2621 results about "Color image" patented technology

Disclosed is a suppression method for a multipath signal of an image mode based on correlation peaks of a satellite baseband signal, including the steps of: constructing a real-world direct-multipath two-dimensional color image mode data set; and building a multipath suppression model of a deep learning network based on a long-short term memory (LSTM) and a self-attention mechanism module, and training the model. In the present disclosure, a satellite signal can be quickly captured without losing the sensitivity of the captured signal, and a Beidou satellite baseband signal can be captured from a signal recorded in the real world, which enables the model to learn complex signal patterns, improving the accuracy and robustness of multipath signal suppression in urban complex scenes.

The invention discloses a panchromatic sharpening method based on multi-resolution panchromatic feature guidance, which comprises the following steps: firstly, designing a multi-resolution feature extraction network based on a spatial frequency Transform module, and respectively extracting multi-scale features from panchromatic and up-sampled multispectral images; secondly, in order to restrain and optimize the extracted detail features, a multi-head self-attention-based texture injection module is adopted to extract a dependency relationship between panchromatic and multispectral features, and more similar detail features are guided to be injected; thirdly, in order to solve information loss caused by multispectral image up-sampling, multi-level detail features are injected into a multispectral reconstruction network, the detail features are learned step by step and reconstructed to a panchromatic image size, and a spectrum fusion module based on adaptive channel attention is utilized to reconstruct a fused image in a high-quality mode; and finally, optimizing the model performance by combining reconstruction loss based on L1 norm, structural similarity constraint and transmission perception loss in training. According to the method, the spectral fidelity and the spatial resolution of the fused image are remarkably improved.

The invention relates to the technical field of multi-target tracking, in particular to a dynamic multi-target tracking and trajectory prediction method. Comprising the following steps: acquiring a color image and a depth image, and preprocessing; constructing a dynamic threshold double-flow neural network, extracting features from the preprocessed color image and depth image to obtain a visual feature set and a depth feature set, fusing the visual feature set and the depth feature set through an adaptive attention mechanism to obtain a fused feature set, and combining a dynamic threshold to obtain an observation information list of a target; updating the observation information list through a self-motion decoupling mechanism to obtain a real information list; establishing space association and time association between the target and the track based on the real information list to obtain a newest track set; and analyzing target characteristics, environmental constraints, social behaviors and historical trajectories, generating a prediction trajectory of each target and a corresponding multi-factor score, generating a time-varying influence graph, and performing planning and decision making by adopting a double-layer decision-making mechanism.

The invention provides an aluminum alloy surface oxidation spot defect identification method and device based on machine vision, and relates to the field of intelligent manufacturing and industrial automation, and the method comprises the steps: obtaining an aluminum alloy surface color image, and carrying out the preprocessing of the image, so as to extract a brightness component image; self-adaptive threshold segmentation of local contrast enhancement is carried out on the brightness component image, a defect area binary mask is generated, morphological connected domains are extracted according to the mask, and three basic feature indexes of the area pixel value, the contour Fourier descriptor complexity and the area gray scale standard deviation contrast of each connected domain are calculated; and extracting and marking a connected domain boundary, verifying a boundary closed topological structure, and dynamically generating a curvature-driven self-adaptive sampling point through multi-scale B-spline curvature extreme value detection. Through optical-algorithm-process three-level collaborative innovation, the curved surface reflection false alarm rate is reduced, the pinhole detection rate is increased, and the boundary precision is + / -0.2 pixel.

The invention discloses a three-dimensional scene reconstruction method and device based on structure perception. The method comprises the following steps: constructing a sparse point cloud generation model according to a diffusion model based on a Unet architecture; inputting the information into a sparse point cloud generation model, and generating camera internal and external parameters and sparse point cloud data of the RGB image frame under the corresponding view angle; constructing a rough three-dimensional structure grid according to the sparse point cloud data; carrying out random sampling on the rough three-dimensional structure grid, and carrying out Gaussian field initialization on the Gaussian primitives according to a random sampling result to obtain an initialized Gaussian field; the loss between the current image frame and the RGB color image is calculated, parameters of Gaussian primitives of all the RGB image frames in the information are iteratively optimized in sequence according to the loss, all the optimized Gaussian primitives are combined, and a reconstructed three-dimensional scene is obtained. According to the invention, the spatial consistency and accuracy of reconstruction results are improved.

