366 results about "Single view" patented technology

The invention provides a tumor area double-view detection method and system based on image modalities and views, and relates to the field of medical image analys.The method comprises the steps that multi-modal image data are collected from the body of a patient, and image data of different view angles at the same position are obtained according to time frames; extracting multi-modal features from the image data of different modalities and different view angles, and performing weighted fusion on the extracted multi-modal features to form a fused feature map; inputting the fused feature map to train and predict the 3D-CNN network so as to identify and position a tumor region, and marking the tumor region by using a mask output by the segmentation model; calculating the pixel number of the tumor area in each layer of image according to the marked mask, and multiplying the pixel number by the volume of each pixel to obtain the volume of the tumor area; and the segmentation results of each layer of image are superposed, and a three-dimensional model of a tumor region is generated by using 3D reconstruction, so that the limitation of single-mode and single-view image data in tumor detection is overcome.

The invention relates to the technical field of three-dimensional modeling, and provides a satellite three-dimensional reconstruction method in a complex illumination environment, which comprises the following steps: acquiring images under different illumination conditions, and extracting image features by combining multi-view collaborative observation; performing feature point and multi-view data registration, generating a three-dimensional model and analyzing illumination change; performing system initialization by using the initially matched feature points, and establishing an initial three-dimensional point cloud model; sparse point cloud is recovered through multi-view stereoscopic vision, and motion recovery is carried out; a 2D diffusion model is used to complement the shielded area, and laser radar data is combined to carry out densification processing on the sparse point cloud; a three-dimensional grid model is generated, and texture mapping is carried out by a multi-view image; light compensation is adopted, a polarized light separation technology is used, and ambient light and specular reflection light are separated; according to the method, through multi-view-angle collaborative observation, the shielding problem under a single view angle is reduced, the 2D diffusion model is used for complementing the shielded area, and the completeness and precision of three-dimensional reconstruction are ensured.

The invention relates to the technical field of natural language processing and information retrieval, and discloses a deep dense document recall method based on multi-view vector fusion, comprising the following steps: S1, preprocessing user query and candidate documents to generate a standardized text; s2, respectively constructing multi-view semantic vector representation of the query and the document, wherein multiple views at least comprise a keyword view, a semantic extension view and an intention view; s3, calculating a similarity score between the query and the document for each semantic view, and fusing the scores of all the views through a dynamic weight; and S4, sorting the documents according to the fusion score, and returning a front Top-K result. By constructing multi-dimensional semantic representation of keywords, semantic extension and an intention perspective, multiple semantic information such as term accurate matching, context association and task target consistency is effectively fused. The dynamic weight distribution mechanism adaptively adjusts the contribution degree of each view angle according to the query content, and overcomes the defect of insufficient semantic coverage of a single view angle.

The invention discloses a pig single-view three-dimensional reconstruction method based on deep learning, and the method is called Magfup. In order to solve the problems that single-view three-dimensional reconstruction viewpoint learning is unstable and training is difficult due to lack of real three-dimensional data, an implicit-explicit combined three-dimensional representation method is adopted, high-dimensional data features subjected to super-resolution optimization are combined, a high-density pig three-dimensional grid can be reconstructed only through a single image, and the problem that training is difficult is solved. And data acquisition cost and equipment requirements are reduced. According to the method, the three-dimensional grid surface is optimized through the Laplacian smoothing method, and the reconstruction quality is improved. According to the invention, the model can learn the general shape and posture characteristics of the pig, predicts the three-dimensional posture, shape and texture information of the specific pig, and more accurately and comprehensively reflects the behavior characteristics of the pig. Therefore, the method can adapt to complex and changeable environments and pig behaviors in a pig farm, and a more comprehensive and accurate data basis is provided for pig behavior analysis.

The invention discloses a single-view large-scale outdoor scene three-dimensional reconstruction method based on three-dimensional Gaussian splashing, and the method comprises the steps: collecting a pseudo aerial image, and constructing a panoramic multi-mode supervision end-to-end single-view three-dimensional reconstruction model; meanwhile, panoramic consistency supervision, semantic constraint depth regularization and a radial weighted luminosity loss and Gaussian cutting mechanism are introduced, so that the defect of insufficient geometric constraint of traditional single-view three-dimensional reconstruction is effectively overcome, and high-efficiency and high-fidelity three-dimensional modeling of a large-scale outdoor scene under single image input is realized; the method is suitable for various actual scenes such as smart city construction, automatic driving simulation, virtual reality / augmented reality, digital twinning and the like.

