12154 results about "Image sensor" patented technology

The invention relates to a backside illuminated image sensor, a preparation method thereof and electronic equipment. The backside illuminated image sensor comprises a substrate, a grating, photodiodes and target isolation structures, wherein the photodiodes and the target isolation structures are located on the first surface of the substrate, are alternately arranged in the first direction parallel to the first surface and extend towards the substrate; the substrate internally comprises a plurality of trench isolation structures which extend into the substrate through the first surface and are arranged at intervals in the first direction; the trench isolation structures and the target isolation structures over the trench isolation structures are arranged in a one-to-one manner; the photodiode comprises a SiAs laminated layer and a SiAs cover layer which are arranged along a direction away from the substrate, and a SiP layer which penetrates through the SiAs laminated layer along a first direction and a direction towards the substrate; the target isolation structure comprises a protruding part higher than the top surface of the photodiode; the grating and the protruding parts are prepared in the same process step at the same period. Signal crosstalk of the BSI image sensor and unnecessary damage caused by IMP can be avoided, and photoelectric conversion efficiency can be improved.

The invention relates to an aerial photography target detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring an aerial image acquired by an image sensor; inputting the aerial image into a pre-trained target detection model to obtain a target detection result; the target detection model comprises a backbone network, a neck network and a head network; wherein the backbone network is used for performing feature extraction on an aerial image to obtain image extraction features; the neck network is used for performing multi-scale fusion on the image extraction features to obtain a plurality of fusion features of different scales; the neck network comprises a bidirectional information transfer network, the bidirectional information transfer network comprises a separation and attention enhancement module, and the separation and attention enhancement module is used for generating fusion features corresponding to tiny targets; and the head network comprises a detection head corresponding to the tiny target and is used for obtaining a target detection result corresponding to the fusion feature based on the fusion feature. By adopting the method, the detection accuracy under the complex aerial image can be improved.

The invention provides a neuromorphic visual target tracking method and system based on image processing, and relates to the technical field of neuromorphic calculation, and the method comprises the steps: fusing the input of an event camera and a conventional image sensor, constructing a combined input tensor, and introducing a multi-scale convolution and synaptic event driving mechanism. Quick response and stable feature extraction of a high-speed moving target are realized, and coding block mistaken deletion and tracking loss caused by quick movement of the target are effectively avoided, so that system delay is reduced. Meanwhile, in combination with significance entropy difference evaluation and a dynamic brightness enhancement mechanism, the target judgment accuracy under low illumination and complex backgrounds is improved; redundant noise blocks are screened out through a significance weight function and confidence calculation, the redundancy calculation burden is relieved, and the lightweight characteristic of the system is guaranteed. A memory trajectory tensor and dynamic template adjustment mechanism based on a recurrent neural network is further introduced, time sequence consistency verification and self-adaptive updating are achieved, and the stability and robustness of the tracking process are enhanced.

The invention relates to the technical field of phytotron control systems, in particular to a seed seedling phytotron control system and a control method thereof, which integrate multi-dimensional environment perception, intelligent analysis and decision, precise environment regulation and control, a central controller and a data storage and analysis platform. A seedling raising environment is monitored in real time through temperature and humidity, illumination, CO2 concentration, soil moisture and an image sensor, an intelligent analysis decision module preprocesses data, establishes a growth model, generates a control strategy and optimizes a target by using an advanced algorithm, and an accurate environment regulation and control module executes the strategy and regulates temperature, humidity, illumination and the like. The central controller coordinates operation of the modules and realizes man-machine interaction, and the data storage and analysis platform is responsible for storing data and supporting cloud analysis so as to realize efficient seedling culture management and significantly improve the seed emergence rate from 75% of a traditional method to 92%, and greatly reduce seed waste.

The invention discloses a self-isolation type image sensing structure, a sensor and a preparation method, and belongs to the field of semiconductors, the self-isolation type image sensing structure is characterized in that a shallow trench isolation structure is exposed after the back surface of a substrate is thinned, then a first semiconductor layer is extended on the back surface of the substrate, and a first isolation structure is prepared in the first semiconductor layer; sequentially depositing a second semiconductor material layer and a metal grating material layer on the first semiconductor layer; sequentially etching the metal grating material layer and the second semiconductor material layer to form a plurality of metal grating structures distributed at intervals and corresponding self-isolation structures; extending a plurality of photosensitive layers in the photosensitive region groove between the adjacent self-isolation structures to form a photosensitive region; and then activating, preparing a filter layer and the like are carried out to obtain a complete back-illuminated image sensor. The self-isolation photodiode is obtained by changing the structural distribution of the photodiode, unexpectedly, the problem that the substrate is damaged by doping is solved, meanwhile, the crosstalk effect is greatly reduced, and the performance of the image sensor is improved.

