1628 results about "Real image" patented technology

The invention provides a scene reconstruction method based on delayed rendering and three-dimensional Gaussian. The method comprises the following steps: S1, generating initial three-dimensional point cloud data based on a multi-view image; s2, constructing a trainable structural body for three-dimensional Gaussian modeling; s3, normal initialization and residual optimization are carried out on the Gaussian ellipsoid primitives, depth consistency constraint is combined, and a differentiable and learnable normal reconstruction mechanism is realized, so that the geometric expression ability of illumination modeling is enhanced; s4, introducing a reflection training mechanism based on ambient light and a reflection direction, and generating a Gaussian attribute based on a visual angle; and S5, a final image is generated through a differentiable Gaussian sputtering rendering algorithm, and optimization is carried out through pixel loss of the final image and a real image. According to the method, the reality sense and geometric consistency of the Gaussian sputtering model under the complex illumination condition are remarkably improved, and the technical problems of unreal rendering effect, inaccurate surface normal estimation, weak propagation capability and the like of the existing three-dimensional Gaussian sputtering model under the complex illumination condition are solved.

The invention discloses a multi-target scene visual SLAM (Simultaneous Localization and Mapping) method fusing target semantics and Gaussian splashing. The method comprises the steps that input data are preprocessed, two-dimensional space masks and semantic information corresponding to the two-dimensional space masks are extracted, global semantic identifiers are distributed to the two-dimensional space masks corresponding to a background and each target, and a target semantic segmentation map is obtained; reading first frame RGBD data as a current frame, acquiring a target semantic segmentation map corresponding to the current frame, and initializing camera pose parameters; respectively establishing an initial target Gaussian splashing model for the background and each target; adopting a Gaussian splash algorithm to render all the target Gaussian splash models to a next frame, and generating an RGB rendering image, a depth rendering image and a target semantic rendering image of the next frame; and designing a loss function between the next frame of rendered image and the corresponding real image, optimizing the pose of the camera, and updating the parameters of the target Gaussian splash model. According to the invention, simultaneous positioning and map construction of multiple target scenes are realized.

The invention discloses a three-dimensional Gaussian sputtering method for sparse visual angle semantic priori. The method comprises the following steps of: 1, acquiring a target scene image, constructing a real image set as a data set, and manually selecting an interested object in a target scene to perform semantic three-dimensional reconstruction to obtain an initial semantic image of a sparse view angle; a multi-view image is collected, camera external parameters and scene sparse point clouds are obtained, a plurality of views are selected and input into the SAM2 segmentation model, and a view set Is with semantic images is obtained; 2, using a pre-trained SAM2 segmentation model as an interactive image sequence segmentation model, and initializing a three-dimensional Gaussian primitive according to the scene sparse point cloud; and step 3, training parameters of semantic three-dimensional Gaussian sputtering based on the trained three-dimensional Gaussian sputtering model and the interactive image sequence segmentation model, and reconstructing a target scene. According to the method, the high-quality semantic model is efficiently reconstructed. And the reconstruction result can be easily corrected through the interactive graphical interface in the training process.

The invention discloses a deep counterfeiting detection model training method, a deep counterfeiting detection method and a deep counterfeiting detection system, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining a training data set containing a real image and a plurality of counterfeit images, and enabling the image to be provided with a label for representing the authenticity; in the training process, the deep forgery detection model can be in contact with various types of image samples, so that wider and more complex image features and forgery modes can be learned, a forgery reason is further marked for a forgery image, and the deep forgery detection model can be helped to deeply understand essential features of forgery content in the training process. Therefore, the problem of insufficient detection capability for well-designed and high-quality counterfeited contents can be solved, and the technical effect of improving the accuracy of counterfeited detection is achieved.

The invention discloses a deep forgery detection method based on a visual language model, and relates to the field of image forensics. The deep forgery detection method based on the visual language model aims to combine multi-source information to improve the discrimination capability of the model on a real image and a generated image. The method comprises the following steps: firstly, extracting image features through an image encoder of a pre-trained CLIP model; meanwhile, a frequency domain enhanced counterfeit perception adapter is embedded in the image encoder to mine potential anomalies of counterfeit images in the image domain and the frequency domain. Secondly, a manual feature extraction module is provided, discriminative low-dimensional features are extracted from the four aspects of the edge, the texture, the frequency and the symmetry of the image, and the discriminative low-dimensional features are used as auxiliary information input in the forgery detection process, so that the robustness and the interpretability of the model are improved; meanwhile, the text cue words are converted into feature vectors through a text encoder of a pre-training CLIP model; and finally, the model predicts a forgery score by calculating the cosine similarity between the image features and the text features so as to realize the discrimination of the authenticity of the image. According to the method, the problem that the detection capability of the model on the cross-dataset is insufficient is effectively improved.

