938results about How to "Improve the extraction effect" patented technology

The invention relates to the technical field of marine environment monitoring, in particular to a time-space diagram neural network anomaly detection method and system for marine ecology. The method comprises the following steps: acquiring ocean monitoring data; performing data preprocessing on the acquired marine monitoring data; constructing a space-time diagram structure based on the preprocessed marine monitoring data; performing spatio-temporal feature extraction on the constructed spatio-temporal diagram structure by using an anomaly detection model; carrying out feature fusion on the extracted spatial-temporal features; and carrying out local anomaly and global anomaly scoring on the marine ecosystem based on a multi-scale anomaly scoring mechanism. According to the method, a unified data processing framework is designed, multi-source heterogeneous data such as satellite remote sensing data, water quality monitoring data and biological survey data can be processed at the same time, information contained in various kinds of data is fully utilized, and the comprehensiveness and accuracy of anomaly detection are improved. Through the construction of a space-time diagram structure, the incidence relation between different data sources is effectively captured, and more comprehensive feature expression is provided for anomaly detection.

The invention discloses an autoregressive visual target tracking algorithm based on token fusion, and the algorithm specifically comprises the following steps: 1, obtaining a public visual target tracking data set, and converting the data set into a central coordinate format; 2, preprocessing an initial template of a given video sequence, an online template at the current moment and a search image to obtain visual embedding of the initial template, a dynamic template and the search image; the method comprises the following steps: preprocessing an angular coordinate sequence of a target frame in previous historical N frames of a current search image of an input video to obtain historical track word embedding of the current search image; 3, constructing an autoregressive visual target tracking model based on token fusion, and designing a loss function; step 4, obtaining a final autoregressive visual target tracking model based on token fusion; the method solves the problem of insufficient model feature extraction capability caused by insufficient utilization of spatio-temporal information and redundant background information of a search area in an existing method.

The invention relates to a PCB defect detection method and system based on a YOLOv8 network, and belongs to the technical field of target detection. Collecting and preprocessing a PCB sample image data set for training a PCB defect detection model; inputting a to-be-detected image into the trained defect detection model to obtain a detection result; the to-be-detected image is generated by collecting and processing image data of the to-be-detected PCB; wherein the defect detection model comprises a trunk network, a neck network and a head network, and the trunk network extracts features through a mobile reverse bottleneck convolution module and a C2f module to generate an intermediate feature map; and the neck network performs splicing fusion on the middle feature map to generate a spliced feature map, the head network performs re-weighting on the spliced feature map through a convolution kernel attention fusion module to generate a third feature map, and then the detection module outputs a detection result. The method not only improves the precision of the defect detection model in detecting the features of the tiny target, but also improves the detection speed and generalization ability, and reduces the calculation cost and complexity at the same time.

The invention discloses a segmentation method for rail defect detection based on improved YOLOv10 and SETR, and the method comprises the steps: S1, collecting image data, carrying out the enhancement processing of the image data, carrying out the segmentation processing, and making a data set; s2, an improved YOLOV10 algorithm model is built and trained; s3, using the improved YOLOV10 model weight to detect image data to be detected, judging whether defects exist or not, if the defects are detected, storing detection frame information of the defects, and outputting a target detection frame and confidence; and S4, transmitting the detection frame information as prompt information into the SETR model, segmenting the target by adopting the SETR model to obtain a segmentation result, and outputting a mask pattern. The improved YOLOv10 algorithm and the SETR algorithm are adopted to detect the rail defects, the calculation complexity and the efficient detection speed are kept, the detection precision is improved, and the robustness of the model is improved.

The invention relates to a Mama-based seismic image super-resolution reconstruction method, which comprises the step of constructing a local feature extraction module, a global feature extraction module and a high-quality seismic section reconstruction module so as to solve the problem of insufficient resolution caused by high-frequency attenuation and sparse acquisition. According to the invention, a Mama model is introduced, and a novel super-resolution reconstruction network is constructed. The local feature extraction module extracts shallow feature information through a convolutional layer; the global feature extraction module obtains global features through the multi-stage residual state space block; and the high-quality seismic image reconstruction module realizes reconstruction of a high-resolution seismic image through pixel recombination and convolution operation. In addition, a Charbonnier loss function is adopted, the gradient disappearance problem is effectively avoided, and the loss function of traditional Mamba is improved. According to the method, the seismic image resolution can be improved, and meanwhile, the calculation complexity and the time cost are remarkably reduced.