The invention provides a single-view image three-dimensional modeling method and system based on comparative learning, and the method comprises the steps: starting from a single input image, generating a multi-view candidate image through a pre-training two-dimensional diffusion model in combination with score distillation sampling; performing positive and negative sample division on the candidate image pairs based on texture fidelity and structure coherence; constructing a perceptual contrast learning mechanism, calculating perceptual loss between the images, and optimizing texture consistency in a three-dimensional content generation process; proposing a number-aware triple loss function, and dynamically adjusting the contribution ratio of positive and negative samples in training; then, a super-resolution reconstruction module is embedded to perform detail enhancement on the image, and the quality of a contrast gradient signal is improved; and finally, performing joint training on the perception comparison loss and a neural radiation field or volume rendering module to generate a high-quality three-dimensional grid or volume rendering result. The problems that in an existing diffusion generation method, multi-view textures are inconsistent, structure reconstruction is not coherent, and the semantic distinguishing capacity is insufficient are effectively solved.

The invention discloses a structural deformation measurement method based on geometric correction of a multi-view panoramic camera. A method for measuring bridge displacement by using a single multi-view panorama camera is provided, multi-view segmentation is performed on a panorama image according to a panorama camera equivalent model and a cube projection method, and independent calibration and correction are performed to obtain a distorted image capable of covering a 360-degree full-field range; therefore, a single panoramic camera is equivalent to a vertical four-camera system without overlapped view fields, so that the reference target in any direction can be used for displacement correction. The invention further provides a camera disturbance elimination method based on a multi-view circle center array target and pose transformation matrix purification optimization, and a coordinate system transformation matrix after purification optimization is obtained through a high-precision multi-feature point tracking algorithm from camera single-view moving target pose measurement. And obtaining a multi-view geometric correction theoretical model of any view of the measurement target and the reference target in a 360-degree range by utilizing a multi-view equivalent camera fixed connection relationship.

The invention discloses a single-view three-dimensional dressed human body reconstruction method based on pixel and voxel feature fusion. The method comprises the steps of data set preprocessing, data set dividing, three-dimensional dressed human body reconstruction network construction, three-dimensional dressed human body reconstruction network training, three-dimensional dressed human body reconstruction network testing and reconstruction result displaying. In the pixel feature extraction network, the coordinate attention and the stacking hourglass network are combined, so that the pixel feature extraction precision is improved; in the voxel feature extraction network, a three-dimensional channel and space attention are introduced into the voxelization human body parameterization model network, and a multi-scale residual feature extraction module is combined to extract voxel features. And splicing the extracted pixels and voxel feature vectors, inputting the spliced pixels and voxel feature vectors into a multi-layer perceptron, and reconstructing a three-dimensional dressed human body model through Marking Cubes, thereby solving the technical problem of insufficient precision of the existing single-view three-dimensional dressed human body reconstruction method, and being applicable to the technical fields of artificial intelligence and computer vision.

Methods, devices, and processor-readable media for method for performing a 3D reconstruction from a single view image, comprising progressively denoising a randomly initialized set of 3D-gaussian representations with continuous guidance from an input image.

The invention discloses a rapid automatic focusing module and method suitable for a fluorescence microscope. The automatic focusing module comprises a laser, a diaphragm, a collimating lens group, a reflector, a dichroic mirror, an objective lens, a sample to be measured, a telescope lens group, a CCD2 detector, a control system and a displacement platform which are sequentially arranged along a focusing light path, and the sample to be measured, the objective lens, the dichroic mirror, a tube lens and the CCD2 detector which are sequentially arranged along an imaging light path; the collimated laser beam is emitted to the plane where the sample to be detected is located through the objective lens, and then the laser beam is reflected back to the telephoto lens group and converged to the CCD2 detector. According to the offset of the center of the reflected light beam, the relative distance between the objective lens and the to-be-tested sample is moved through a control system, and automatic focusing of a single view field of the to-be-tested sample is achieved; the to-be-measured sample is moved to the focal planes of different object view fields in the X direction and the Y direction and is quickly focused in real time, so that the large-size to-be-measured sample is always kept at the focal plane position, and the image quality and the working efficiency are improved.

