80results about How to "Reduce labeling costs" patented technology

The invention provides a phytoplankton chromatography sequence identification method and a phytoplankton chromatography sequence model building method, and belongs to the technical field of image enhancement identification. The method comprises the following steps: firstly, acquiring microscopic chromatography sequence data of phytoplankton, performing view field extraction and serialization recombination, and constructing a three-dimensional data set; then, constructing a three-dimensional recognition model containing physical perception and a sequence aggregation mechanism, extracting single-frame semantic features by the model by adopting a parameter-shared twin network, and introducing a physical definition prior module to calculate a space-frequency domain quality score of a slice; secondly, designing a deep perception sequence aggregation module, and adaptively aggregating key features of a high signal-to-noise ratio by taking definition scores as gating signals and combining spatial context information between slices; and finally, training and optimizing the model based on the image-level weak supervision label to obtain an optimal model. According to the method, the problems of information truncation and out-of-focus noise interference caused by extremely shallow depth of field of high-power microscopic imaging are solved, and full-depth-of-field stereoscopic perception can be realized under the condition that frame-by-frame fine labeling is not needed.

The invention relates to a Fourier contour syntax guided remote sensing target detection method, equipment and a medium, belongs to the technical field of remote sensing image target detection, and can effectively detect obviously symmetrical targets such as airplanes or ships in a remote sensing scene lacking contour / target mask labels. According to the invention, in a small sample scene, detection of a target category and positioning of a target can be realized, and a closed contour of the target can also be output. Fourier low-frequency spectrum is used to realize contour coding, and only 128-dimensional descriptors are used to replace traditional discrete contour point prediction. Through a triple Fourier guidance mechanism formed by cyclic alignment loss, low-frequency amplitude regularization and symmetric constraint, the noise interference of a weak supervision false contour is effectively relieved, the model is forced to preferentially learn the global shape and symmetric structure of a target, and the adaptability to scale change, any rotation angle and complex background interference is remarkably improved. And meanwhile, the detection accuracy in a small sample scene is improved, and practical value and theoretical value are both considered.

A kind of flow video active interaction control method, equipment and medium for the scene of helping blind, construct the hierarchical task system for the scene of helping blind, train the semantic understanding and behavior alignment of multimodal large model to specific task of helping blind;Introduce the active interaction control mechanism for flow video, under the premise of not changing the bottom model parameter, realize the trigger output of environmental perception by the dynamic suffix switching of determination mode and generation mode;Finally, the incremental context updating strategy is used to optimize the inference process, while keeping the timing consistency and reducing the calculation redundancy.The invention solves the technical problems of lack of special data, passive interaction logic and calculation redundancy of multimodal large model in flow environment through the synergistic effect of data-driven task adaptation and active interaction mechanism.The general framework proposed in the invention provides an important theoretical basis and technical paradigm for the paradigm evolution of flow video understanding from "passive analysis" to "active interaction".

This invention belongs to the field of industrial manufacturing technology, specifically providing a method and system for visual detection of industrial defects by decoupling defect features from imaging conditions. The method includes: collecting defect samples of industrial products under different operating conditions; wherein the defect samples include defect images and corresponding defect annotations; based on the defect samples, constructing a region-guided causal decoupling network model by combining a feature decoupling mechanism guided by defect regions and a causal invariance learning mechanism based on imaging simulation; performing multi-supervised loss joint optimization training on the causal decoupling network model to obtain a converged causal decoupling network model; and detecting defects in industrial products under target operating conditions based on the converged causal decoupling network model to obtain defect detection results. This invention can significantly improve the generalization and robustness of defect detection algorithms in complex industrial environments, enabling rapid and low-cost model transfer between different operating conditions.

This invention discloses a target detection method, electronic device, and medium based on knowledge distillation, comprising: inputting a target detection image to be detected into a pre-trained target detection model to obtain target detection results; wherein, the training process of the target detection model includes: acquiring target detection data and constructing it into a labeled dataset and an unlabeled dataset; training a teacher model using the balanced labeled dataset; inputting the unlabeled dataset into the teacher model to obtain confidence-labeled data; combining the unlabeled dataset and the confidence-labeled data, and after filtering, obtaining a confidence dataset; setting a distillation loss function, and training a student model based on the distillation loss function using the confidence dataset to obtain a target student model, i.e., a target detection model; wherein the distillation loss function is the sum of bounding box loss, target loss, and classification loss; wherein the target loss and classification loss are both constructed based on the confidence output by the target teacher model.

