255 results about "Region selection" patented technology

The invention discloses a texture perception state space modeling method for an image restoration task, and the method comprises the steps: 1, constructing a region selection mechanism based on texture complexity, and enabling the region selection mechanism to be used for distinguishing a flat region and a high-texture region in an image; 2, introducing a texture modulation mechanism, and performing explicit adjustment on a state transition matrix in the state space model; 3, enhancing the context modeling capability of the model through a multi-direction sensing module; and 4, by combining position embedding and a sequence modeling structure, the capability of the model in the aspects of image structure understanding and spatial information maintenance is improved. The method can effectively alleviate the problem of information loss when a traditional image restoration method processes texture details, improves the structure restoration capability of a complex region, gives consideration to the restoration quality and the calculation efficiency, is suitable for multiple image restoration scenes such as image super-resolution, image rain removal, low-light image enhancement and the like, and improves the image restoration efficiency. And the method has good engineering adaptability and actual deployment value.

The invention discloses an image processing method, computing equipment, electronic equipment and a storage medium, and relates to the field of image generation and image processing. The method comprises the steps that a region selection instruction acting on a target region on an initial scene image is responded, a mask matrix of the target region and a target description text of a target object are acquired, and the target description text is used for describing the target object; inputting the mask matrix, the target description text and the initial scene image into an image generation model, and generating an initial object scene image by using the image generation model; the initial scene image and the initial object scene image are fused, a target object scene image is obtained, and a target area in the target object scene image comprises a target object. According to the invention, the technical problems of high cost, complex user operation process and poor fusion effect of the target object and the scene when the image for fusing the target object into the scene is generated in the prior art are solved.

The invention is suitable for the technical field of terminals, and particularly relates to a region selection method, electronic equipment and a computer readable storage medium. In the method, a first image feature of a target image and interaction information for the target image can be acquired, and the first image feature and the interaction information are input into a first area mask generation model for processing to obtain a first mask result corresponding to the interaction information. Besides, the target image and the first mask result can be input into a second area mask generation model for processing, a second mask result corresponding to the interaction information is obtained, and a target area corresponding to the interaction information is determined according to the second mask result. Wherein the second area mask generation model can optimize the first mask result according to the target image and perfect the edge of the first mask result and / or the missing area in the first mask result, so that the target area which the user wants to select can be accurately determined according to the second mask result, and the edge of the determined target area is smooth.

The present invention discloses an infrared and visible light image fusion method based on multi-modal features, including: 1. An encoder-decoder network for extracting multi-modal features; 2. Measuring the multi-modal features using entropy, gradient, and saliency and designing an adaptive loss function; 3. Constructing a fusion weight learning model incorporating an embedded Transformer fusion strategy; 4. Using the saliency map of the infrared image as a label and adding this label as the region selection for optimizing the fusion network; 5. Cascade the fusion weight learning model with the embedded Transformer fusion strategy and the encoder-decoder to construct an infrared and visible light image fusion network and train it. This method utilizes the Transformer to capture the global correlation of multi-scale features, taking into account both local and global information to enhance the overall visual effect of the fused image; and uses a multi-modal adaptive fusion strategy to retain the multi-modal feature information of the image and improve the quality of the fused image.

The invention discloses a power grid tower cross-domain adaptive type selection method, system and device and a medium. The method comprises the steps that tower data and multi-domain environment data are collected; inputting the tower data and the multi-region environment data into a domain adversarial neural network, and constructing a tower-region calculation model; based on the multi-region environment data, obtaining environment parameters of a target region through an LSTM prediction model, and obtaining tower parameters through a multi-objective evolutionary algorithm; and according to the environmental parameters and the tower parameters, generating a three-dimensional tower model, and performing safety analysis on the three-dimensional tower model to generate an optimal cross-domain adaptive type selection scheme. According to the method, multi-region data cross-domain sharing and privacy protection are realized through federated learning and homomorphic encryption, the domain adversarial neural network, LSTM prediction and a multi-objective evolutionary algorithm are combined, and a BIM modeling and knowledge graph reinforcement scheme is matched, so that the cross-region type selection suitability and precision of the power grid tower are improved, the type selection is safe and efficient, and the method is suitable for large-scale popularization and application. The problems that traditional model selection data are limited, and dynamic optimization is weak are solved.

