2256 results about "Image pre processing" patented technology

The invention relates to the field of remote sensing image processing, in particular to a high-resolution remote sensing image semantic segmentation method based on multi-scale feature fusion, which comprises the following steps: acquiring a public remote sensing image data set, preprocessing the image, and constructing a training and testing set of semantic segmentation; a CTMFNet is designed, an encoder is composed of a lightweight residual module and an MS-Transform, and local space details and global context information are extracted; rID is adopted to reduce spatial information loss, LSFE is introduced to improve spatial positioning capability, and feature calibration is carried out in space and channel dimensions through DecoderAttn to realize boundary fine segmentation; inputting the training sample into the network for training to obtain a converged optimal semantic segmentation model; and inputting the test set into the model to obtain a semantic prediction map, and outputting a fine segmentation result of the remote sensing image through multi-scale fusion and boundary restoration. According to the method, the precision and robustness of ground feature extraction are effectively improved, the calculation cost is remarkably reduced while high segmentation precision is kept, and the method has good practical value and popularization prospects.

The invention provides a fire early warning and intelligent fire extinguishing method based on image recognition. The fire early warning and intelligent fire extinguishing method comprises the steps that a camera network is used for covering a target monitoring area, flame and smoke characteristic parameters are input, noise is removed through image preprocessing, and image data are standardized. A heat source point is selected as a camera installation position in combination with a fire propagation mode, and layout is optimized. And calling image data, performing real-time analysis based on a dynamic flame sequence, extracting abnormal response, calculating a fire risk value by using a mode recognition algorithm, and generating peak fire probability data. And collecting real-time fire characteristic data, comparing the data with a risk value after filtering and noise reduction, and setting multi-level threshold values to generate an alarm result. And analyzing a false alarm reason, optimizing the position of the camera and the starting condition of the fire extinguishing device, and generating a fire risk and fire extinguishing efficiency report. According to the invention, the accuracy and timeliness of fire early warning can be improved, the false alarm rate is reduced, and the fire extinguishing efficiency is enhanced.

The invention discloses a bridge crack identification and automatic evaluation method based on image identification and AI modeling, and the method comprises the following steps: S1, obtaining an original image of a bridge structure surface, and carrying out the image preprocessing; s2, inputting the standardized image into an image recognition model, performing pixel-level segmentation on a crack region in the image, and outputting a crack mask graph; s3, performing feature extraction processing on the crack mask graph, extracting geometric feature parameters of the crack, and constructing a crack feature vector; s4, constructing an evaluation model based on a supervised learning method, and training the evaluation model; and S5, inputting the crack feature vector into an evaluation model, evaluating the structural risk level of the crack, and outputting a structural risk label. According to the method, image recognition and AI modeling are fused, automatic crack recognition and evaluation are achieved, and the method has the advantages of being high in precision, clear in boundary and intelligent in evaluation.

The invention relates to the technical field of PCB welding spot defect detection, and discloses a PCB welding spot defect detection system and method based on image recognition, and the system comprises an image preprocessing module which is used for obtaining an original image flow and dividing an interested detection area; the feature fusion module is used for extracting multi-modal features to form a fusion set; the defect judgment module is used for establishing a mapping index and obtaining a judgment result; the parameter calibration module is used for verifying the detection parameters and adjusting the mapping index; and the report output module is used for generating a defect detection report. The method comprises the steps of image preprocessing, feature fusion, defect discrimination, parameter calibration, report generation and the like. According to the system and the method, the PCB welding spot defects can be efficiently and accurately detected, the detection precision and stability are improved, a structured report is generated, and an effective solution is provided for PCB quality detection.

The invention provides a self-adaptive nonlinear image enhancement method and system for a low-light scene of a mobile terminal, and relates to the technical field of image enhancement, and the method comprises the steps: carrying out the image preprocessing and noise reduction, and carrying out the graying and noise suppression of an input color image through a local variance self-adaptive algorithm; adaptive down-sampling is carried out, and the down-sampling proportion is dynamically adjusted according to the image resolution and the content complexity, so that the processing efficiency is improved; brightness adaptive enhancement is carried out, and the overall brightness of the image is rapidly improved by adopting an Otsu method and a lookup table; contrast nonlinear enhancement: enhancing image details and contrast in combination with a Laplace operator and local mean adjustment; and color restoration: restoring the resolution through bilinear interpolation and performing weighted fusion to realize natural color reconstruction. And finally, a high-quality image of which the brightness, the contrast ratio and the color are remarkably improved is output. According to the invention, the recognition accuracy and processing efficiency of the low-illumination image are improved.

