23results about How to "Shorten recognition time" patented technology

The invention discloses a multi-source ship target identification method based on two-stage collaborative fusion, and relates to the technical field of signal processing, and the method comprises the steps: firstly collecting target sea area data, including original optical image and radar image data, by means of an optical camera and a radar sensor on an aircraft, and then carrying out the preprocessing of the collected data through Gaussian filtering; performing sea-land segmentation by using a deep learning algorithm, constructing a loss function optimization network parameter to obtain a segmentation mask, and removing a land background area according to a mask result to obtain processed optical and radar images; an improved SURF feature and graph cut algorithm combined registration method is adopted, optimal transformation parameters are obtained by screening feature point pairs and solving the minimum value of an energy function, and image registration is achieved; for the registered images, fusing the images based on an adaptive fusion rule; and finally, extracting corresponding features through manual feature extraction and feature extraction based on deep learning, and fusing the features to obtain a final target recognition result.

The invention relates to the technical field of RFID chip manufacturing, in particular to an RFID chip image anomaly detection method and system based on intelligent learning. The method comprises the following steps: acquiring a real-time RFID chip sampling image set, performing feature alignment based on a registration template, generating an average grey-scale map by using an alignment image set, and obtaining a chip edge mask image set by using an edge search algorithm; segmenting the edge mask image set of the RFID chip to obtain an effective edge mask variance image set of the RFID chip; integrating the variance image set to obtain a pre-generated variance image; acquiring field environment data of the RFID chip and performing sensitivity coefficient estimation to obtain an edge sensitivity coefficient; and performing convolution filtering smoothing processing on the pre-generated variance image to obtain a chip edge variance image, and constructing an RFID chip image anomaly detection model based on the edge variance image and the average grey-scale image. According to the invention, the detection accuracy and stability are improved.

The invention discloses a method, a processor and a system for detecting the welding quality of a stud. The method comprises the following steps: acquiring an image of a target detection area; the obtained target detection area image is input into a pre-trained detection model, and target stud welding information of the target detection area is recognized; respectively comparing the welding information of the identified target stud with corresponding standard stud welding information preset in a detection model; whether the target stud is qualified or not is determined according to the comparison result of the welding information of the target stud and the welding information of the standard stud; and outputting the quality state information of the target stud in the target detection area. The method has the advantages of being low in cost, high in efficiency, large in detection coverage rate, high in accuracy and good in human-to-effect ratio.

The invention relates to an optical cable cutover auxiliary method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: measuring temperature information of a to-be-cut optical cable on the optical cable distribution frame in real time by adopting temperature detection equipment positioned on the optical cable distribution frame; cooling the candidate optical cables at the to-be-cut-over position in sequence by adopting refrigeration equipment, and judging whether the temperature information is changed or not; the candidate optical cable represents an unknown optical cable including the to-be-cut optical cable; and under the condition that the temperature information changes, determining the candidate optical cable which is currently cooled as the to-be-cut optical cable. By adopting the method, the optical cable cutover efficiency can be improved.

The invention discloses a lane changing intention recognition method and system and a computer storage medium, and the method comprises the steps: carrying out the data fusion, feature extraction and initial marking processing of collected auxiliary driving information according to a preset time sequence, and carrying out the recognition, so as to execute the lane changing control. The method has the advantages that the problem that an existing marking method lacks robustness is solved, compared with a traditional time window marking method, the recognition time is effectively shortened, the lane changing intention recognition model based on the multi-head attention mechanism is established, and higher accuracy can be obtained; the defects caused by the fact that a time window and other single-feature marking methods need to be overcome in the prior art of a lane changing initial point marking method are overcome, the method is crucial for a driving assistance system to predict lane changing risks and provide timely suggestions, and meanwhile more response time is provided for a driver.

The invention provides an ASR-based early media identification method and system. The ASR-based early media identification method comprises the following steps: collecting audio data corresponding to a target media in real time, comparing the audio data and repairing the audio data to obtain the repaired audio data; inputting the repaired audio data into an ASR model to obtain text data corresponding to the audio data; performing keyword and phrase recognition from the text data to obtain target keywords and phrases; the target keywords and phrases are compared with preset keywords and phrases and an emotional tendency table for recognition, an emotional tendency result is obtained, and the emotional tendency result is an early media recognition result; and pushing the media content according to the emotional tendency result. The system comprises modules corresponding to the steps of the method.