The invention discloses a video signal de-interlacing method and device and electronic equipment, and the method comprises the steps: S100, obtaining a directional correlation interpolation result P directional interpolation of a to-be-interpolated pixel point in a current field, and obtaining a median filtering result P median filtering of the to-be-interpolated pixel point in the current field; and S200, acquiring a motion coefficient M of a to-be-interpolated pixel point in the current field, and selecting an interpolation mode and an interpolation result of the to-be-interpolated pixel point in the current field according to an interval in which the motion coefficient M is located. According to the method, interpolation results of direction correlation interpolation and median filtering are calculated respectively, then motion detection is carried out, a motion area of a pixel point to be interpolated is divided, and then a proper interpolation algorithm is selected according to the divided area, namely, an optimal mode to be interpolated is selected through motion detection. Therefore, the final interpolation suitable for various motion scenes is obtained, and the interpolation mode and the interpolation effect during de-interlacing are greatly optimized.

The invention belongs to the technical field of sleep health monitoring, and particularly relates to an optimal area selection chest / abdomen respiration signal extraction method based on human body pressure distribution. The method comprises the steps that data signals of human body pressure distribution changing along with time during sleeping are collected; the collected original pressure data are preprocessed, and clear and accurate pressure distribution data are obtained; for the preprocessed data, based on calculation of parameters such as the frequency, the power and the signal-to-noise ratio of the chest and abdomen area, an optimal breathing area selection algorithm is used for calculating respective band-shaped areas, most suitable for breathing signal extraction, of the chest / abdomen area; for the selected area, respiratory waveforms are extracted through a pressure signal fusion method based on weighted summation, and thoracic / abdominal respiratory signal monitoring is achieved; the sleep breathing monitoring device can accurately monitor sleep breathing in the form of a non-contact mattress under the condition that sleep quality is not interfered, and has a wide clinical application prospect.

The invention provides a gene expression analysis method and device, electronic equipment and a storage medium. The gene expression analysis method comprises the following steps: clustering a plurality of capture sites according to spatial transcriptome data of a target slice to obtain a biological function region; generating a region distribution diagram according to the biological function region and the spatial position information of each capture site; receiving area selection operation to determine a target analysis area; determining a biological gradient direction, and dividing the target analysis region into sub-regions according to the biological gradient direction; according to the spatial transcriptome data, obtaining the region expression quantity of the target gene in each sub-region; performing gene expression analysis according to the biological gradient direction and the region expression quantity corresponding to each sub-region; the gene expression analysis is used for describing the variation trend of the gene expression of the target gene in the target analysis region along the biological gradient direction. According to the embodiment of the invention, gene expression analysis can be carried out in the target analysis area based on the gradient direction.

The invention discloses a full-closed-loop control method for a humanoid robot joint module based on an FPGA (Field Programmable Gate Array), which relates to the technical field of robot control and comprises the following steps of: performing second-order active low-pass filtering and programmable gain amplification on analog signals of an encoder, a gyroscope and a force sensor to convert the analog signals into digital signals; a multi-dimensional feature sequence is constructed, temperature signals of a joint driver and an encoder are collected through a temperature sensor interface, a temperature drift model is constructed, and zero drift compensation parameters are obtained; further obtaining a joint elastic deformation error, and applying the joint elastic deformation error to an encoder signal for online offset correction; outputting a reconfiguration region selection signal based on the lightweight neural network; and triggering bit stream local refresh corresponding to the PR Region according to a region selection signal output by the lightweight neural network. The problem that a control loop is difficult to eliminate flexible errors due to the fact that a traditional rigid model or a constant parameter model is difficult to reflect deformation characteristics under the heat-lubrication-load combined action in real time is solved.

The invention discloses a multi-unmanned ship area coverage path planning method considering dynamic collision avoidance, belongs to the field of unmanned surface ship path planning, and aims to solve the problems that a coverage dead zone exists, backtracking cannot be adapted and dynamic collision avoidance is not considered when a traditional DARP area coverage algorithm is adopted to plan a multi-unmanned ship path. The method comprises the following steps: step 1, loading electronic chart data, and extracting static obstacle information in the electronic chart data; rasterization modeling is carried out; 2, according to the initial positions of the m unmanned ships, a task area is allocated to each unmanned ship based on a DARP algorithm; 3, for each task area, selecting a heuristic fusion algorithm with the minimum moving step number and action cost to obtain a static path, and completing full coverage of areas outside static obstacles; and step 4, introducing a DWA algorithm, performing real-time dynamic collision avoidance path planning on the basis of the static path, and when the angle of the advancing direction and the connecting line of the target point is too large, performing re-planning by using an APF algorithm so as to prevent the unmanned ship from falling into local optimum.

