680 results about "Sample graph" patented technology

The invention relates to the technical field of industrial defect detection, and discloses a memory matching industrial defect detection method based on adaptive feature fusion, and the method comprises the steps: generating a plurality of unknown defect samples through an enhanced image-level defect simulation strategy, and helping a model to effectively distinguish a normal mode from an abnormal mode; the method comprises the following steps: extracting multi-scale features of a to-be-detected sample image, a normal sample image and a defect sample image, designing an adaptive hierarchical memory bank architecture, embedding the multi-scale features into a grid memory bank in a layered manner, further improving the reasoning speed through an adaptive core set sampling strategy, and combining multi-scale feature fusion and a defect positioning optimization technology to obtain a defect positioning algorithm. And the difference between the multi-scale features and the normal mode is fully utilized, so that the accuracy of defect detection and positioning is remarkably improved. Compared with the prior art, the defect detection precision and the positioning accuracy can be improved, and meanwhile, the real-time detection requirement can be met.

The invention provides a mathematical problem solving method and device based on a multi-modal large model and electronic equipment, and relates to the technical field of artificial intelligence. The method comprises the following steps: determining a mathematical element image of a mathematical problem; inputting the mathematical element image into an image coding model to obtain an image vector output by the image coding model; the image coding model is obtained by training based on a sample mathematical element image and a positive sample text description and a negative sample text description corresponding to the sample mathematical element image; inputting the image vector into a self-adaptive module to obtain an image conversion coding vector output by the self-adaptive module; the adaptive module is obtained based on training of a sample image vector and a sample text vector; determining question stem characters of the mathematical question, and inputting the question stem characters and the image conversion coding vector into the large language model to obtain a prediction answering process output by the large language model; the large language model is obtained based on sample question stem characters, sample image conversion coding vectors and sample answering process training, and the mathematical problem solving capability of the multi-modal large model can be improved.

The embodiment of the invention relates to the technical field of data processing and AI vision, and provides a warehouse inventory intelligent management method and device based on AI vision monitoring and a terminal, and the method comprises the steps: obtaining inventory management basic data and external dynamic influence data of a to-be-monitored warehouse; based on an AI visual technology, according to the sample image data set and the label image data set, performing dynamic optimization processing on current inventory data in the inventory management basic data to obtain dynamically optimized inventory data; according to the inventory management basic data and the dynamic optimization inventory data, carrying out inventory structure evaluation to obtain inventory structure evaluation data; performing real-time dynamic adjustment processing on the inventory structure evaluation data according to the external dynamic influence data to obtain inventory dynamic adjustment data; according to the dynamic optimization inventory data, the inventory structure evaluation data and the inventory dynamic adjustment data, an inventory health evaluation report is generated, and the purpose of more accurate and more comprehensive warehouse inventory intelligent management is achieved.

The invention relates to the technical field of defect detection, and particularly discloses an electronic component defect detection method, system and device, and the method comprises the steps: collecting a multiband original image set based on multispectral imaging, inhibiting batch material difference and surface reflection through orthogonal complementary space projection, and obtaining a de-noised image set; performing feature extraction on the de-noised image set, and identifying and marking all fragmented defect areas; based on a graph neural network space attention mechanism, establishing an association relationship between fragmented defect areas to form a complete defect feature chain; gray frequency value sequences of the to-be-detected image and the standard sample image are constructed respectively, and a first fitting curve and a second fitting curve are obtained through fitting of a skewed distribution function; and finally, comprehensively judging whether the electronic component has defects or not by combining the defect characteristic chain and the difference characteristics of the two fitting curves. According to the method, interference can be effectively suppressed, fragmented defects can be accurately identified, and the detection accuracy and reliability are remarkably improved through multi-dimensional feature fusion.