The invention relates to the technical field of image conversion, and discloses a multi-view conversion method based on a multi-view consistency diffusion model, and the multi-view consistency diffusion model is trained through a unified multi-view-to-multi-view conversion framework based on the diffusion model. The conversion framework has a new self-supervised training strategy and a consistency and antagonism supervision strategy, and the strategies utilize an existing 2D single-view image converter and a 3D Gaussian splash technology to generate pseudo-truth values as supervision signals, so that view consistency and high-frequency details are enhanced; the multi-view consistency diffusion model trained by the conversion framework further comprises a potential multi-view consistency module, and the potential 3DGS module is used as a 3D representation of a bottom layer to promote information exchange across multiple views, so that 3D priori is introduced into the diffusion model to ensure view consistency. Finally, a joint optimization strategy is provided, and better balance between consistency and authenticity can be realized.

The invention relates to a robot environment perception method based on single-view three-dimensional scene generation, and the method comprises the steps: collecting a two-dimensional image containing target environment information through employing a monocular camera, generating multi-view information through combining depth estimation, normal prediction and a two-stage semantic guidance diffusion model, and constructing a high-quality three-dimensional scene. And reconstructing three-dimensional point cloud data through the neural radiation field, and performing texture rendering optimization on the point cloud data. A Point Net + + network is used for carrying out semantic analysis on point clouds, a multi-frame time sequence point cloud registration and Kalman filtering tracking method is introduced, modeling is carried out on a dynamic target, and a dynamic semantic map with a motion state is constructed. And finally, structured output environment information is used for robot navigation, path planning and task execution. The problems of high cost, high complexity and insufficient real-time performance and robustness of a three-dimensional scene generation technology in the field of robot environment perception are solved, and the method is high in structuring degree, standard and unified in output format and high in universality and engineering adaptation capacity.

The invention provides a three-dimensional model retrieval method based on multi-view aggregation, and the method comprises the steps: 1, designing a three-dimensional model retrieval network based on views, and enabling the three-dimensional model retrieval network to extract the features of a three-dimensional model from the view representation of the three-dimensional model for three-dimensional model retrieval; the three-dimensional model retrieval network MVAT comprises a single-view feature extraction network and a multi-view feature aggregation network, the single-view feature extraction network is responsible for independently extracting local features of a three-dimensional model from each view, and the multi-view feature aggregation network is responsible for generating global feature representation through information fusion of multiple views; 2, weight freezing and contrast learning technologies are introduced, and a three-stage training method is adopted to train the three-dimensional model retrieval network; and 3, performing performance evaluation on the trained three-dimensional model retrieval network on a test set. The three-dimensional model retrieval method has the advantages of being high in feature expression ability, efficient in training method, high in precision and good in generalization.

The invention discloses a cultural relic handicraft three-dimensional reconstruction method based on multiple views and deep learning, and relates to the technical field of cultural relic handicraft three-dimensional reconstruction methods, and the method comprises the steps: carrying out the image collection of cultural relic handicrafts through employing a multi-view shooting method, and obtaining a structured image data set; performing feature matching and camera parameter estimation on the obtained multi-view image set by adopting a sparse reconstruction method to obtain camera internal parameters, external parameters and sparse point cloud data; performing fine processing on the obtained sparse point cloud data by adopting a dense reconstruction method to obtain a 3D dense point cloud model with a texture map; analyzing the obtained 3D dense point cloud model by adopting a depth map generation method to obtain a depth map and a rendering image corresponding to each image; image acquisition is performed on the cultural relic handicraft by adopting a multi-view shooting method, so that information such as geometric shapes and texture details of the cultural relic handicraft is comprehensively captured, and the problem of information loss caused by a single view angle is effectively solved.

The invention discloses a single-view human body three-dimensional reconstruction method based on Gaussian surface elements, and the method comprises the steps: collecting image data from a plurality of human body data sets, carrying out the preprocessing through employing a targeted preprocessing strategy according to the data characteristics corresponding to each image data, and obtaining a processed human body data set; constructing a human body 3D Gaussian attribute prediction model, wherein the human body 3D Gaussian attribute prediction model comprises a hierarchical feature extraction module, a feature fusion network and a 3D Gaussian function decoder; training the human body 3D Gaussian attribute prediction model based on the processed human body data set to obtain a trained human body 3D Gaussian attribute prediction model; and performing three-dimensional reconstruction on an input human body picture based on the trained human body 3D Gaussian attribute prediction model, and outputting a reconstructed human body three-dimensional image. According to the method, the manufacturing cost of the virtual digital human is greatly reduced, and the efficiency and accuracy of constructing the dynamic human body model are improved.

