902 results about "Product image" patented technology

The invention provides a commodity information processing and querying method and system. Belongs to the technical field of information processing. The method comprises the steps of collecting text information of commodities, preprocessing the text information of the commodities through natural language processing information, and constructing a structured commodity text database; based on the high-definition image of the commodity, key features of the commodity are extracted through machine vision and are associated with information in the text database to form multi-modal commodity information representation; the method comprises the following steps: encoding a commodity text by adopting a BERT model based on Transform, and obtaining a deep semantic representation of the text; performing feature extraction on the commodity image by using a pre-trained convolutional neural network to obtain an advanced visual feature of the image; through natural language processing and machine vision technologies, the text information and the image information of the commodity are subjected to structured and multi-modal fusion, so that the processing efficiency of the commodity information is greatly improved.

The invention relates to the technical field of product quality inspection, in particular to a product quality detection system based on deep learning and machine vision, which comprises a multi-modal image acquisition module, a deep learning defect detection module and a dynamic resource scheduling module. The multi-modal image acquisition module is used for acquiring product images in all directions by using a high-resolution camera array and a self-adaptive light source; the deep learning defect detection module accurately identifies defects through dual-path residual denoising, multi-scale context sensing and graph topology defect classification; the dynamic resource scheduling module realizes reasonable scheduling of personnel and materials and balance of a production line according to a detection result, a skill map, an ant colony algorithm and the like; the augmented reality interaction module visually displays defects and provides maintenance navigation through holographic projection and multi-mode interaction; the self-evolution detection model can update the weight based on a new sample, and the multi-physics field simulation module simulates the influence of defects on the mechanical property of a product. The detection precision is high, the efficiency is high, and the product quality detection and production management level is remarkably improved.

Provided are an advertisement generation method and system, a medium and a device. The method comprises: acquiring standard-product information and environment information of at least one standard product (S201); generating a standard-product image corresponding to the standard-product information on the basis of artificial intelligence generated content (AIGC), measuring each dimensionality of the standard-product image to obtain label information of each dimensionality of the standard-product image, and performing semantic combination on the label information of each dimensionality and the environment information to generate an advertisement copywriting (S202); and matching an advertisement template from an advertisement template library according to the label information of each dimensionality, and combining the standard-product image and the advertisement copywriting into the advertisement template to generate a standard-product advertisement (S203). The solution has the technical advantages of simplicity and high efficiency.

A system and method of automatic product attribute recognition receive training images having bounding boxes associated with one or more products in the training images, receive attribute values for each of the one or more products in the training images, and train a first convolutional neural network (CNN) model to generate bounding boxes for and identify each of the one or more products with the training images until the accuracy of the first CNN model is above a first predetermined threshold. The system and method further train a second CNN model for each of the products associated with the cropped images until the second CNN generates attribute values for the one or more attributes with an accuracy above a second predetermined threshold, and automatically recognize the one or more attributes for a new product image by presenting the product image to the first and second CNN models.

An online concierge system trains a fine-tuned generative image model for distinct categories of items based on a generative image model that takes a textual query as input and outputs and an associated image. Training of the fine-tuned generative image model is additionally based on a small set of representative images associated with the various categories, as well as textual tokens associated with the categories. Once trained, the fine-tuned generative image model can be used to generate realistic representative images for items in a database of the online concierge system that are lacking associated images. The fine-tuned model permits the generation of different variants of an item, such as different quantities or amounts, different packaging or packing density, and the like.

The present invention relates to a system (100) and method (300) for automated product validation, identification, and transaction management in shopping environments. The system (100) integrates a smart shopping cart (102) equipped with at least one camera (106), a load cell (108), and a user interface (110). An AI model trained on a dataset of high-resolution, 360° product images generate unique image embeddings for each product. The system performs multimodal validation by comparing the extracted image embeddings, measured weight data, and scanned barcode information with reference data stored in a vector database. Further, discrepancies, such as unscanned products or mismatches, are flagged, and payment is blocked until resolved.

The invention discloses an industrial image anomaly detection method and device, equipment and a storage medium, and relates to the field of image recognition. Obtaining a defect-free product graph, making a comparison data set based on the defect-free product graph, and performing pre-storage processing; performing feature point shape matching on the comparison data set based on the received to-be-detected image and the extracted feature point data, and determining a comparison image of the to-be-detected image; taking the to-be-detected image as a target object, correcting a product image area in the image, and aligning the product image area with the product image area of the to-be-detected image according to a pixel shift operation; and comparing the aligned product image areas, and identifying abnormal defects in the to-be-detected image. According to the scheme, the comparison data set of the defect-free product image is constructed, the feature point matching and pixel-level alignment technologies are combined, the problem of false detection caused by product position deviation or form change in a traditional method is effectively solved, the adaptability to illumination change is improved through brightness adjustment and difference matrix analysis, and the detection accuracy is improved. The method has the advantages of high detection precision, high environmental adaptability and high processing efficiency.