The invention provides a mountainous area tunnel construction safety intelligent monitoring and early warning method and system, and relates to the technical field of construction safety monitoring, and the method comprises the steps: collecting visible light and depth images of tunnel surrounding rock, and carrying out the segmentation and extraction of crack features through a depth attention network after image preprocessing and data fusion; extracting parameter time sequence data based on the crack spatial position and the type feature; determining fracture evolution characteristics and critical state parameters by combining wavelet transform and stress-rate coupling analysis; and adopting deep reinforcement learning to calculate the instability probability and generate early warning information. According to the invention, intelligent identification, instability prediction and risk early warning of tunnel surrounding rock cracks are realized, and the safety monitoring accuracy and early warning timeliness are improved.

The invention provides an image restoration and super-resolution reconstruction system and method based on deep learning, and belongs to the technical field of digital image processing. The invention aims to solve the problems of high calculation complexity and resource consumption, limitation of long sequence processing, high training difficulty and texture scene deficiency when a multi-scale residual network based on a Transform architecture is used for image resolution conversion. The reconstruction system comprises: an image preprocessing module performing window division and video memory optimization on an input low-resolution image; the multi-layer fusion network dynamically adjusts the characteristics of the low-resolution image, captures channel information in different scenes, performs interactive fusion, performs comparison supervision, establishes an information communication channel, dynamically adjusts and optimizes parameters through negative feedback, and obtains a super-resolution image. And the loss function module maximizes the similarity of the super-resolution image and the high-resolution image in the segmentation feature space to obtain a final super-resolution image.

The invention discloses an SMT welding spot defect detection method based on image data. The method comprises the following steps: S1, acquiring an image of an SMT welding spot area; s2, carrying out image preprocessing; s3, carrying out welding spot region segmentation by adopting a self-adaptive region growing algorithm, and carrying out modeling on a welding spot region contour in combination with a boundary fitting optimization algorithm to generate a welding spot contour model; s4, multi-level comprehensive features are extracted from the welding spot area, and the multi-level comprehensive features are constructed into a feature vector set; and S5, inputting the feature vector set into a welding spot defect detection network, predicting a defect mode existing in the current batch of welding spots by adopting an improved ProtoNet model, and completing classification and identification of welding spot defects by adopting adaptive feature filtering. According to the method, the adaptive region growing algorithm and the improved ProtoNet model are adopted, accurate detection of the welding spot defects is achieved, and the method has the advantages of being high in detection precision, high in adaptability and good in robustness.

The invention provides a medical image segmentation method based on geometry and topology cooperative guidance. The medical image segmentation method comprises the following steps of image preprocessing and data enhancement; a shared encoder; a dual-path cooperative decoder; carrying out multi-mode deformation iterative refining; and a multi-objective composite loss function and an optimization strategy. The method has the beneficial effects that the performance can be remarkably improved: through a unique geometry and topology collaborative refining mechanism, the segmentation precision and the boundary definition are far superior to those in the prior art, the topology correctness of an anatomical structure can be actively maintained and repaired, clinically unacceptable errors are remarkably reduced, and the reliability of a result is improved; in addition, operation can be simplified, stability and generalization are enhanced, and advanced application is promoted.

The invention discloses an image analysis method and system for concrete apparent quality defect detection, and relates to the technical field of concrete apparent quality defect detection.The method comprises the steps that a camera device is used for collecting concrete surface images in a specified distance interval, and the optical axis of a camera lens is controlled to be perpendicular to the concrete surface; carrying out image preprocessing on the collected concrete surface image, and carrying out defect type image marking; carrying out key feature extraction on the preprocessed concrete surface image by adopting a convolutional neural network to obtain key feature information; constructing a multi-algorithm comparison verification framework, performing cross validation in combination with the key feature information to obtain defect detection results and evaluation results of a plurality of defect detection models, optimizing model parameters in combination with a confusion matrix, and determining a final concrete surface detection model; obtaining an apparent quality defect detection result based on the defect detection result and a preset quality defect grading threshold value; the efficiency of concrete apparent defect detection is improved.