The invention provides a multi-view structure learning method based on multi-expert cooperation, and relates to the technical field of graph neural networks and multi-view learning, and the method comprises the steps: reconstructing initial multi-view data, and obtaining multi-view structure data; respectively training the single-view expert model and the shared expert model by utilizing a first loss function and a second loss function based on the multi-view structure data to obtain a trained single-view expert model and a trained shared expert model; constructing a collaborative decision model by using the trained single-view expert model and the trained shared expert model; training the collaborative decision model by using a third loss function to obtain a trained collaborative decision model; and analyzing the graph data by using the trained collaborative decision model to obtain a joint decision result, and completing learning of the multi-view structure. According to the method, the problems of large structural noise interference, isolated expert model information and insufficient node classification accuracy when an existing graph neural network processes multi-relation graph data are solved.

The invention discloses a single-view unknown object 6D pose estimation method based on segmentation and new view angle synthesis, and relates to the field of object pose estimation. The method comprises the following steps: designing a segmentation module and a new view angle synthesis module of a pose estimation network, and obtaining a segmentation result of a target object by using the segmentation module; generating a target object multi-view-angle embedding template by using a new view angle synthesis module; matching the embedding representation zquery'of the segmentation result with a multi-view embedding template of the target object to find out the embedding representation most similar to zquery ', and determining a preliminary rotation attitude estimation result R of the target object; and estimating the relative translation vector t of the target object, and combining the R to obtain the relative pose (R, t) of the target object. According to the method, robust 6D pose estimation of the unknown object in a complex visual scene can be realized only by taking a single reference image as input, and the pose estimation precision and robustness of the unknown object in the complex scene are improved while the real-time requirement is met.

The invention discloses a single view reconstruction and rendering method, which belongs to the field of view reconstruction and rendering, and comprises the following steps of: firstly, generating multi-view feature representation with strong geometric consistency from a single input image by introducing an image diffusion module of a cross attention mechanism; a point cloud reconstruction module with self-attention and cross-attention is utilized, and multi-view information is fused to reconstruct an accurate three-dimensional point cloud; and finally, constructing differentiable three-dimensional Gaussian representation based on the point cloud, rendering the differentiable three-dimensional Gaussian representation, and outputting a new view angle image, a normal map and a depth map. According to the system, a staged training strategy is adopted, and a composite loss function including multi-scale bidirectional consistency smooth loss and feature consistency loss is innovatively used for optimization. According to the method, the problems of low geometric accuracy, multi-view inconsistency, detail missing and the like in single-view reconstruction are effectively solved, and the method can be widely applied to the fields of virtual reality, digital twinning, cultural heritage digitization and the like.

The invention provides a layer focusing illustration single-view 3D reconstruction method, which comprises the following steps of: performing region segmentation on an original illustration single view in a data set through SAM to obtain a region text pair fine-tuning CLIP model comprising the original illustration single view and a layer channel of the original illustration single view; extracting an unnatural contour line, semantic information and layer information from each original illustration single view in the data set T; selecting a basic element model to initialize a 3D point cloud according to a role posture in a single view, segmenting the human body point cloud into different parts through point cloud segmentation, carrying out noise growth and color perturbation on corresponding point cloud regions according to position information of an unnatural contour line, respectively mapping the point cloud regions into a submerged space, and obtaining a final potential representation by adopting LDM; and finally, the final potential representation passes through a decoder to obtain a final 3D representation, and 3D assets are reconstructed. According to the invention, the corresponding 3D assets can be generated according to a given illustration single view.

