107 results about "Digit recognition" patented technology

The invention discloses a speed limit sign detection and identification method based on YOLO and PPOCR. The method comprises the following steps: capturing a traffic scene image; adjusting the size of the original image, and scaling the image to a set resolution by adopting a nearest neighbor interpolation algorithm; the adjusted image with the set resolution is input into an improved YOLO model for speed limit sign detection, and detection frame position information of the speed limit sign is obtained; cutting the original image according to the detection frame information; carrying out image preprocessing on the cut image; adjusting the size of the preprocessed image, and scaling the image to a set resolution by adopting a nearest neighbor interpolation algorithm; and carrying out digital identification on the image with the adjusted size by using the improved PPOCR model. According to the method, the precision and adaptability of speed limit sign detection and recognition are remarkably improved, efficient and accurate detection and recognition of the speed limit sign and the speed limit value of the speed limit sign are achieved, and the method has wide application prospects.

The invention relates to a method for testing a participant's ability to accurately detect speech under background noise, comprising: (a) audibly presenting a series of 2-bit numbers, 3-bit numbers, 4-bit numbers and / or 5-bit numbers in a random or fixed order to a participant's first ear at a fixed or varying signal-to-noise ratio (SNR) in a quiet or noisy environment, (b) audibly presenting a series of 2-bit numbers, 3-bit numbers, 4-bit numbers and / or 5-bit numbers in a random or fixed order to the second ear of the participant at a fixed or random signal-to-noise ratio (SNR) in a quiet or noisy environment, where the numbers are presented in the native language of the participant or the fluent language of the participant, (c) recording the numbers or sequence of numbers correctly recognized by the participant, and (c) storing the recorded numbers or sequence of numbers at the second ear of the participant. And (d) calculating a score as a function of the percentage of the number or sequence of numbers correctly replicated by the subject, or as a function of the sound intensity difference between the speech and noise required to achieve the correct recognition of 50% sequence of numbers. The invention also relates to a device and a method for measuring the digital recognition capability related to hearing loss, and to a computer-implemented method for evaluating the speech recognition capability of a participant.

The invention provides an intelligent submission method and device for manual measurement and reading data. Synchronously collecting data of dam monitoring points; performing target detection, number identification, pointer identification and scale identification on an instrument area in the image to generate structured measurement and reading data; intelligent matching and automatic filling of the structured measurement and reading data and report fields are realized through a natural language processing technology; performing anomaly detection and threshold judgment on the filled report data, identifying a potential risk level, and selecting a submission path through a preset grading submission mechanism according to the risk level; and synchronously uploading the hash value of the original image data and the structured data to a block chain system, executing double-chain storage, and automatically starting an intelligent contract of an emergency process when a preset triggering condition is met. According to the method, the dam monitoring data submission efficiency and accuracy can be effectively improved, intelligent identification and real-time hierarchical transmission of multi-source data under complex working conditions are realized, and the credibility and traceability of the data are guaranteed through a block chain technology.

The invention discloses a method and system for improving image classification performance by using a spiking neural network, and the method comprises the steps: focusing on a neuron activation mode and synaptic plasticity in an unsupervised handwritten numeral recognition research process based on a triple pulse time-dependent plasticity learning rule, and after the fluctuation of accuracy is observed, carrying out the recognition of an unsupervised handwritten numeral recognition model, and carrying out the recognition of the unsupervised handwritten numeral recognition model. According to the method, key influence factors such as weight initialization, STDP randomness and hyper-parameter sensitivity are deeply analyzed; according to the invention, a common SNN structure is optimized, a pulse Inception module is realized, and the SNN is trained through a triple STDP; according to the method, the simulated annealing algorithm and the STDP are innovatively combined to be used for neuron parameter optimization, an optimal hyper-parameter combination is established through SA, after STDP training, the performance is remarkably improved under the condition of a limited data set, and particularly, the high efficiency of one-time perceptual classification of hundreds of digits is shown.

