331 results about "Image database" patented technology

The invention relates to the field of intelligent city planning, and discloses a street space quality intelligent evaluation and image generation method and system based on deep learning, and the method specifically comprises the following steps: movably collecting a street space image, and recording the geographic position information; performing perspective correction and multi-view image segmentation on the acquired street image data, and performing spatial matching with geographical location information to obtain a fused image database; and performing semantic segmentation on the image in the fused image database based on a deep learning model, calculating objective and subjective visual perception indexes and facility function indexes of the image, and generating a street space quality evaluation result. The method solves the problems that in the prior art, evaluation dimensions are limited, and an automatic response generation mechanism is lacked, and has the advantages of being intelligent, efficient and comprehensive in subjective and objective evaluation.

The invention provides an image searching method, which comprises the following steps of: when a new label is added to an image in a first image database, acquiring a first semantic feature of the new label; based on the first semantic feature, performing image retrieval in a first image database to obtain a first retrieval result and a confidence coefficient of the first retrieval result; if the confidence coefficient is smaller than the preset confidence coefficient, performing secondary retrieval in the first retrieval result based on the existing tag in the first retrieval result to obtain a second retrieval result; based on the second retrieval result, adding the newly added tag into an image corresponding to the secondary retrieval result to obtain a second image database; and after an image search instruction of the user is obtained, performing image retrieval in the second image database based on the image search instruction. The problems that in the database maintenance process, a new label needs to be added, the workload of adding the label again to the data is increased while the new label is added, and the database is difficult to maintain in an existing method are solved.

Camera positions can be recovered for video frames. Keypoint tracks can be generated for a first video frame and subsequent video frames. Metric depth can be determined for the video frames. An image databased can be generated using the keypoint tracks and the metric depth. The image database can include the video frames. A first video frame having a highest number of keypoint tracks can be identified. A first projection into a three-dimensional model can be generated for the first video frame and a first matching video frame. A camera position can be generated using the first projection. A second video frame having a next-highest number of keypoint tracks can be identified. A second projection into a three-dimensional model can be generated for the second video frame and a second matching video frame. A camera position can be generated using the second projection.

The invention discloses a wind turbine blade fault diagnosis method based on aerodynamic noise and image recognition, and relates to the technical field of wind turbine blade fault diagnosis, and the method comprises the steps: building a plurality of typical fault models of a wind turbine blade based on a numerical simulation platform, and extracting simulation acoustic image data; the position of the aerodynamic noise source is positioned through an acoustic camera, and corresponding blade image information is collected by an unmanned aerial vehicle; and carrying out feature training on the image sample and the acoustic image data by using a deep neural network and executing fault matching judgment. According to the method, a fault acoustic image database under multiple working conditions is constructed through numerical simulation, outfield data acquisition is performed in combination with an acoustic camera and an unmanned aerial vehicle, and joint feature training and fault matching of acoustic frequency spectrums and images are realized by using a deep neural network. The method can break through the limitations of the prior art in the aspects of real-time performance, accuracy and intelligence, and achieves the efficient and reliable diagnosis of the fault of the wind turbine blade.

The invention discloses an image secret type cloud storage method and device based on feature block encryption, and the method comprises the steps: a cloud server receives and stores an encrypted image, an encrypted image feature set and a feature inner product, and forms an image database; the cloud server receives a feature ciphertext query feature uploaded by the authorized user when the authorized user needs to query the image; the cloud server performs retrieval according to the received ciphertext query features, separately calculates the similarity between color and texture sub-features in the ciphertext query features and features in each encrypted image feature set in the image database by using a hierarchical earth moving distance algorithm, and calculates the comprehensive similarity corresponding to each encrypted image; and screening out a plurality of encrypted images according to the comprehensive similarity and returning the encrypted images to the authorized user. According to the method, safe and efficient search of the image can be realized, and the image retrieval performance is improved.