Disclosed in embodiments of the present application are a multi-modal retrieval model generation method and apparatus, a device, and a storage medium. The embodiments of the present application can be applied to various scenarios such as artificial intelligence, intelligent transportation, and assisted driving. The multi-modal retrieval model generation method comprises: by means of a prefix vector module, performing feature recognition on first sample image modal data to obtain a first image prefix vector; by means of a modal identification module and on the basis of the first image prefix vector, the first sample image modal data, and first sample text modal data, generating a first retrieval character; by means of a constrained decoding module, acquiring from a pre-generated database a first sample retrieval result associated with the first retrieval character; and on the basis of a first reference retrieval result and the first sample retrieval result, adjusting the model parameters corresponding to the prefix vector module and the constrained decoding module to obtain a retrieval model after the adjustment. The present application can improve the training efficiency of multi-modal retrieval models.

The present application relates to the field of autonomous driving, and in particular relates to an occupancy grid prediction method, an occupancy grid prediction apparatus for implementing the method, a computer storage medium for implementing the method, and a smart device having the occupancy grid prediction apparatus. The method comprises: acquiring a pure visual image of the surroundings of a vehicle body; inputting the pure visual image into an occupancy grid prediction model; and using the occupancy grid prediction model to determine an occupancy result of an object in the pure visual image as a model output. The model output is an occupancy result in a cylindrical form, and the occupancy grid prediction model is constructed on the basis of a training dataset comprising sample images and truth value information of the sample images. The truth value information is directly generated from point cloud data or converted from occupancy information in a voxel form generated on the basis of the point cloud data.

The invention discloses a few-sample classification method research based on feature cache optimization, and the method comprises the steps: (1), employing an improved ResNet-based SwAV model as a backbone, and enabling the SwAV model to be used for generating auxiliary features; (2) taking the image data and the text description as original input, and generating text input with rich downstream language semantics by utilizing GPT-3 to serve as text prompt of a CLIP model; (3) through a feature selection method based on feature similarity and difference, the problems of feature redundancy and inaccurate selection in the feature selection process are solved, feature dimensions with high selection value are identified, and normalization and enhancement zooming processing are performed on the features; and (4) utilizing visual contrast knowledge of SwAV, introducing a learnable cache model, and adaptively mixing prediction results from CLIP and SwAV. According to the method, under the CLIP framework without extra training, the accuracy of few-sample image classification is effectively improved through feature cache optimization and a self-adaptive hybrid strategy.

The embodiment of the invention provides a model training method and device based on visual reinforcement learning, equipment and a medium. Comprising the following steps: acquiring a sample image frame and semantic category information, inputting the semantic category information into a visual large language model to obtain a first convolution kernel parameter, and inputting the sample image frame into a first feature convolution kernel to obtain a first feature thermodynamic diagram; obtaining a second convolution kernel parameter and a second feature thermodynamic diagram of the sample image frame through a preset visual reinforcement learning model; constructing a first distillation loss based on the first convolution kernel parameter and the second convolution kernel parameter, and constructing a second distillation loss based on the first characteristic thermodynamic diagram and the second characteristic thermodynamic diagram; through prediction and calculation of the sample action data and the sample state data, self-supervision loss and target strategy loss are constructed; and based on the first distillation loss, the second distillation loss, the self-supervision loss and the target strategy loss, performing parameter adjustment on a preset visual reinforcement learning model to obtain a target visual reinforcement learning model.

A semantic segmentation model training method and apparatus, an electronic device and a storage medium are provided. The semantic segmentation model training method includes: acquiring a sample image, and extracting visual image features corresponding to the sample image by a semantic segmentation model to be trained; processing the sample image to obtain a text image feature corresponding to the sample image, the text image feature being an image feature generated from language description text for the sample image; fusing the visual image features with the text image feature to obtain multimodal features, and performing image segmentation prediction based on the multimodal features to obtain a target loss; and training the semantic segmentation model to be trained based on the target loss to obtain a target semantic segmentation model.