The invention discloses a license plate recognition method and system based on deep learning, and the method comprises the following steps: collecting license plate image data, and generating an image data set; performing semantic decomposition operation, dividing into region code units, letter units and number units, and constructing a semantic structure chart; constructing a multi-path character recognition model, and taking an improved PCNN model as a trunk feature structure; respectively inputting the image data set into a multi-path character recognition model, outputting an area code recognition result, a letter recognition result and a number recognition result, and generating a candidate license plate recognition result; if illegal character combinations or structural unit missing information exists, path screening processing is executed, and a candidate path set is generated; reasoning and filling processing is executed, and a complemented path set is generated; and combining and outputting a license plate recognition result and a confidence alternative path set. According to the method, the character recognition accuracy is improved, meanwhile, the robustness to structure deficiency is enhanced, and the method has the advantages of being high in recognition precision and high in fault-tolerant capability.

The invention provides a general meter reading automatic identification method, and relates to the technical field of computer vision and deep learning, and the method comprises the steps: carrying out the image collection of a meter to obtain an original image containing an instrument panel region of the meter, and carrying out the image recognition of the original image, obtaining an integer area and a decimal area in the instrument panel area and corresponding coordinates thereof, and determining physical layout positions of the integer area and the decimal area in the instrument panel area according to the coordinates; segmenting a plurality of numeric character regions from the integer region; and performing number identification on each number character area and decimal area, and sequentially splicing a plurality of numbers obtained by identification according to corresponding physical layout positions to obtain the meter reading. The method has the beneficial effects that a multi-stage cascade detection method is adopted, interference is filtered layer by layer (positioning-segmentation-classification digital recognition) through cooperative work of multiple processes, and the problem that cross-specification universal recognition is difficult to realize by a traditional single detection model is solved.

The invention provides a meter reading detection method based on a cascaded detection model, and relates to the technical field of intelligent metering of the Internet of Things, and the method comprises the steps: constructing a cascaded detection model which comprises a dial detection network, a character segmentation network and a digital classifier which are cascaded in sequence; the method comprises the following steps: acquiring a front image of a meter, inputting the front image into a cascade detection model, dynamically adjusting a feature perception range by a dial detection network according to a dial diameter of the meter, generating a dial region-of-interest frame, inputting the dial region-of-interest frame into a character segmentation network, and obtaining a character segmentation image of each character contained in the dial region-of-interest frame; and inputting the character cut images into a number classifier to obtain numbers contained in the character cut images, and taking a number sequence formed by combining the numbers according to the positions of the numbers in the region-of-interest frame of the dial plate as a reading detection result. The method has the beneficial effects that the problems of large dial positioning error, inaccurate character cutting, unstable number recognition and the like are effectively solved, the model volume is reduced by about 95% compared with a traditional single-stage model, and the calculation power demand is reduced.

The invention provides an implementation method based on light-induced linear reconfigurable artificial synapses, and belongs to the technical field of neuromorphic electronic devices and intelligent sensing signal preprocessing. The method comprises the following steps: providing an artificial synapse transistor capable of regulating and controlling conductance through voltage pulses, irradiating visible light in a channel or a conductance modulation area of the artificial synapse transistor, applying continuous voltage pulses under an illumination condition, reading the conductance in a preset time window after the last pulse is ended, and taking the conductance as a weight value of a corresponding pulse sequence; by adjusting illumination parameters, conductance is converted from nonlinearity to linearity along with the pulse number relation, the nonlinear factor is reduced compared with the dark state, the linear interval is widened, and linearity reconstruction in the weight updating process is achieved. According to the method, reconfigurable linear weight modulation can be obtained only by means of a light field and simple pulse driving, the method is suitable for high-fidelity compression coding of 5-bit and above time sequence pulses, feature separability and recognition accuracy can be remarkably improved in handwritten number recognition, handwriting identification and other scenes, and training time and energy consumption are reduced.