The invention provides a weld defect intelligent detection method based on high-definition image deep learning, which comprises the following steps: S1, collecting a high-definition weld image, manually marking the selected image, and constructing a weld defect image database; s2, performing first fine tuning on the pre-training weight GLIP-L by adopting a GLIP model; s3, a self-made welding test piece is made, a high-definition camera and ultrasonic detection equipment are used for collecting a weld joint surface high-definition image of the self-made welding test piece and ultrasonic echo characteristic data of internal defects, and a high-definition weld joint image and ultrasonic multi-mode database is established; s4, introducing a multi-layer perceptron module into the GLIP model, constructing an image and ultrasonic multi-modal feature alignment mechanism, and performing second fine tuning to obtain a trained GLIP model; and S5, performing defect detection on the welding seam by using the trained GLIP model, and outputting a detection result. According to the invention, through multi-modal data fusion, the accuracy and robustness of weld defect detection are improved, and the method is suitable for automatic and accurate industrial detection scenes.

The invention discloses a power equipment state monitoring method based on image recognition, which belongs to the field of power equipment monitoring, and comprises the following steps: firstly, acquiring surface images of power equipment according to a preset interval through an image acquisition module, and constructing a time sequence image database; performing alignment and standardization preprocessing on the image to generate a standard time sequence image sequence; inputting the sequence into a differential convolutional neural network, and extracting the feature variation of the defect region to obtain a defect evolution feature sequence; the sequence is analyzed based on a defect evolution dynamic model, the evolution rate of the defect is quantified, and the residual safe life of the equipment is predicted; and finally, automatically generating a graded early warning and maintenance decision according to a life prediction result. Therefore, the problem that the dynamic evolution rule of the gradual change defect of the equipment is difficult to capture and quantify in the prior art is solved, the transformation from post-event monitoring to predictive maintenance is realized, and the accuracy of state monitoring and the perspectiveness of operation and maintenance decision are remarkably improved.

Provided is a system and method for digital content generation and recommendation. A first plurality of stories are collected from various content sources over a network. A second plurality of stories are selected from the first plurality of stories such that a degree of similarity between any two stories of the second plurality of stories is below a determined threshold. A summary and a headline are generated for a first story of the second plurality of stories. A set of images are selected, from an image database, for the first story based on the generated summary and headline. A multimedia item including the summary, the headline, and a first image of the set of images is created. The multimedia item is presented on a lock screen interface of a user device, displaying the generated summary, the generated headline, and the first image on the lock screen interface.

The invention discloses an unmanned aerial vehicle camera repositioning method and device based on image retrieval and a storage medium. The method comprises the steps of collecting image data and constructing an image database. A global feature extraction module is used for extracting global features to obtain the global features, similarity calculation sorting is carried out on the global features and the global features of the images in the database, and the most similar images are selected to form an image pair. After local feature extraction, relative translation and relative rotation are predicted through a relative pose regression network. And finally, converting the relative pose of the retrieved picture into an absolute pose through a relative pose conversion algorithm. According to the overall method, image feature extraction and pose prediction are realized through a deep learning network, and efficient relocation of the unmanned aerial vehicle camera is realized.

The invention discloses a remote sensing image retrieval method and device, electronic equipment and a medium. The method comprises the steps that a remote sensing image retrieval request is acquired; analyzing the retrieval request through the target large model to obtain an initial retrieval condition; according to the initial retrieval condition and a pre-constructed interest point database, determining a target geo-fence retrieved by the current image; and based on the initial retrieval condition and the target geo-fence, performing retrieval in a remote sensing image database to obtain a target remote sensing image. Therefore, retrieval request analysis of complex geographic description and fuzzy text description is realized through the target large model, so that the remote sensing image retrieval intention of a user is accurately understood, and meanwhile, an accurate target geographic fence is obtained in combination with the pre-constructed interest point database, remote sensing retrieval is locked in a more accurate geographic position range, and the remote sensing retrieval accuracy is improved. Therefore, the retrieval precision of the target remote sensing image is improved.