The invention relates to the technical field of image generation, and discloses a country style and appearance design image generation method and system based on a knowledge graph and a large model, and the method comprises the steps: constructing a core element covering country style and appearance design and a knowledge graph; constructing a multi-level label system based on the knowledge graph; pre-processed rural style and appearance sample images are obtained, the rural style and appearance sample images are labeled based on a multi-level label system, and a special training set in the field of rural style and appearance design is obtained; constructing a basic diffusion large model architecture, and training the basic diffusion large model architecture by using the special training set for the country style and appearance design field to obtain a trained country style and appearance design image generation model; and generating a rural style and appearance design image by using the trained rural style and appearance design image generation model. According to the method, the problems that a general large model is insufficient in domain knowledge support and insufficient in data labeling specialty in rural style and appearance design image generation are solved, and the rural style and appearance design image generation efficiency and quality are improved.

The embodiment of the invention provides a training method and device of an open vocabulary detection model and electronic equipment. The open vocabulary detection model training method comprises the steps of obtaining sample data; the sample data comprises a sample image, a sample text and label information, inputting the sample data into a neural network model to be trained, and extracting text feature information of the sample text and image feature information of the sample image; generating dynamic text prompt information according to the image feature information; performing fusion processing on the text feature information and the dynamic text prompt information to obtain semantic fusion feature information; predicting an object in the sample image according to the semantic fusion feature information; and performing iterative training on the neural network model according to the object prediction result, the label information, the text feature information, the dynamic text prompt information and the semantic fusion feature information to obtain an open vocabulary detection model. According to the invention, the context sensing, semantic generalization and fine-grained understanding capabilities of the open vocabulary detection model can be improved.

A semantic segmentation model training method and apparatus, an electronic device and a storage medium are provided. The semantic segmentation model training method includes: acquiring a teacher semantic segmentation model that is pre-trained, the teacher semantic segmentation model including a first teacher network and a second teacher network, the first teacher network having structural characteristics of low depth and high width, and the second teacher network having structural characteristics of high depth and low width; processing a sample image based on the teacher semantic segmentation model to obtain a first segmentation map and a second segmentation map; and training a student semantic segmentation model that is lightweight according to the sample image, the first segmentation map and the second segmentation map, so as to obtain a target semantic segmentation model.

The invention relates to the technical field of target recognition, in particular to a target recognition and model training method and device based on global perception graph convolution. The training method comprises the following steps: acquiring sample images and category labels, counting the co-occurrence probability of one or two same category labels in all the sample images, and generating a label adjacency matrix; inputting the sample image into a feature extraction network to obtain a first image feature, and generating a corresponding first adjacent matrix based on each local feature of the first image feature; inputting the first image feature and the first adjacent matrix into a first image convolutional network to obtain a second image feature; fusing the first image feature and the second image feature, and inputting the fused first image feature and second image feature and the label adjacent matrix into a second image convolutional network to obtain a prediction category of the target object; and calculating the difference degree between the prediction category and the category label, and adjusting the parameters of the model to obtain a trained target recognition model. The problem that a traditional classification network method is difficult to consider classification accuracy and efficiency in a complex scene is solved.

The invention relates to a generation method and device of a defective image, electronic equipment and a storage medium, and the method comprises the steps: obtaining a defective sample image, a defect-free sample image, a sample control condition and a sample instruction, the sample control condition is used for indicating a defect background, a defect position and a defect form in the defective sample image, and the sample control condition is used for indicating the defect background, the defect position and the defect form in the defect-free sample image; the sample instruction is used for describing a main category and a sub-category of the defect; sequentially performing coarse training and fine training on the initial image generation model based on the defective sample graph, the defect-free sample graph, the sample control condition and the sample instruction to obtain a trained image generation model; and inputting the defect-free image, the target control condition and the target instruction into the trained image generation model to obtain an output image with defects. According to the invention, the generation efficiency of the defect graph is improved.