The application discloses a kind of man-machine collaborative multi-modal data intelligent labeling and quality checking method, it is related to man-machine collaborative technical field.The method first obtains at least containing two modalities of original data of image, text, audio, generates multi-modal joint data sample after pre-processing and modal alignment;Input pre-trained multi-modal AI pre-labeling model generates pre-labeling result with confidence;Through cross-modal consistency check calculation consistency score, sample is divided into high, medium, low confidence three categories;High confidence sample is directly adopted and enters the sampling pool, and medium and low confidence sample is based on labeling personnel ability portrait and AI model dynamic capability evaluation and carries out intelligent task distribution.The application effectively solves the problem that efficiency and quality are difficult to balance in multi-modal data labeling, significantly improves labeling accuracy and automation rate, reduces labeling cost, and can meet the construction demand of large-scale high-quality multi-modal data set.

The application discloses a material mechanical property inference method based on multi-modal pre-training representation. In view of the problems that the existing method needs to rely on expensive scanning electron microscope images in the inference stage, and the prediction accuracy is low in the small sample scene, the application constructs a multi-modal network containing an image encoder, a table encoder, a shared projection branch and a modal specific projection branch, and carries out joint pre-training through cross-modal alignment loss, intra-modal decoupling loss based on contrast logarithmic ratio upper bound mutual information estimation and table mask recovery loss. Then, the pre-trained table encoder and its auxiliary projection head are migrated to the downstream multi-task regression network, and the mechanical properties can be predicted only by inputting process parameters. The method can also maintain stable performance in the small sample scene of iterative sample collection, significantly reduces the material representation cost, and improves the prediction accuracy and generalization ability.

This application discloses a method for detecting apple disease spores using microscopic data based on multimodal and semi-supervised learning. The method includes: acquiring raw microscopic data of apple disease fungal spores, performing edge detection and texture enhancement to generate texture-enhanced data; extracting features from the raw microscopic data and texture-enhanced data using a dual-branch encoder, and fusing them using a cross-attention mechanism to obtain enhanced visual features; inputting the textual description information of the disease fungal spores into a text encoder for encoding to obtain global text features; aligning the enhanced visual features and global text features across modalities based on a multimodal object detection network, outputting multimodal fused features, and inputting them into a semi-supervised learning framework to train a student-teacher model using labeled and unlabeled data; inputting the microscopic data to be detected into the trained model and outputting the detection results. This method improves the detection accuracy and robustness of microscopic data with extremely low annotation costs.

The invention relates to the technical field of image recognition, and discloses a wind power fan blade defect recognition method and system based on image recognition, and the method comprises the steps: carrying out the blocking cutting, illumination normalization and image enhancement processing of an original image, constructing a defect-free sample and a defect sample, and dividing the samples into a training set and a test set; the method comprises the following steps: taking a GANopen network as a basic framework, fusing the lightweight design of Mamba-YOLO, constructing a joint loss function by adversarial loss, reconstruction loss and coding loss based on a training set, carrying out unsupervised training, optimizing network parameters until convergence, and obtaining a defect identification model; inputting the preprocessed to-be-detected fan blade image into the trained defect recognition model, performing defect recognition and positioning, and outputting the defect type and position; based on an output result of the defect identification model, dynamically adjusting an early warning level and response measures through a self-adaptive early warning mechanism; according to the invention, the efficiency and accuracy of wind power fan blade defect identification are improved.

The application discloses a benthic animal rare species image recognition method and system based on small sample learning, relates to the technical field of computer vision and artificial intelligence, and comprises the following steps: S100, data preparation and preprocessing; S200, small sample training based on meta learning; S300, domain self-adaptive data enhancement; S400, small sample rapid adaptation based on meta learning; S500, attention-guided difficult example mining; S600, online incremental learning mechanism; through the collaborative design of meta learning and a multi-level feature fusion network, the common species data is used for meta training, and cross-species general visual knowledge is learned; the new rare species can be rapidly adapted through a small amount of labeled samples and several gradient updates, the labeling cost is reduced while the recognition accuracy is improved, and the double bottlenecks of rare species recognition difficulty and labeling cost are effectively broken through.