The invention relates to the technical field of disinfection devices, and discloses a disinfection device control method, a disinfection device and a computer readable storage medium, the disinfection device control method comprises the following steps: receiving an area selection instruction, and determining a target processing area according to the area selection instruction; receiving a mode selection instruction, and determining a target processing mode according to the mode selection instruction; according to the invention, a user can autonomously select the target area according to actual requirements, so that energy waste caused by idling of a chamber which does not need to be treated is avoided, and the treatment requirements of different spaces can be met in a targeted manner; the target processing mode is accurately matched through the mode selection instruction, so that the disinfection device only executes the corresponding function in the selected area, it is ensured that the processing effect meets the actual requirement, the operation process is simplified, and on-demand processing can be achieved without complex setting. And the disinfection device can be flexibly adapted to diversified use scenes through the control logic of double autonomous selection.

The invention relates to the technical field of automobile multi-screen interaction, and discloses a multi-screen interaction method and system applied to an intelligent automobile, and the method comprises the steps: capturing frame images of all display screens, calculating the difference, and selecting an interaction region; information entropy evaluation complexity is calculated, a high-efficiency or low-power-consumption encoder is dynamically allocated based on the complexity, and a matched compression algorithm is selected for encoding; and decoding and updating the received compressed data on the target display screen to realize collaborative response and real-time feedback among multiple screens, and the system comprises an image acquisition module, an interaction area selection module, an information entropy calculation module, an encoder task allocation module, a compressed encoding module, a data transmission module and a synchronous display module. According to the invention, a multi-region dynamic coding scheduling technical scheme based on image difference analysis and information entropy evaluation is adopted, and refined coding processing of image regions with different complexity is realized.

Methods, systems, and devices for wireless communications are described. Generally, the described techniques provide for efficiently determining a region in which a user equipment (UE) is located. The UE may perform measurements on reference signals received from a first network entity to determine the region in which the UE is located. In some cases, the UE may provide the measurements or other information to a second network entity, and the second network entity may indicate the region in which the UE is located to the UE based on the measurements or the other information. The second network entity or the UE may also indicate the region in which the UE is located to the first network entity. The UE and the first network entity may then select suitable beams for communicating based on the region in which the UE is located.

The invention provides an image enhancement method and device, a storage medium and electronic equipment, and the method is applied to the technical field of computers, and comprises the steps: obtaining a first sample data set which comprises a first sample image, a first sample text and a sample region coordinate, performing model training on the initial region selection large model based on the first sample data set to obtain a region selection large model, and obtaining a second sample data set which comprises a second sample image, a second sample text and a sample clear image, and performing model training on the initial image enhancement large model based on the second sample data set to obtain an image enhancement large model, and performing image enhancement processing based on the region selection large model and the image enhancement large model. According to the method, the local image can be determined through the area selection large model, the image enhancement large model is adopted to perform enhancement processing on the local image, the pertinence of image enhancement is improved, and the operation amount of image enhancement is also reduced.

The invention discloses an energy consumption optimization path planning method and system for unmanned aerial vehicle routing inspection, and relates to the technical field of unmanned aerial vehicle routing inspection path planning, and the method comprises the steps: calculating an evaluation function during node expansion in each step: a heuristic function represents the accumulated cost from a starting point to a current node, and a heuristic function represents the estimated cost from the node to a target point; the heuristic function is composed of a distance component and an energy consumption component, and the expression of the heuristic function is as follows: the linear distance from the current node to the target node, the minimum additional energy consumption estimated according to the height change and the flight resistance, and the weight coefficient; the improved heuristic function can preferentially avoid a high-energy-consumption area, a flatter and smoother air route is selected, and accumulated energy consumption and flight time of a global path are remarkably reduced; meanwhile, due to the introduction of the multi-dimensional cost, the global planning is more accurate and reliable, and the search efficiency of the algorithm in a complex three-dimensional environment is improved.