The invention relates to a part defect detection method and system, and relates to the technical field of part defect detection. Comprising the steps of classifying and sorting a part processing library by using a part information sorting module, and constructing a generative adversarial network by using a sample image expansion module, generating a defect sample image to expand a defect sample database, constructing an image defect recognition model by utilizing an image recognition module to perform defect recognition, and performing defect state scoring and grade division operation on the part by utilizing a defect grade determination module. The defect sample database is expanded by constructing the generative adversarial network, data guarantee is provided for subsequent recognition, and recognition efficiency and accuracy are improved; besides, based on the score of the related defect of each feature surface, the state of the part is evaluated on the whole, and a data basis is provided for the subsequent defect grade and degree of the part, so that the part is reasonably defined, the treatment pertinence of the defective part is improved, the invalid loss is reduced, and the cost is reduced.

A method of clustering patterns of an integrated circuit includes; providing a pattern image and numeric data, as input data corresponding to a first pattern to a first model, wherein the first model is trained by a plurality of sample images and a plurality of sample values, obtaining a content latent variable using the first model, and grouping a plurality of content latent variables corresponding to a plurality of patterns into a plurality of clusters based on a Euclidean distance, wherein the numeric data represents at least one attribute of the first pattern.

The invention is suitable for the technical field of artificial intelligence, and provides an abnormal sample image generation method, electronic equipment and a storage medium, and the method comprises the steps: setting a material parameter library and a scene parameter library, and constructing a paired industrial defect sample data set in combination with three-dimensional geometric models of a plurality of sample workpieces; constructing category text description, defect text description and material category text description of various workpieces, and constructing and training an abnormal sample image generation model in combination with the paired industrial defect sample data set; obtaining a target normal image, a candidate defect area mask image, a target category text description, a target defect text description and a target material category text description of a target category workpiece, and inputting the target normal image, the candidate defect area mask image, the target category text description, the target defect text description and the target material category text description into an abnormal sample image generation model for processing to obtain a target defect image and a target defect area mask image; the training precision and flexibility of the abnormal sample image generation model are improved, and then the efficiency and precision of abnormal sample generation are improved.

A sample processing method includes: obtaining a positive sample image; obtaining, based on the positive sample image, an anomaly labeling mask map corresponding to the positive sample image; and generating a forged negative sample image based on the positive sample image and the anomaly labeling mask map, where an anomaly region in the forged negative sample image corresponds to an anomaly labeling region in the anomaly labeling mask map. In this way, a large quantity of forged negative sample images can be automatically generated, to provide sufficient sample datasets for task detection in a scenario with scarce samples.

The invention relates to the field of fabric printing and dyeing flaw detection, and discloses a fabric printing and dyeing flaw multi-scale lightweight detection method and system based on improved YOLO, and the method comprises the following steps: firstly, collecting fabric printing and dyeing flaw pictures, carrying out the labeling and data set division of the collected pictures according to the corresponding flaw types, and constructing sample image data; performing data enhancement on the sample image data; on the aspect of a model structure, in combination with a multi-scale context aggregation module, a multi-scale context diffusion fusion pyramid network, a C3-Star module and an Officient Local Attention, structural innovation is carried out on a YOLOv5 model, and an improved YOLOv5s model is obtained. According to the method, the global context features of the defect features are obtained by using the multi-scale context aggregation module according to the multi-scale characteristic of the fabric printing and dyeing flaws, and the global context features are diffused to each detection scale by using the multi-scale context diffusion fusion pyramid network, so that the detection capability of the model on a multi-scale target is improved.

The invention provides a knowledge distillation-based model training method and device, electronic equipment, a computer readable storage medium and a computer program product. The method comprises the steps of obtaining a first sample data set; the first sample data set comprises a plurality of first labeled texts, and a first sample image corresponding to each first labeled text has different visual effect types and comprises a plurality of objects; calling a pre-trained teacher model, and generating a first generation image based on the first annotation text; generating a second generation image based on the first generation image and the first annotation text through a to-be-trained student model; determining knowledge distillation loss of the student model based on the first generated image and the second generated image; and performing model parameter updating on the student model based on the knowledge distillation loss to obtain a trained student model. According to the method and the device, the images which have different visual effects and contain a plurality of objects can be generated through the trained student model, and the generation quality of the images can be improved.