The invention provides a deep counterfeit detection method based on self-mixing, and compared with the prior art, the method generates a mixed image containing counterfeit artifacts by using a self-mixing technology, and carries out the precise detection and recognition of an artifact region of an input image through combining a backbone network OfficientNet-b3 and a counterfeit artifact detection module. The method comprises the following steps of: firstly, performing framing processing on a real image, generating a source image and a target image by utilizing a self-mixing technology, and generating a mixed image with counterfeit artifacts by combining a mask generator with an alpha blowing model; and then, the backbone network extracts global features from the mixed image, and the fake artifact detection module reinforces local feature expression of a mixed region through an artifact space cooperation attention module, so that an artifact region is accurately positioned. And finally, fusing global and local features by using residual connection, and transmitting the fused features to a full connection layer to complete classification. According to the method, various forged image types can be effectively detected, and excellent generalization ability is shown in cross-dataset and unknown scenes.

An AI-based method for visual inspection of parts manufactured on a shop floor includes acquiring a set of real images of nominal parts manufactured on the shop floor to create training datasets. A self-supervised pre-trainer module is used to pre-train a loss computation neural network in a self-supervised learning process using a first dataset on pretexts defined by real-world conditions pertaining to the shop floor. The first dataset is labeled by automatically extracting pretext-related information from image metadata. A main anomaly trainer module is used to train a main anomaly detection neural network to reconstruct a nominal part image from an input manufactured part image in an unsupervised learning process using a second dataset. The main anomaly training measures a perceptual loss between an input image and a reconstructed image by measuring a difference between feature representations thereof at one or more layers of the pre-trained loss computation neural network.

The invention discloses a rainy day target detection method based on paired image data generation and knowledge distillation, and belongs to the technical field of automatic driving target detection. The method comprises the following steps: acquiring a real image of a road scene and a semantic segmentation mask; inputting the semantic segmentation mask and the weather condition text information into a trained improved diffusion model to obtain a road scene generation image under a rainy day condition; introducing a CoT module and an ODConv module into the target detection model for optimization, and then improving the target detection model by adopting a dynamic weighted aggregation loss function to obtain an optimized target detection model; performing knowledge distillation to obtain a rainy day target detection enhancement model; and performing target detection by using the rainy day target detection enhancement model. The method provided by the invention is excellent in performance in a rainy day scene, has good generalization ability, can provide higher-precision target detection in various complex scenes, and effectively improves the detection accuracy and stability.

The invention relates to an introspection lifelong SLAM method and system based on visual language model hidden space representation, and the method comprises the steps: extracting a semantic tag based on an RGB-D image through a semantic encoder, and generating a scene map and a semantic topological graph based on the RGB-D image and the semantic tag; generating a dynamic mask based on the scene map, obtaining a dynamic mask coverage rate, and screening key frames with high static confidence values based on the coverage rate; calculating camera pose estimation corresponding to the key frame in real time, sampling the key frame to realize layering of the key frame, and performing layering rendering by using a NeRF model to obtain a virtual view; the hidden space difference degree of the virtual view and the corresponding real image is calculated, whether error introspection needs to be carried out or not is judged based on the hidden space difference degree, and the system is used for achieving the method. Compared with the prior art, the method has the advantages that open semantic reasoning of VLM, high-precision reconstruction of NeRF and real-time positioning of SLAM are combined, and positioning and mapping accuracy is improved.

The invention relates to a pig image BDE reconstruction system and method based on differentiatable image rate filtering, and belongs to the field of image processing, and the system comprises an image preprocessing module which is configured to process an original low-bit image; the neural network module acts on the processing and reconstruction tasks of low-bit pig images, the characteristics of each image are collaboratively optimized in a task dividing and conquering mode, and finally a high-quality image is obtained; the loss function module is configured to optimize the network model performance by minimizing the difference between the network output image and the real image; the training module is configured to optimize network model parameters and a learning process by adopting a staged training strategy; and the test and evaluation module is configured to evaluate the image quality through the peak signal-to-noise ratio and the structural similarity index and measure the reconstruction effect of the model. According to the method, a two-stage training strategy is adopted, an image reconstruction task is decomposed into two sub-tasks which cooperate with each other, and spatial resolution enhancement and bit resolution improvement are achieved.