The invention discloses a remote sensing image enhancement processing method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring layout condition information of a remote sensing image corresponding to a remote sensing task type; performing feature extraction on the layout condition information by using a layout encoder to obtain a corresponding first layout condition feature vector; analyzing the first layout condition feature vector by using a layout condition generator to obtain a plurality of second layout condition feature vectors; and analyzing the first layout condition feature vector and the plurality of second layout condition feature vectors by using an image generator to obtain a first enhanced remote sensing image and a plurality of second enhanced remote sensing images corresponding to the remote sensing image. The technical problem that a remote sensing task model obtained based on remote sensing image training is high in cost and poor in performance due to the fact that enhancement processing cannot be efficiently carried out on the remote sensing image at low cost in related technologies is solved.

The present disclosure provides a data labeling, data generation method, device, equipment and storage medium, data fragmentation processing is carried out for the first type of data, N data fragments corresponding to the first type of data are obtained, and the fragmentation characteristics of each data fragment in the N data fragments are extracted; from the data feature library corresponding to the second type of data, a data feature set whose similarity with the fragmentation characteristics of each data fragment in the N data fragments satisfies a preset condition is obtained, N data feature sets corresponding to the N data fragments are obtained; the data features in each data feature set in the N data feature sets are respectively counted, the occurrence times of the data features in the N data feature sets are counted, and the first type of data is labeled based on the second type of data corresponding to one or more data features with the most occurrence times.

The application is a medical image segmentation method based on multi-modal self-supervision. First, the multi-modal medical image of the lesion tissue is obtained, including A-mode image and B-mode image, and the image is preprocessed. Then, a cycle-consistent modal contrast domain translation network is constructed, including two generators and two discriminators. The generator is used to convert the image of one mode into the image of another mode, including an encoder, an intermediate shared module and a decoder. The discriminator is used to judge the source of the input. Then, the cycle-consistent modal contrast domain translation network is pre-trained, the training loss is calculated, and the loss function includes multi-modal semantic consistency loss, adversarial loss, cross-domain translation loss and cycle consistency loss. Finally, the A-mode segmentation network and the B-mode segmentation network are constructed, the pre-trained weights are migrated to the two segmentation networks, and the trained two segmentation networks are respectively used for medical image segmentation of the corresponding mode. The contrast cross-domain translation is used as a multi-modal self-supervised pre-training task to learn more comprehensive modal features, promote the network to better learn modal characteristics and common knowledge, and improve the segmentation ability.

The present application relates to a kind of heart ultrasound video semantic segmentation pseudo-label generation method based on optical flow guide, belong to computer vision and medical image processing field.The method includes: selecting two key frames in heart ultrasound video frame sequence, and obtaining the segmentation mask of two key frames by artificial labeling;Optical flow model is fine-tuned using two key frames, the frame between two key frames, the frame of first key frame left side preset quantity and the frame of second key frame right side preset quantity;Based on the fine-tuned optical flow model, the forward optical flow sequence from first key frame to second key frame and the reverse optical flow sequence from second key frame to first key frame are predicted;Based on forward optical flow sequence and reverse optical flow sequence, generate forward propagation mask sequence and reverse propagation mask sequence, and carry out position weighted fusion, obtain the pseudo-label of unlabelled frame.The technical problem that the present application aims to solve is that the label of heart ultrasound video semantic segmentation is difficult to obtain and the quality of pseudo-label obtained is poor.