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 belongs to the technical field of image analysis, and discloses an old people health state prediction and alarm method based on AI image analysis, and the method comprises the steps: obtaining a depth image, a color image and a pressure image of an activity environment of old people, carrying out the combined analysis of the depth image and the pressure image, recognizing a terrain semantic region in a monitoring region, and carrying out the prediction of the health state of the old people; and carrying out dynamic segmentation on the gait contour of the old people. And the terrain gait coupling degree is calculated by analyzing the depth change characteristics and the motion period characteristics of the gait sub-regions, so that the abnormal gait sub-regions are screened out. And for the abnormal regions, the terrain gait coupling degree, the dynamic texture features of the color image and the pressure distribution features of the pressure image are fused, and a cross-modal health risk vector is constructed. Based on the vector, the health state of the old people is accurately predicted, and a corresponding alarm signal is generated. According to the invention, precise evaluation and timely early warning of the health state of the old people are realized, and intelligent guarantee is provided for the home safety of the old people.

A scene flow estimation method includes: inputting a frame pair into an artificial intelligence (AI) network, and obtaining therefrom a motion embedding feature and a non-occluded-category label embedding feature corresponding to a target pixel in the frame pair; and estimating a scene flow corresponding to the frame pair based on the motion embedding feature and the non-occluded-category label embedding feature, the frame pair includes a first frame and a second frame, the first frame including a first color image and a first depth image and the second frame including a second color image and a second depth image, the non-occluded-category label embedding feature includes category information of an object corresponding to a pixel pair in the frame pair, the pixel pair includes a first pixel of the first frame and a second pixel of the second frame, and the second pixel corresponds to the first pixel.

The invention provides an ocean near-surface sub-mesoscale process flow field inversion method and device. The method provided by the invention comprises the following steps: screening an ocean near-surface sub-mesoscale representative sea area; obtaining the structure image information of the frontal surface and the filament of the phytoplankton in the marine sub-mesoscale process; acquiring displacement velocity field information at the same position; the structure image information and the displacement velocity field information are matched, and a first training sample set is constructed; correcting the first training sample set based on the second data set and the third data set to obtain a second training sample set; training the initial model by using the second training sample set to obtain a flow field inversion model; obtaining a plurality of sub-mesoscale sea surface water color images of the sea area to be detected; and processing the sub-mesoscale sea surface water color image by using the flow field inversion model to obtain a displacement velocity field. The invention provides an ocean near-surface sub-mesoscale process flow field inversion method and device. The method and device are used for solving the problem that a current ocean flow field inversion algorithm is not high in sub-mesoscale process inversion precision.

The invention discloses a mechanical arm autonomous grabbing system based on a visual language model and a control method of the mechanical arm autonomous grabbing system. The method comprises the steps that firstly, a mechanical arm is initialized, and a color image and a depth image of a working interval are collected; secondly, based on the color image and the natural language description, generating a target object bounding box and confidence, and judging whether a target object exists in the scene or not; and when the target object does not exist, instance segmentation and capture feasibility detection are carried out on the visible object in the current scene. And then the depth image and the object binary mask passing through grabbing feasibility detection are sent to an interaction object selection strategy and an action selection strategy to obtain an object to be interacted and a pushing action direction. And finally, center coordinates of the to-be-interacted object and center coordinates of the mechanical arm are obtained, the obstacle is moved in the pushing action direction until the target object appears and can be grabbed, and the mechanical arm grabs the target object and places the target object at a designated position. According to the method, the object specified by the human natural language can be accurately recognized and grabbed.

The invention belongs to the technical field of robotics and computer vision, and relates to a multi-source information fusion outdoor inspection scene three-dimensional color geometric model reconstruction method, which comprises the following steps: (1) inertial measurement unit IMU internal reference calibration initialization, (2) laser radar and inertial measurement unit IMU combined external reference calibration, (3) state estimation, (4) using an i-Octree structure to store an updated map, and (5) performing multi-source information fusion. (5) carrying out loopback detection to solve the mileage drift problem; (6) carrying out data fusion of the laser radar and the color camera; and (7) carrying out 3D global positioning. The method has the advantages that the information of the laser radar and the information of the color camera are fused, the one-to-one mapping relation between each space point in the three-dimensional point cloud and a certain pixel point in the color image is established, visual information is more completely described, and the visual information is more accurately and accurately positioned by using a 3D global positioning method. And high-precision reconstruction of the three-dimensional color geometric model is realized.