To provide an artificial intelligence-based collection server and its operation method that enable the collection of meter readings of a meter more conveniently and efficiently, without requiring a meter reader to directly obtain meter readings from the meter.SOLUTION: A collection server 110 includes: a collection event generation unit that generates a collection event for collecting meter readings at preset collection cycle intervals; a capture request unit that transmits a capture request command via the collection event generation unit, instructing a camera to capture and transfer an image of the area on the meter where the meter reading is displayed; an identification unit that, upon the transfer of the captured image of the area on the meter where the meter reading is displayed from the camera in response to the capture request command, identifies a first meter reading composed of numerical values from the captured image based on a preset number identification model; and a history storage processing unit that, after generating meter reading collection history information at the time the collection event occurred, stores the meter reading collection history information in a history database in which multiple pieces of meter reading collection history information are stored.SELECTED DRAWING: Figure 1

The invention provides a multivariate data regulation and control processing method based on three-dimensional visualization, belongs to the technical field of image data processing, and aims at solving the problem that an existing AI intelligent inspection unit mainly depends on a two-dimensional image to carry out instrument detection and digital recognition. In order to overcome the defects of inaccurate state analysis, insufficient multi-view data fusion and the like caused by target occlusion misjudgment and spatial information loss in the prior art, a multivariate data system is constructed by fusing a three-dimensional point cloud, a depth map and a multi-view two-dimensional image, and three-dimensional reconstruction, spatial feature extraction and a multi-modal data fusion algorithm are combined, so that the multi-view data fusion is realized. Accurate detection of a power distribution station house equipment target, three-dimensional visual analysis of state parameters and dynamic regulation and control processing are realized. According to the method, the anti-shielding capability of target detection, the accuracy of state analysis and the real-time performance of data regulation and control are effectively improved, the intelligent level of operation and maintenance management of the power distribution station house is remarkably optimized, and the labor cost and the fault risk are reduced.

The present invention discloses a fully analog circuit system for handwritten digit recognition based on a storage-computing integrated architecture, comprising a first analog circuit and a second analog circuit. The first analog circuit is composed of the following circuit modules: an input drive circuit, a first resistor array with a scale of m columns and 2n rows, a first current-voltage conversion circuit, a first subtraction circuit, and a first activation function circuit; the second analog circuit is composed of the following circuit modules: a second resistor array with a scale of n columns and 20 rows, a second current-voltage conversion circuit, a second subtraction circuit, a second activation function circuit, and a voltage comparison circuit. This system can eliminate the data handling problems brought about by the traditional von Neumann architecture and does not require the assistance of any digital modules, achieving ultra-low latency and accurate recognition. This system has very broad application prospects in the field of new neural network hardware system research, and is expected to provide a feasible non-von Neumann hardware solution for deep neural networks and edge computing.

This invention discloses a license plate recognition method and system based on deep learning, comprising the following steps: acquiring license plate image data to generate an image dataset; performing semantic decomposition to divide the data into region code units, letter units, and number units, and constructing a semantic structure graph; constructing a multi-path character recognition model with an improved PCNN model as the backbone feature structure; inputting the image dataset into the multi-path character recognition model respectively, and outputting region code recognition results, letter recognition results, and number recognition results to generate candidate license plate recognition results; if there are illegal character combinations or missing structural unit information, performing path filtering processing to generate a candidate path set; performing inference imputation processing to generate a complete path set; and merging and outputting the license plate recognition results and the set of confident candidate paths. This invention improves the accuracy of character recognition while enhancing robustness to structural defects, possessing the advantages of high recognition accuracy and strong fault tolerance.

The present application relates to a kind of mixed STDP training method and system based on threshold increment cycle counting, construct including input layer, hidden layer and classification output layer STDP network architecture, and the threshold voltage increment of neuron is counted and controlled to the unsupervised learning process of STDP network architecture, the supervised learning process of combination classification output layer adjusts the weight value corresponding to neuron, by applying threshold increment cycle counting and neuron responsivity screening, effectively constrain weight update, reduce the weight update frequency, to greatly reduce the overall amount of computation of training, help to improve training learning efficiency.The present application method is applicable to handwritten numeral recognition, compared with traditional STDP training algorithm, can reduce 2.5 to 3.5 times amount of computation and improve about 3% classification accuracy.