This invention discloses an online detection system for airbag coating defects based on machine vision and deep learning, belonging to the field of airbag coating detection technology. It includes: a multispectral imaging module containing visible and infrared light sources, forming a ring-shaped multispectral light source array for acquiring visible and infrared spectral images of the airbag coating surface; an FPGA-accelerated lightweight deep learning model, including adaptive convolution kernels and channel-parallel computing modules, achieving real-time inference at <50ms / frame; an adaptive illumination control module integrating an ambient light sensor and deep learning model output, adjusting light source parameters in real time through a closed-loop control algorithm; and a multi-angle imaging and image fusion module, including a ring camera array and a compressed sensing encoder, for acquiring multi-angle images of the airbag surface and generating a 3D defect distribution map. This invention achieves high-precision, high-speed, and highly robust online detection of minute defects in airbag coatings.

This application discloses a method for identifying and diagnosing abnormal states in fish by fusing multimodal data. The method is implemented through a multimodal data-fused fish abnormal state identification model, comprising a multimodal encoder, a conditional modulation meta-network, a shared classifier head, and a text decoder. The method includes: constructing a multimodal dataset; jointly training the multimodal encoder and text decoder using the multimodal dataset, and freezing the parameters of the multimodal encoder after training; constructing multiple meta-tasks based on the multimodal dataset; jointly training the conditional modulation meta-network and the shared classifier head, used to generate modulation parameters for the multimodal encoder, based on K samples from the current meta-task, to obtain the trained multimodal data-fused fish abnormal state identification model; and using this trained model to identify and diagnose abnormal states in the fish under monitoring. This method can identify unknown anomalies, reduce annotation costs, and improve the accuracy of fish abnormal state identification and diagnosis.

The invention discloses a coarse-grained power utilization data non-intrusive load monitoring method and system based on a YOLO deep neural network. According to the method, an encoding mode for converting a one-dimensional power consumption time sequence into a two-dimensional load image is constructed, so that coarse-grained power consumption data can be efficiently utilized by a mature convolutional neural network and a target detection framework; a set of automatic labeling mechanism based on power change characteristics is designed, and bounding boxes and category labels required by training are generated on the premise of not depending on manual labeling; a non-intrusive load monitoring problem is uniformly modeled as a target detection task, equipment category identification and operation time interval regression are realized through YOLOv5 or an improved structure thereof, and the overall identification performance is improved; and a complete system design scheme is given. According to the invention, the data acquisition and communication cost is obviously reduced, and the system has the advantages of simple model structure, high training efficiency, easy transplantation, engineering landing and the like, and can be widely applied to scenes of energy efficiency monitoring, demand response, power consumption behavior analysis and the like of families, buildings and parks.

The invention discloses a black vehicle-oriented real-time point cloud fusion detection method, system and device, and a medium. The method comprises the following steps: step 1, obtaining point cloud data through a laser radar; 2, scanning the point cloud data, marking the continuous point cloud blocks with the reflection intensity of 0 as unlicensed car identifiers, and storing the unlicensed car identifiers in point cloud attribute positions; step 3, performing dual-thread compensation, performing clustering operation on the point cloud data set with the unlicensed car identifier by using DBSCAN, and performing imaging processing on a clustering operation result by using Point Pill to obtain a three-dimensional image model; forcibly outputting an unlicensed vehicle detection frame image according to an image processing result or improving the confidence coefficient as an unlicensed vehicle compensation confidence coefficient, and preferentially adopting a model result; when the two conditions do not belong to the above two conditions, performing weighted fusion on the operation result; and step 4, fusing operation results in the step 3 into a complete three-dimensional frame image. Therefore, the problem of missing detection is solved, and the detection rate and detection efficiency are improved.

The invention discloses a data annotation and model training scheduling method and device based on agent collaboration, and the method comprises the steps: constructing an initial network architecture of an agent through employing image data and a knowledge graph, performing parallel training on a neural network in the initial network architecture by using the labeled first image data to obtain a network architecture of the intelligent agent; the intelligent agent is used for performing target detection on the image data by using a convolutional neural network to obtain a target in the image data, and performing anomaly classification on the target by using a deep learning network to obtain a labeling result; the intelligent agent is also used for determining the confidence coefficient of the labeling result according to the type of the neural network in the network architecture, the type of the image data, the target and the labeling result; inputting the second image data into the intelligent agent to obtain an initial labeling result and confidence output by the intelligent agent; and obtaining a labeling result of the second image data according to the initial labeling result and the confidence coefficient.

