1507 results about "Aerial image" patented technology

The invention relates to the technical field of target detection and tracking, in particular to a highway abnormal event detection and tracking method under the view angle of an unmanned aerial vehicle, which comprises the following steps: establishing an aerial photography historical data set of the view angle of the unmanned aerial vehicle; constructing a target detection model and a target tracking model, and training the target detection model and the target tracking model based on the historical data set; acquiring a real-time aerial image of a highway, inputting the real-time aerial image into the trained target detection model, and acquiring position information and environment information of a target vehicle; inputting the detected target vehicle information into the trained target tracking model, and obtaining the motion track information of the target vehicle; according to the position information, the environment information and the movement track information of the target vehicle, analyzing and judging whether the target vehicle has an abnormal event or not; and outputting a detection result of the abnormal event. The technical scheme of the invention can effectively capture the dynamic state of the vehicle and provide effective support for timely discovery and processing of abnormal events, thereby improving the safety and efficiency of road operation.

The invention discloses an unmanned aerial vehicle small target aerial photography detection method and system based on RT-DETR, a medium and equipment, and belongs to the technical field of unmanned aerial vehicle visual detection, aerial photography images are collected based on an unmanned aerial vehicle, the images are input into a trained target detection model, and target position and category information is obtained. The method comprises the following steps: firstly, extracting low-layer features of an image, and simultaneously capturing information of a channel dimension and a space dimension based on an efficient multi-scale attention mechanism; and outputting features through the convolution residual block. Key features are processed based on a single-scale feature interaction module; for the coded feature map, fusing information of a shallow layer and information of a deep layer step by step in an up-sampling and transverse connection mode; in a down-sampling stage, context semantic information of a target is reserved, and global and local information interaction is enhanced. According to the method, the accuracy and robustness of small target detection are remarkably improved, and the practicability and deployment value in actual application scenes such as unmanned aerial vehicle aerial photography and remote sensing monitoring are expanded.

The invention discloses an improved YOLOv11-based lightweight aerial image target detection method, which comprises the following steps of: firstly, acquiring an aerial image data set and converting the aerial image data set into a YOLO format, and then constructing an aerial image detection model which takes a YOLOv11 model as a reference network and comprises a backbone network, a neck network and a head network; in the backbone network of the aerial image detection model, a Conv module in the backbone network of the YOLOv11 model is replaced by a DBR Down module, and an MSFLB module is added in the neck network; and finally, performing iterative training on the constructed aerial image detection model to obtain a trained aerial image detection model, and detecting a to-be-detected target in the aerial image by using the trained aerial image detection model to obtain a detection result. The aerial image detection model constructed by the invention can effectively improve the precision of aerial image target detection under the condition that edge equipment resources are limited.

The invention relates to the field of river and lake water quality monitoring, and discloses a river and lake remote sensing monitoring multi-source data fusion and analysis method, which comprises the following steps: firstly, collecting satellite remote sensing images, unmanned aerial vehicle aerial images and ground monitoring station data of a river and lake area, and preprocessing to form a feature data set; and then, constructing an image classification model based on a convolutional neural network (CNN), a water quality evaluation model based on a support vector machine (SVM) and a water quality prediction model based on time sequence analysis, and respectively classifying, evaluating and predicting water quality. And finally, by defining a multi-source data fusion algorithm, carrying out fusion analysis on a model output result, obtaining a comprehensive water quality evaluation result, and generating a river and lake remote sensing monitoring report. The advantages of multiple data sources are fully utilized, comprehensive, rapid and accurate monitoring of the water quality of rivers and lakes is achieved, powerful support is provided for environmental protection and water resource management, and the method has wide application prospects and important practical value.

The invention relates to an unmanned aerial vehicle aerial image small target detection method and a computer readable storage medium. The method comprises the steps of obtaining unmanned aerial vehicle aerial image data, and dividing the data into a training set and a verification set after processing; yOLOv8n is used as a basic model, traditional convolution structures of a shallow layer and a middle layer are replaced by full-dimensional dynamic convolution in a backbone network of the YOLOv8n model, an enhanced space attention mechanism based on routing is introduced into the backbone network of the YOLOv8n model, and an original SPPF structure of the backbone network is replaced by a multi-scale context modulation module. Performing collaborative design of a multi-scale structure and a detection head in a neck network and a head network of the YOLOv8n model to obtain an improved YOLOv8n model; training and verifying the improved YOLOv8n model by using the training set and the verification set to obtain a trained unmanned aerial vehicle aerial image small target detection model; and inputting a to-be-detected unmanned aerial vehicle aerial image into the trained unmanned aerial vehicle aerial image small target detection model to obtain a detection result. According to the invention, the small target detection precision and speed are improved.