The invention relates to the technical field of image generation, and discloses a high-quality human body image generation method and device, and computer equipment. The method comprises the following steps: constructing a training data set, wherein the training data set comprises a sample image of a human body, a text description of a corresponding human body attribute, a human body analysis graph and a plurality of attribute tags; an image generation network is constructed, the image generation network comprises an encoder, a UNet and a decoder which are connected in sequence, the image generation network further comprises a time feature aggregation module and an attribute perception reward module, and the attribute perception reward module is used for predicting a target reward score and a prediction reward score; training an image generation network by minimizing the difference between the target reward score and the predicted reward score; and inputting the text description of the human body image to be processed and the human body analysis graph into the trained image generation network to obtain a target image. By adopting the method, the high-quality human body image with space alignment and consistent attributes can be generated.

The invention discloses a knowledge distillation-based model training method and electronic equipment. The method comprises the following steps: performing feature extraction on a first sample image through a plurality of teacher models to obtain foreground supervision sub-features and background supervision sub-features of N local category objects; performing feature extraction on the first sample image through a first student model to obtain first foreground features and first background features of the N local category objects; performing foreground knowledge distillation on the first student model according to the foreground supervision sub-feature and the first foreground feature, and performing background knowledge distillation on the first student model according to the background supervision sub-feature and the first background feature; the first student model after foreground knowledge distillation and background knowledge distillation is a second student model; and performing image prediction on the second sample image through the second student model, and adjusting model parameters of the second student model according to a prediction result of image prediction. According to the invention, on the premise of a limited number of samples, the student model with high concurrency capability and high precision can be distilled based on a plurality of teacher models.

The invention relates to the technical field of artificial intelligence, and provides a method and system for improving generalization of a small computer vision model based on a text graph model, and the method comprises the steps: S1, constructing and optimizing a large text graph model; s2, constructing positive and negative sample text descriptions according to a text graph large model on the basis of features and scene requirements of a target detection object; s3, inputting the positive and negative sample text descriptions into the optimized text graph large model to generate corresponding positive and negative sample images, labeling the positive and negative sample images, and carrying out preprocessing and data enhancement on the labeled positive and negative sample images; s4, loading a CV small model, and performing model training according to the preprocessed and data enhanced positive and negative sample images; s5, evaluating the trained CV small model, and if an evaluation result does not meet a preset condition, adjusting hyper-parameters and returning to the step S4 for re-training; if a preset condition is met, the trained CV small model is stored, and the problems that training data is insufficient and samples lack real scene diversity are solved.

The invention relates to the field of electrical technology, and discloses a drawing-to-configuration AI generation method and system based on a CV large model, and the method comprises the steps: constructing an original sample set based on a single-cabinet drawing, and screening out a complex primitive sample set from the original sample set; secondly, labeling basic primitives and non-detachable combined primitives in each sample graph of the original sample set and complex primitives in each sample graph of the complex primitive sample set; and training the CV visual large model based on the labeled original sample set and the labeled complex primitive sample set to obtain a basic primitive recognition model and a complex primitive recognition model. And finally, the complex primitive recognition model and the basic primitive recognition model are sequentially adopted to recognize the target system diagram, the type and coordinates of each primitive and the topological relation between the primitives are obtained, and a configuration diagram in a vector format is generated. When the method and the system disclosed by the invention are applied in a complex circuit design scene, the accuracy and the reliability of electrical drawing identification can be remarkably improved.