The invention discloses a YOLOv8 improved model training method and a ship detection method, and belongs to the technical field of target detection. According to the method, on the basis of a sample ship remote sensing image and a sample ship labeling remote sensing image, a YOLOv8 improved model comprising an OBB rotation detection model, a feature extraction network RFAnet, a dynamic attention scale sequence feature fusion network DSASS F and an SPPFLSKA module is trained. The method can effectively cope with the diversity of the target ship in scale, shape, direction and attitude, accurately captures the characteristics of the ship, and improves the ship detection precision and robustness in a complex marine environment.

The invention belongs to the field of target detection, and particularly relates to an infrared weak and small target detection method based on a double-branch attention mechanism. The method comprises the following specific steps: step 1, preparing data sets: performing data enhancement on a first infrared data set and a second infrared data set for pre-training, and performing model fine adjustment on a third infrared data set; 2, constructing a network model, wherein the whole model is composed of a target analysis path, a splicing path and a target reconstruction path; according to the method, a global semantic enhancement large-kernel attention module is introduced into a coding module of a target analysis path, global information is obtained and utilized from four angles of a channel, a transverse direction, a longitudinal direction and a receptive field, and a double-branch single-head attention module is introduced into a splicing path to obtain association degrees among different scales; the purpose of eliminating the semantic difference between the target analysis path and the target reconstruction path is achieved, the difference between the weak and small target and the background is expanded, and the detection precision of the infrared weak and small target is improved.

The invention discloses a rainy day target detection method based on paired image data generation and knowledge distillation, and belongs to the technical field of automatic driving target detection. The method comprises the following steps: acquiring a real image of a road scene and a semantic segmentation mask; inputting the semantic segmentation mask and the weather condition text information into a trained improved diffusion model to obtain a road scene generation image under a rainy day condition; introducing a CoT module and an ODConv module into the target detection model for optimization, and then improving the target detection model by adopting a dynamic weighted aggregation loss function to obtain an optimized target detection model; performing knowledge distillation to obtain a rainy day target detection enhancement model; and performing target detection by using the rainy day target detection enhancement model. The method provided by the invention is excellent in performance in a rainy day scene, has good generalization ability, can provide higher-precision target detection in various complex scenes, and effectively improves the detection accuracy and stability.

The invention relates to the technical field of artificial intelligence and the field of financial science and technology, and discloses a speech synthesis method based on a gated attention mechanism, and the method comprises the steps: obtaining text data and speech data, converting the text data into a text symbol sequence through a text encoder, speech features of the speech data are extracted through a speech encoder trained based on a self-supervised learning model, and the speech features are quantized into a discrete speech symbol sequence; then, respectively organizing the text symbol sequence and the voice symbol sequence into a text sequence and a voice sequence; the corresponding relation between the text symbols and the voice symbols is established through preliminary alignment processing, the matching degree between the text symbols and the voice symbols is dynamically adjusted through a gating attention mechanism, and finally a final voice signal is generated through a decoder. According to the invention, through the voice encoder trained by the self-supervised learning model, the extraction capability of voice features is effectively improved, and effective feature representation can still be learned from unlabeled data especially in a scene lacking a large amount of labeled data.

The invention provides an overhead transmission line defect detection method based on multi-scale feature fusion, and the method comprises the steps: carrying out the correction and registration of a lead image and a laser point cloud image, carrying out the data enhancement, and constructing a detection data set; the method comprises the following steps: respectively inputting two images into a target detection network for training, adding an attention mechanism in a feature extraction network Backbone so as to carry out correlation calculation on a target, and meanwhile, adopting a multi-scale feature fusion detection method in a backbone part of a YOLOV8 operation network, so that the improvement of precision and accuracy is realized on the premise of ensuring the size of a model, and the accuracy of the model is improved. And the requirement of actual overhead transmission conductor defect detection is met.