The invention specifically relates to the technical field of intelligent control, and discloses an intelligent control-based mold forming optimization system, which comprises a defective finished product image acquisition module, a defect intelligent identification module, a defect score quantification module, a process parameter synchronous acquisition module, a process risk dynamic analysis module and a process association closed-loop optimization module, the method comprises the following steps of: calculating a single-parameter risk index and a comprehensive risk index based on a process risk dynamic analysis module through a process parameter synchronous acquisition module in real time of full-flow process parameters of an injection molding process, identifying abnormity when the parameters deviate from an optimal target value, immediately triggering early warning, quickly positioning core cause parameters through a defect-process incidence matrix, and quickly positioning the core cause parameters through the defect-process incidence matrix. The influence of adjustment of different parameters on comprehensive defect scores is simulated within an equipment constraint range through a reverse optimization algorithm, a reverse optimization scheme is generated, a full-link traceable data link is formed through timestamps, stop loss time and production cost are greatly reduced, and accurate assessment and prediction of risks are achieved.

The invention relates to the technical field of industrial manufacturing digital management, in particular to a machine vision production line performance evaluation and optimization management system, which comprises a data acquisition module for triggering a high-speed industrial camera array, a vibration sensor and an RFID reader through a central synchronous controller to synchronously acquire product images, equipment operation and material circulation data; the data processing and fusion module extracts product quality features based on CNN, and fuses multi-modal data through time sequence alignment normalization and an attention mechanism; the dynamic efficiency evaluation module calculates OEE, FPY and a production line balance rate in real time by means of a deep neural network; the optimization strategy generation module is used for reinforcing the learning agent to output optimization instructions such as equipment parameter adjustment; and the control execution module converts the instruction into an industrial protocol format, issues the instruction to the PLC, and verifies the effect to form a closed loop. According to the method, the data relevance and the evaluation real-time performance are improved, the dynamic state of the adaptive production line is optimized, and the efficiency improvement is facilitated.

Systems and methods for generating three-dimensional (3D) models based on images are disclosed herein. An example computing system can generate, using a trained machine learning model and based on at least one image, a vectorized object corresponding with the object and a parametric list that can include a sequence of predefined operations and associated parameters to construct a corresponding computer-aided design (CAD) object. The parametric list can be employed, and is modifiable, in a CAD software or CAD workflow to construct a 3D model of the CAD object.

The invention relates to the technical field of risk early warning, and discloses an intelligent electronic product risk early warning system and method based on artificial intelligence, and the system comprises an acquisition module which determines a target product image of a to-be-detected intelligent electronic product, and an analysis module which determines the target product image when identifying that the target product image is suspected to accord with calibration; a target product image is divided into a plurality of sub-quality detection areas, each sub-quality detection area is analyzed, the sub-quality detection areas are deleted according to an analysis result, a processing module extracts all quality pixel values of the remaining sub-quality detection areas after deletion, and the quality pixel values of the remaining sub-quality detection areas are calculated. Each two quality pixel values on the first quality sequence or the second quality sequence are used as to-be-associated quality pixels, whether the to-be-associated quality pixels are associated is verified based on the quality evaluation model, and the calculation module determines whether to perform early warning on the to-be-detected intelligent electronic product according to the comprehensive quality value. According to the invention, risk early warning is carried out on the intelligent electronic product through the artificial intelligence learning model, and the early warning accuracy is improved.

The invention belongs to the technical field of precision marketing and intelligent recommendation, and discloses a B2B customer deep insight and precision reaching method based on a multi-modal large model, and the method comprises the specific steps: S1, carrying out the fusion and deep analysis of multi-modal data; s2, performing customer multi-dimensional preference modeling; s3, an intention and emotion analysis engine; s4, generating a personalized marketing strategy; s5, recommendation execution and verbal skill optimization; s6, feedback collection and reward function drive optimization; and S7, applying a cold start solution and transfer learning. According to the invention, through integration of multi-mode data of client official websites, news information, social media dynamics, mail communication, product images and conference recording, panoramic insight of clients from a business level to behavior details is realized; in combination with deep analysis of a pre-trained large model on texts, images and audios, explicit demands can be obtained, and industry features, cultural characteristics and potential concerns of customers can be understood.