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention discloses a tunnel detection method based on a color image, and relates to the technical field of tunnel detection, and the method comprises the following steps: obtaining color image data in a tunnel, and carrying out the illumination condition calibration of the image data, so as to recognize the illumination change caused by natural light, an artificial light source and a light source fault. According to the method, through illumination calibration, adaptive color correction and dynamic contrast enhancement technologies, the problems of image color distortion and detail loss under the complex illumination condition of the tunnel are solved, accurate extraction of color features and clear presentation of details of each region are ensured, and the robustness and precision of defect detection are remarkably improved. In combination with edge detection, color feature extraction, shape analysis and a machine learning algorithm, automatic application of a multi-modal identification technology is realized, misjudgment areas are automatically filtered, a defect detection report with high confidence is generated, and the defect detection report comprises crack length, leakage area and deformation classification. Accurate and comprehensive data support is provided for tunnel safety assessment and maintenance decision.

The invention belongs to the technical field of three-dimensional reconstruction, and relates to an inspection scene multi-view three-dimensional reconstruction method based on adaptive feature enhancement, which comprises the following steps of: (1) constructing a depth map fusion framework based on laser radar and monocular camera self-calibration, obtaining an inspection scene color image and depth map data, the method comprises the following steps: (1) constructing a routing inspection scene multi-view three-dimensional reconstruction network based on adaptive feature enhancement, (3) carrying out transfer learning fine tuning on the routing inspection scene multi-view three-dimensional reconstruction network, and (4) dividing a routing inspection scene data set and configuring an experimental environment, and carrying out network model training and testing. According to the multi-view three-dimensional reconstruction network provided by the invention, the integrality index on a public data set is superior to that of other network models in a pre-training stage, and each evaluation index on an inspection scene data set is lower than values of a reference network and before fine adjustment in a transfer learning fine adjustment stage, so that the network is more suitable for inspection scene reconstruction through fine adjustment; and an ideal effect is achieved.

The invention relates to the technical field of foamed silicone rubber surface detection, and discloses a foamed silicone rubber surface detection method based on image visual identification, which comprises the following steps: acquiring a color image of a foamed silicone rubber surface, carrying out graying and normalization processing, and generating a diffuse reflection effective detection area by using a polarization filter difference technology; color abnormity is detected based on an image gridding and gray statistical method, meanwhile, pore and microbubble defects are detected in combination with minimum value seed point extraction and an eight-neighborhood region growing algorithm, finally, various masks are fused, the defect area and number are counted, and a quality inspection report is generated; the method can effectively inhibit highlight interference and accurately identify uneven colors and pore microbubbles, the detection process does not need external training, calculation is efficient, and the method is suitable for rapid detection and quality control of surface defects of foamed silicone rubber.

The invention discloses a remote sensing image fishpond extraction method based on an adaptive edge enhanced neural network. The method comprises the following steps: selecting a sentinel No.2 satellite multispectral band to synthesize a false color image; constructing a fishpond region labeling data set; establishing an adaptive edge enhanced neural network architecture, wherein the architecture fuses an edge detection module capable of learning a threshold and semantic feature extraction; an edge perception double attention module and a pyramid pooling module are integrated, and the multi-scale feature representation capability is enhanced; optimizing a training process by adopting a multi-level depth supervision and prediction fusion strategy; and finally, extracting a fishpond area through the training model and generating a standardized result. According to the method, the fishpond boundary segmentation precision is remarkably improved, the adaptability to fishponds with different scales is enhanced, the detection rate of small-scale fishponds is particularly improved, the model convergence process is accelerated through the depth supervision strategy, misclassification of similar water bodies such as rivers and ditches is effectively reduced, and the accuracy of fishpond boundary segmentation is improved. The technical problems that in the prior art, edges are fuzzy, multi-scale adaptability is poor, complex background interference is sensitive, and the omission ratio of a small-scale fishpond is high are solved.