The invention discloses an infrared and visible light fusion method based on multi-feature extraction. A multi-feature extraction network is composed of a fusion network, a semantic segmentation network, a content loss function and a semantic loss function. The fusion network comprises a feature extraction part and a feature reconstruction part, and the feature extraction part comprises a convolutional layer with an activation function, an adaptive gradient feature extraction module and a space channel reconstruction convolution and residual gradient extraction module; the feature reconstruction part is composed of an attention mechanism and four convolution layers connected in series; the method comprises the following steps: S1, inputting an infrared image and a visible light image into a fusion network, and generating a fusion image through feature extraction and feature reconstruction; s2, transmitting the fused image to a segmentation network for segmentation; and S3, comparing the segmentation result with a semantic tag, and evaluating richness of semantic information contained in the fused image. The contour of the fused image is clearer, the target is clearer, and the index is remarkably improved.

The invention discloses an entity relationship extraction method fusing semantic information and syntactic information, and belongs to the technical field of language processing. According to the method, the information in the sentences is learned, so that the fusion of semantic and syntactic information is realized, and the accuracy of relationship classification between entities is improved. According to the method, an external syntax device and word vector information are combined, the influence of noise in a text is reduced by using a graph convolutional neural network of a self-attention pooling layer and a residual shrinkage network, and long-distance dependence between entities is improved. Meanwhile, loss is calculated in an interpolation prediction mode, and interaction between the BERT and the graph neural network is perfected. Experiments show that semantic vectors and syntactic structure information are fully fused, so that the relation extraction effect is remarkably improved compared with other reference models, and the problems that the recognition rate of the long-distance relation between entities in sentences is low and noise exists are solved.

The invention discloses a multi-scale perception feature anomaly positioning method based on knowledge distillation and VAE, relates to the technical field of industrial image anomaly detection, and is used for unsupervised image surface defect anomaly detection. Comprising the steps of image generation, pixel-level abnormal score calculation, teacher clone network, student encoder, multi-scale feature fusion, abnormal region segmentation and the like. According to the method, useful feature representations are extracted from input data by using a clone network C, activation values of features of each layer are extracted to a VAE encoder, and a multi-level double attention module is introduced for an image reconstruction module. The innovation not only relieves the problem of shallow feature interference existing in the model, but also improves the accuracy of the model for obtaining the image perception features and positioning the abnormality at the pixel level. Finally, the method achieves an excellent anomaly detection effect on a public data set, is obviously improved compared with other VAE methods, and proves the practicability and effectiveness of the method in a real scene.

The invention provides a polyp segmentation method and device. The method comprises the following steps: extracting global features of a polyp image by using a pyramid vision Transform model PVT, extracting local features of the polyp image by using a convolutional neural network U-Net, and fusing the global features and the local features by using a fusion module; a multi-scale enhancement module is used for processing a fusion result of the fusion module through a space attention mechanism so as to emphasize space information layer by layer, and a decoding module is used for decoding a processing result; and a boundary enhancement module is used to process a fusion result of the fusion module through cavity convolution and an attention mechanism to emphasize boundary information, and a processing result of the boundary enhancement module and a decoding result of the decoding module are fused and then decoded again to obtain a segmentation result of the polyp image. The accuracy and efficiency of polyp segmentation are improved.

The invention provides a mining area earth surface crack identification method and device and a computer program product, and the method comprises the steps: obtaining a mining area earth surface crack image of a target mining area earth surface, and carrying out the cutting processing of the mining area earth surface crack image, and obtaining a plurality of earth surface sub-images, the target mining area earth surface being the mining area earth surface which is currently subjected to crack identification; sequentially inputting the earth surface sub-images into an earth surface crack recognition model to recognize cracks in the earth surface sub-images by using the earth surface crack recognition model, and outputting sub-crack recognition results corresponding to the earth surface sub-images; and splicing the sub-crack identification results corresponding to the ground surface sub-images to obtain a crack identification result of the ground surface of the target mining area. According to the method, the problem of low mining area surface crack identification accuracy in the prior art is solved.