The invention relates to a continuous learning method and device for a handwritten number recognition model, and the method comprises the steps: generating a main recognition model according to the training of an annotated data set, and selecting image samples which are wrongly recognized by the main recognition model from the annotated data set; training according to the error sample training set to generate an auxiliary recognition model, and cascading the main recognition model and the auxiliary recognition model which can be complementary with the main recognition model in performance to obtain a teacher model; based on the pseudo label set and the real label set, using the teacher model to guide the student model to train to obtain a distilled student model; and under the condition that the recognition performance of the student model after distillation reaches the recognition performance of the teacher model, replacing the main recognition model with the student model after distillation, returning to the model training cascade step and waiting for a new round of unlabeled data set for continuous learning, thereby not only endowing the intelligent model with continuous self-learning ability, but also improving the learning efficiency of the intelligent model. And the purpose of overcoming the disastrous forgetting phenomenon is also achieved.

The present application relates to the technical field of digital recognition, and particularly provides a nixie tube content recognition method and device, electronic equipment and storage medium, wherein the method comprises: acquiring a continuous video image sequence of a nixie tube, acquiring position information of a nixie tube character display segment according to a preset frame number by using a template matching algorithm; recognizing bright and dark states of the nixie tube character display segment by using a support vector machine (SVM); acquiring a brightness sequence unit of the nixie tube character display segment under the preset frame number according to a preset nixie tube arrangement mode; classifying the brightness sequence unit by using a deep learning method and outputting a recognition result; and the device comprises: a nixie tube and a PC terminal connected with the nixie tube, the nixie tube and the PC terminal are configured in an asynchronous state, and the PC terminal comprises: an acquisition module, a position recognition unit, a brightness recognition unit and a training module. The purpose is to solve the misrecognition problem caused by uneven brightness and defects in the nixie tube content recognition process.

The invention discloses a medical B-ultrasonic image automatic analysis system and method based on deep learning. The system comprises an image preprocessing module, a follicle detection module, a digital recognition module, an OCR text extraction module, an intelligent matching module and a report generation module. The method comprises the following steps: performing tilt correction, region extraction and perspective transformation on a B ultrasonic image, detecting follicle positions and morphological parameters by using a YOLO-11 model based on a rotating bounding box, and identifying digital labels; extracting an image bottom size text by adopting a CnOCR engine, and correcting to obtain structured data; intelligently matching follicles, numbers and sizes by calculating the Euclidean distance, and establishing triple association; and automatically generating a structured JSON report in combination with an anomaly detection mechanism. According to the method, full-process automation of follicle monitoring is achieved, the analysis efficiency, accuracy and consistency are remarkably improved, manual dependence is reduced, and reliable support is provided for clinical decision-making of assisted reproduction.

The invention discloses a new building electrical installation acceptance method and device, and relates to the technical field of electrical installation acceptance, the method comprises the following steps: obtaining house numbers of different rooms and code numbers and resistance values of resistor devices preset in different rooms to obtain a data file, the code numbers and resistance values of the resistor devices having uniqueness; measuring and recording actually measured current values shown by the corresponding electricity meters after the electric brake master control room opens the electric brake of each room; and extracting the code number of the resistor device in the data file and the house number of the room where the resistor device is located by using a digital identification model, calculating to obtain a theoretical current value, and comparing the theoretical current value with the actually measured current value to judge whether the ammeter is correctly installed. The method is low in cost and easy and convenient to operate, manual errors are reduced, and the efficiency and accuracy of electrical installation acceptance are improved.

The application provides an image recognition simulation model and system, the image recognition simulation model comprising: an optical diffraction neural network for a handwritten digit recognition task, the optical diffraction neural network comprising: an input layer, a diffraction layer and an output layer; the input layer modulates incident light into a wavefront array carrying image information; the diffraction layer performs multiple diffractions on the light output by the input layer to simulate a convolution weight matrix, and adjusts a phase distribution in a manner of error back propagation; and the output layer generates a focused light spot corresponding to the number of categories, the focused light spot focusing on a number corresponding to a plurality of detection points, and a detection surface light spot intensity distribution of the focused light spot is used to represent a classification probability. Through nano structure design, high degree of freedom regulation of light field amplitude, phase and polarization is realized in a two-dimensional plane; meanwhile, through multi-layer cascading extension network depth, feature abstraction capability is improved, nonlinear mapping is realized through multi-layer cascading diffraction, and a classification light intensity distribution is directly output, and the light intensity distribution represents a classification result.