The invention discloses an intelligent manufacturing defect automatic detection and classification method based on machine vision, and particularly relates to the technical field of defect automatic detection and classification, by constructing a high-resolution multi-source sample data set and introducing image preprocessing operation, defect expressions under different manufacturing batches, surface states and illumination conditions are covered, and the defect detection and classification accuracy is improved. Generating a defect probability heat map through an image segmentation network, extracting a primary defect candidate region, calculating a pseudo defect high-frequency interference coefficient by combining a high-frequency pseudo defect feature tensor, and calculating a multi-class defect overlapping coupling coefficient based on multi-classification confidence distribution and semantic adjacency; pseudo defect interference intensity and multi-class defect boundary fuzzy degree in the defect candidate area are accurately described, a sample label pollution risk assessment model is constructed to realize automatic identification and screening of high pollution risk samples in training data, and interference of mistakenly labeled samples on deep model training is significantly reduced; and erosion of error feature-label mapping on the generalization ability of the model is effectively prevented.

The invention discloses an SPR response region identification method based on image semantic segmentation and time sequence alignment, and the method comprises the following steps: collecting SPR image frame sequence data, and constructing an original image sequence; performing image preprocessing operation on the original image sequence, and outputting a standardized image sequence; constructing a time sequence window image set composed of multiple continuous frames; inputting the time sequence window image set into an improved SegFormer model, and generating a response region segmentation mask image corresponding to each frame; executing cross-frame time sequence alignment operation of the response area, and outputting time sequence consistency identification mapping of the response area; performing area statistics, intensity analysis and time positioning operation; and generating a structured response region recognition result. The spatial-temporal evolution process of the response area in the SPR image sequence can be effectively recognized, the accuracy and stability of response area recognition are improved, and the method is suitable for high-precision biological detection and real-time molecular analysis scenes.

The invention belongs to the technical field of traffic safety monitoring technologies, and discloses a defogging method and equipment based on infrared light and visible light image fusion. The method comprises the following steps: image preprocessing: denoising and enhancing a foggy image; inputting the preprocessed picture into a defogging model based on infrared light and visible light image fusion to obtain a defogged image; the defogging model based on infrared light and visible light image fusion carries out the following processing on an input picture: processing an infrared light image, enhancing the penetration effect of the infrared light image in a fog environment, and highlighting the contour information of a target; and processing the visible light image, and recovering the color and texture details of the visible light image in the fog environment. According to the method, the definition and the visual effect of the foggy day image can be remarkably improved, the target is prominent, the color is natural, the contrast ratio is effectively improved, the detail information of the image is reserved, rapid defogging processing is realized, and the accuracy of fog concentration estimation is enhanced.

The invention provides a Transform and CNN fused crack detection and structure evaluation system and method. The system comprises an image preprocessing module, a crack feature extraction module, a crack positioning and classification module, a crack boundary refinement module and a structure integrity evaluation module. Through combination of the CNN and the Vision Transform, local and global features in the image can be extracted at the same time, and the precision of crack detection is enhanced. The dynamic attention mechanism is used for refining fracture boundaries and improving fracture positioning and recognition effects. And the structure health assessment module combines crack information and structure stress analysis, performs structure risk assessment by using a support vector machine or a random forest, and outputs a structure health state and a repair suggestion. The invention further provides an evaluation scheme of the system. The method improves the precision and robustness of crack detection, has higher multi-scale detection capability, noise robustness and real-time performance, and is suitable for automatic monitoring and health management of civil infrastructures.