The invention discloses a plant disease and insect pest detection device and method based on unmanned aerial vehicle high-altitude texture imaging, and belongs to the technical field of intelligent agriculture. The plant disease and insect pest detection device comprises an unmanned aerial vehicle body, a holder, a high-altitude texture camera and a small host; the high-altitude texture camera is arranged below the holder, the holder is arranged below the small host, the small host is arranged below the unmanned aerial vehicle main body, the unmanned aerial vehicle main body is provided with a front high-definition camera, a battery and an illumination sensor, and the high-altitude texture camera is wirelessly provided with a handle and a flight remote controller through Bluetooth; according to the plant disease and insect pest detection method, high-precision recognition of disease and insect pests is achieved on the basis of high-altitude texture feature fusion in combination with an environment perception driven data enhancement module, a self-adaptive multi-scale image convolution feature extraction and fusion module and a disease and insect pest area attention fusion module. The technology effectively supports precise detection of diseases and insect pests in agricultural production, and provides a scientific basis for agricultural management and pesticide application.

The invention discloses a remote sensing change detection method based on edge enhanced cross attention and multi-dimensional loss. The method comprises the following steps: inputting a dual-time-phase image into a trained twin backbone network, and extracting multi-scale backbone features of the dual-time-phase image; inputting the multi-scale backbone features into a trained non-sharpening mask cross attention fusion model to extract spatial information and semantic information of the dual-temporal image through cross attention operation, and extracting edge information of the dual-temporal image through a sharpening convolution kernel to obtain multi-level differential features with different scales; inputting the differential features into a trained multi-scale hierarchical connection decoder to obtain a multi-scale preliminary prediction result of the change area; and carrying out weighted fusion on the multi-scale preliminary prediction result to obtain a change prediction result. According to the invention, the multi-scale information of the twin backbone network can be fully utilized, and the capability of extracting the edge information of the dual-time-phase image can be improved, so that the change detection accuracy can be improved.

The invention provides a ViT distillation training method, system and device and a readable storage medium, and the method comprises the steps: selecting a long-tail data set, and carrying out the preprocessing of the long-tail data set; training the convolutional neural network to generate a teacher model; generating a data sample based on the long-tail data set, inputting the data sample into a teacher model, and generating a soft label; constructing a student model based on a visual Transform, and configuring a knowledge distillation function and a loss function for the student model; generating a data sample based on the long-tail data set, inputting the data sample into a student model, generating a classification result, and optimizing student model parameters by using a loss function based on the classification result and the soft label to train the student model; and after training is completed, performing model verification and output on the student model. According to the method, the technologies of long-tail data set preprocessing, teacher network training, knowledge distillation, loss reweighting and the like are combined, so that the classification performance and generalization ability of the ViT model on the long-tail data set are effectively improved.

The invention provides a three-dimensional geologic model automatic reconstruction method based on a conditional spectrum normalized generative adversarial network, which relates to the field of three-dimensional geologic modeling and comprises the following steps: preprocessing a sample three-dimensional geologic model to obtain a conditional data set, and setting random noise; constructing a conditional spectrum normalized generative adversarial network, and training the conditional spectrum normalized generative adversarial network through the conditional data set and the random noise to obtain a trained conditional spectrum normalized generative adversarial network; and inputting condition data of a to-be-reconstructed three-dimensional geologic model into the trained condition spectrum normalization generative adversarial network to obtain a reconstructed three-dimensional geologic model. According to the method, a discriminator loss function and a generator joint loss function are designed to train and update a discriminator and a generator, the gradient dissipation problem caused by a traditional loss measurement mode is solved, and the similarity between a reconstruction result and condition data is improved by calculating an extra loss value for a condition data position.

The invention discloses a point switch part detection method and device based on a YOLOv8 model and a medium, and the method comprises the steps: marking a target part in a point switch part image, and generating a target image; the method comprises the following steps: replacing an original backbone network darknet53 of a YOLOv8 model with a Swin transfer network, and introducing a residual attention module into the Swin transfer network; the method comprises the following steps of: constructing an improved YOLOv8 model by combining a spatial pyramid pooling layer on the basis of a Swin transformer network and replacing an original loss function CIoU of the YOLOv8 model with a new loss function Focal-EIoU at the same time; and inputting the target image into the improved YOLOv8 model, and generating a prediction result. And accurate detection of the target part is realized based on the detection result output by the improved YOLOv8 model.