According to the hardware-network collaborative optimization FPGA digital recognition system and method, the LeNet-5 network architecture is improved, the number of channels is adjusted to be a multiple of 4 so as to be matched with an FPGA parallel computing array, a ReLU activation function is adopted to replace traditional Sigmoid / Tanh, and 8-bit integer quantization processing is implemented; a four-level assembly line architecture (convolution-quantization-pooling-activation) is adopted in the aspect of hardware, a snakelike movement strategy and a double-Block dynamic storage scheme are combined, the energy efficiency ratio and the reasoning speed are remarkably improved while high recognition precision is kept, an end-to-end real-time processing function chain from image collection, neural network reasoning to result display is achieved, and the method has the advantages of being high in recognition precision and high in practicability. The data reuse rate is obviously improved, the storage access bandwidth is reduced, a referenceable low-power-consumption and high-performance solution is provided for edge computing applications such as intelligent monitoring and industrial quality inspection, and high real-time performance and practicability are achieved.

The invention relates to the technical field of artificial synapses, in particular to a composite artificial synapse which comprises a core functional layer, the core functional layer is a composite material layer, and the composite material layer is formed by mixing a SrAl2O4-based long afterglow material and a NaYF4-based up-conversion material; eu < 2 + > and Dy < 3 + > are doped in the SrAl2O4-based long afterglow material; yb < 3 + > and Tm < 3 + > are doped in the NaYF4-based up-conversion material; under excitation of near-infrared light, the NaYF4-based up-conversion material transmits energy to the SrAl2O4-based long-afterglow material through up-conversion luminescence, so that the SrAl2O4-based long-afterglow material generates long-afterglow luminescence, and a synaptic function is realized; through thermal stimulation, the release process of defect trap electrons in the SrAl2O4-based long afterglow material is regulated and controlled, and thermally induced synaptic plasticity is realized. According to the invention, SrAl2O4: Eu < 2 + >, Dy < 3 + > and NaYF4: Yb < 3 + >, Tm < 3 + > are coupled, so that long afterglow luminescence (LPL) of near-infrared triggering and thermal regulation is realized, and information encryption, handwritten number recognition and mechanical arm gesture control are realized by artificial synapses based on photo-thermal synergistic stimulation.

Device for external control of speed and / or performance parameters, comprising a physical identification means (starter chip card) and / or a digital identification means (app), both of which can be used individually or in combination.

The present invention provides an optical character recognition system and method. By performing processing such as thinning and segmenting on a captured image and combining two recognition models, an artificial neural network and a K-nearest neighbor algorithm, the system and method include the following steps: S1: image acquisition; S2: image preprocessing; S3: image thinning and segmentation, thinning digits with width in the image and dividing the refined digits into several line segments; S4: inputting the angle information between adjacent line segments into the neural network and the K-nearest neighbor algorithm model for digit recognition; S5: the neural network and the K-nearest neighbor algorithm model generate possible preliminary recognition results and corresponding confidence levels; S6: selecting the preliminary recognition result with the highest confidence level as the final recognition result and outputting it. This system primarily addresses the technical problem that existing optical character recognition technology cannot adapt to various harsh real-world environments and produces inaccurate recognition results, thereby improving character recognition accuracy in complex environments.

A roller cutter for a raise boring tool includes a journal and a cutter shell positioned radially around the journal. The journal includes an external surface from which a journal end protrudes from. A seal retainer is positioned between the journal and the roller cutter, and wherein at least one digital identification tag is located on the roller cutter.