The application relates to the technical field of data processing, and discloses a radar real-time data transmission processing classification identification method and system.The method comprises the following steps: performing track mode identification on narrowband data of a target radar to obtain a real-time motion mode category of the narrowband data; performing directional region selection on synchronous wideband echo data of the target radar based on the real-time motion mode category to obtain a signal segment of the synchronous wideband echo data; performing sparse decomposition on the signal segment to obtain compact features of the signal segment; performing motion mode compatibility quantification on the compact features based on the real-time motion mode category to obtain category scores of the compact features; performing feedback adjustment on the real-time motion mode category based on the category scores and the compact features to obtain an optimized motion mode of the real-time motion mode category; and performing confidence fusion judgment on the optimized motion mode to obtain a classification identification of the real-time motion mode category; and the application can improve the efficiency of radar real-time data transmission processing classification identification.

The invention discloses a wide-angle video correction method and device based on area selection, relates to the technical field of network wide-angle cameras, and mainly aims to improve the definition of key details in a video so as to enhance the safety of remote operation. According to the main technical scheme, an area video viewing request is obtained, the viewing request comprises view angle configuration parameters of an area, and the view angle configuration parameters specify the coordinates and the size of the area to be intercepted in an original video stream of a wide-angle camera; intercepting a to-be-intercepted area from an original video stream output by the wide-angle camera according to coordinates and sizes in the view angle configuration parameters to obtain a to-be-corrected video area; extracting a local mapping table corresponding to the to-be-corrected video area from a preset global distortion correction mapping table according to the view angle configuration parameters; performing real-time distortion correction on the to-be-corrected video region by using the local mapping table to generate a corrected video stream; and outputting the corrected video stream through a network interface of the wide-angle camera.

The present disclosure provides an image processing method, a system, an electronic device, a storage medium, and a program product. A method comprises: in combination with image content of a target image, performing point sampling on the target image to obtain sampling point information; performing full image segmentation on the target image on the basis of the sampling point information; displaying, on a client interface, the target image which has undergone full image segmentation, the target image which has undergone full image segmentation comprising multiple segmented selectable regions; and in response to a region selection operation triggered by a user for the multiple selectable regions, performing image matting of the target image on the basis of at least one selected region, to obtain a matted image. By using the present solution, the segmented target image can be adaptively changed on the basis of the image content of the target image, which can improve the full image region division effect of the target image, and balance the time consumption of functions; moreover, interaction between the target image which has undergone the full image segmentation and the user can be used to clarify an actual intention of the user on the basis of the region selected by the user, so that excessive matting or missing matting is avoided, and better image matting service can be provided for the user.

The embodiment of the invention discloses a virtual object control method and device, a terminal and a storage medium, and belongs to the technical field of computers. The method comprises the following steps: displaying a master control virtual object in a first orientation in a virtual scene; in response to a first trigger operation on the perspective skill control, displaying a perspective area selection control; in response to an area selection operation on the perspective area selection control, controlling the master control virtual object to change from the first orientation to a second orientation, the second orientation being the direction of the perspective area indicated by the area selection operation; and controlling the master control virtual object to release a perspective skill in the perspective area in the virtual scene, wherein the enemy virtual object in an invisible state in the perspective area has a perspective display effect. By adopting the scheme provided by the embodiment of the invention, the perspective skill release flexibility can be improved, and the display form of the virtual object in the virtual game is enriched.

The invention discloses a physiological signal extraction method based on self-adaptive ROI and ViM dual paths, which comprises the following steps: constructing a first quality perception space-time diagram of a first user and a second quality perception space-time diagram of a second user based on a self-adaptive region-of-interest selection mechanism and a low-rank tensor completion method; extracting a first time sequence prediction signal of the first quality perception space-time diagram and a second time sequence prediction signal of the second quality perception space-time diagram through a time path of the ViM dual-path network structure, and extracting a first frequency domain prediction signal of the first quality perception space-time diagram and a second frequency domain prediction signal of the second quality perception space-time diagram through a frequency path; and fusing the first time sequence prediction signal and the first frequency domain prediction signal to obtain a first physiological signal of the first user, and fusing the second time sequence prediction signal and the second frequency domain prediction signal to obtain a second physiological prediction signal of the second user. According to the method, the robustness, precision and efficiency of physiological signal extraction are improved, and different scene requirements can be considered.