The invention discloses a permanent magnet wheel hub motor demagnetization fault diagnosis method considering multi-source signal fusion, belongs to the technical field of motor fault diagnosis, and aims to perform permanent magnet wheel hub motor demagnetization fault diagnosis by using a multi-source heterogeneous signal feature fusion method and solve the problem of dependence on additional sensors. The method mainly comprises the following steps: collecting multi-source signals such as current, back electromotive force and vibration under different conditions; performing symmetric point mode processing on the multi-source signal preliminarily, performing adaptive optimization and fusion on SDP parameters according to image fitness, and performing imaging processing again; the image database of the multi-source signals is used for fault diagnosis model training, and the diagnosis accuracy of each signal is obtained; a dynamic decision-making environment is established by integrating environmental conditions, image fitness and fault diagnosis accuracy, and a model is trained to realize optimal selection of multi-source heterogeneous signals under different conditions. According to the invention, through signal selection and fusion under different conditions, the accuracy and efficiency of motor fault diagnosis are improved.

The application provides a Pod rollback version determination method and device, electronic equipment and a storage medium. The method comprises the following steps: when the current version of Pod code does not match the previous version of Pod code, inputting the file information of the current version of Pod in the image database into a pre-trained version selection model; obtaining a first-level classification version selection probability corresponding to the current version through a first-level classification sub-model, and obtaining a second-level classification version selection probability corresponding to the current version through a second-level classification sub-model; determining the version selection probability of each version corresponding to the current version based on the first-level classification version selection probability and the second-level classification version selection probability; and determining a target rollback version corresponding to the current version based on the determined version selection probability of each version. It can be seen that the efficiency of determining the rollback version can be intelligently improved, and the rollback version determined by the version selection model can be more accurate and closer to the truth.

To generate a progress report capturing a change in a lesion, etc., hard to capture just by a local area of a medical image.SOLUTION: A medical information system includes an imaging apparatus, an image server, an image database, a report server, a report database, a medical terminal and an interpretation terminal connected to one another interposing a network. In the medical information system, a document preparation support device 20 includes a medical information acquisition part 32, a progress information acquisition part 33 and a report generation part 35. The medical information acquisition part is configured to acquire a medical image in a time-sequence, an interpretation report of each medical image, and medical information. The progress information acquisition part is configured to acquire progress information of the entire medical image in the time-sequence according to the medical image in the time-sequence, an interpretation report of each medical image, and the medical information. The report generation part is configured to generate a sentence and words showing a progress observation status of a patient according to the medical image in the time-sequence, an interpretation report of each medical image, the medical information and the progress information to generate a progress report showing a change in time-sequence in the entire medical image.SELECTED DRAWING: Figure 3

This application discloses a method, apparatus, electronic device, and medium for retrieving remote sensing images. The method includes: acquiring a retrieval request for remote sensing images; parsing the retrieval request using a target big data model to obtain initial retrieval conditions; determining the target geofence for the current image retrieval based on the initial retrieval conditions and a pre-constructed point-of-interest (POI) database; and performing a search in the remote sensing image database based on the initial retrieval conditions and the target geofence to obtain the target remote sensing image. Thus, by using a target big data model, retrieval requests with complex geographical descriptions and ambiguous textual descriptions can be parsed, thereby accurately understanding the user's remote sensing image retrieval intent. Simultaneously, by combining this with a pre-constructed POI database, a precise target geofence can be obtained, locking the remote sensing retrieval within a more accurate geographical location range, thereby improving the retrieval accuracy of the target remote sensing image.