The present application relates to the field of artificial intelligence and the field of financial technology, and discloses a speech recognition method, device and equipment and a storage medium, the method comprising: splicing a first embedding vector and a second embedding vector to generate a third embedding vector, reconstructing the third embedding vector to generate a fourth embedding vector; obtaining a predicted speech recognition text output by a speech recognition model based on the fourth embedding vector, obtaining a first loss value and a second loss value between the predicted speech recognition text and a preset speech recognition text; generating a total loss value of the predicted speech recognition text according to the first loss value and the second loss value, training the speech recognition model based on the total loss value; obtaining a current speech and a current field label sent by a customer service system, inputting the current speech and the current field label into the trained speech recognition model, and obtaining a current speech recognition text output by the trained speech recognition model. The present application is beneficial to improving the efficiency of speech recognition.

The present invention belongs to the field of computer technology, and specifically relates to a face recognition method, system, computer equipment and storage medium. The method comprises: acquiring captured image data; initializing the acquired image data, judging whether there is a rotation flag; if not, cutting out the face and reading the characteristic value of the captured face; according to the processed characteristic value and the face direction recognition judgment, obtaining the rotation value, assigning the image and saving the rotation flag; if yes, performing the corresponding angle rotation according to the value of the rotation flag to obtain normal image data. The present invention can quickly and efficiently complete the adaptive normal display of the face direction, and all big data operations such as image processing are processed in an embedded neural network processor (NPU), which greatly improves efficiency, reduces power consumption, can identify living bodies, avoids the situation that the recognition can be passed by using photos, and greatly improves the recognition security.

The invention discloses a fault processing method and device, and relates to the technical field of network management and control, and the method comprises the steps: after a root alarm in a network element is reported to a management and control system, analyzing and recognizing a fault which causes a non-atomic function to generate the root alarm in the network element, starting a network element fault diagnosis process, and meanwhile, reporting the fault to the management and control system. The management and control system performs a fault identification process based on network connection after receiving the root alarm in the network element, and eliminates the fault causing the root alarm to be generated by the non-atomic function according to the fault diagnosis state of the network element, so that the fault processing efficiency is improved.

The invention discloses an equipment mirror image burning and testing method and system and a development panel, and the method comprises the steps: obtaining the information of a target container, building a connection with the target container, and obtaining the initial mirror image information of a stored initial mirror image disk; analyzing the burning instruction to obtain target mirror image information, and adding a corresponding target mirror image disk to carry out mirror image burning; and inputting a plurality of test instructions to obtain a plurality of mirror image burning results, and generating a final test result. According to the method and the device, the mirror image disks in the multiple containers and the target container are obtained, the corresponding connection is established, then mirror image burning is performed, and the burning process is tested from multiple angles such as the containers, the mirror images and the directories, so that the efficiency of mirror image burning on the equipment is improved, and the data integrity is ensured.

The invention relates to the technical field of small animal monitoring and early warning in substations, and discloses a small animal invasion monitoring and early warning method and system for substations. The system comprises an image preprocessing module considering micro-meteorological factors, an intrusion target detection module based on image recognition and target tracking, and a trajectory prediction module for a non-monitored area. The invention adopts an improved YOLOv5s algorithm as a benchmark detection model, and adopts an improved EIOU loss function to replace the defect of the loss function in the model. At the same time, the defect that ordinary convolution is difficult to extract irregular target features is solved by replacing the deformable convolution, and the recognition accuracy of the model is improved, so as to realize the detection of targets in the substation and judge whether they are intrusive small animals, effectively reduce the recognition time, and improve the accuracy, so as to meet the growing intelligent demand.

The present invention discloses a method and system for intelligently identifying road surface defects. The method detects defects on an input road surface image based on a target detection model, and returns the defect area position coordinate information, defect type, and defect confidence of the road defect. The defect area is accurately located using a small frame of a fixed size, which is convenient for calculating the length or area of ​​a specific defect. The result of the accurate location is post-processed, including removing isolated frames and identical items, to obtain the final recognition result. The present invention can automatically identify defects, reduce the false alarm rate of detection, and improve detection efficiency.