The invention discloses a valve support quality detection method based on image recognition, and particularly relates to the technical field of valve support quality detection.The method comprises the steps that real-time temperature data and image data in the valve support quality detection process are acquired; outputting a temperature-image relation model through joint calibration of a temperature sensor and an image sensor; compensating the image data of the valve bracket based on the real-time temperature data and the temperature-image relation model; building an initial defect identification model, adjusting model parameters based on the real-time temperature data and the preliminarily compensated image, using a time sequence causal relationship model to predict image quality degradation, adjusting the model parameters in advance, and outputting a trained defect identification model; controlling a receptive field and a feature fusion coefficient of the defect identification model; the feature fusion coefficient is used for adjusting the feature fusion weight of each scale; and the training priority is managed by using clustering analysis, and the detection precision of the temperature anomaly region is optimized.

The invention relates to the technical field of machine vision structure health monitoring, and discloses a steel structure weld fatigue cracking real-time monitoring system based on machine vision. The system comprises a space-time registration and fusion module, a multi-scale feature analysis module, a health monitoring module, a crack deduction calculation module and a regulation and control strategy generation module. Performing space-time registration and pixel-level fusion through the visual data of the plurality of image sensors to generate a synchronous multi-source image stream; a multi-level welding seam characteristic spectrum is constructed through multi-scale characteristic analysis, and a welding seam structure knowledge base is dynamically updated; the knowledge base and the real-time characteristic spectrum are used for monitoring the welding seam health state, and abnormity is recognized; deducing a crack initiation position and an evolution path in combination with historical damage data; and real-time load information is fused to pre-estimate the remaining service life, and a structural integrity regulation and control strategy is generated online. According to the invention, high-precision fusion of the multi-source visual data and active prediction of the crack trend are realized, and the monitoring accuracy and the early warning capability are improved.

The invention discloses a transformer fault intelligent detection method and device based on deep learning multi-mode fusion, computer equipment and a readable storage medium. The method comprises the steps that firstly, RGB image data of a target transformer are collected by means of an image sensor, infrared thermal imaging image data are collected by means of an infrared sensor, and sound signals are collected by means of a sound sensor; and preprocessing the data, taking the preprocessed data as multi-modal detection data, and inputting the multi-modal detection data into a pre-trained multi-modal fault recognition model which comprises a sensing layer, a fusion layer, a backbone network and an output layer which are cascaded in sequence, so as to obtain a fault detection result of the target transformer. According to the method, multi-modal data are fused, the deep learning advantage is exerted, the comprehensiveness and accuracy of transformer fault detection are improved, and an effective means is provided for intelligent operation and maintenance of a power system.

The invention relates to the technical field of slope disaster monitoring, particularly provides a slope landslide geological disaster monitoring method and system based on image intelligent identification, and solves the problems that image identification and hyperspectral analysis are depended, multi-modal data fusion is lacked, and the monitoring accuracy is high. The method comprises the following steps: acquiring multi-modal data through an unmanned aerial vehicle hyperspectral camera, a ground image sensor, a vibration sensor, a displacement sensor and a meteorological sensor; carrying out denoising, calibration and formatting processing on the acquired multi-modal data; extracting landslide related features from the image data, the hyperspectral data, the vibration data, the displacement data and the meteorological data; fusing the multi-modal data through an intelligent fusion algorithm to generate a landslide risk assessment result; and early warning information is generated according to a landslide risk assessment result, and an emergency response mechanism is triggered, so that the precision and efficiency of image recognition and hyperspectral data analysis are improved, and the real-time performance and response speed of the system are improved.

The invention relates to the technical field of goods shelf detection, in particular to a self-detection method and device for goods shelf settlement, and provides the following scheme: obtaining a top view image through an image sensor arranged right above the top of a goods shelf, dividing the image into a plurality of grid units, and positioning a rectangular geometric shape by utilizing Hough transform; and screening a plurality of to-be-detected areas in combination with the edge features. For an area to be measured, homographic registration and ortho-rectification are carried out based on a reference image, a displacement field is obtained by adopting sub-pixel-level dense registration, and a geometric parallax component field corresponding to imaging parameters is obtained through robust estimation. And under the hypothesis of small deformation, inverting the parallax into a pixel normal distance, and carrying out weighted aggregation on the local region to obtain a local distance measurement result. And by iteratively combining adjacent grids, determining a settlement area boundary, and finally outputting a settlement detection result. Millimeter-level settlement quantification can be realized under a single-frame image, hardware transformation is avoided, and the method is suitable for automatic detection and long-term monitoring of multi-specification goods shelves.