The application discloses an ethylene cracking furnace tube infrared intelligent analysis system, which comprises, which are connected in sequence, an image acquisition module, a temperature monitoring module, a coking diagnosis module and a health management module; the image acquisition module acquires infrared images of cracking furnace tubes and burners by using an infrared thermal imaging online monitoring system, and establishes a cracking furnace tube infrared image database; the temperature monitoring module extracts and records the outer surface temperature of the ethylene cracking furnace tube from the processed infrared images of the ethylene cracking furnace tube by using an infrared image processing and recognition method; the coking diagnosis module combines the technologies of artificial intelligence and data mining, establishes a coking degree diagnosis and prediction model of the ethylene cracking furnace tube, and realizes early warning and targeted tracking of the coking abnormal conditions; the health management module carries out health evaluation of the cracking furnace tube according to the results obtained by the coking degree diagnosis and prediction model of the ethylene cracking furnace tube, and provides maintenance decision support. The application can realize integrated management of cracking furnace tube detection, diagnosis and maintenance.

A computer architecture for interactive image based database searching includes a first server that acquires category information indicating a category of a vehicle owned by a user. The first server also acquires part information for identifying a vehicle part located at a position specified by a user operation within an image of a vehicle displayed on a terminal device of the user. A second server is in communication with the first server and searches for products by accessing a storage storing thereon product information on vehicle part products and by using the acquired category information and the acquired part information as search criteria. The system transmits search result information indicating results of the search to the terminal device.

The invention discloses a full-process visual monitoring and intelligent management method for a glass bracket production line, and relates to the technical field of industrial quality control, and the method comprises the steps: carrying out the continuous image collection of all stations of the glass bracket production line through an image collection unit, and constructing a full-process image data sequence; detecting the size of the glass bracket by using the qualified glass bracket size image database, and judging whether the size is abnormal or not; further performing defect detection on the glass bracket with the normal size, and determining whether an abnormal defect exists or not through pixel value difference analysis; and finally, summarizing the glass brackets with abnormal sizes or defects and related information thereof, and generating a visual quality report, so as to quickly position problems and optimize the production process. The comprehensive monitoring and intelligent management of the glass bracket production process are realized, and the production efficiency and the product quality are improved.

The invention provides an intelligent analysis method and device for population around a nuclear energy project factory site, and the method comprises the steps: constructing a factory site peripheral region image database through integrating a satellite map, GIS data and an unmanned aerial vehicle aerial image; automatically distinguishing buildings from non-buildings by using an image recognition algorithm, and intelligently classifying the buildings according to civil, office, commercial, industrial, cultural and other types; the population density or empirical parameters of various buildings are combined to calculate the total population number of single buildings and regions, and future population prediction based on the annual growth rate is supported. The system is further provided with a visual interaction interface, and parameter input, data storage, result query and report automatic generation are supported. According to the method, the automation, precision and high-efficiency level of demographic statistics around the nuclear power plant site is improved, the manpower and time cost is reduced, and scientific, dynamic and sharable data support is provided for nuclear power project site selection decision.

The invention belongs to the technical field of automatic driving testing, and particularly relates to an automatic driving system long-tail image generation method based on heterogeneous data fusion. Comprising the following steps: S1, extracting data input information for heterogeneous data fusion; s2, selecting a pre-training diffusion model as a heterogeneous data fusion backbone network, disassembling a reasoning diffusion process of the heterogeneous data fusion backbone network, and constructing a reasoning pipeline of a first stage; s3, refining and enhancing intermediate fusion features of the diffusion model; s4, developing a reasoning pipeline of a second stage; s5, screening and de-weighting the long-tail image samples, and establishing a high-quality long-tail image database; according to the method, a long-tail image database can be established, the method is used for high-efficiency and high-coverage test requirements of an automatic driving perception system, and industrialization of an automatic driving automobile is accelerated.

This invention discloses a text image restoration method based on generative adversarial networks (GANs), comprising the following steps: S1: Collecting images containing inscriptions and random occluded images, performing scale normalization to generate k×k pixel images, and establishing an inscription image database, where k is a positive integer; S2: Constructing a generative adversarial network (GAN); S3: Using images from the inscription image database as training samples, training the GAN and optimizing the parameters of the generator and discriminator in the GAN; S4: Replacing the pixel values ​​of the occluded or damaged areas in the text image to be restored with the pixel values ​​of the corresponding areas in the generated text image. This invention effectively simulates the structural features of text images, improves the accuracy and quality of text image restoration results, and effectively restores damaged or contaminated text images, providing a new method and approach for image restoration.