This invention discloses a method and system for identifying threat targets in a driving environment based on the coordination of electroencephalography (EEG) and eye movement. The method includes the following steps: S1, acquiring real-time video of the road conditions in the foreground and performing multi-target detection and tracking; S2, displaying flashing arrows of different stimuli on the target and following its movement, collecting eye movement data and EEG data; S3, processing the eye movement data to obtain eye movement results, and processing the EEG data to obtain EEG results; S4, combining the eye movement results and EEG results to output the target tracking result. This invention has advantages such as reducing the false positive rate, shortening the target recognition time, and improving the recognition accuracy.

This utility model relates to the field of kitchen utensil technology and discloses a frosted glass for kitchen stovetops, including a stovetop body, a countertop, multiple thermochromic materials, and metal gaskets. The countertop is made of frosted glass and is detachably and fixedly installed on the top of the stovetop body, covering the cooking operation area of ​​the stovetop body. Multiple thermochromic materials are embedded and fixed inside the countertop, corresponding to the cooking heating area of ​​the stovetop body. The metal gaskets are fixedly embedded at the four corners of the countertop and located inside the countertop. This utility model, through a concentric circle distribution and increasing diameter structure design, combined with the reversible thermochromic characteristic of turning red above a certain temperature and transparent below a certain temperature, reflects the temperature difference around the stovetop through the color range. The thermochromic materials in the central area show color first, and the outer thermochromic materials show color later, converting temperature into a visual color display, allowing users to directly determine the boundary of the high-temperature zone through the red coverage area.

The invention provides a bolt position identification method and device and a bolt identification system. The method comprises the steps that point cloud data of a current mine card are acquired, and the point cloud data are obtained based on scanning of a laser sensor; template point cloud data is obtained, and the template point cloud data is data of a template of a pre-configured bolt; and performing global matching on the point cloud data and the template point cloud data to obtain the position of the bolt. According to the scheme, the problem of low efficiency of manually identifying the position of the bolt of the mine card in the prior art is solved.

The invention relates to the technical field of computer vision and robots, and provides a pharmacy unmanned vehicle path planning and drug matching method based on JPS and RRT algorithms, and the method comprises the steps: constructing a pharmacy environment map, and determining the real-time position of an unmanned vehicle; performing unmanned vehicle path planning by adopting a JPS-RRT * algorithm to obtain a final driving path; the method comprises the following steps: acquiring a target drug image, constructing a drug feature template library, periodically acquiring drug images in a current visual field, pre-processing the drug images, and matching the pre-processed drug images with the drug feature template library by adopting an SAD template matching algorithm to obtain a drug classification result; and controlling the unmanned vehicle to move to the target position according to the final driving path, and executing a corresponding medicine grabbing or sorting task according to a medicine classification result.

The invention provides a vehicle diagnosis method and related equipment, and the method comprises the steps: connecting a vehicle-mounted automatic diagnosis system interface of a to-be-diagnosed vehicle, and judging whether a specified interface specification exists or not; in response to the absence of the specified interface specification, sending a diagnosis service request according to a preset interface specification sequence; responding to received response information fed back by the to-be-diagnosed vehicle according to the diagnosis service request, generating inquiry information according to a first interface specification corresponding to the response information and outputting the inquiry information; and in response to a determination instruction triggered by the object according to the inquiry information, setting the first interface specification as a specified interface specification, and performing function diagnosis on the to-be-diagnosed vehicle to obtain a diagnosis result. And the diagnosis service request is preferentially sent to the to-be-diagnosed vehicle according to the specified interface specification, so that the vehicle type can be quickly and accurately identified. In addition, the interface specification with the high weight in the preset interface specification sequence is preferentially scanned, so that the overall recognition time is shortened, and the efficiency and accuracy of vehicle diagnosis are remarkably improved.

The present invention discloses a vibration feedback control system of a gantry-type dicing machine, and the present invention relates to the technical field of vibration measurement and control of dicing machines. The vibration feedback control system of the gantry-type dicing machine includes a vibration detection module, a vibration signal processing module, a vibration analysis module, and a vibration control decision generation module. The vibration detection module is composed of sensors arranged at the spindle, the body, and the processing surface of the dicing machine, a signal adjustment unit, and a data acquisition card. The vibration analysis module includes a vibration model and an analysis unit. The present invention realizes the integration of multiple groups of vibration signals, which facilitates the subsequent identification unit to perform overall identification of the correlation coefficient waveforms of multiple vibration sources. Based on the overall identification of a single vibration source, the identification time and identification process are greatly shortened and simplified, and the integration of dicing machine vibration and the shortening of signal data processing time are realized.