The disclosed invention relates to a method for processing image data of an image sensor comprising a matrix of light sensitive elements and a plurality of lens elements and / or filter elements arranged in a pixel matrix in front of a finer sub-pixel matrix of light sensitive elements. A group of light sensitive elements are placed behind a common lens element and / or a common filter element to provide sub-pixel values for a respective position in the pixel matrix. Said image sensor is adapted to capture image data for a plurality of views, wherein each view comprising a matrix of a selected group of sub-pixel values of the pixel matrices captured by the matrix of light sensitive elements.According to the invention, for each captured view of the image, first variations of sub-pixel values in the respective view are determined separately from other views of the same image. Second variations of sub-pixel values related to the same position in the pixel matrix behind a respective lens element and / or filter element are determined in a set of views of the same image. The image data for the image are processed by use of the determined first and second variations.

The invention provides a three-dimensional automatic focusing method and device based on multi-modal data fusion and Kalman filtering and a readable storage medium, and high-precision real-time focusing is realized through multiple steps: collecting sample three-dimensional phase information and different focal plane image data by using a phase and image sensor, and calculating phase depth and definition; constructing a multi-plane Kalman filtering cluster, modeling the focus state of each focal plane, and predicting the focus by fusing the neighborhood coupling influence; the focus state is updated and corrected in combination with the image definition and the phase gradient, and the optimal three-dimensional focus position is determined by means of a collaborative optimization decision maker; dynamically adjusting the noise matrix and the coupling coefficient, and controlling the objective lens to complete focusing. According to the method, through multi-modal fusion and multi-plane cooperation, the problems of focus drifting and jumping are effectively solved, the focusing precision and stability of an imaging system in a complex sample and a dynamic environment are remarkably improved, and the method is suitable for equipment such as a biomedical full-automatic microscope and a digital pathological section scanner.

The invention provides an offshore wind turbine generator fault diagnosis method and system based on multi-source sensor data fusion. The method comprises the steps that a vibration signal from at least one component of a wind turbine generator is acquired through a vibration sensor; performing time-frequency conversion on the vibration signal by applying synchronous compression wavelet transform to obtain time-frequency representation of the vibration signal; when the reconstruction error exceeds a preset threshold value, it is judged that an abnormal event exists in the vibration signal; obtaining the position of a part corresponding to the abnormal event; starting an image sensor and an acoustic sensor according to the position of the component, and acquiring an image signal and a sound signal of the component according to the image sensor and the acoustic sensor; according to the DS evidence theory, the vibration signal, the image signal and the sound signal, obtaining the confidence of the fault type; the fault type of the component is judged according to the maximum confidence allocation principle, high-resolution time-frequency analysis can be achieved through synchronous compression wavelet transform (SST), and the fault feature identification degree is improved in combination with the self-encoding neural network and the D-S evidence theory.

Embodiments are directed to perceiving scene features using event sensors and image sensors. Enclosures mounted may be on structures arranged to establish a boundary where each enclosure includes an event camera, a beam generator, or a frame camera. The beam generators may be employed to scan paths across objects in a scene that may be outside the boundary. Events may be determined based on detection of beam reflections corresponding to the objects. Trajectories associated with the objects may be determined based on the paths and the events. Objects that may be authorized may be determined based on trajectories associated with authorized objects. Objects in the scene that may be unauthorized may be determined based on trajectories associated with the unauthorized objects. A representation of the unauthorized objects may be determined such that a position and an orientation of the unauthorized objects in the scene may be based on the trajectories.