The invention belongs to the technical field of image processing and deep-sea mining, and discloses a deep-sea polymetallic nodule single-view three-dimensional reconstruction method and system. Aiming at the problem that the real three-dimensional data of the underwater polymetallic nodule is difficult to acquire, a multi-view generation model is adopted as 2D prior, 3D Gaussian is optimized through fractional distillation sampling loss, any three-dimensional data training is not used, the problem of three-dimensional data acquisition is solved, and three-dimensional reconstruction of the polymetallic nodule is realized. In addition, the invention provides a method only through image fine tuning, function decoupling of a noise prediction module (Unet) and a condition control network (multi-view model) is realized based on DreamBooth, and multi-view generation stability can still be maintained through pre-training view angle parameters under empty condition input; and the problem of parameter acquisition of the underwater camera is avoided through a conditional blanking mechanism, so that the model can be finely adjusted only by depending on a single-view image, and the data acquisition cost is reduced.

The invention provides a multi-view image generation method and a multi-view image generation model training method. The multi-view-angle image generation method comprises the steps that a target single-view-angle image of a target object is obtained, the target single-view-angle image is input into a pre-trained multi-view-angle image generation model, m-view-angle images of the target object are output, the image of one view angle comprises n-domain images, m and n are integers larger than 1, and m is an integer larger than 1. The images of different domains of the target object under the same view angle have consistency in physical attributes. A single view angle can be expanded to multiple view angles, and richer visual information is provided; moreover, the physical attributes of the domain images in the multi-view image are consistent, so that vivid illumination and material performance can be ensured, and the details of the generated image are richer and more accurate.

The invention relates to the technical field of city data representation. The invention discloses an urban space general representation learning method and device based on multi-modal spatio-temporal data fusion, a terminal and a storage medium, which can improve the applicability of urban general representation and enable the urban general representation to be applicable to diversified urban analysis tasks. The method comprises the following steps: acquiring multiple modal spatio-temporal data of each space unit in a target city, and setting a corresponding view for each modal spatio-temporal data; on the basis of each modal spatio-temporal data of each space unit, generating a representation of each space unit in a single view under a view corresponding to each modal spatio-temporal data; generating a multi-view fusion representation corresponding to each space unit based on the representation of each space unit in a single view under the views corresponding to all modal spatio-temporal data, and forming a multi-view fusion representation matrix by the multi-view fusion representations corresponding to all the space units; and performing global aggregation processing on the multi-view fusion representation matrix to obtain urban general representation.

Disclosed are methods, devices, and computer-readable media for detecting lanes and objects in image frames of a monocular camera. In one embodiment, a method is disclosed comprising receiving a sample set of image frames; detecting a plurality of markers in the sample set of image frames using a convolutional neural network (CNN); fitting lines based on the plurality of markers; detecting a plurality of vanishing points based on the lines; identifying a best fitting horizon for the sample set of image frames via a RANSAC algorithm; computing an inverse perspective mapping (IPM) based on the best fitting horizon; and computing a lane width estimate based on the sample set of image frames using the IPM in a rectified view and the parallel line fitting.

The invention provides a cross-view-angle sight line estimation method based on feature decoupling and an attention mechanism, double-view-angle face images are collected through double cameras, a view angle blind area under a single view angle is effectively eliminated, and the ambiguity problem caused by insufficient single-view-angle information is relieved; face features are decoupled into personal appearance features, head posture features and sight line related features, and accurate separation of different types of features is achieved; the adaptive capacity of the model in diversified scenes is enhanced through a feature decoupling strategy, and it is ensured that the model still keeps high-performance under the conditions of different individuals and large-amplitude head posture changes; then, adaptive weighting processing is carried out on the three types of features obtained through decoupling by applying a cross attention mechanism, and supplement and enhancement of sight line information are realized through fusion of double-view-angle sight line features; meanwhile, the influence of personal factors and head movement is compensated by combining the processed appearance features and head posture features, so that the precision and robustness of sight line estimation are remarkably improved.

The invention discloses a multi-article binding recommendation method based on graph contrast learning, and relates to the technical field of e-commerce application. According to the method, through heterogeneous binding relation modeling, a user-article-binding ternary interaction graph is constructed, direct preference of a user to an article and indirect association of binding driving are jointly learned, and the problem of relation splitting caused by multi-graph splitting in AMCBR and MGCRL is solved; according to the method, light-weight projection head optimization is carried out, double-layer linear projection is adopted to replace a traditional multi-layer perceptron (MLP), the over-smoothing risk is reduced while the contrast characterization distinction degree is enhanced, and the redundancy problem caused by fusion or knowledge modules in MultiCBR and KEMB-Rec is solved; the method introduces a multi-objective loss function, combines main task recommendation loss, cross-view comparison loss and binding comparison loss, and collaboratively optimizes local and global embedding representation, is more comprehensive than EBRec single view loss, and effectively alleviates sparsity and over-smoothing problems.