The invention relates to an underwater image detection system and method based on a YOLOv8s model, and the system comprises an underwater image collection and preprocessing subsystem which is used for collecting underwater image data and carrying out the preprocessing; the target detection subsystem is used for carrying out target detection by adopting an improved YOLOv8s model, and the improved YOLOv8s model is used for obtaining a feature map by adopting a mode of combining a denoising mechanism and a dynamic adjustment receptive field mechanism based on the preprocessed underwater image data, carrying out feature enhancement by adopting a dual attention mechanism based on the feature map, and carrying out target detection by adopting an improved YOLOv8s model; performing feature fusion and detection to obtain a target detection result; the performance monitoring and evaluation subsystem is used for monitoring and evaluating the target detection result; and the detection result display and output subsystem is used for outputting and displaying the target detection result. Compared with the prior art, the method has the advantages of improving the depth and precision of feature extraction, improving the underwater image detection performance and the like.

The invention relates to a low-illumination image enhancement method based on central difference convolution, and belongs to the field of image processing. The method comprises the following steps: firstly, dividing a brightness component of an image in an HSV color space into a bright region and a dark region through a binary mask, and calculating the average brightness of each region; performing exposure of different degrees on the brightness component of the image according to the average brightness of different areas through an exposure method based on a binary mask; fusing the brightness components with different exposure degrees by the central difference convolutional neural network under the guidance of the fusion loss function; and finally, the amplified noise is suppressed and texture details in the image are enhanced under the guidance of the content loss function through the other central difference convolutional neural network. According to the method, noise can be suppressed, texture details can be enhanced, and the image definition can be improved; moreover, the method has a better enhancement effect on images under a non-uniform illumination condition and an extremely low illumination condition.

The invention discloses a water conveyance project operation risk affair graph construction method and system based on an inspection text, and relates to the technical field of affair graph construction, and the method comprises the following steps: S1, data collection and preprocessing; s2, risk event extraction based on a large language model; s3, classifying risk events based on word vector coding; s4, event relation identification based on association rule mining; and S5, storing the mined risk event pairs in a graph database to construct a affair graph. According to the method, related technical processes from event extraction, event classification, event relation identification to graph construction are realized, the evolutionary process of the operation risk of the water conveyance project can be researched and analyzed, and technical support is provided for risk disposal decision.

The invention discloses a hyperspectral image classification method based on super-pixel multi-scale image fusion Transform, and belongs to the technical field of image processing and computer vision. The invention aims to provide a hyperspectral image classification method based on super-pixel multi-scale image fusion Transform, which realizes accurate classification of hyperspectral images through multi-scale image network feature extraction and super-pixel feature global modeling. According to the method, features of a hyperspectral image are divided into a plurality of superpixel areas through superpixel segmentation, a superpixel-based image structure is constructed, and global information fusion is carried out on extracted multi-scale superpixel features through a Transform network self-attention mechanism by applying multi-scale image convolution operation, so that a final classification result is generated. According to the invention, a multi-scale image convolution feature extraction network is developed, context feature extraction is carried out on a superpixel feature pyramid, the spectral feature extraction capability is improved, and the robustness of the model to different scale feature changes is enhanced.

The invention relates to a medical image target detection method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining a to-be-detected medical image and a medical image target detection model, according to the medical image target detection model, an expandable residual module is added in a backbone network, a multi-branch attention mechanism module is added to a head in a neck network, and at least part of conv modules in the backbone network and the neck network are replaced by YOLOv8 models of deep down-sampling modules. The medical image target detection model is obtained through joint regression loss function training; and inputting the to-be-detected medical image into the medical image target detection model to obtain a medical image target detection result. According to the medical image target detection method provided by the invention, the accuracy of medical image target detection is improved.