1. Name of the Design Product: Graphical User Interface for Item Selection of an Electronic Device. 2. Use of the Design Product: For an electronic device. 3. Design Key Points of the Design Product: Lies in the content displayed on the user operation interface. 4. Picture or Photograph Best Illustrating the Design Key Points: The front view of Design 1. 5. The other views are of conventional designs, and the rear view, top view, bottom view, left view, and right view are omitted. 6. Design 1 is designated as the basic design. 7. Use of the Graphical User Interface: The graphical user interface is used to select items in a store. In the front view of Design 1, when the user obtains selectable items, it enters the first state change diagram of the Design 1 interface. In the first state change diagram of the Design 1 interface, click on the middle area to select an item and enter the second state change diagram of the Design 1 interface. In the second state change diagram of the Design 1 interface, click on the "Confirm Selection" button at the bottom to enter the third state change diagram of the Design 1 interface. In the third state change diagram of the Design 1 interface, click on the second button from left to right at the lower left corner to enter the fourth state change diagram of the Design 1 interface, and the selected item is displayed at the lower right corner. In the front view of Design 2, when the user obtains selectable items, it enters the first state change diagram of the Design 2 interface. In the first state change diagram of the Design 2 interface, click on the middle area to select an item and enter the second state change diagram of the Design 2 interface. In the second state change diagram of the Design 2 interface, click on the temporarily retract button at the lower right corner to enter the third state change diagram of the Design 2 interface, and an exclamation mark icon will appear for prompting.

The present disclosure provides a region of interest selection method and device based on vehicle navigation, equipment, medium and product, relating to the technical field of artificial intelligence, especially in the field of autonomous driving. The specific implementation scheme is: determining the standard recognition result corresponding to the road video collected by the vehicle-mounted camera; using a region selection strategy to select a candidate region of interest for the road video to obtain at least one candidate region of interest; performing lane line recognition on the candidate region of interest to obtain a to-be-compared recognition result, and determining the selection result of the corresponding candidate region of interest according to the to-be-compared recognition result and the standard recognition result; determining the target recognition result that meets the result selection condition according to the selection result corresponding to each of the at least one candidate region of interest, and determining the candidate region of interest corresponding to the target recognition result as the target region of interest. The embodiment of the present disclosure realizes efficient and accurate selection of the target region of interest.

Provided is an image processing device (60) for detecting a condition outside a vehicle on the basis of image information acquired by a detector (1a) mounted on the vehicle, the image processing device (60) being provided with: an extraction unit (142) for extracting feature points of an object included in the image information; a selection unit (143) that selects a feature point to be used for processing from among the plurality of feature points extracted by the extraction unit (142); and an adjustment unit (144) that divides the image information into a plurality of regions and adjusts the number of feature points selected in each region on the basis of the distance to the object included in the plurality of regions.

System and method for automatic selection of training region selection for deep learning model is disclosed. An identification module 110 identifies equipment shutdowns and excludes buffer region data. A segmentation module 120 estimates operational regions and divides data into predefined operating regions using K-means clustering. A feature analysis module 130 determines top contributing tags and identifies low and high values within operational regions. A domain comparison module 140 compares contributing tags with domain knowledge, excluding regions associated with mapped failures. A data cleaning module 150 ensures data integrity by recalculating statistical measures and eliminating outliers. A filtering module 160 refines data and consolidates regions to reduce training ranges. This integrated approach enhances equipment monitoring and predictive analytics, enabling proactive maintenance strategies and optimizing operational efficiency.

The invention discloses a multi-label classification method based on key region selection and cross-layer feature fusion. Comprising the following steps: carrying out standard preprocessing operation on a chest radiograph image, and carrying out multi-label coding processing on an image label; inputting the preprocessed image into an OfficientNet-B0 network to extract features, performing dimensionality reduction to obtain an original feature sequence, and splicing the original feature sequence with learnable category embedding and position codes to form an embedding sequence; inputting the embedded sequence into a Transform encoder, and extracting deep features of the image through a multi-head attention mechanism and a multi-layer perceptron; constructing a key area selection module, selecting a discriminative area by using an attention mechanism of an encoder and filtering irrelevant information; constructing a cross-layer feature fusion module, fusing features of different levels in the encoder, and inputting the features into a classifier to obtain a multi-label classification result; and designing adaptive class balance loss, dynamically adjusting the attention on different classes of positive and negative samples, and optimizing model parameters through back propagation. The method is an optimization method for effectively improving image multi-label classification performance.

