacency graph during network training, avoiding the high computational cost of reconstructing the entire search tree.

The application discloses a kind of real-time high-resolution portrait matting methods based on deep neural network, including obtaining training dataset, and marking generation training groundtruth alpha matte;Training data set is data enhanced;Network model is trained in step phase;Using trained network carries out matting.Through embedding ConvLSTM module in network configuration, using Max Pooling Indices, high-definition detail optimization is carried out using PRM, semantic segmentation task is added, and the core technology of high-precision real-time portrait matting is created, simultaneously, data set and data enhancement method are innovated, and training is carried out in stages, from simple to complex, from rough to fine, the training effect of algorithm is strengthened, the innovation and application of the three aspects interact, mutually unified, the performance and practicality of algorithm are comprehensively improved, and powerful technical support is provided for high-precision real-time portrait matting application.

The present invention discloses an image segmentation method based on a global perception U-Net model, specifically relating to the technical field of image segmentation. The technical key points are as follows: The global perception U-Net model includes an encoder, a decoder, a CFGC module, and a CSCP module. The first layer of the encoder adopts a first convolution module and a max pooling module connected in sequence. The remaining four layers of the encoder all adopt a CFGC module and a max pooling module connected in sequence. The last layer of the decoder adopts a second convolution module. The remaining four layers of the decoder all adopt a transposed convolution module, a CSCP module, and a CFGC module connected in sequence. The first four layers of the encoder are respectively connected to the first four layers of the decoder by skip connections; The CFGC module includes H a dimensional feature selection unit and W a dimensional feature selection unit, which is used to select feature information in the H and W dimensions at different scales, and based on H a dimensional feature selection unit and W a dimensional feature selection unit to perform feature enhancement on the global features in the form of matrix multiplication.

This invention provides a deep spiking neural network method for ECG classification based on attention and integer training pulse inference, comprising the following steps: acquiring raw ECG signal data and preprocessing the raw ECG signal data; performing three rounds of convolution and corresponding max pooling on the ECG data features; performing block-based local self-attention processing on the ECG data features to obtain feature associations in local regions of the ECG data features; performing global self-attention processing on the ECG data features to obtain feature associations across the entire sequence of the ECG data features; performing two rounds of convolution and corresponding max pooling on the ECG data features; classifying the integrated ECG data features using a classification head, and outputting the ECG data classification result. This invention can effectively mine long-range dependency information of ECG data and retain shallow feature information through residual fusion, avoiding the gradient decay problem in deep networks, thereby enabling deep spiking neural network learning.

The present invention discloses a method, device, storage medium and product for identifying structural damage of a cable-supported bridge. The identification method includes constructing a sample data set, where the input quantity of each sample data is an image obtained by splicing the time-frequency domain images converted from the acceleration response signals of the main girder and the stay cables respectively; constructing a damage identification model, which includes an input layer, an initial convolutional layer, a max pooling layer, a first combination layer, a second combination layer, a third combination layer, a fourth combination layer, a first global average pooling layer, a first fully connected layer and an output layer connected in sequence. The initial convolutional layer has 6 input channels, and each combination layer includes a convolutional block, an SE attention mechanism and a residual block connected in sequence; using the sample data set to train and verify the damage identification model to obtain a target damage identification model; using the target damage identification model to identify damage to the actual spliced image. The present invention significantly improves the damage identification accuracy.

Methods, systems, and / or apparatuses are described for detecting relevant motion of objects of interest (e.g., persons and vehicles) in surveillance videos. As described herein input data based on a plurality of captured images and / or video is received. The input data may then be pre-processed and used as an input into a convolution network that may, in some instances, have elements that perform both spatial-wise max pooling and temporal-wise max pooling. The convolution network may be used to generate a plurality of prediction results of relevant motion of the objects of interest.

The present invention discloses an entity relation joint extraction system and method based on hybrid feature representation. The system includes: a feature extraction module for extracting character-level feature vectors and word-level feature vectors from industrial text data; a feature fusion module for fusing the character-level feature vectors and word-level feature vectors using max pooling operation to generate hybrid feature vectors; a model construction module for constructing an entity relation joint extraction model based on a bidirectional LSTM encoder, a head entity recognition unit, an entity type classification unit, and a relation-tail entity recognition unit; and a joint recognition module for inputting the hybrid feature vectors into the entity relation joint extraction model to identify all entities and relations in the industrial text data. The present invention integrates feature information at multiple granularity levels, effectively handles the problem of overlapping triples, and can improve the accuracy of entity relation extraction.