The application discloses a behavior fingerprint digital feature extraction method, system, medium and equipment, and the method comprises the following steps: acquiring a behavior fingerprint sequence of a user and extracting digital feature information of the user, constructing a digital feature vector matrix for a to-be-matched user set and a known user set respectively, extracting a feature vector set of the user for the acquired behavior fingerprint sequence of the user, calculating the similarity distance between the digital feature vector of the known user and the digital feature vector of the to-be-matched user based on a similarity distance formula, and judging the identity of the user according to a preset matching strategy. The application expands the behavior fingerprint of the user, solves the problem of the sparsity of the behavior fingerprint of the user to a certain extent, and achieves the effect of improving the recognition accuracy.

The invention relates to the technical field of artificial intelligence and the field of emotion recognition, and discloses an emotion category recognition method and device based on a Transform architecture, equipment and a medium, and the method comprises the steps: obtaining a current coding feature vector through a coder; the current coding feature vector is reconstructed through a decoder, a current decoding feature vector is obtained, and the current decoding feature vector comprises a reconstruction vector corresponding to the current missing modal feature vector and a reconstruction vector corresponding to the current normal modal feature vector; determining a first weight coefficient and a second weight coefficient based on the cosine similarity; and based on the first weight coefficient and the second weight coefficient, fusing a reconstruction vector corresponding to the current missing modal feature vector and a reconstruction vector corresponding to the current normal modal feature vector to obtain a fused feature vector, and identifying the fused feature vector to obtain an emotion category of the current multi-modal data. The method is beneficial to extracting the recognition efficiency of emotion recognition.

The present invention provides a smart grid attack detection method and device based on 5G communication, wherein the method includes: collecting power parameters of each device in the smart grid system based on 5G communication; using the Monte Carlo simulation method to predict the state of each subsystem at a future time according to the power parameters of each device in the smart grid system and the random state probability transfer matrix of each subsystem in the smart grid system established based on the Markov model; if there is a partial available state in the state of the subsystem at the future time, the unpowered energy loss value and the load value loss value of the subsystem in the partial available state within the simulation time are calculated; according to the unpowered energy loss value and the load value loss value of the subsystem in the partial available state within the simulation time, the risk of the smart grid being attacked from the outside is predicted. The present invention can quickly detect whether the smart grid is attacked from the outside, and greatly shorten the recognition and response time of the smart grid when it is attacked.

The application belongs to the technical field of crop disease and pest identification, and discloses a wheat disease and pest intelligent identification method and an intelligent identification system based on meta learning. The collected leaf and stem images of the wheat to be identified are subjected to filtering processing by using an adaptive Wiener filtering method; the filtered leaf and stem images of the wheat are subjected to gray scale processing to obtain the gray scale images of the leaf and stem of the wheat; the filtered gray scale images of the leaf and stem of the wheat are subjected to enhancement processing by using a cross line gray scale image definition improvement model; and the diseases and pests, symptoms, disease spots and pest body characteristics of the leaf and stem of the wheat are identified according to the enhanced images. The application can be used in agricultural actual production, can provide a reference for further chemical control and biological control of wheat diseases and pests, and can provide a reference for the application of automatic distinguishing and identifying plant stress.

The invention relates to cell detection, and particularly provides a cell recognition method based on adaptive ROI (Region of Interest) feature image extraction, which comprises the following steps of: (S1) adaptively extracting a cell feature image to obtain a cell ROI feature image; (S2) the quality of the ROI cell feature image is evaluated, if an evaluation value is smaller than a first threshold value, the cell ROI feature image is reserved, and if the evaluation value is not smaller than the first threshold value, the cell ROI feature image is abandoned; and (S3) carrying out naive Bayesian classification on the characteristics of the reserved ROI cell image, and identifying cells and positions thereof. The method has the advantages of accurate recognition and the like, and is applied to various cell detection.