The invention provides a depth map generation method and device based on a large model, a three-dimensional reconstruction method and device, electronic equipment and a storage medium, relates to the technical field of artificial intelligence, in particular to the technical fields of computer vision, deep learning, large models and the like, can be applied to real-time road scene depth perception, environment three-dimensional reconstruction and obstacle avoidance, and can be applied to real-time road scene depth perception. And virtual and real scene fusion and other scenes can be realized. The specific implementation scheme is as follows: performing visual coding on a monocular image to obtain a coded image; inputting the coded image and the target text into a pre-trained large language model for fusion to obtain fusion features; generating global guide features based on the fusion features, wherein the global guide features comprise joint semantic information of visual features and text features; adding noise to the color image of the monocular image to obtain a noise feature sequence; de-noising the noise feature sequence under the condition of the global guide feature, and generating an implicit feature matched with the joint semantic information; a depth map is generated based on the implicit features.

The invention discloses a thyroid intraoperative real-time navigation method and a thyroid intraoperative real-time navigation system based on multi-mode optical fusion. The thyroid intraoperative real-time navigation method comprises the following steps: outputting visible light through an endoscope and coupling light waves of a narrow-band multispectral light source to irradiate an operative field, exciting parathyroid glands to generate near-infrared fluorescence, and receiving reflected visible light, split light, near-infrared fluorescence and laser speckle signals through an endoscope probe. And separating the composite optical signal into four channels, and respectively generating an anatomical structure color image, a blood vessel spectroscopic image, a parathyroid gland near-infrared fluorescence image and a laser speckle image. Performing decorrelation processing on the laser speckle image to generate a blood flow dynamic pseudo-color decorrelation speckle image; an anatomical structure, a blood vessel center line, a parathyroid gland contour and blood flow dynamic feature points are extracted through a multi-modal registration technology, after affine transformation space alignment is conducted, a comprehensive imaging map containing the anatomical structure, blood vessel distribution and parathyroid gland function marking information is generated through a wavelet fusion algorithm, and intraoperative multi-dimensional real-time tissue navigation is achieved.

The invention discloses a multi-mode teenager idiopathic scoliosis screening method based on a back RGB-D image, and belongs to the technical field of image processing and scoliosis screening, and the method specifically comprises the steps: collecting an RGB-D image pair of a subject in an Adams anteflexion posture, carrying out the manual marking of an RGB color image, constructing and training a back key point detection model, and carrying out the detection of the back key point. And establishing a calculation rule of a maximum trunk rotation angle, a minimum trunk rotation angle and a back contour asymmetry index, and establishing an AIS classification rule based on the RGB-D data set, thereby completing AIS classification of the to-be-detected subject. According to the non-contact radiation-free AIS screening method, comprehensive utilization of back two-dimensional and three-dimensional information is achieved by means of data complementation of the RGB mode and the D mode, the problem of feature deficiency in a single mode is solved, and the method has the advantages of being fast, efficient, robust and objective.

The invention provides a commodity identification method and device, and the method comprises the steps: carrying out the multi-view collection and preprocessing, and constructing a second color image set and a depth image set; performing three-dimensional reconstruction and volume estimation based on the second color image set and the depth image set, and judging a commodity packaging form; extracting a commodity packaging semantic and price information area based on the second color image set, constructing a space-semantic joint code, and generating global packaging semantic features; based on the second color image set, a multi-view visual coding network is adopted, and a visual prediction SKU index is output; carrying out structure-semantic fusion modeling based on multi-source heterogeneous features, and outputting commodity category labels; and matching a commodity price based on the commodity category label, generating an interaction prompt sign in combination with a confidence threshold, and triggering interaction feedback. According to the invention, commodity vision, space and price activity information are fused, the identification accuracy and price verification intelligence are improved, and the method is suitable for the field of intelligent cashier and retail automation.