The invention discloses a multi-view three-dimensional target detection method and system for an operation robot, and computer equipment, and the method achieves the fusion of multi-angle information through the installation of an image sensor and the simultaneous collection of multi-view images from different angles. The multi-view-angle image fusion technology can effectively compensate for a recognition blind area caused by shielding or complex object form under a single view angle, so that the accuracy of target detection is improved; the spatial feature extraction module can adaptively process differences of the target objects in size and form, has high robustness in detection of large-size and small-size target objects, and avoids the problem of insufficient sensitivity to different target sizes in other methods. The collected images are transmitted to the remote server in real time through the local area network for calculation processing, the bottleneck that local calculation resources are limited is avoided, and the efficiency of model reasoning and target detection is improved through the powerful remote calculation capacity.

The invention discloses a scene depth estimation method and device, image processing equipment and a storage medium, and belongs to the technical field of computer vision, and the scene depth estimation method comprises the steps: selecting a single image with shielding, reflection or perspective from a multi-view image set for depth fusion; the improved depth information is spread to other views in a re-projection mode, so that the time overhead is reduced while the reconstruction effect is improved; performing semantic division on the index image to obtain the weight of each semantic factor; the problem that feature points are difficult to match due to a special area is solved by introducing single-view depth estimation; fusing a single-view estimation result and a multi-view depth estimation result under the constraint of semantic information, and spreading the fused result to other view angles in a re-projection manner to obtain a consistent depth estimation result corresponding to the current scene; consistency estimation can be accurately carried out on the depth of each area in the scene.

The invention relates to the technical field of computer vision, in particular to a gymnastic training real-time feedback and error correction system based on image recognition and tracking, and the system comprises a data collection module which is used for obtaining a three-dimensional space coordinate system of a training field, and synchronously collecting the training actions of gymnastic athletes through a plurality of cameras, so as to obtain a multi-view real-time training video stream; and the image processing module is used for carrying out frame image decomposition on the multi-view real-time training video stream to obtain continuous multi-view action frame images. According to the invention, through the data acquisition module and the skeleton generation module, real-time three-dimensional attitude skeleton data under a three-dimensional space coordinate system of a training field can be obtained, so that the defects that the traditional video playback lacks space depth information and cannot truly restore the three-dimensional attitude are fundamentally overcome; meanwhile, the problems that the visual angle is single and motion details cannot be captured in all directions during visual observation of a coach are solved, and a complete three-dimensional space data basis is provided for subsequent accurate analysis.

The invention discloses a construction period landslide disaster advanced prediction and rapid networking monitoring method, and the method comprises the steps: collecting synthetic aperture radar data, digital elevation model data and satellite precision orbit data of a monitoring region, carrying out the data preprocessing, and obtaining a single-view complex data image set; constructing an interference image pair connection graph, performing interference processing, and generating a differential interference graph and a coherence coefficient graph; performing interferometric phase unwrapping processing on the differential interferogram and the coherence coefficient graph according to a minimum cost flow algorithm to obtain earth surface deformation information; ground control points are selected, and the surface accumulated deformation quantity and the surface deformation rate are obtained; interpreting and delineating a potential deformation area, establishing an interpretation mark, selecting a typical deformation area, and extracting a time sequence deformation data set; and analyzing the time sequence deformation data set according to the neural network model to obtain a deformation prediction result and a disaster risk assessment result. According to the invention, the monitoring accuracy and prediction capability of landslide disasters can be improved.

The invention discloses a multi-modal information fusion target detection method based on multiple visual angles, which comprises the following steps: taking front views and aerial views of a camera, a laser point cloud and a millimeter wave point cloud as input of a sensing system, designing a multi-scale fusion module to fuse front view and aerial view features of multiple sensors, fully extracting semantic and spatial information, and detecting the target based on the multi-modal information fusion. And a self-attention mechanism is used to adaptively distribute weight coefficients of front view and aerial view features. Compared with target detection under a single view angle, semantic information and spatial position information are fully extracted in a target detection task under multiple view angles. Therefore, the method effectively combines the features of the multi-sensor front view and the aerial view, and further improves the target detection precision in a complex road environment.