The invention belongs to the field of computer vision and industrial defect detection, and discloses a steel coil end face defect detection method and system based on CBAM-BiFPN and multi-loss optimization, and the method comprises the steps: image preprocessing and region extraction: extracting a steel coil end face region through OpenCV and other image processing methods; dividing the high-resolution image into a plurality of small blocks and performing data enhancement; the method comprises the following steps: constructing a YOLOv11 network based on CBAM-BiFPN, introducing a CBAM attention mechanism and BiFPN feature fusion structure, and constructing an improved YOLOv11 detection network; multi-loss function joint optimization training is carried out, and model training is carried out in combination with loss functions such as Focal Loss and WIoU; and fusion of detection results and defect reconstruction output: reconstructing original image defects of all tile image detection results in a space coordinate mapping and redundant region fusion mode, and realizing high-precision overall detection output. Compared with a traditional method, the method still has the high recognition capability in a complex background and low-contrast scene, the detection precision and stability are obviously improved, and higher industrial adaptability and practical value are achieved.

The invention belongs to the technical field of machine vision and precision measurement, and particularly discloses a non-contact part arc parameter high-precision measurement method and system, and the method comprises the following steps: S1, constructing a three-dimensional measurement mobile platform; s2, establishing a dynamic scanning reference; s3, performing spatial pose optimization based on the three-dimensional morphology characteristic parameters of the part, and driving a moving guide rail to dynamically scan the surface of the part to obtain a three-dimensional point cloud image of the part; s4, cutting and screening the point cloud image to obtain an arc region point cloud image; s5, preprocessing the point cloud image of the arc region; s6, carrying out dimension reduction processing; s7, outputting a circle center position vector and a radius calibration value to determine a corresponding circle center coordinate and a radius parameter; s8, constructing a radial deviation evaluation function; and S9, judging that the measured part meets the standard. The non-contact part arc parameter high-precision measurement method and the non-contact part arc parameter high-precision measurement system have the advantages of high precision, high efficiency, automation and the like, and are suitable for arc parameter detection of complex curved surface parts.

The invention discloses a device and a method for intelligently investigating types and quantity of fishes. The device comprises a self-adaptive sonar detection module, a three-dimensional image acquisition module, an image preprocessing module, a fish detection module, a main body segmentation module and an identification and classification module. The sonar module is integrated with a three-frequency-band transducer, can dynamically switch frequency according to fish school depth, and realizes target tracking counting and quantity estimation in combination with an algorithm; the image acquisition module constructs a fish school three-dimensional image model by combining a depth camera with a light attenuation compensation algorithm; the preprocessing module fuses acoustic and optical features and expands a data set; the detection and segmentation module is used for accurately positioning fishes and removing impurities; the identification and classification module realizes type identification based on transfer learning and supports incremental learning of new fingerlings. According to the method, through cooperation of multiple modules, the problems of insufficient detection precision, poor image quality and the like in traditional investigation are solved, efficient and accurate investigation of fish species and quantity is realized, and technical support is provided for fishery resource management and ecological protection.

The invention discloses a two-stage infrared and visible light image registration method, system and device. The method comprises the following steps of image preprocessing, contour extraction, angular point detection, feature description and matching, affine transformation estimation, multi-scale optical flow estimation, optical flow constraint and loss function, reverse resampling and fusion and error evaluation. According to the invention, rough registration is carried out by using contour angular point features, so that a preliminary alignment result can be quickly obtained; refined alignment is carried out in combination with an unsupervised optical flow network, and sub-pixel-level registration precision is achieved. The contour angular points are based on shape information of an image target, are natural and are not influenced by spectral differences, and the matching stability is enhanced through main direction and angle features. The unsupervised depth optical flow model estimates a pixel displacement field by learning consistency characteristics of an input image, and does not need to depend on annotation data. The combination can effectively eliminate the difference between infrared light and visible light, and improves the robustness and adaptability of registration.

The invention relates to a crack tracking and predicting method based on point cloud registration, belongs to the technical field of image processing, and solves the problem that an existing three-dimensional point cloud model is low in precision and lacks automatic crack tracking capability. The method comprises the following steps: after preprocessing a plurality of images regularly acquired by an unmanned aerial vehicle, identifying the plurality of images by using a deep learning model to obtain crack positions, and then extracting crack features; performing dynamic blocking and differential feature extraction and matching on the plurality of images, and constructing a point cloud model; mapping the crack position and the crack feature obtained in each period to a point cloud model constructed in a corresponding period, and storing the point cloud models in a point cloud model library according to a time sequence; the point cloud models of every two adjacent periods are registered, and a change area of crack characteristics is analyzed; and predicting the change trend of the crack in the future time according to the environmental data of the time sequence, the crack characteristics and the crack characteristic change rate. And improvement of the accuracy of the point cloud model and automatic tracking of the crack are realized.