The invention discloses an intelligent welding control system and method based on multiple sensors and electronic equipment, and belongs to the technical field of automatic welding, the system comprises a sensing layer, a decision-making layer and an execution layer, through cooperative work of a front laser vision sensor, a rear laser vision sensor and a molten pool image sensor, a groove three-dimensional model can be accurately established before welding, and the welding precision is improved. And dynamic information of the molten pool is continuously collected in the welding process, a dual monitoring mechanism for the geometric characteristics of the welding line and the state of the molten pool is formed, and multi-source data collection and fusion in the whole welding process are achieved. The control method comprises the steps of pre-scanning modeling, feedforward parameter planning, real-time feedback adjustment of a molten pool, data fusion optimization and the like, and high-precision self-adaptive control over welding parameters is achieved by combining deep learning and the Kalman filtering technology. According to the method, the welding adaptability, the control precision and the intelligent level can be remarkably improved, and the method is suitable for high-precision welding scenes such as pipelines, pressure containers and steel structures and has remarkable engineering application value and popularization prospects.

The invention belongs to the field of image generation, particularly relates to a three-dimensional image generation method based on spherical reflection cavity parameter optimization, and aims to solve the problems of low imaging precision and poor adaptability caused by lack of a collaborative optimization mechanism in the prior art. The method comprises the following steps: acquiring optical characteristics of the inner wall of a cavity to generate a reflection path offset comparison table, measuring deformation deviation of the cavity to construct a geometric compensation mapping relation, and monitoring environmental disturbance in real time and converting the environmental disturbance into a noise intensity coefficient; inputting the target three-dimensional image into a pre-compensation processor, synchronously executing optical path compensation, geometric distortion correction and environmental noise suppression, and outputting a pre-corrected image to projection equipment; capturing an actual projection through an image sensor, extracting difference data with the pre-corrected image, and decomposing the difference data into a system error component and an environment interference component; and feeding back the updated parameters to the processor to regenerate a final three-dimensional image. According to the invention, multi-error-source cooperative correction and dynamic optimization are realized, and the imaging precision and adaptability are significantly improved.

Stacked devices and methods of fabrication are provided. Die-to-wafer (D2W) direct-bonding techniques join layers of dies of various physical sizes, form factors, and foundry nodes to a semiconductor wafer, to interposers, or to boards and panels, allowing mixing and matching of variegated dies in the fabrication of 3D stacked devices during wafer level packaging (WLP). Molding material fills in lateral spaces between dies to enable fan-out versions of 3D die stacks with fine pitch leads and capability of vertical through-vias throughout. Molding material is planarized to create direct-bonding surfaces between multiple layers of the variegated dies for high interconnect density and reduction of vertical height. Interposers with variegated dies on one or both sides can be created and bonded to wafers. Logic dies and image sensors from different fabrication nodes and different wafer sizes can be stacked during WLP, or logic dies and high bandwidth memory (HBM) of different geometries can be stacked during WLP.

The invention relates to the field of hydrogen leakage and combustion detection, and discloses a hydrogen leakage and combustion identification method, which comprises the following steps: S1, collecting multiple types of detection signals from acoustic, electrochemical and infrared thermal image sensors; s2, preprocessing the collected signals to generate a multi-dimensional feature space; s3, constructing a topological structure in the multi-dimensional feature space, extracting topological features, and generating topological descriptors; s4, analyzing the time dynamic change of the topology descriptor based on a Markov chain model; s5, calculating the topological similarity between the real-time signal and the known mode, and carrying out mode matching; and S6, calculating a signal state through a comprehensive decision function, judging whether a hydrogen leakage or combustion event exists, and triggering a corresponding alarm. According to the invention, through multi-sensor cooperative monitoring, signal preprocessing, topological structure analysis and dynamic modeling, high-precision real-time monitoring and reliable alarm of hydrogen leakage and combustion are realized, and the system has the advantages of high sensitivity, low missing report rate and rapid response.

The invention discloses a cradle head tracking method, system and device, a storage medium and a program product, and relates to the technical field of cradle heads, and the method comprises the steps: obtaining the target position information of a tracking target in continuous multi-frame image frames collected by an image sensor, and the size of a detection frame, determining a pan-tilt rotation angle of a target pan-tilt carrying an image sensor when multiple image frames are acquired; determining a target position offset between the target position and the center of the image based on the target position information, and mapping the size of the detection frame into a target distance between the tracking target and the image sensor; inputting the target position offset and the holder rotation angle corresponding to the multiple image frames into a motion estimation model to obtain a predicted position; and determining a pan-tilt angle value of the target pan-tilt based on the predicted position and the target distance, and driving the target pan-tilt to rotate based on the pan-tilt angle value. According to the invention, the cradle head keeps stable tracking while quickly responding to target movement, so that the stability and reliability of cradle head tracking control are improved.