The application provides a single sample learning path construction method for medical image key point detection. The method comprises: feature extraction and sample similarity measurement on images in a data set to obtain a similarity score; according to the similarity score, a single typical sample representative in a key point area structure is screened out from the data set; a pseudo label generator is trained based on the typical sample; pseudo label information of key points of other samples in the data set is generated by using the pseudo label generator; the pseudo label information is used as a supervision signal to train a key point detector, and a detection network of the key point detector adopts a U-Net network as a backbone network and a pre-trained VGG19 encoder as a feature extractor of the network; and the key point detector is used to detect key points of a target image and predict a key point heat map and a coordinate offset map. The method of the application reduces labeling cost, improves the precision and stability of key point detection, and enhances the generalization ability.

The application discloses an active domain adaptation semantic segmentation method, system, device and storage medium, selects and labels taking superpixels as units, which is different from image-level and pixel-level labeling methods, and the superpixel-level labeling greatly improves labeling efficiency by only assigning a semantic category to each superpixel; in addition, different from the existing scheme based on the uncertainty selection labeling strategy, the application focuses on difficult example samples in the domain adaptation scene, and proposes a selection strategy based on domain information quantity to label superpixels most valuable for domain adaptation learning; by adopting the superpixel-level labeling method and the selection strategy based on the domain information quantity, the application greatly reduces the labeling cost while improving the labeling quality, and guarantees the performance of the domain adaptation semantic segmentation.

This invention relates to the fields of unmanned aerial vehicles (UAVs) and computer vision technology, and particularly to a UAV-based method for classifying bridge components. The technical problem is that existing point cloud-based automatic classification methods for bridge components suffer from issues such as scarce samples, high annotation costs, insufficient model generalization ability, and low processing efficiency. The technical solution is a UAV-based method for classifying bridge components, comprising a data acquisition step, a data augmentation step, a data preprocessing step, a model training step, and a component classification step. In the model training step, preprocessed 3D point cloud data and its corresponding component category labels are used to train a deep learning-based point cloud semantic segmentation model, enabling the model to learn the feature representations of different bridge components. This invention uses a systematic data augmentation strategy to simulate real-world acquisition interference, combined with an efficient point cloud deep learning model, to achieve accurate, robust, and efficient classification of various bridge components.

This invention discloses a cross-modal data processing system for the safe operation of hydrogen refueling stations, including a raw data acquisition and processing module, a full-variable safety scanning module, a physical relationship coupling diagnosis module, an adaptive operating condition clustering module, a question-answer pair construction module, a hydrogen refueling station time-series command data acquisition module, and a fault type identification module. By constructing a three-layer semantic enhancement logic and model adaptation strategy, this invention effectively solves the technical problems in the prior art, such as the lack of supervision signals in the raw data of hydrogen refueling stations, the difficulty in identifying hidden faults under complex operating conditions, and the difficulty in adapting heterogeneous feature space models.

The application discloses a mass spectrum combined characterization qualitative and quantitative method and system of unified hidden state, relates to the cross field of analytical chemistry, bioinformatics and artificial intelligence data processing, the method obtains original data and instrument / method metadata containing LC-MS / MS, encodes to form a condition vector and is used for conditional modulation, constructs wave encoder and particle encoder, respectively Tokenizes continuous chromatographic signal and discrete fragment peak set into wave encoder and particle encoder to obtain wave representation and particle representation, realizes cross-modal interaction through wave-particle fusion module, forms unified hidden state or object-level unified hidden state set, adopts two-stage training of self-supervised pre-training and task fine-tuning, and applies conditional quantitative operator and qualitative operator to unified hidden state or object-level unified hidden state set in the inference stage, and parallelly outputs quantitative results and identification score / probability, and is suitable for end-to-end qualitative and quantitative analysis in DDA / DIA scene.