A device receives a real image (e.g., photograph or video frame) that includes a human. The device creates a synthetic image corresponding to the real image. The synthetic image includes a synthetic environment and a humanoid shape that correspond to the human. The device predicts, using a trained viewpoint neural network and based on the synthetic image, a predicted viewpoint heatmap. The device predicts, using a trained pose neural network and based on the synthetic image, a predicted pose heatmap. The device provides, as input to a random synthetic environment, the predicted viewpoint heatmap and the predicted pose heatmap and creates a reconstructed three-dimensional pose based on the predicted viewpoint heatmap, the predicted pose heatmap, and the random synthetic environment. The device classifies the reconstructed three-dimensional pose as a particular type of pose of the human in the real image.

The invention discloses a flow field measurement method based on an event camera, and the method comprises the steps: generating a PIV data set, each time sequence sample sequence comprising a plurality of frames of continuous particle images, a corresponding velocity vector field, and particle event data at all moments; establishing a flow field data acquisition device based on an event camera and a high-speed camera, acquiring real event data and real image data which are synchronous in time so as to adjust parameters of an event simulator, and verifying and updating particle event data in the PIV data set according to the adjusted event simulator so as to obtain a flow field data acquisition result; obtaining the updated PIV data set as a training data set; building an event camera optical flow method model, and training by adopting the training data set; and on the basis of the trained event camera optical flow method model, event sequences in two adjacent time periods are used as inputs to calculate a velocity vector field corresponding to a middle moment. According to the invention, the flow field velocity field at the required moment can be obtained based on the event data within a period of time.

The invention discloses a high-precision three-dimensional scene and object reconstruction method, system, equipment and medium, belongs to three-dimensional scene reconstruction in the technical field of computer vision, and aims to solve the technical problem of low three-dimensional scene reconstruction quality in the prior art. The method comprises the steps of calculating external parameters of a camera, determining coordinates and colors of sampling points closest to the surface of a scene, updating and optimizing parameters and generating a scene geometric model and a rendering graph, the rendering graph is generated based on an SDF MLP network and a Color MLP network in combination with a Gaussian sputtering technology, and parameter optimization is carried out on Gaussian primitives, the SDF MLP network and the Color MLP network by utilizing the generated rendering graph and a real graph. And the Gaussian primitive after parameter optimization, the SDF MLP network and the Color MLP network are utilized to finally generate a high-precision scene geometric model and a new visual angle image with high fidelity rendering quality, so that the technical problem that geometric precision and rendering quality cannot be obtained at the same time in a three-dimensional reconstruction process is effectively solved, and the reconstruction quality of a three-dimensional scene is remarkably improved.

The invention belongs to an image processing technology, and particularly relates to a deep forgery detection method and system based on face embedded difference guidance, and the method comprises the steps: generating a forgery image according to an input real image, and calculating a difference image and a semantic difference vector of two images; learning the features of the real image and the forged image by using a first classifier, and learning the features of the real image and the difference image by using a second classifier; splicing and fusing the semantic difference vector and image features extracted from the difference image through a channel to obtain fused features, and learning the difference between the real face features and the fused features by using a third classifier; and calculating the distillation loss between the fusion feature and the image feature of the forged image, and training in combination with the loss of each classifier. According to the method, multi-level feature fusion, a three-classifier architecture and a knowledge distillation mechanism are adopted, the strong generalization ability across data sets is achieved, and the system can output a high-precision deep forgery judgment result.

The invention discloses a road and bridge settlement displacement monitoring system and method based on image detection, and the method comprises the steps: selecting a plurality of static background reference points in a stable background region of a monitoring scene, so as to construct a virtual and stable image internal reference system; furthermore, by accurately tracking image coordinate changes of the background reference points in the initial reference frame and the current frame, a transformation matrix capable of accurately describing disturbance of the camera from the initial pose to the current pose is reversely calculated, and the transformation matrix is applied to observation coordinates of a monitored target point; therefore, the virtual displacement component introduced by the camera disturbance is accurately stripped from the total displacement, and finally the real image displacement generated only by the motion of the structure is obtained. By means of the mode, the system can effectively resist interference of external factors such as environment vibration and temperature change, it is ensured that the height of the finally calculated physical displacement is close to the real settlement value of the structure, and therefore the accuracy and reliability of the monitoring result are greatly improved.