The present invention discloses a three-dimensional object detection method for multimodal input and spatial partitioning, and proposes the following targeted strategies: using the original point cloud data and the RGB three-channel color image as multimodal input; partitioning the space of the original point cloud data, indexing the point cloud grouping row by row and column by column, randomly sampling K points, extracting features, and using the max pooling layer to reduce the dimension of the obtained K local-global feature vectors; slicing the RGB three-channel color image, indexing the slices row by row, and inputting them into the two-dimensional feature extractor VGG16 to only extract the shallow-layer relevant features of the texture color of the 8th layer, obtaining K color texture feature vectors; fusing the local-global feature vectors and the color texture feature vectors to obtain the fused feature vectors; passing through the fully connected layer, outputting the prediction results, and drawing the BBox according to the confidence level to complete the post-processing task. The present invention reduces the computational amount and improves the accuracy of classification and detection.

The embodiment of the specification provides a maximum pooling processing method and device for protecting privacy data. The method comprises the following steps: based on a local slice of an input matrix, a local slice of a first comparison matrix is determined by multiple parties, which indicates a comparison result of horizontally adjacent elements in the input matrix; based on the local slice of the first comparison matrix, a local slice of an intermediate result matrix is determined by multiple parties, an element of the intermediate result matrix being a larger value of horizontally adjacent elements in the input matrix; based on the local slice of the intermediate result matrix, a local slice of a second comparison matrix is determined by multiple parties, which is used for indicating a comparison result of vertically adjacent elements in the intermediate result matrix; and based on the local slice of the second comparison matrix, a local slice of a pooling result matrix is determined by multiple parties, an element of the pooling result matrix being a larger value of vertically adjacent elements in the intermediate result matrix. The communication overhead can be reduced, and the overall calculation efficiency can be improved.

The present invention discloses a three-dimensional reconstruction method based on fusing multi-view features and deep learning, including: collecting multi-view image data and corresponding SDF data; performing convolution operations on the input image data using a convolutional network to respectively obtain one-dimensional global features and multi-layer two-dimensional image features under multiple views; performing max pooling operations on the global features under multiple views to obtain the overall global features; projecting the input three-dimensional query points, querying the multi-layer image features with two-dimensional query points to obtain local point information, and splicing to obtain local feature information; performing point convolution operations to obtain the point features of the model; combining the point features with the global features and local features respectively to obtain the global features and local features with point feature information; combining the global features with the local features respectively and generating the final feature information by voting; finally generating a three-dimensional model.

The invention discloses a point cloud semantic segmentation method based on stage information fusion transformer and grouping normalization, and aims to improve the accuracy of point cloud semantic segmentation and reduce the operation cost. A traditional U-net network is adopted to divide the process into a coding stage and a decoding stage for five times, coding firstly adopts core point convolution to extract initial features of point clouds, then downsampling is carried out, features before and after sampling and grouping are utilized to carry out mutual normalization, then the features are linearly combined, and finally, the initial features of the point clouds are extracted; and finally, carrying out adaptive combination of maximum pooling and learnable weight pooling. And then transform attention operation is carried out, information of different coding stages is fused into an attention result, operation opposite to coding is carried out during decoding, attention calculation is carried out after up-sampling is carried out, information fusion is added, and finally segmentation is carried out.

This invention discloses a bridge bending detection method based on a lightweight multi-scale sparse gating network, relating to the field of bridge structural health monitoring. The method includes: acquiring multimode fiber speckle images corresponding to different bending states of the bridge and preprocessing them to obtain standardized speckle images; obtaining an initial feature map based on a lightweight multi-scale sparse gating network through initial convolutional layers and max pooling layers; extracting and fusing bending-sensitive features through a multi-level multi-scale feature fusion module to obtain a multi-scale fused feature map; using a learnable sparse gating module for feature selection to obtain a sparse enhanced feature map; modeling global dependencies through a global context enhancement module to obtain a globally enhanced feature map; and inputting the globally enhanced feature map into a dual-task prediction module to output the corresponding result. This method achieves synchronous, lightweight, and high-precision detection of bridge bending degree and location, effectively decoupling the problem of multi-parameter cross-sensitivity.