The invention relates to the technical field of computer vision and three-dimensional space modeling, and discloses a real-time multi-instance segmentation method and device based on a Gaussian splash radiation field model. The method comprises the following steps: based on a two-dimensional Gaussian splash radiation field model, rendering a visual angle with continuous spatial change to obtain an image sequence, and obtaining a multi-visual-angle consistent two-dimensional instance segmentation mask of the image sequence; and assigning an instance tag to each Gaussian primitive based on the two-dimensional instance segmentation mask. And for a two-dimensional Gaussian splash radiation field model with an instance label, acquiring a coarse instance segmentation mask and a color image at any view angle by using a Gaussian splash algorithm, inputting the mask and the image into a lightweight post-processing network for edge detection and region connectivity repair, and outputting a target two-dimensional instance segmentation mask with a complete structure and a label consistent with a three-dimensional scene. The method does not depend on any training or distillation process, directly acts on a Gaussian splash radiation field model, and has the advantages of high reasoning speed, high semantic consistency, support of multi-target continuous tracking and the like.

The invention provides a middle and primary school multi-person interactive teaching method and system based on a VR technology, and relates to the technical field of virtual reality, and the method comprises the steps: generating a model through deep learning based on pre-obtained teaching aid model data, determining subcomponents, combining a teaching aid generation engine, and constructing a virtual teaching aid; a color image and a depth image are obtained through a structured light depth camera, two-dimensional pixel coordinates are obtained, depth information is combined, a three-dimensional skeleton curve of limb movement of a student is generated, an optical motion capture result is obtained, an inertial motion capture result is obtained through inertial drift calibration based on the three-dimensional skeleton curve, and a limb movement three-dimensional track of the student is reconstructed; generating a force feedback signal by solving a kinetic equation through a collision detection algorithm based on the three-dimensional trajectory of the limb movement of the trainee; and rendering the virtualization model into a virtual teaching scene, synchronizing the limb movement three-dimensional track and the force feedback signal in real time, and generating a multi-person collaborative interaction process of the virtual teaching aid.

The embodiment of the invention belongs to the technology of display equipment, and provides liquid crystal display equipment based on field sequential display and a color image display method thereof.The liquid crystal display equipment comprises a liquid crystal display screen, a backlight module and a processor, the liquid crystal display screen is divided into a plurality of backlight partitions used for displaying images, and the backlight module is used for emitting backlight; the backlight is used for adjusting the backlight brightness of the display image corresponding to the four-field sequence. And the processor is used for acquiring RGBW image pixel data of the image in the backlight partition, and acquiring backlight brightness data corresponding to the RGBW color based on a preset backlight extraction algorithm according to the RGBW image pixel data. And the processor is also used for determining image compensation pixel data corresponding to the RGBW color based on a preset pixel compensation algorithm and the backlight brightness data corresponding to the RGBW color. And the liquid crystal display screen is used for displaying a color image according to the image compensation pixel data corresponding to the RGBW color. The power consumption of the display equipment is reduced, and the user experience is improved.

The invention belongs to the field of agricultural information perception, and relates to a plant leaf scab segmentation method and system based on RGB-D cross-modal fusion, and the method comprises the steps: collecting a color image and a depth image of a crop through a synchronous collection device; registering the color image and the depth image to obtain a registered color image and a corresponding registered depth image; constructing an initial segmentation network, and performing model training on the initial segmentation network; the initial segmentation network comprises a cross attention-based feature aggregation module and an attention-guided adaptive feature fusion module; inputting the registered color image and the registered depth image into a trained segmentation network for segmentation to obtain a segmentation result; through heterogeneous data fusion of a depth sensor and a visible light camera, a multi-dimensional feature system covering two-dimensional textures and three-dimensional deformation is constructed; in combination with a cross-modal feature complementation mechanism, the recognition robustness of weakly dominant diseases and insect pests is enhanced, and the segmentation precision in a complex illumination and branch and leaf shielding scene is significantly improved.

The invention relates to a stereoscopic warehouse checking method and system based on an unmanned aerial vehicle, and the method comprises the steps: receiving a cargo checking task, and obtaining the layout information of a stereoscopic warehouse; determining the position of a to-be-checked goods location in the goods checking task in the stereoscopic warehouse; according to the layout information of the stereoscopic warehouse and the position of the to-be-checked goods location in the stereoscopic warehouse, determining a safe flight area and a hovering point of the unmanned aerial vehicle based on a path planning algorithm, and generating a flight task; issuing a flight task to the unmanned aerial vehicle, and calculating pose information of the unmanned aerial vehicle during execution of the flight task in real time through an inertial measurement unit; based on the observation image of the unmanned aerial vehicle visual system, correcting the accumulated error of the pose information through a Kalman filtering algorithm; rGB color images and depth images collected by the unmanned aerial vehicle at the to-be-checked goods locations are obtained in real time; determining whether the box stack is in an abnormal condition according to the depth image; and for the box stack which is not in the abnormal working condition, the number of goods in the box stack is determined according to the RGB color image.