The invention relates to the technical field of image forgery detection, in particular to an image forgery detection method based on self-supervised contrast learning. According to the technical scheme, the method comprises the steps that an image to be detected is input into a trained image forgery detection model, and an image forgery detection result of the image to be detected is obtained; the image forgery detection model comprises a feature extractor which is pre-trained through self-supervised contrast learning and is used for extracting an image feature I of an image to be detected; the classifier is used for classifying the to-be-detected image into a real image or a forged image based on the image features I extracted by the feature extractor; the self-supervised contrast learning pre-training takes a real image set as a positive sample, and takes the real image set and a forged image set as negative samples. According to the method, feature distribution of a real image and a forged image is mined through self-supervised comparative learning, and the problem of generalization of a detection model is solved.

The invention discloses an image segmentation method for evaluating a hepatocellular carcinoma neutron therapy dose, and relates to the technical field of neutron therapy. A 3D U-Net GAN network model is trained through a medical image data set; a generator model and a discriminator model in a 3D U-Net GAN network model are alternately trained by using a real image and a false image randomly generated by the generator model, so that the trained generator model can generate a segmentation result which is more accurate and rich in details for a medical image; the trained discriminator model can more accurately evaluate the authenticity of the output of the generator model, and the trained network can accurately reflect the anatomical structure of the patient, the distribution condition of 10B in the body and the radionuclide dynamics condition when segmenting the medical image, so that the information in the medical image can be accurately and fully displayed, and the medical image segmentation efficiency can be improved. The application is convenient.

The invention belongs to the technical field of sea temperature complementation, and discloses a sea temperature complementation method and system based on an asynchronous diffusion Schrodinger bridge, and the method comprises the steps: firstly, generating a weight anomal which reflects the abnormal degree of a pixel through a preprocessing step S1, so as to guide a subsequent diffusion process; s2, establishing a bidirectional diffusion path between the initial complementation image and the real image based on a diffusion Schrodinger bridge theory, and dynamically adjusting a diffusion coefficient according to anomay to generate an intermediate state xt; predicting a score function pred through a U-Net network S3, and reconstructing a current image x0 for updating a state or calculating loss; model parameters are optimized through iteration during training, multi-round denoising reconstruction is carried out during inference, and finally a complete high-quality sea surface temperature image SSTrecon is output. According to the invention, local details are fully reserved, and the accuracy of image completion is improved.

The invention discloses an unsupervised domain adaptive semantic segmentation method, system and device based on inter-domain consistency and a medium. The method comprises the following steps: translating source domain image data to a target domain style while keeping the content by using a GAN-based multi-scale generative adversarial network; inputting the source domain image data into an inter-domain consistency training network based on a Transform model for adaptive training, wherein the source domain image data subjected to data enhancement is sent to a student network for source domain semantic segmentation training, and an EMA strategy is used for updating a teacher network weight; inputting the target domain label-free image data into a teacher network to generate a pseudo label so as to further guide students to train the network; randomly combining the source domain image data and the target domain image data by using a hybrid strategy to guide students to learn domain invariant knowledge in a network; and finally, adjusting and optimizing the student network according to the consistency of the attention feature map, outputting an unsupervised field adaptive semantic segmentation model, and completing segmentation of target domain image data under the condition of not using a target domain label. According to the method, a model with excellent performance can be trained by using fewer real image data sets in the unmanned driving field, so that the training time and cost are greatly reduced, and the working efficiency is improved.

The invention discloses a titanium alloy microscopic structure prediction method and system based on a conditional generative adversarial network and a storage medium, and belongs to the following steps: firstly, constructing a process-structure mapping model, and taking the output of the model as a rule constraint condition; inputting the random noise vector and the rule constraint condition into a conditional generative adversarial network to generate a prediction image; according to the generative adversarial network, thermal dynamic constraints based on physical quantities of microscopic structures are introduced in the training process, so that the interpretability of a prediction result is improved. And carrying out quantitative comparison on the predicted image and the real image, verifying the consistency of the statistical characteristics, and if the verification is passed, outputting a prediction result. According to the method, end-to-end prediction from process parameters to microscopic structure images is realized, the limitation that only symbolization or parameterization prediction can be carried out in a traditional method is broken through, and the intuition, the interpretability and the engineering application value of the method are remarkably enhanced.