The application provides a text processing method and device, a model training method and device, and a storage medium, and relates to the technical field of neural networks. The text processing model is trained by using a training sample text added with a separation mark. Since the training sample text is labeled with label information and position information of the separation mark, the label information of the separation mark indicates whether the text at the position of the separation mark needs to be merged, and the label information of the separation mark is generated according to the real semantics of the text at the position of the separation mark in the training sample text, and the accuracy of the label is high. Therefore, based on the label information and position information of the separation mark labeled by the training sample text, the text processing model obtained by training can be used for accurate merging processing of a target processing text. The training sample text can be obtained by concatenating multiple lines of text, so that the text processing model obtained by training can be applied to the merging processing of multiple lines of text, and the efficiency of the merging processing of multiple lines of text is improved.

This invention provides a method and system for detecting internal defects in continuously cast billets based on a backdistillation network, comprising the following steps: inputting an image of the continuously cast billet to be tested into a backdistillation network to obtain a multi-scale feature set; constructing a pixel-level anomaly score map based on the obtained multi-scale feature set; and detecting internal defects in the image of the continuously cast billet to be tested based on the pixel-level anomaly score map. The backdistillation network includes a teacher branch network and a student branch network, with an enhanced cross-mapping feature fusion module between the teacher and student branch networks; the student branch network includes an asymmetric guided jump connection module. This invention not only effectively solves the problem of dependence on a large amount of labeled data in traditional defect detection methods but also improves the model's detection capability under conditions of few samples.

The present application relates to the technical field of computer vision and image processing, and discloses a remote sensing weakly supervised fine-grained target detection and recognition method and device, based on a general text prompt corresponding to a small amount of labeled remote sensing data samples and a large amount of unlabeled remote sensing data samples, by extracting the geometric features of the samples and the cross-modal features representing the visual text differences, combining the fine-grained class labels of the labeled remote sensing data samples, prior knowledge prototypes of various fine-grained classes are constructed; then, the prior knowledge prototypes of various fine-grained classes are used to construct fine-grained soft labels of the unlabeled remote sensing data samples, which are used as supervision signals to realize the training of the model, solve the cost consumption problem of fine-grained labeling, improve the label labeling efficiency of the unlabeled remote sensing data samples, and further improve the model training efficiency and target recognition efficiency.

The application discloses an infrared small target detection method based on space-time context perception and compact geometric representation, comprising the following steps: acquiring three adjacent images in a sequence of infrared images to be detected; inputting a pre-trained small target detection model to output a center heat map, a center offset and an effective radius prediction result, and completing positioning and scale estimation of the infrared small target; the small target detection model is used to extract multi-time space features through a backbone network and a feature pyramid sharing weights, obtain space-time representation features suitable for infrared small target detection by introducing space-time context perception information and constructing a time domain difference enhancement and global gating adjustment mechanism, and realize direct prediction of the center position and the effective radius of the small target in combination with a decoupled geometric parameter prediction network. The infrared small target detection task is modeled as target center position and effective radius prediction, so that effective detection is realized while reducing model calculation complexity and labeling cost.

The application provides a training method, a target detection method, a device, a medium and a robot, and a training method of a target detection model, which comprises the following steps: acquiring a training set, wherein the training set comprises positive samples, benchmark samples, negative samples and spliced samples obtained by splicing the positive samples, the benchmark samples and the negative samples; acquiring a support set; training a preset detection model by using the positive samples, the benchmark samples and the negative samples, to obtain a first training model; training the first training model by using the spliced samples, to obtain a second training model; and training the second training model by using the support set, to obtain the target detection model.

The invention provides a method for automatically extracting question data from a heat supply ERP system by using a large model, and belongs to the technical field of heat supply management. The method comprises the following steps: automatically extracting index-SQL template mapping data from a system back-end source code based on LLM and RAG technologies; inputting the system screenshot, the index list and the professional knowledge base into a multi-modal large model, and automatically generating a question and answer pair set containing indexes, numerical values, time and questions and answers; for each question and answer pair, calling LLM to instantiate a corresponding SQL template into an executable statement, checking the consistency of a result and an answer through the LLM after execution, and automatically adjusting a wrong SQL to generate an accurate question-SQL-answer triple; and further in combination with scores of service experts on candidate SQL, differences are analyzed by using LLM, experience is summarized, an expert knowledge base is dynamically updated, and SQL generation quality is continuously optimized. According to the method, the manual marking cost can be greatly reduced, and high-quality training data is provided for the Text-to-SQL model in the heat supply field.