The invention discloses a fusion device high-temperature superconducting magnet quenching detection method based on deep learning, and the method comprises the steps: obtaining a magnet quenching voltage noise signal, a magnet current signal, a plasma current signal and an eddy current signal from the historical operation data of a fusion device, and taking the signals as a data set to train a deep learning model; a magnet current signal, a plasma current signal and an eddy current signal during operation of the device are continuously collected in real time, and a magnet quenching voltage noise signal is calculated in real time by using an offline trained deep learning model; and deducting the magnet quench voltage noise signal calculated in real time from the magnet quench voltage detection signal acquired in real time to obtain a real magnet quench voltage signal, thereby realizing accurate detection of the high-temperature superconducting magnet quench of the fusion device. According to the method, quench of the high-temperature superconducting magnet is detected timely and accurately by means of the strong learning ability of deep learning, and safe and stable operation of the high-temperature superconducting magnet in a fusion device is guaranteed.

The invention discloses a breast tumor benign and malignant identification system based on a double-branch attention distillation network, belongs to the field of medical image processing, and particularly relates to the breast tumor benign and malignant identification system based on the double-branch attention distillation network. The invention aims to solve the problem that the existing method based on deep learning is insufficient in feature extraction capability and cannot accurately focus a lesion area. The system comprises an ultrasonic image preprocessing main module used for preprocessing collected breast ultrasonic images, obtaining ultrasonic imaging areas, unifying the image size of the obtained ultrasonic imaging areas, obtaining preprocessed breast ultrasonic images, and taking the preprocessed breast ultrasonic images as a training set; the double-branch convolutional neural network main module is used for building a double-branch attention distillation network model; the model training main module is used for obtaining a trained double-branch attention distillation network model; and the model test main module is used for loading the trained double-branch attention distillation network model and classifying breast ultrasound images to be diagnosed.

The invention discloses a strip steel surface defect detection method based on an RAC-YOLO algorithm, and belongs to the technical field of industrial automatic detection and computer vision. Comprising the steps that an image data set is acquired and preprocessed; the method comprises the following steps: constructing a RAC-YOLO network based on YOLOv8, and replacing an original feature pyramid module SPPF with an intra-scale feature interaction module AIFI; meanwhile, an attention mechanism module SE is introduced behind the AIFI module; a lightweight upsampling operator CARAFE is used for replacing an original upsampling model; replacing an original C2f module with a context anchor attention feature extraction and fusion module; meanwhile, the network loss function is replaced by the MPDIOU; and completing the training of the RAC-YOLO network by using the preprocessed image data set. Various defects on the surface of the strip steel can be rapidly and accurately detected on a high-speed production line, the production efficiency and the product quality are effectively improved, and a more reliable quality control solution is provided for industrial manufacturing.

The invention relates to the technical field of image processing, in particular to a multi-modal remote sensing image semantic segmentation method and system, and the method comprises the steps: building a data set which comprises a plurality of image pairs, and each image pair comprises an SAR image and an optical image; constructing a multi-modal semantic segmentation model, wherein the multi-modal semantic segmentation model comprises a basic feature transformation layer, a multi-scale semantic acquisition layer, a significant feature cross fusion layer, a feature deep coding layer and a feature decoding layer which are connected in sequence; training and optimizing a multi-modal semantic segmentation model based on the data set to obtain a target multi-modal semantic segmentation model; and performing joint semantic segmentation on the SAR remote sensing image and the optical remote sensing image by adopting the target multi-mode semantic segmentation model to obtain a semantic segmentation result graph. The embodiment of the invention is at least beneficial to reducing the parameter quantity and calculation quantity of semantic segmentation of the multi-modal remote sensing image.

The invention relates to a method and system for detecting a target grabbed by a mechanical arm, and the method comprises the steps: collecting a target material image, and obtaining an initial data set; preprocessing the initial data set by adopting a data enhancement mode to obtain a target data set; taking the generative residual convolutional neural network model as a reference model to construct an improved generative residual convolutional neural network model as a mechanical arm grabbing target detection network model; calculating each output loss and total loss by using an improved loss calculation method, obtaining a difference between the output loss and the total loss and practical feasible capture information, and obtaining a training weight; and updating the weight of the model by using a loss calculation result to obtain an optimal training weight, and inputting a test set into the trained mechanical arm grabbing target detection network model to obtain a mechanical arm grabbing target detection result. According to the improved mechanical arm grabbing target detection network model, the grabbing target detection precision is improved, and the grabbing target detection speed is increased.