Provided are systems and methods for controlling material attributes such as roughness, metallic, albedo, and transparency in real images. This method leverages the generative prior of text-to-image models known for their photorealistic capabilities, offering an alternative to traditional rendering pipelines. As one example, the technology can be used to alter the appearance of an object in an image, making it appear more metallic or changing its roughness to create a more matte or glossy finish. This can be particularly useful in various fields where the ability to manipulate the appearance of products in images can be a powerful tool.

The invention discloses a face forgery detection algorithm based on style-guided multi-view fusion processing. The method comprises the following steps: firstly, carrying out standardized preprocessing on a face video sample, and extracting multi-scale image features based on an OfficientNet-B4 backbone network; by constructing a local texture map, an attention enhancement map and a style vector sequence, precise modeling and discrimination of a forged area are realized. The algorithm further utilizes a multi-branch sequence convolutional network to carry out time sequence modeling on fusion features, and outputs global style change representation for classification of forged and real images. The method comprehensively fuses the spatial texture, the semantic style and the time feature, has the advantages of high detection precision, strong generalization ability, good robustness and the like, and is suitable for complex and diverse depth forgery detection tasks.

Devices, systems, and methods for image processing; wherein a method can include generating a synthetic image based on the 3D point set of a same geographical region as the 2D real image and performing a coarse registration to grossly register the synthetic image to the 2D real image, the coarse registration including determining, for a plurality of synthetic image tiles that span the synthetic and at each of a plurality of offsets in a search region, a correlation score resulting in a plurality of correlation score arrays, determining a weighted combination of scores in the correlation score arrays resulting in a combined correlation score array, identifying an offset of the plurality of offsets based on the combined correlation score array, and moving the synthetic image relative to the 2D real image by the identified offset.

The utility model discloses an out-of-focus protection desktop far-vision read-write device which comprises a plane spectroscope, a first concave reflector and a second concave reflector. Wherein the light reflected by the table top and an object placed on the table top is emitted to the plane spectroscope from the lower part and then is split; a first light ray formed by reflection of the plane spectroscope is reflected by the first concave reflector, then penetrates through the plane spectroscope and is emitted to an exit pupil position; a second light ray formed after penetrating through the plane spectroscope is reflected by the second concave reflector, is reflected by the plane spectroscope again and then is emitted to an exit pupil position; one of the first light and the second light emitted to the exit pupil position is imaged at a virtual image position not less than 3 meters in front of the exit pupil position, and the other one is imaged at a real image behind the exit pupil position to form positive defocus. According to the out-of-focus protection desktop far-vision read-write device, the coaxial optical system is used, an object placed on a desktop is amplified, projected and imaged, positive out-of-focus stimulation is provided, and myopia prevention and control are achieved.

The invention discloses a laser radar-camera fusion calibration method and system, and belongs to the field of calibration. According to the method, after a target is shot through a laser radar and a camera, multiple pairs of corresponding three-dimensional point clouds and two-dimensional images are obtained; angular point coordinates in the phase unwrapped image are obtained through the phase unwrapped image and the two-dimensional real image, three-dimensional angular point coordinates in the three-dimensional point cloud are further obtained, the two-dimensional angular point coordinates and the three-dimensional angular point coordinates are input into an external parameter calibration program of the laser radar and the camera, a rigid matrix between two sensor coordinate systems is obtained, and calibration is completed. According to the method, a fast inversion method of angular points on a phase unwrapped graph based on a Gaussian elimination method is designed, a calculation strategy of laser radar point cloud-phase unwrapped graph-angular point detection-angular point three-dimensional coordinates is provided, after three-dimensional point cloud and two-dimensional images of a target are collected, data can be imported into a calibration program for automatic calculation, and the accuracy of calibration is improved. The method has the advantages of less human intervention, high automation degree, fast operation and high efficiency.