The present application belongs to the technical field of artificial intelligence, and discloses a wood board sorting method and system based on machine learning, which comprises: establishing a multi-scale normal wood board image database; training wood board detection models for each size specification to form a multi-scale wood grain deviation detection model set; obtaining a wood board image to be sorted, determining the optimal segmentation size according to the size; segmenting the wood board image into multiple sub-images; selecting a corresponding model to judge the sub-images and mark the defect images; calculating the overall defect index, defect distribution index and defect continuity index to comprehensively evaluate the wood board quality; and determining the wood board grade according to the evaluation result and sorting. The method not only considers the number of defects, but also analyzes the position distribution and size continuity of the defects, realizes multi-dimensional quality evaluation, can more accurately reflect the actual use value of the wood board, and improves the scientificity of wood board grading and the utilization efficiency of wood resources.

This invention provides an occluded pedestrian re-identification method based on single-scale feature representation, belonging to the fields of pattern recognition and computer vision. A spatial memory module is embedded into the feature extraction network to obtain more discriminative pedestrian feature maps. An improved triplet loss is calculated on the pedestrian feature vectors before batch normalization, and a classification loss is calculated on the normalized pedestrian feature vectors to complete training. The Euclidean distance between the query pedestrian features and pedestrian features in the image database is calculated, and the features are sorted in ascending order of distance to obtain the nearest neighbor list of the query pedestrian, thus completing pedestrian re-identification. The advantages are that using single-scale pedestrian features to solve the occlusion problem in pedestrian re-identification not only reduces training time and model complexity but also reduces inference time, making it more suitable for re-identification systems. The spatial memory module only requires a small amount of memory to improve model accuracy.

In a method, performed in a data processing system, an object marker is authenticated. The method comprises receiving object marker image data of the object marker. From an original marker image database, original marker image data of an original marker are retrieved. Marker comparison parameters based on a comparison of the object marker image data to the retrieved original marker image data are determined. The marker comparison parameters are input to a plurality of input nodes of an artificial neural network, ANN. The ANN has been trained to discriminate between object marker image data of the original marker, and a copy or clone of the original marker. The ANN provides output data, representative of the extent of authenticity of the object marker, at its output node. An authenticity signal based on the output data of the ANN is output.

The invention provides an image searching method. The method comprises the following steps: acquiring input data of a user; performing semantic extraction processing on the input data to obtain a global semantic vector and at least one target semantic vector corresponding to the input data; based on the global semantic vector and the at least one target semantic vector, performing semantic search in an image database to obtain a search result corresponding to the input data; and returning the search result. According to the image searching method and device, the problems that when a certain target is searched in an existing image searching method, the corresponding image can be accurately inquired in the database only by knowing the corresponding retrieval elements of the target in the database, the image data inquiring difficulty is increased, and the image searching efficiency is low are solved.

The invention provides an intelligent grading and judging method for wooden furniture plates, which comprises the following steps of: constructing a high-quality image database which is acquired by an industrial camera at multiple angles and is bound with material labels, performing multi-stage denoising, enhancement and normalization preprocessing, extracting a key texture feature channel through a lightweight convolutional neural network and a channel attention mechanism, and performing classification and judgment on the key texture feature channel; according to the method, weight distribution and online optimization are carried out on feature channels in combination with a dynamic feature reweighting network, intelligent compensation of feature expression ability after lightweight pruning and quantification of the model is realized, in addition, a weight drift monitoring and local recalibration mechanism is arranged, the discrimination robustness of the model in detection of different batches of plates is guaranteed, and the detection accuracy is improved. According to the method, the edge deployment efficiency is improved, and meanwhile, the accuracy and adaptability of automatic judgment of the plate grade are enhanced.