The invention discloses a method for identifying traditional Chinese medicinal materials by combining infrared spectroscopy with clustering analysis, which comprises the following steps of: acquiring original infrared spectral data, averaging the original infrared spectral data to obtain single-sample original spectral data, constructing a sample graph and a wavelength graph based on standardized spectral characteristics, and fusing the sample graph and the wavelength graph to obtain the single-sample original spectral data. Obtaining fusion image data, inputting the fusion image data into a pre-trained image neural network model, extracting a low-dimensional feature vector of a target sample through forward propagation, and inputting the low-dimensional feature vector into a pre-trained dynamic cluster diffusion module to obtain a clustering label and distance data; and determining and outputting the quality grade of the rhizome traditional Chinese medicinal material sample based on the clustering label and the distance data. Therefore, interference can be effectively reduced, key features can be extracted, associated features of fused graph data are extracted in combination with a graph neural network, and the quality grade of traditional Chinese medicinal materials can be accurately determined in combination with clustering analysis of a dynamic cluster diffusion module.

A data processing method includes obtaining N sample image sets and an image recognition model, obtaining a sampled sample image from the N sample image sets, inputting the sampled sample image and the wrong class label into the image recognition model to generate a first probability vector of the sampled sample image for the N class labels, obtaining, from the N probability elements in the first probability vector, a first probability element indicating the wrong class label, and adjusting the sampled sample image based on the first probability element, to obtain an adversarial sample image corresponding to the sampled sample image.

The invention relates to the technical field of image recognition, in particular to an animal and plant image recognition method based on deep learning and electronic equipment, and the method comprises the steps: obtaining and preprocessing sample image data, and constructing a sample image database; and constructing a data model, setting training parameters, and inputting image data and labels for training. And evaluating the model by using the verification set, and outputting after optimization. And evaluating the performance by using the test set, and deploying the model for intelligent identification. According to the invention, data diversity is increased by using a data enhancement technology, a model structure and a training strategy are optimized, and an under-fitting problem is solved. And by increasing the complexity of the neural network, selecting a proper weight initialization method and a proper learning rate strategy, and increasing training rounds, the generalization ability of the model is improved. Meanwhile, different attitude changes are simulated by using a data enhancement technology, multi-attitude image data are collected, an attention mechanism and an attitude estimation model are introduced, and differential recognition of animals and plants under different attitudes is realized by combining a multi-scale feature extraction and fusion strategy.

Systems and methods for a sample database are provided, where the sample database is for matching a target coating. In one embodiment, the system comprises a sample database stored on a storage device. The sample database includes a sample coating formula and a sample image feature with at least one sample coating formula linked to at least one sample image feature. At least one sample image feature includes a spatial micro-color analysis that includes a value determined by a sample pixel feature difference between at least two sample pixels.

A computer-implemented method is provided. The method includes: obtaining a sample text and a sample image corresponding to the sample text; labeling a true semantic tag for the sample text according to a first preset rule; obtaining a text feature representation of the sample text and a predicted semantic tag output by a text coding sub-model; obtaining an image feature representation of the sample image output by an image coding sub-model; calculating a first loss based on the true semantic tag and the predicted semantic tag; calculating a contrast loss based on the text feature representation of the sample text and the image feature representation of the sample image; adjusting parameters of the text coding sub-model based on the first loss and the contrast loss; and adjusting parameters of the image coding sub-model based on the contrast loss.

The invention discloses a radar weak target detection and positioning method based on four-polarization time-frequency feature fusion. The method comprises the following steps: constructing an initial four-polarization time-frequency feature detector; the initial four-polarization time-frequency characteristic detector is constructed by introducing four parallel Backbones into a YOLOv11 network; one parallel Backbone correspondingly processes one polarization channel; training the initial four-polarization time-frequency feature detector by using a pre-constructed training label set to obtain a four-polarization time-frequency feature detector; calculating detection statistic distribution based on the confidence corresponding to each pure clutter sample map, and determining a detection threshold according to the detection statistic distribution; based on a to-be-detected time-frequency diagram, a radar weak target detection and positioning method which is sufficient in feature utilization, high in detection capability and stable is provided by using a four-polarization time-frequency feature detector and a detection threshold.