404results about How to "Improve the extraction effect" patented technology

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 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 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 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 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 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 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 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 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 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.

The invention provides an extraction device containing a penthorum chinense pursh extract and a beverage, and relates to the field of food processing. According to the extraction device containing the penthorum chinense pursh extract, through the design of a closed-loop transmission system, continuous feeding, extraction and discharging of penthorum chinense pursh materials are achieved, a driver provides a stable power source through a bevel gear transmission mechanism, it is ensured that the materials continuously move and are stirred in an extraction pipe, and the extraction efficiency is improved. Penthorum chinense pursh materials can be continuously fed and extracted without shutdown treatment. Compared with the prior art, continuous extraction is achieved, the production efficiency is improved, the operation process is simplified, human intervention is reduced, the ultrasonic extraction effect is enhanced, the extraction efficiency is improved, real-time monitoring and feedback are achieved, and it is ensured that the extraction process is stable.

The invention relates to the technical field of electric power operation and maintenance, and particularly discloses an unmanned aerial vehicle insulator defect identification method based on improved yov8n, and the method comprises the steps: firstly, improving a down-sampling module and feature map processing, enhancing the capability of a model for extracting insulator defect features, and improving the detection accuracy; and secondly, a Focaler-SIoU loss function is introduced, the target positioning performance is optimized, and particularly, the robustness of the model is improved when samples difficult to classify are processed. In addition, the model is lightened through the LAMP algorithm, and the parameters and the calculation amount of the model are reduced, so that the model is more suitable for running on mobile equipment such as an unmanned aerial vehicle and the like. And finally, through training verification and deployment into an unmanned aerial vehicle detection system, real-time and efficient defect identification is realized, so that the problems of low efficiency, poor real-time performance and the like in the prior art are solved, the insulator defect detection precision can be improved, and meanwhile, the complexity and the calculation amount of the model are reduced so as to adapt to the real-time detection requirement of an unmanned aerial vehicle platform.

The invention relates to the technical field of deep learning, in particular to a lithium battery fault recognition method and system based on a recurrence plot and an improved AlexNet model, and a storage medium. The method comprises the following steps: acquiring one-dimensional time sequence data from various sensors of the lithium battery; preprocessing the one-dimensional time sequence data to generate original fault waveform data; converting the original fault waveform data into a recurrence plot; the recurrence plot is input into a pre-trained improved AlexNet model for feature extraction, the improved AlexNet model processes feature vectors in the recurrence plot through a multi-head self-attention mechanism, and the processed feature vectors are input into a classification layer for fault type prediction of the battery; and obtaining a battery fault type prediction result output by the classification layer. The objective of the invention is to solve the problem of how to predict possible faults in a lithium battery.

The invention relates to a kidney component segmentation method and system based on deep learning, belongs to the technical field of medical image segmentation, and solves the problem of low accuracy of kidney component segmentation in the prior art. The method comprises the following steps: acquiring a kidney pathological image and corresponding component labeling information, cutting the kidney pathological image, screening a target image from the cut image, and constructing a sample set based on the target image and the corresponding component labeling information; constructing a multi-channel coding fusion image segmentation model, and training the multi-channel coding fusion image segmentation model based on the sample set to obtain a kidney component segmentation model; cutting a to-be-segmented kidney pathological image, screening a target image from the cut images, and inputting the screened target image into the kidney component segmentation model to obtain a segmentation result corresponding to the target image; and obtaining a component segmentation map of the to-be-segmented kidney pathological image based on the segmentation result. And high-precision kidney component segmentation is realized.

The invention discloses a wheat growth period identification method based on multi-modal features, and belongs to the technical field of crop growth period identification. Image data features of a wheat growth period are acquired by using a multi-scale convolution sum attention mechanism technology, and the wheat growth period is identified by adding asymmetric multi-channel convolution. Multi-scale extraction of canopy image features is carried out and a nonlinear expression capability is increased at the same time; the attention mechanism module is introduced to enable the network to pay more attention to main effective features, interference features are filtered, the capability of extracting key information from redundant information is enhanced, the recognition accuracy is improved, the problem of canopy image area weight dispersion is solved, the obtained wheat growth period image data features are more accurate and comprehensive, and the wheat growth period image data feature extraction efficiency is improved. According to the image data features of the wheat growth period, on one hand, the image features can be directly classified to identify the growth period, and on the other hand, the image data features of the wheat growth period and serialized environmental data features can be fused; the method realizes small difference and high similarity of morphological characteristics of plant canopies in the transition period of adjacent growth periods, and also can accurately identify the growth period in which the plant canopies are located.