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention provides an L1-L4 lumbar vertebra segment-oriented multi-feature fusion diagnosis system and method, and the system comprises an image preprocessing module which is used for receiving a lumbar vertebra CT image sequence of a patient; a centrum anatomy partition module; the multi-dimensional image feature extraction module is used for extracting four types of quantitative features from each sub-region; the clinical multi-modal data coding module is used for independently acquiring and processing three types of clinical data: a multi-modal graph attention fusion network; and the segment-level diagnosis output module outputs diagnosis results of three levels. Through a parallel processing architecture and an optimized feature extraction algorithm, the whole diagnosis process only needs 45 seconds from data input to report generation, time is saved compared with manual film reading, and the consistency of diagnosis results is remarkably improved.

The invention provides a YOLO and Transform fused nori cultivation scene detection method, belongs to the technical field of image recognition and intelligent agriculture, and solves the technical problems of low efficiency and insufficient precision of traditional nori cultivation monitoring. According to the technical scheme, the method comprises the following steps: S1, collecting and preprocessing a laver cultivation image, and constructing a labeling data set containing laver, inserted links and raft net targets; s2, a detection model fusing a YOLO module and a Transform module is constructed; s3, based on the training data set, using an optimization algorithm to iteratively train model parameters until the performance reaches the standard; and S4, pre-processing a to-be-detected image, inputting the pre-processed to-be-detected image into the trained model for detection, and filtering out an overlapping frame through a non-maximum suppression algorithm to obtain a final result. According to the invention, key targets such as laver, inserted links and raft nets can be accurately monitored, and the monitoring automation level and accuracy are improved.

The invention discloses a multi-modal large model-based seal detection and identification method and system, and the method comprises the steps: carrying out the size standardization processing of obtained document image data through an image preprocessing module, and generating a standardized image which accords with the input specification of a multi-modal large model; the standardized image and the structured recognition instruction are combined and then input into a multi-modal large model subjected to fine tuning training for end-to-end reasoning, and a recognition result character string conforming to the JSON format specification is output; and analyzing the recognition result character string to extract the bounding box coordinate, the type label, the text content and the text recognition confidence coefficient of each seal object, performing hierarchical decision processing based on the text recognition confidence coefficient, and finally outputting a structured recognition result. According to the invention, the accuracy and the automation level of seal identification are obviously improved, the normalization and the reliability of an output result are ensured, and the problems of low seal identification precision and non-standardized output in a complex scene are effectively solved.

The invention discloses an occlusion perception 3DGS and neural SDF semantic level hybrid reconstruction method, which comprises the following steps: image preprocessing: initially obtaining an image video frame through a camera, starting from image preprocessing, processing an input image, including semantic segmentation, depth estimation, normal estimation and internal and external parameter calibration, a corresponding semantic graph, a depth graph, a normal graph and camera parameters are generated; aligning the 3DGS with the neural SDF, after preprocessing is completed, performing spatial alignment on geometric information generated based on the 3DGS and a neural symbol distance function, and performing restoration in combination with regional densification and a hierarchical depth map; and finishing the final three-dimensional object modeling and rapid reconstruction by adopting a Marking Cubes algorithm. According to the occlusion perception 3DGS and neural SDF semantic level hybrid reconstruction method provided by the invention, each object in a scene can be quickly reconstructed with high quality, and applications such as downstream editing tasks, interaction and the like are supported at the same time.

The invention discloses a PDF (Portable Document Format) drawing recognition and information structured extraction method. The method comprises the following steps: generating a high-resolution bitmap through image preprocessing; positioning and classifying a text region, a table region and a symbol region in the drawing based on a target detection model of transfer learning; hough transform is combined with SIFT feature matching to identify engineering symbols, and sub-pixel positioning is realized through an RANSAC algorithm; after the oblique text is corrected through affine transformation, the content is extracted through OCR; reconstructing a table structure based on OPTICS clustering and projection analysis; constructing an RDF knowledge graph according to a coordinate association rule; and using U-Net difference to detect and position an omission area and complementing the omission area. According to the method, deep learning and image processing technologies are fused, the problems of low rotating text recognition rate, table structure loss and semantic association deficiency in a traditional method are solved, through lightweight model compression and TensorRT acceleration, the analysis accuracy is remarkably superior to that of the traditional method, and the method can be widely applied to the fields of constructional engineering, petrochemical engineering and the like and has wide application prospects. And the drawing information processing efficiency and the data integrity are improved.