A system for creating online vectorized maps for autonomous vehicles includes an image sensor and an electronic control unit (ECU). The image sensor captures a series of image frames. The ECU includes a memory, a central processing unit (CPU), and a transceiver. The memory stores a semantic segmentation deep learning model and a vectorization post-processing module as computer readable code. The CPU executes the semantic segmentation deep learning model and the vectorization post-processing module to output a vectorized map of an external environment of a vehicle. The transceiver uploads the vectorized map to a server such that the vectorized map can be accessed by a second vehicle that uses the vectorized map to traverse the external environment.

According to the metal plate self-adaptive straightening method and technology based on the line laser contourgraph, the laser scanning contourgraph is arranged above a feeding part of a straightening machine, a line laser transmitter and an image sensor are arranged in the laser scanning contourgraph, and the line laser transmitter projects laser lines to a metal plate; the image sensor is used for capturing a plate image with a laser reflection light strip to assist the straightening control system to adjust the pressing roller, straightening of the metal plate can be adjusted in real time, and the straightening precision is improved; image data collected by an image sensor is transmitted to a data processing module, and the data processing module analyzes an image to obtain actual contour data of a plate before straightening; the actual contour data are compared with a preset ideal straightened plate contour model, the deviation value of each point is calculated, and a deviation matrix is generated; the straightening machine control system controls the position conditions of all the pressing rollers independently according to the deviation matrix, the accuracy of plate straightening can be greatly optimized, and continuous adjustment can be achieved.

The invention discloses an embedded full-coverage engineering project intelligent auditing and supervising system, and particularly relates to the technical field of engineering project auditing and supervising. A data acquisition layer comprises an Internet of Things sensor unit which is used for deploying various sensors on an engineering project site, acquiring environment data and engineering structure data in an engineering construction process in real time, and transmitting the environment data and the engineering structure data to a data processing layer; the sensors include but not limited to a temperature sensor, a pressure sensor, a displacement sensor, a humidity sensor, a vibration sensor and an image sensor, and data are transmitted to the data storage layer through the wireless communication module. Intelligent auditing supervision of the whole process of the engineering project is achieved by integrating advanced technologies, responsibility definition and tracing are enhanced, auditing efficiency and accuracy are remarkably improved, project risks are reduced, and compliance and safety of the engineering project are ensured.

A system for providing privacy measures for a computing device comprising: a memory a display configured to display images and / or data which may be subject to privacy concerns; at least one image sensor configured to provide image data of a user operating the computing device and image data of the environment surrounding the user within an image sensor field of view; at least one processor configured to: receive the image data to segment the image data to isolate the facial features of a person comprising a user and generate a user facial vector representative of characteristics of the segmented facial image data; and compare the user facial vector with facial vector data associated with one or more authorised device users stored in the memory; and a privacy controller module configured to determine a privacy threat alert based on the facial vector comparison; wherein, on receipt of a privacy threat alert from the privacy controller, the processor is configured to modify the display of the electronic device to preserve the privacy of the displayed image and / or data.

The invention relates to the technical field of camera control, and discloses a megapixel camera control method based on multifunctional fusion. The method comprises the following steps: acquiring original image data of an image sensor, and preprocessing to generate an initial brightness distribution diagram; and calculating a scene dynamic range to determine a target exposure parameter set containing exposure time, a gain coefficient and an aperture value, synchronously analyzing color temperature information, matching a preset white balance model to output a correction coefficient, and combining the two to generate a first control instruction to be sent to the image sensor. Receiving a touch screen focusing signal, and extracting a focus area coordinate to adjust a stroke parameter of a focusing motor; ambient light intensity is monitored in real time, and when the ambient light intensity is lower than a preset threshold, a flash lamp compensation module is activated and a compensation light quantity value is calculated. In the photographing mode, the output power of the flash lamp is adjusted according to the compensation light quantity value, the target exposure parameter set is recalibrated based on the focus area coordinates, and the camera complex scene adaptation and imaging quality are improved.

The invention discloses a motion capture system, a motion capture method and a readable storage medium, and belongs to the technical field of motion capture. The motion capture system comprises a motion capture sensor group and a motion capture processing unit, the motion capture sensor group comprises at least one invisible light image sensor and at least one visible light image sensor, the invisible light image sensor is used for sensing a detected target in a near-infrared or infrared band, and the visible light image sensor is used for sensing the detected target in a near-infrared or infrared band. The visible light image sensor is used for sensing a detected target in a visible light wave band and outputting a visible light image; and the motion capture processing unit is connected with the motion capture sensor group and is used for determining motion posture data of the detected target based on the visible light image and the near-infrared or infrared band image. According to the invention, the motion attitude data of the measured target is obtained based on the sensing data of different modes, and the precision and robustness of motion capture of the measured target by the motion capture system are improved.