The invention relates to the technical field of industrial detection, and discloses a multi-modal data fusion industrial defect intelligent detection system, which comprises a multi-modal data acquisition module which comprises a high-resolution visible light camera, an infrared thermal imager and an ultrasonic sensor and is used for acquiring product images and data information from different dimensions, the devices are reasonably arranged on a production line to realize comprehensive acquisition; the preprocessing module is used for denoising and normalizing the collected data; the limitation of single-mode detection is overcome through the multi-mode data acquisition module, and omnibearing detection is realized; an advanced fusion strategy is adopted, and feature interference and insufficient information interaction are avoided; the environment self-adaptive adjustment module enables the system to stably work in a complex environment; the parallel computing module improves the data processing efficiency and meets the real-time detection requirement; the encryption module ensures data security, the modular design facilitates maintenance and upgrading, and the data backup and recovery module ensures production and data integrity.

The invention discloses a concave-convex point detection and defect evaluation system based on multistage detection and feature change, and the system comprises an image collection unit which is used for capturing a product image in real time; the image preprocessing unit is used for carrying out noise reduction processing on the image and improving the brightness and the contrast ratio of the image; the primary product defect positioning module is used for selecting Gaussian difference extraction, dynamic threshold extraction, fixed threshold extraction, template matching and a deep learning algorithm module to extract and position suspicious defects according to the size of the product and the characteristic form of the defects; the secondary defect classification and judgment module is used for carrying out preliminary classification and type judgment on the defects in the region; the third-stage defect contour extraction unit is used for performing fine contour extraction on each detected defect area after the defect classification is clear; and the four-stage defect feature analysis and evaluation unit is used for deeply extracting key features of each defect and carrying out scientific and quantitative evaluation on the key features.

The invention discloses a rendering-based artificial industrial product defect synthesis method and device. Firstly, modeling and generalization are carried out on the reasonable shape of a 3D model of a normal sample; the modeling defect result of the target sample is simulated by modifying the shielding, the metallization and the roughness, the three variables are designed into parameters or variables which can be updated, and defect simulation is carried out on the corresponding area according to a subsequent loss function. And projecting the sample 3D model into which the defect is introduced into a standard space to obtain an industrial product image of the multi-view defect product with the standard resolution. Calculating image similarities of different artificial samples and a small number of real defect samples; label-based discrimination is carried out on a real sample and a defect sample through an adversarial network of an image, and the similarity between a generated defect and an actual defect is improved through loss functions of the two parts. According to the method, a multi-view defect generation result is optimized instead of evaluating and simulating defects in pixel.

Methods, systems, and computer-readable storage media for receiving a product image depicting a product that is to-be-inspected for defects, transmitting a request to a LLM system, the request including the product image and a reference image, receiving, from the LLM system, a textual response, the textual response being generated by the LLM system at least partially by processing the product image and the reference image, processing the textual response and the product image through a RIS model to provide an output image that includes one or more masks, each mask indicating a defect in a product depicted in the product image, and providing the output image with an indication that the product is defective.

An online system performs an inference task in conjunction with the model serving system and / or interface system to generate relevant product images for query auto-completion and query suggestion to help users better navigate their search experience. The online system generates a collection of query suggestions using search query log mining. For each query suggestion in the collection of query suggestions, the online system retrieves one or more catalog images that depict the query suggestion from a product catalog. The online system constructs a prompt to a text-to-image model including the query suggestion, and a request to generate one or more query images based on the query suggestion. The online system receives the query images from the text-to-image model and ranks the catalog and query images to identify an image to display to the user in association with the query suggestion.

The invention relates to the technical field of big data analysis, and more particularly discloses a package design optimization system and method based on big data analysis, which analyze and optimize package design by using a product image and a package design image. The attractive force and the spreading effect of product packaging are improved in the modes of color adjustment, pattern optimization, information transmission improvement and the like, so that the high requirement of consumers for visual experience is met, and a packaging design scheme with better attractive force and information transmission effect is provided for the fields of beauty makeup commodities and the like.

The invention discloses a product defect unsupervised semantic segmentation method and device based on a pre-training model, and is used for solving the technical problem that the defect classification precision is not ideal due to an existing industrial defect unsupervised semantic segmentation method. The method comprises the following steps: acquiring a to-be-detected industrial product image, an industrial product reference image corresponding to the to-be-detected industrial product image and a plurality of to-be-matched defect categories; a preset segmentation network of a self-learning point generation prompter based on defect potential space features is adopted to generate a target defect segmentation mask according to a to-be-detected industrial product image and an industrial product reference image, and a target cutting image is generated based on the target defect segmentation mask, so that the target cutting image with visual prompt can be generated; and processing the target cutting image and a plurality of defect categories to be matched through a preset defect classification network based on a self-learning defect category prompt module and a defect image-text cross-modal feature interaction module, and combining segmentation and classification, thereby improving the defect classification precision.