The video creation method includes a setting step of setting, in an imaging region of a first video having a first angle of view, regions having a second angle of view smaller than the first angle of view, a selection step of selecting a selection region which is an imaging region of a recorded video from among the regions, a switching step of reselecting the selection region from among the regions to switch the selection region after the selection step, a first recording step of recording a video of the selection region in a first period, in which the first recording step is executed between a time when the switching step is executed and a time when the first period elapses, a second recording step of recording a video of the selection region in a second period other than the first period, in which the second recording step is executed the second period, and an adjustment step of adjusting at least one condition of an exposure amount of the selection region, white balance of the video of the selection region, or focus of the imaging lens in each of the first period and the second period, where in the adjustment step executed in the first period, an adjustment speed of the condition is faster than in the adjustment step executed in the second period.

The application discloses a robust online learning ship tracking method based on a twin network, which firstly extracts a series of candidate target frames by using an anchor frame-based method, then extracts features of templates and detection frames respectively by using a twin network, uses a region selection network for classification and anchor frame regression, introduces an adaptive online update network UpdateNet, fuses SiamRPN classification scores and online classifier scores, and establishes a shielding judgment mechanism according to the classification fusion scores, finally tests on an inland ship dataset and evaluates tracking performance. The application improves the SiamRPN tracking model, so that it is suitable for inland water scenarios, thereby improving the target tracking drift problem under the mutual shielding condition of inland ships. The application applies the twin method to inland ship tracking, and the performance is better than that of existing methods, and the application shows strong robustness under the shielding scene.

The invention discloses a multilayer semantic information region selection method, system and device and a storage medium, and belongs to the technical field of deep learning adversarial attacks, and the method comprises the steps: for all convolution layers in a convolution layer set, firstly obtaining the gradient of a channel, then adding all gradients along the channel direction, and obtaining Gi; the Gi is normalized to be between [0, 1], up-sampling to an input image size is obtained, and a semantic mask mi to be output through a binary function delta (x) is obtained; and performing element-by-element product on the obtained mi of all the convolution layers to obtain a final mask M '. According to the method, the disturbance applying area is controlled through the mask, and multi-round iterative optimization is performed in combination with the gradient direction, so that the generated confrontation sample has attack effectiveness and also has good sparsity, imperceptibility and mobility. Experiments show that the method is superior to existing methods (such as DAG, FGSM and the like) in white-box and black-box scenes.

1. Name of the product in this design: Map Merging Graphical User Interface for Electronic Devices. 2. Intended use of this design: for use in an electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: This graphical user interface is used for map merging. Design 1's main view shows the interface where one area of ​​the map is selected. When the user clicks on another area of ​​the map, the "Confirm" button is activated, as shown in Design 1's interface state change diagram 1. When the user clicks the "Confirm" button, a reminder pop-up appears, reminding the user that non-adjacent areas cannot be merged, as shown in Design 1's interface state change diagram 2. Design 2's main view is a region selection interface with a pop-up reminder window; Design 3's main view is an interface for selecting one region on the map; Design 4's main view is an interface for selecting all regions on the map. Users can achieve corresponding product functions by operating the corresponding controls.

The invention relates to a multi-agent collaborative sensing method based on spatio-temporal joint selection. The method comprises the following steps: acquiring original sensing data of a plurality of agents; constructing a spatial sparse region selection mask; generating a joint sparse feature map; sparse features of the intelligent agent are obtained; obtaining a fused feature map; and carrying out intermediate-stage three-dimensional target detection, and carrying out later-stage fusion on the intermediate-stage three-dimensional target detection and an independent detection result of the collaborative agent to obtain a final collaborative perception detection result. According to the method, the space sparse region is constructed to select the mask, so that the relatively high sensing performance can still be kept in a scene with extremely limited bandwidth; the communication burden is reduced, the transmission content is more focused on a dynamic target which really contributes to collaborative perception, and the information utilization rate is improved; the detection precision is improved by using multi-view complementary information, the missed detection and false detection risks caused by time delay or shielding in intermediate fusion are reduced through a later compensation mechanism, and finally a more accurate and more complete 3D target detection result is obtained.