The present invention discloses a super-resolution reconstruction method for industrial CT images based on deep learning, and its application relates to the field of industrial CT image technology, in order to solve the problem of low image resolution caused by equipment limitations. The steps include data acquisition and preprocessing, data enhancement, edge enhancement module construction, convolution downsampling module construction, RRDB residual module construction, upsampling and reconstruction, model training and optimization, and splicing trace processing. By using the present invention, super-resolution reconstruction of multi-material CT images can be achieved, and the resolution and clarity of the images can be improved.

The invention provides an effective signal filtering and noise reduction method and system for a micro-seismic event of a tunnel water and mud inrush disaster, and relates to the technical field of signal processing. Comprising the following steps: acquiring original signals of water-inrush and mud-inrush micro-seismic events; performing modal decomposition on the original signal by using a variational modal decomposition method to obtain a plurality of modal components; calculating a cross correlation coefficient between the original signal and each modal component; based on the center frequency of each modal component and in combination with the cross correlation coefficient, performing primary noise reduction on the plurality of modal components; performing de-trend fluctuation analysis on the modal components after the primary noise reduction screening to obtain scale indexes of the modal components after the primary noise reduction screening, and performing secondary noise reduction based on the scale indexes; and reconstructing the modal component after the secondary noise reduction screening to obtain a micro-seismic signal after noise reduction filtering. According to the method, the problem that noise and effective signals in part of modal components are mixed is solved, and the weak signal extraction capacity is improved.

The invention discloses a petroleum pipe microscopic fracture morphology identification method, system and device and a medium, and belongs to the technical field of failure analysis image process.The method comprises the steps that a deep learning classification network with a residual module is constructed and trained, and a deep learning classification network loaded with a network connection weight matrix is obtained; and finally, the deep learning classification network is utilized to efficiently and accurately identify the to-be-tested object region image. According to the method, massive data sets can be quickly processed and analyzed by using the high-speed processing capability of a modern computer, so that the efficiency is greatly improved. Compared with a traditional method, the automatic fracture classification method can reduce subjective interference of people and enhance reliability and consistency of results. And meanwhile, the identification method is simple, engineering application integration is facilitated, and good practicability and expansibility are achieved.

The invention provides a perovskite passivation layer and a preparation method thereof. According to the invention, a perovskite passivation layer is arranged on the surface of a perovskite absorption layer, and the material of the perovskite passivation layer contains an alkyl diamine compound and a phenylethyl ammonium compound; the phenethyl ammonium compound is selected from p-trifluoromethyl phenylethylamine hydrochloride and / or p-fluorophenethyl hydriodate. According to the perovskite solar cell and the preparation method thereof, the alkyl diamine compound and the phenylethyl ammonium compound are cooperated to form the passivation layer, the simultaneous effect of surface passivation and interface passivation can be exerted, the carrier recombination of the surface and the interface is reduced, the electron extraction capability is improved, and the energy conversion efficiency of the perovskite solar cell is further improved.

The invention discloses a tool blade grinding completion condition detection system based on deep learning, and relates to an intelligent detection technology, the tool blade grinding completion condition detection system comprises an image acquisition module which at least comprises a tool blade camera device, an automatic clamp device, an image preprocessing device and an image marking device; the database module is mainly used for storing the detection information, the grinding completion information and the abnormal information of the detected cutting edge of the cutter; the feature learning module is used for training and recognizing a grinding completion state and an abnormal condition; the detection module is used for detecting whether the cutting edge of the cutter is in a completely-ground state or abnormal conditions; a background module; a management device module; and a user terminal module. The method has the advantages that the complete grinding state of the cutting edge of the cutter can be monitored in real time, the problem of excessive complete grinding or insufficient complete grinding can be intelligently recognized, and through a novel network structure, an attention mechanism is added, so that the extraction capability of complete grinding features of the cutter is improved.

The invention provides a method for simultaneously detecting perfluorinated compounds in mouse tissues and organs, and belongs to the technical field of analysis and detection. The method provided by the invention comprises the following steps: mixing a mouse visceral organ tissue sample, an alkaline buffer solution and an internal standard solution, and homogenizing to obtain homogenate; mixing the homogenate with tetrabutylammonium hydrogen sulfate and methyl tert-butyl ether, and carrying out liquid-liquid extraction to obtain extract liquor; and sequentially drying and redissolving the extract liquor, and carrying out ultra-high performance liquid chromatography-tandem mass spectrometry analysis to obtain a result of the perfluorinated compound in the mouse tissue visceral organs. The result of the embodiment shows that the adding standard recovery rate of 14 target objects in the method provided by the invention is 64.9%-120.3%, the RSD of 6 repeated tests is 1.0%-14.0%, the matrix effect of the target objects is 83.7%-134.0%, and the matrix effect is relatively low; and the detection result has excellent accuracy and reproducibility.