The present invention proposes a handwritten digit recognition system and method based on an anti-interference convolutional neural network. A handwritten text collection system obtains handwritten digit images and real digit labels written by different users. A generative adversarial network for interfering handwritten digit images is constructed, and the handwritten digit images are passed through the generative adversarial network for interfering handwritten digit images to predict the predicted labels of the noisy weakness feature images of the handwritten digit images. A loss function model is constructed in combination with the real digit labels, and the network is optimized using the RMSprop optimizer to obtain an optimized network. The handwritten digit images are collected by the handwritten text collection system and output to a host computer to obtain the predicted labels of the real-time handwritten digit images. The present invention utilizes a generative adversarial network to generate interference images using a special inverse mapping, and performs weakness training on the label convolutional neural network. This can recognize handwritten digit images with sloppy handwriting and stains, thereby improving the handwritten digit recognition accuracy of the handwritten digit recognition system.

The invention discloses an artificial intelligence-oriented neural network method. The method comprises the following steps of S1, constructing related contents on an FPGA (Field Programmable Gate Array) device by using an RISC-V instruction set; s2, designing an assembly line and a bus based on the specific structure of the Xuanting E902 processor of the RISC-V architecture; s3, determining neural network parameters according to verification; s4, the FPGA implementation and digital identification verification process of the E902 kernel is divided into an ov5640 camera image acquisition and data processing part and a neural network hardware implementation part and is integrated into an IP core on the whole; s5, verifying the database minst through the artificial neural network, and achieving the recognition of the handwritten characters on the FPGA. The flexibility of a kernel is improved, the language compiled by itself is strictly matched between the two platforms, the incompatibility situation is avoided, the system stability is improved, the effect in the aspect of gesture recognition is more prominent, and the method is suitable for popularization and application. The method has a wide application prospect and is beneficial to popularization and application.

The invention relates to the technical field of artificial intelligence, and provides a handwritten numeral recognition method and related equipment based on multi-modal large model guidance, and the method comprises the steps: constructing a meta-training data set; performing style analysis on the support set image based on a multi-modal large model, generating a style description text, and encoding the style description text into a first prompt vector; performing off-line training on the small model according to the support set image and the first prompt vector based on a meta-learning framework; in an online recognition stage, generating a personalized style description text according to an unlabeled handwritten digital image of a new user through a multi-modal large model, and encoding the personalized style description text into a second prompt vector; adopting a self-supervised learning mechanism to carry out personalized adaptation processing on the trained meta-learning small model based on a second prompt vector; and recognizing a subsequently input handwritten digital image through the adapted meta-learning small model to generate a recognition result. According to the method and the device, high-precision recognition of personalized handwritten numbers can be quickly realized under the condition of no labeled sample, and meanwhile, the consumption of computing resources is reduced.

The invention discloses a method and a related device for gradient detection and positioning of a contact network positioner, and relates to the technical field of rail transit detection equipment.The method comprises the steps that an obtained to-be-detected image data set is input into a contact network recognition model YOLOv5E-L, and a category label and a picture set of a position detection frame are obtained; cutting the picture set of the category label and the position detection frame to obtain an intercepted locator picture and an intercepted pillar number plate picture set; carrying out LSD-based linear detection on the intercepted locator picture to obtain a locator slope value; and inputting the intercepted pillar number plate picture set into a number plate number identification model YOLOv5E-d to obtain the number on the pillar number plate. According to the invention, the geographic position and the angle data value of the positioner can be automatically detected in a non-contact manner, and the state digitization and informatization of the catenary positioner are realized.

The present invention relates to a method for a driver of an unattended vehicle loading station to urgently abort the loading operation without getting out of the vehicle, including: collecting environmental noise; horn sampling; constructing a horn digital recognition model; detecting a suspected horn; duration judgment; frequency judgment; amplitude judgment; position judgment; and determining it as a horn. The present invention monitors the sound in the surrounding environment during the loading process, discriminates the sudden change of the sound with an increased amplitude based on factors such as duration, frequency, amplitude, and position to determine whether there is a horn for warning and requiring the interruption of the loading process, forming a complete horn alarm system. The present invention uses continuous horn blowing to warn the surrounding personnel in the most convenient and fast way and notify the control system of the loading station to automatically perform emergency avoidance. When sounding the horn, the driver does not need to open the door and get out of the vehicle, or even open the window, and can quickly stop the loading operation. This is particularly important for effectively avoiding personal injury in case of an accident during loading.