The invention relates to the technical field of commodity visual identification, in particular to a vending cabinet commodity visual identification system based on a machine learning model, which comprises a feature extraction module used for determining position information of a target identification commodity, shooting a picture of the position of the target identification commodity to obtain a commodity image, and performing feature extraction on the commodity image to obtain a feature extraction result; constructing a target identification commodity feature network; the database construction module is used for collecting the feature information and the mutual relation of each commodity in the vending cabinet, processing the feature information and the mutual relation of each commodity, and storing the processed feature information and the mutual relation to a feature database; the matching identification module is used for establishing an association relationship between the image feature information association network and the standard feature information association network, determining feature information matched with the target commodity feature information from the feature database, and determining an identification result of the target commodity based on the commodity corresponding to the feature information, the method can achieve the accurate and efficient recognition of the commodities in the vending cabinet.

The invention relates to a layout image generation method and device, computer equipment and a storage medium, and the method comprises the following steps: carrying out the element analysis of original image data, and obtaining image element information; performing feature extraction on the image element information to obtain image features and text features; performing hypergraph structure construction according to a preset design rule, the image features, the text features and the image element information to obtain hypergraph structure information; performing style parameter adjustment and coordinate adjustment on the hypergraph structure information according to the matching similarity corresponding to the element embedding vector to obtain to-be-rendered information; and performing image layout rendering according to the to-be-rendered information to obtain an effective layout image. The method can be applied to application scenes of financial science and technology and digital medical systems, and insurance product propaganda images or disease prevention propaganda images which effectively express image elements and features can be effectively, accurately and quickly generated according to original insurance product images or health education images in the system.

The invention provides a defect detection model parameter construction method, a controller, a medium and a product. The method comprises the following steps: acquiring a plurality of product images; inputting the plurality of product images into a deep learning model to obtain a reasoning result, the reasoning result comprising a defect category; setting an initial strict threshold value, an initial balance threshold value and an initial loose threshold value according to a reasoning result; and based on the defect category, correcting the detection parameters of the deep learning model according to the initial strict threshold value, the initial balance threshold value and the initial loose threshold value to obtain corrected defect detection model parameters. The corrected defect detection model parameters are obtained based on the model reasoning result and analysis of actual production requirements, so that the defect detection model has the flexible adjustment capability of defect detection in the actual production process, the detection threshold value can be adjusted in real time according to customer requirements, and it is ensured that the detection result meets the specific quality standard, and the detection efficiency is improved. And the dynamic requirements of different batches are met, and accurate and efficient detection is realized.

The invention belongs to the technical field of printing process optimization, and discloses a printing process optimization decision-making system based on big data. Comprising an image acquisition module used for acquiring a printed product image; the feature extraction module is used for analyzing the printed product image and extracting product feature data; the quality detection module is used for analyzing the product feature data and identifying quality defect problems; the reason tracing module traces the reason of the quality defect according to the quality defect problem; the process optimization module is used for dynamically optimizing printing process parameters based on quality defect reasons; according to the method, the advantages of advanced technologies such as computer vision, machine learning and intelligent optimization can be fully played, intelligent monitoring and dynamic optimization of the whole printing production process are achieved, the method can rapidly adapt to the changing production environment, the quality defect problem in printed products is reduced, and the printing quality and the production efficiency are improved.

A system and method of automatic product attribute recognition receive training images having bounding boxes associated with one or more products in the training images, receive attribute values for each of the one or more products in the training images, and train a first convolutional neural network (CNN) model to generate bounding boxes for and identify each of the one or more products with the training images until the accuracy of the first CNN model is above a first predetermined threshold. The system and method further train a second CNN model for each of the products associated with the cropped images until the second CNN generates attribute values for the one or more attributes with an accuracy above a second predetermined threshold, and automatically recognize the one or more attributes for a new product image by presenting the product image to the first and second CNN models.