The invention discloses an interpretable deep learning-based innate immune gene expression prediction method, which comprises the following steps of: 1, collecting and preprocessing a DNA (Deoxyribose Nucleic Acid) promoter sequence and epigenetic modification data, and dividing a preprocessed data set into a training set and an independent test set so as to verify the prediction performance of a model; 2, constructing a human innate immune gene expression prediction network based on residual connection, a convolutional neural network and an attention mechanism; 3, performing model training on a plurality of cell lines to obtain an optimal human innate immune gene expression prediction model, and evaluating the prediction effect of the model on the independent test set; and 4, carrying out post interpretability analysis on the model through an integral gradient method, and identifying a key motif which is most likely to influence gene expression. According to the method, DNA sequence information and epigenetic modification information are extracted more effectively, and a new tool is provided for deeply understanding a regulation mechanism of an innate immune gene and developing individualized medical treatment.

The invention provides an image recognition method and system based on self-supervised multi-scale attention learning, and the method comprises the steps: inputting an input image into ResNet for processing, and obtaining the output features of each stage; inputting the output feature of a predetermined target stage in each stage into the corresponding convolution block for processing to obtain a first feature of the predetermined target stage; inputting the first feature of the predetermined target stage and the output feature of the final stage in each stage into a corresponding attention module, processing to obtain a corresponding multi-scale attention map, and inputting the multi-scale attention map into a self-supervised multi-scale attention learning module; obtaining global feature representation of the corresponding stage according to the multi-scale attention maps corresponding to the predetermined target stage and the final stage and the output features, and inputting the global features corresponding to the predetermined target stage and the final stage into corresponding classifiers to obtain probability distribution of prediction categories; and obtaining a final prediction category of the picture according to the probability distribution of the prediction categories of the predetermined target stage and the final stage.

The invention discloses a battlefield semantic situation rapid generation method based on image analysis, and belongs to the technical field of battlefield situation generation. The method comprises the following steps: dividing a battlefield area into a plurality of local battlefields; reconnaissance is carried out on each local battlefield area; generating a multi-source heterogeneous image database; performing time-space relationship unification on the multi-source heterogeneous image database; constructing a battlefield situation element composition system; constructing a battlefield situation element extraction technology system; carrying out adaptive extraction on battlefield situation elements; generating a local battlefield situation; a global battlefield situation is generated based on the local battlefield situation. The invention provides a battlefield situation element extraction framework based on deep learning, designs a battlefield situation generation method based on deep learning, and provides a conflict situation fusion method and a battlefield region division method based on an attention mechanism and a complete space-time relationship unification mode. A whole set of battlefield semantic situation adaptive generation method is constructed, and decision-making assistance can be provided for direct commanders and fighters.

The invention relates to the field of remote sensing image processing, and discloses a high-resolution remote sensing image semantic segmentation passive domain self-adaption method and device, and the method employs a prototype guiding consistency learning module of teacher and student knowledge distillation, optimizes pseudo label generation and quality through knowledge transmission, data enhancement and a prototype guiding mechanism, and improves the semantic segmentation precision of a high-resolution remote sensing image. Feature alignment of a source domain and a target domain is improved, and particularly, the segmentation effect is remarkably improved in small ground feature recognition; through a cross-domain feature enhancement module, the feature extraction capability of complex boundaries and small ground features is enhanced by using channel adaptive weight distribution, so that the segmentation precision of a target domain is improved; a passive domain adaptive strategy is adopted, under the condition that source domain data cannot be accessed, a target domain is optimized based on a pre-trained source domain model, the data privacy and safety problems are solved, and the generalization performance of cross-domain semantic segmentation is improved. According to the method, the condition that source domain data are unavailable is effectively handled, and the segmentation precision of the cross-domain difficult-to-classify region in the remote sensing image is remarkably improved.

The invention relates to the technical field of computer vision, in particular to a behavior recognition method based on a space-time mixed attention module, which comprises the following steps of: S1, dividing joint points into regions: dividing each joint point into regions, and respectively performing feature enhancement on the sub-regions to obtain corresponding feature weights; and S3, space-time stream fusion: combining the attention scores of the space-time streams to obtain a space-time attention score statt, and endowing the obtained space-time attention score statt to an input feature map to realize enhancement of time domain and space domain features. According to the method, the perception ability of the model to the local space structure and the time dynamic change is enhanced, the interaction and information exchange between the time domain and the space domain characteristics are enhanced, and the understanding ability and prediction precision of the model to the complex space-time mode are improved.