The invention discloses a method for clearly imaging a mask under an optical objective, and relates to the technical field of optical detection and image processing, and the method comprises the following steps: S1, constructing a light intensity distribution model based on the boundary condition of the field of view of the optical objective in combination with the reflection characteristic and incident angle change rule of a metal edge material, and determining the coverage range of metal edge reflection crosstalk, generating a mask boundary interference prediction map; s2, according to the mask boundary interference prediction map, performing region division on the micro-reflectivity image, extracting brightness gradient characteristics of reflectivity lifting in an interference region, and generating a brightness gradient parameter set required by image filtering; the method is based on light intensity modeling, fusion direction filtering, pixel stripping, gradient reconstruction and credibility weighted fusion, realizes closed-loop control from interference prediction to pixel restoration, has high resolution and adaptivity, can accurately strip edge reflection artifacts and restore a real image structure, remarkably reduces misjudgment and rework rate, and is suitable for large-scale popularization and application. And the mask yield and the stability of the detection system are improved.

The invention discloses a peripheral visual area defocused long-distance imaging optical system, and the system comprises a defocus panel which is located right above an observed object, and comprises a flat plate region and a micro-lens array region; the flat plate area is used for directly transmitting light emitted by an observed object; the micro-lens array area is used for carrying out first imaging on the peripheral area of an observed object to form a real image; the objective lens is positioned right above the defocus panel, and is used for performing first imaging on the observed object to form a virtual image of the observed object, receiving a real image of a peripheral area of the observed object, and performing second imaging to form a virtual image of the peripheral area of the observed object; the eyepiece is located right in front of the human eyes and used for conducting secondary imaging on the observed object, forming an amplified long-distance virtual image in front of the human eyes, receiving a virtual image of the peripheral area of the observed object and forming a real image in front of the peripheral retina in the human eyes. And the purpose of preventing and controlling myopia is better achieved.

The invention discloses a vehicle-mounted VR back-row entertainment method, and relates to the field of VR, and the method comprises the steps: processing image data transmitted by a controller through a DCT plate negative reflection plate lens, and generating aerial imaging data; reflecting a virtual image corresponding to the aerial imaging data by using a plane mirror to generate real image data; performing voice recognition processing on the input voice of the user to obtain voice control instruction data; acquiring image data of user gestures through an infrared sensor, and performing image recognition to obtain gesture control instruction data; generating interaction control data according to the three-dimensional coordinates in the real image data, the voice control instruction and the gesture control instruction; aiming at low image imaging precision in a vehicle-mounted VR entertainment system in the prior art, high-quality aerial imaging is generated by using a DCT plate negative reflection plate lens and a plane mirror, multi-modal interaction and the like are realized by adopting voice recognition and gesture recognition, and a vivid imaging effect is provided.

The invention discloses a low-illumination image enhancement method based on wavelet-Fourier transform. Relates to the technical field of computer vision, in particular to the technical field of low-illumination image enhancement based on wavelet-Fourier transform. The advantages of the two frequency domain transformations are fully exerted, the problems of insufficient illumination adjustment and poor detail retention in the existing method are solved, and the utilization efficiency of the frequency domain information is improved. The method comprises the following steps: acquiring an image data set, wherein the image data set comprises a low-illumination image and a real image; constructing an image enhancement model: inputting the low-illumination image into the image enhancement model, carrying out iterative training, and when the number of iterative training times reaches 200, obtaining a trained image enhancement model; and inputting the low-illumination image into the trained image enhancement model to obtain an enhanced image. Compared with the prior art, the method can better restore image details, adjust image saturation, reduce noise and improve visual effect.

The invention provides a method and a system for generating deviation guide information in an approaching landing section of an aircraft and a readable storage medium, and the method comprises the steps: S1, real image collection and processing: capturing an outboard environment image through an airborne visual sensor, and extracting key feature points of a target landing airport runway; s2, virtual image generation: based on the airport three-dimensional high-precision map, generating an approach glide image under a visual angle of a virtual camera; s3, image feature registration: carrying out spatial alignment on the key feature points of the runway in the real image and the key feature points of the runway in the virtual image; the pixel deviation of the runway center point in the real image and the virtual image is calculated, glide guidance is provided for a pilot or is directly input into an automatic flight system.The method can be used for the aircraft in the approaching landing section, the enhanced outboard visual display is provided for the pilot in the cockpit based on the equivalent ILS deviation guidance method, and the visual guidance effect is improved. And providing approaching glide deviation guide information.