The invention discloses a warehouse in-out rechecking management system based on automatic visual scanning, which relates to the technical field of warehouse rechecking management, and comprises a pose acquisition unit, a visual calibration unit, a cargo reading unit, a record management unit, a warehouse-in rechecking unit and a warehouse-out rechecking unit, the pose acquisition unit is used for acquiring a color image and a depth image of a target cargo in a to-be-identified area so as to obtain pose information of the cargo, and the visual calibration unit is used for obtaining a relation matrix of a camera coordinate system and a mechanical arm coordinate system by calculating a rotation matrix and a translation matrix. And space coordinates in an image collected by the camera are converted into a mechanical arm coordinate system. The automatic visual scanning and mechanical arm technology are combined, the intelligent level in the warehouse management process can be greatly improved, the method is more suitable for actual complex warehouse scenes, the cargo warehouse-in and warehouse-out speed can be increased, and the cargo checking accuracy can also be improved.

The invention discloses a stone cultural relic disease identification method based on a hyperspectral image, and aims to solve the problem that stone cultural relic disease investigation depends on artificial experience and is time-consuming and labor-consuming. The method comprises the steps that a hyperspectral image of a stone cultural relic to be explored is collected and preprocessed; extracting spectral features; selecting a central wave band of three wave bands of blue light, green light and red light to construct a true color image; extracting texture features and color features of the true color image; fusing the spectral features, the texture features and the color features to obtain combined features; establishing a typical disease feature database; training a disease classification model by using a random forest, a support vector machine, a gradient elevator and a convolutional neural network; carrying out typical disease identification by using the intelligent identification model and drawing a disease cloud picture; and calculating the typical disease area proportion of the to-be-detected area to carry out disease evaluation. According to the invention, in-situ, lossless and rapid identification of stone cultural relic diseases is realized, manpower and material resources are saved, and an intelligent identification method is provided for cultural relic protection exploration work.

The invention relates to the field of 3D rendering, in particular to an intelligent scenic area three-dimensional image rendering method, which introduces a differentiable discrete decision into 3D fusion, supports end-to-end learning of a k value, performs discrete-continuous optimization based on an activation function, predicts an optimal k value of each voxel, introduces feature adaptive fusion based on dynamic neighborhood bidirectional retrieval, and realizes the 3D image rendering of the scenic area. The alignment of the color image and the point cloud is enhanced, the false detection rate of a small target is reduced, the detail reconstruction capability of a large target is improved, and the comprehensive rendering capability of a scenic spot is improved; according to the method, a lightweight grid is adopted to express a scenic spot subject, residual Gaussian is introduced to supplement high-frequency detail features, the number of Gaussian is reduced, rendering efficiency and capability are improved, textures are generated based on initial rendering back projection, fuzzy view angle dependence is avoided, hierarchical clustering and contour extraction from bottom to top are adopted on the basis, and the method is more efficient and efficient. The point cloud vertical structure change is dynamically detected, the point cloud is complemented, the accurate contour is extracted, the number of grid vertexes is reduced, and the rendering integrity is improved.

The invention relates to the technical field of automatic spraying, and particularly discloses a spraying system based on multi-modal vision and artificial intelligence self-correction, comprising a multi-modal vision acquisition module for acquiring color image information, depth information and infrared feature information; the control module is used for generating a spraying sensing model according to the information of the multi-modal visual acquisition module; the control module carries out spraying area identification, track planning and spraying parameter decision making based on the spraying sensing model; the spraying execution module is used for spraying; and the feedback self-correction module iterates the spraying parameter decision in the control module based on a reinforcement learning algorithm according to the difference between the actual coating state information and the expected state. The control method based on multi-modal vision and artificial intelligence self-correction is applied to a spraying system based on multi-modal vision and artificial intelligence self-correction. The scheme is used for solving the problems that the spraying quality of complex workpieces is not high due to the single sensing dimension of an existing spraying system, and the production flexibility is poor due to the rigid control mode.