This invention discloses a standardized real-time monitoring and error correction method and system for martial arts routine teaching, relating to the field of image recognition technology. The method includes: S1. Acquiring information on students' learning stages and training objectives, retrieving corresponding standard movement image models and image feature parameters from a multi-dimensional martial arts standard movement image database, and dynamically adjusting the weights of evaluation indicators based on image recognition; S2. Acquiring movement image data through a multi-modal image acquisition array, and after image preprocessing, using a two-stage temporal alignment algorithm based on image semantic attention to achieve accurate temporal alignment between the student's movement image sequence and the standard movement image model; S3. Calculating movement deviations based on image feature matching, extracting error precursor image features using a deep learning image recognition model, and triggering early warnings; S4. Outputting multi-modal error correction feedback based on image overlay, pushing a group common error image analysis report to the teacher's end, generating and dynamically adjusting personalized correction schemes based on the deviation diagnosis results of image recognition, forming a closed-loop error correction.

The invention provides an image search method, which comprises the following steps: in a multi-round image search process, acquiring first input data of a current round, and acquiring second input data of a historical round, an interval time between the historical round and the current round being less than a preset interval time; based on the second input data, extracting auxiliary search semantics; and based on the first input data and the auxiliary search semantics, performing search processing in the image database to obtain a search result corresponding to the first input data, and returning the search result. The image search method and device solve the problems that according to an existing image search method, each round of search is independently carried out based on the input data of the current round, an accurate result can not be obtained in each time of search, the search result accuracy is not high, a user needs to continuously search the input data, and consequently the working efficiency is low.

The application relates to the technical field of blind guiding, and particularly discloses an intelligent blind guiding instrument, which comprises a host computer internally provided with an MCU processor and a power module; a head-mounted device composed of a ring frame and a camera, a microphone and a loudspeaker mounted on the ring frame; and a blind guiding stick in Bluetooth connection with the MCU processor, wherein the MCU processor is in communication connection with a background server or a cloud server and loads an image database; the MCU processor is used for receiving image information sent by the camera and identifying and judging the image category, so as to control the blind guiding instrument to enter a voice navigation mode or a thing recognition mode; the MCU processor controls the blind guiding instrument to enter a voice control mode when detecting that the microphone sends predetermined voice content, so as to start or stop the blind guiding instrument or play predetermined audio. The above-mentioned intelligent blind guiding instrument combines the blind guiding stick with the head-mounted device, collects the images around the blind guiding stick to position a position to be guided, improves the navigation accuracy, has multiple functions, realizes one machine with multiple functions, and reduces the blind guiding cost of users.

The embodiment of the invention discloses a conference image processing method and device, a storage medium and electronic equipment, and the method is applied to a conference image processing program, and comprises the steps: obtaining a conference panoramic image; performing region division on the conference panoramic image to obtain at least one region sub-image; acquiring a first personnel sub-image corresponding to the personnel in the first area sub-image; based on behavior characteristics of personnel in the first personnel sub-image or a first area sub-image corresponding to the first personnel sub-image, determining an identity tag corresponding to the first personnel sub-image, and generating a first target personnel image carrying the identity tag; and according to the at least one area sub-image and the first target person image, generating a conference image database, and by adopting the embodiment of the invention, the image processing speed of conference software is improved.

Disclosed herein is a system and method for processing crawfish, specifically peeling crawfish shells. The device comprises a meat extraction portion, further comprising a clamping subsystem, cutting subsystem, and extraction subsystem designed to extract the crawfish meat from the shell without human interference. In a preferred embodiment of the device, the device further comprises a head / tail separation portion designed to decapitate the crawfish without human interference. The device further incorporates a single-board computer comprising a machine-learning system and related image database designed for improved identification of the crawfish shell components by the device, resulting in greater accuracy in processing individual crawfish of varying sizes.