The invention belongs to the technical field of industrial detection, and discloses a structured knowledge-driven industrial defect detection method and system, and the method comprises the steps: obtaining manufacturing data related to a product, and constructing a knowledge graph which reflects the relation between the defect characteristics of the industrial product and the machining process and environment through the manufacturing data; regarding the established industrial defect knowledge graph as a heterogeneous information network, and performing deep feature aggregation on various entities and relationships in the industrial defect knowledge graph by using a heterogeneous graph neural network technology to form a high-dimensional feature vector for guiding coupling of multiple environmental factors; establishing an industrial image illumination model by using a deep transfer learning technology, and simulating image features of industrial products under different acquisition conditions; an industrial product defect data enhancement framework based on a conditional diffusion model is constructed by training a conditional noise prediction network, a real and diversified industrial product image is generated according to the industrial product defect data enhancement framework, and industrial product defect detection is completed.

The invention discloses a zero-sample industrial defect detection method and device based on visual enhancement and a medium, and the method comprises the steps: collecting an industrial product image and a text semantic tag corresponding to the industrial product image, and constructing a training set and a test set; building a zero-sample industrial defect detection model based on visual enhancement, and training by using the training set; and inputting the test set into the trained model for reasoning and evaluation. The model is composed of a text encoder and an image encoder, and deep interaction and optimization of text and image features are realized by introducing a bidirectional cross attention mechanism, a multi-task fusion module and an image feature refining module. In the training stage, the model learns uniform defect features from part of industrial product categories; in the test stage, defect identification and positioning are carried out on the types of industrial products which are not contacted. According to the method, the dependence on manual labeling is effectively reduced, the adaptability and detection precision of the model in a complex industrial scene are improved, and the method has good generalizability and industrial deployment value.

The invention relates to the technical field of cocktails, discloses a cocktail formula retrieval enhanced generation method based on multi-modal feature fusion, and aims to solve the problems of poor accuracy and low individuation degree of an existing method. The scheme mainly comprises the steps that a cocktail finished product image and an ingredient constraint condition text uploaded by a user side are acquired; visual feature extraction and semantic feature extraction are carried out, fusion coding is carried out, and a unified query vector is generated; searching candidate formulas with similarity scores Top-K from a pre-constructed cocktail formula knowledge base; and inputting the query vector and the candidate formula into a retrieval enhanced generation model, generating a cocktail formula recommendation result including a formula name, main ingredients, alternative ingredient suggestions, making steps, formula difficulty and estimated making time, and returning the cocktail formula recommendation result to a user side for display. According to the method, the accuracy and the individuation degree of cocktail formula retrieval are improved, and the method is suitable for intelligent bartending, catering training and personal customization service scenes.

The invention relates to a control method of an injection molding system and the injection molding system.The method comprises the steps that in response to the situation that injection molding equipment enters a pressure maintaining stage, pressure maintaining is conducted on the basis of one value from 80% to 90% of the maximum filling pressure, and pressure change information in the pressure maintaining duration time is collected; responding to a plastic product forming trigger signal of injection molding equipment, and controlling an image acquisition unit to continuously acquire N frames of plastic product images; on the basis of the pressure change information, the N frames of plastic product images and a pre-trained neural network, determining the adjustment directions of the dwell pressure and the dwell time; and based on the adjusting direction, the pressure maintaining pressure and the pressure maintaining time of the next process of the first-stage injection molding equipment are adjusted, and the first-stage pressure maintaining pressure is adjusted to be 4%-7% of the maximum filling pressure. According to the pressure maintaining device, self-adaptive adjustment of the pressure maintaining pressure is achieved.

The invention discloses an e-commerce commodity image-text generation optimization system based on generative AI, relates to the technical field of image-text optimization, and is used for solving the problems of insufficient image-text attraction and low conversion rate caused by difficulty in generating differentiated image-text content for different user groups. Classifying and marking the commodities, judging attention conditions, extracting commodity core features and user preference features after screening, and determining a user interest weight value of each marked commodity; feedback information of the commodities under the corresponding classification marks is collected, image-text information is generated in combination with user interest weights, updating time is set to collect historical optimization effect data, and the historical optimization effect data is used for updating user behavior data, so that image-text optimization of different commodities is achieved, and the matching degree and the display attraction of contents and user preferences are improved.

The present invention refers to a method of training a neural network to detect anomalies in a manufacturing product comprising: obtaining a dataset including multiple manufacturing product images and annotation files with coordinates of predetermined anomalous regions corresponding to the manufacturing product images, respectively; selecting a test query set of images and a support set of images from the dataset multiple to train a deep learning model, the support set of images comprises pairs of reference images and each pair comprises at least one anomalous manufacturing product image and at least one non-anomalous manufacturing product image; inputting the test query set into a deep learning model to rate a similarity score based on a similarity distance between the pairs of reference images of the support set based on the annotation files; and adjusting parameters characterizing the deep learning model through a model based on the similarity score.