The invention relates to the field of computer vision and image processing, and discloses a remote controller liquid crystal screen image detection method based on computer vision, which comprises the following steps: S1, acquiring an original image containing a remote controller, and carrying out gray level conversion and filtering denoising processing on the original image to obtain a preprocessed image; s2, calculating the local information entropy and the change rate of the image through a sliding window under multiple scales based on the preprocessed image, generating a multi-scale entropy gradient heat map, and screening out a candidate region with high entropy difference as a liquid crystal display region according to a set threshold value; and S3, carrying out vectorization partitioning on the candidate region, and constructing an image matrix. According to the invention, by introducing an image preprocessing mode of low-rank sparse decomposition and normalization processing, the expression ability of structural information in the character image is effectively enhanced, and the feature extraction accuracy under the conditions of complex background, low character contrast and the like is improved.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.

The invention is suitable for the technical field of hydrographic survey, and provides a river and channel water flow velocity measurement method based on video recognition and CFD simulation, and the method comprises the steps: S1, completing data collection and constructing a data set, S2, building a three-dimensional water flow model of a target river or channel, S3, generating a vertical flow velocity distribution model, S4, carrying out the image preprocessing of an obtained video stream, and S5, carrying out the calculation of a vertical flow velocity distribution model. S5, performing target detection on the preprocessed image and calculating an area of the flow velocity, S6, performing optical flow calculation on the extracted specific area and calculating the surface velocity, S7, establishing a distribution relationship between the surface velocity and the vertical velocity, and S8, calculating the real flow velocity of the river channel according to the surface velocity and the vertical velocity distribution. According to the method, the relation between the cross section vertical flow velocity obtained through simulation and the surface flow velocity obtained through calculation of the optical flow method is established, the real flow velocity is finally obtained, multi-source fusion of data is achieved, and the calculation accuracy and the flow velocity measurement precision are remarkably improved.

The invention discloses a financial robot invoice element identification method based on semantic extraction, and the method comprises the following steps: S1, obtaining an original invoice image, and carrying out the image preprocessing; s2, executing optical character recognition operation, and extracting invoice text information; s3, inputting a semantic potential model; s4, constructing a semantic kernel vector set according to preset invoice element categories; s5, generating a potential tensor field based on the semantic kernel vector set; s6, performing iterative semantic migration operation on the character units in the potential tensor field to form a semantic clustering region; s7, calculating a comprehensive confidence score, and outputting an invoice element recognition result; and S8, performing field legality verification on the invoice element identification result, and submitting the invoice element identification result to a financial robot system after verification is passed to drive related business processes. According to the method, semantic potential modeling and context coding technologies are fused, invoice elements are accurately extracted, and the method has the advantages of being clear in structure, high in robustness and high in adaptability.

The invention discloses a low-illumination image enhancement method suitable for a complex night operation scene. The method comprises the following steps: preprocessing a collected low-illumination RGB image to obtain a preprocessed image; multi-scale features are extracted from the shallow convolution projection and the multilayer axial converter unit, and cross-layer attention is used for weighted aggregation to form enhanced features; forward noise addition is executed according to noise scheduling to obtain potential representation representing low-illumination noise distribution, and deep semantic representation is extracted in combination with a U-Net encoder embedded in CBAM; in the decoding stage, details are reconstructed through jump connection and a multi-layer converter, meanwhile, reverse denoising of a diffusion model is introduced to gradually remove noise, and an enhanced image with high brightness and low noise is output; in the training process, bidirectional mapping from low illumination to normal illumination is constructed, a weighted target of self-encoding loss and enhanced loss is introduced to carry out joint optimization on a converter and diffusion parameters, and a low-illumination scene is monitored on a general GPU / edge device.