The invention discloses a monocular three-dimensional human body posture estimation method and device based on a diffusion model and feature fusion. The monocular three-dimensional human body posture estimation method comprises the steps that an input image sequence is acquired from an RGB image sensor; sampling from the Gaussian noise to obtain input three-dimensional attitude noise; performing image preprocessing on the input image sequence to obtain 2D key points, depth features and image features; fixing a pre-trained feature fusion model, fusing the input key points, depth and image features, and taking the fused features as de-noising conditions of a diffusion model; and fixing a pre-trained diffusion model, performing a diffusion model denoising process on the noise posture based on the pre-trained model, and outputting an accurate three-dimensional human body posture.

This disclosure provides systems, methods, and devices for image capture that support image processing. In a first aspect, a method of image processing includes receiving first image data from a first image sensor; receiving second image data from a second image sensor of a second camera, wherein at least one of the first image data or the second image data is received through a variable aperture (VA) having an adjusted aperture setting applied by the first camera or the second camera; and / or determining a first output frame based on the first image data and the second image data by adjusting the second image data to match a characteristic of the first image data. Other aspects and features are also claimed and described.

The invention discloses an intelligent anti-counterfeiting color box printing positioning control method, and relates to the technical field of printing positioning control, and the method comprises the steps: dividing a color box layout into a plurality of detection regions, setting a cross-shaped and circular combined positioning mark in each region, and collecting positioning information in real time through a high-resolution image sensor to establish a feature database; extracting regional deformation and position features by using a multilayer convolutional neural network, calculating actual deviation by combining a residual network, constructing a mapping model of process parameters and position deviation, and establishing a differential compensation model by using a deep reinforcement learning algorithm; and the compensation parameters are distributed to a servo control unit, a motion control instruction is generated, a compensation effect evaluation index is adopted to carry out real-time evaluation on position deviation, a decision model is established to realize compensation parameter adaptive optimization, and regional compensation adaptive adjustment is realized. According to the method, intelligent monitoring and optimization of the anti-counterfeiting printing process are achieved, and the printing quality stability and the production efficiency of the anti-counterfeiting color box in batch production are greatly improved.

The invention discloses a target detection method based on an image semantic matching algorithm, and the method comprises the steps: carrying out the multi-source data collection through an image sensor, an SAR (Synthetic Aperture Radar) radar and infrared remote sensing equipment which are carried by an unmanned plane, and obtaining a visible light image, a high-resolution radar image and thermal imaging data in a low-light environment; carrying out target identification on different data sources by utilizing an integrated learning method; judging whether the target is a tank or not by adopting a soft voting fusion strategy; after the tank is detected, the crossing unmanned aerial vehicle is dispatched to adjust a flight path, and image data with higher resolution are obtained; a deep learning model is trained based on an image semantic matching technology, semantic analysis is performed on a high-precision image, and weak parts of a target are automatically identified and labeled by matching standard part features. According to the method, the multi-modal source data is fused, the reliability of tank recognition is improved, the weak part of the target is accurately positioned by combining the image semantic matching technology, the method can adapt to a complex battlefield environment, and the intelligent level of battlefield intelligence analysis is improved.

According to the RS error correction decoding method and system compatible with the multiple two-dimensional codes, a finite field and module value corresponding table is defined, a corresponding relation table between power and data is constructed, and the decoding requirements of the multiple two-dimensional code types are met. The method comprises the following steps: acquiring image data by using an image sensor, extracting two-dimensional code data, and converting the two-dimensional code data into a data code word stream; and performing error detection and correction on the two-dimensional code data based on a preset power data corresponding table, including judging whether an error exists or not by calculating a syndrome, if an error is detected, calculating an error position polynomial by using a Berlekamp-Massey algorithm, then positioning an error position by using a Chien search algorithm, and finally calculating an error value by using a Forne algorithm and performing correction. According to the method, the decoding efficiency and the fault-tolerant capability of the two-dimensional code are remarkably improved, the method is suitable for various standards, and especially when the two-dimensional code is partially damaged or an error occurs in data transmission, the original coding information can be effectively recovered.