The invention discloses an unmanned aerial vehicle aerial image target detection method based on PSO-DETR, and belongs to the technical field of unmanned aerial vehicle aerial image target detection. Firstly, a parallel patch perception attention feature extraction module is constructed, and an efficient multi-branch backbone network C3KCSPnet is designed by fusing a CSPDarknet53 structure; according to the network, gradient flow is improved through deep optimization, and the capturing capacity of high-level semantic information is enhanced. And secondly, an enhanced channel offset hybrid operator is provided, the dependency relationship between channels is enhanced through a channel shuffling mechanism, and cross-channel interaction of local space information is realized in combination with channel offset operation, so that the feature recovery quality and fusion efficiency in an up-sampling stage are improved, and the problem of missed detection of a shielded target is further relieved. And finally, a re-parameterization hierarchical aggregation network is designed, effective integration of shallow details and deep semantics is realized through an efficient hierarchical fusion mechanism on the premise of ensuring controllable calculation complexity, and the detection performance of the small target is further enhanced.

Processing circuitry may configure a system to implement a CAR-OnMap (“CAROM”) air framework for vehicle localization and traffic scene reconstruction using the aerial video of the traffic scene. Such a system may obtain aerial video of a traffic scene including vehicles that traverse the traffic scene and a satellite map image of the traffic scene as a distinct reference image. In such an example, processing circuitry may determine aerial image reference points within the aerial image which correspond to reference points in the satellite map image of the traffic scene. Processing circuitry may responsively generate calibrated images of the traffic scene from individual frames of the aerial video and determine unique keypoints on the vehicles in the traffic scene. In such an example, processing circuitry may track the vehicles across the individual frames of the aerial video utilizing the unique keypoints. Processing circuitry may output vehicle metrics for the vehicles.

The invention belongs to the technical field of computer vision and artificial intelligence, belongs to the cross technical field of target detection, deep learning and image processing, and particularly relates to a YOLOv8 algorithm improvement method based on an unmanned aerial vehicle aerial image small target detection model, which comprises the following steps of: introducing a user-defined feature enhancement module into a YOLOv8 backbone network, a neck part and a detection head part; the self-defined feature enhancement module comprises a context guide self-adaptive fusion module introduced into a backbone network so as to replace part of traditional convolution operation; a space edge sensing feature up-sampling module and a space sensing enhanced convolution module are adopted in the neck fusion network; a fine-grained dynamic pruning detection head is introduced into a detection head detection network. According to the method, the performance of the model in a small target detection scene is effectively enhanced, and the accuracy, robustness and real-time response capability of a detection system are remarkably improved.

The invention provides an unmanned aerial vehicle aerial image small target detection method, system and device and a medium. The method comprises the implementation steps that an unmanned aerial vehicle aerial image data set is acquired and preprocessed; yOLOv8s is used as a basic network, and SPD convolution is introduced into a backbone network; the neck structure is improved, and the new structure is named as a fused context feature alignment pyramid network (FCAFPN); a feature fusion module SIAF is introduced into the feature fusion module; then, a dynamic up-sampling module (DySample) is used for replacing nearest neighbor sampling (NN); an improved YOLOv8 network is obtained; the improved YOLOv8 network is applied to unmanned aerial vehicle aerial image recognition, and a high-precision small target detection deep learning model is constructed through training; inputting a to-be-detected image into the model for detection to obtain a target detection result; the system, the equipment and the medium are used for implementing the method. According to the invention, accurate identification of the small target of the unmanned aerial vehicle is realized.

The invention relates to an unmanned aerial vehicle multi-modal visual positioning method and system in a GNSS denial environment, and the method comprises the steps: constructing a visual inertial mileage calculation method based on deep learning feature extraction and matching, calculating the relative positioning of an unmanned aerial vehicle, and outputting to obtain high-frequency 6-DoF pose information; performing scene matching of infrared RGB image multi-modal fusion based on the Officient-LoFTR, and obtaining absolute positioning in a world system by solving a relative pose between a current key frame picture and a matched satellite image; a factor graph optimization mode is adopted to carry out self-adaptive weight information fusion according to the confidence coefficient of absolute positioning and relative positioning under a world system, and 6-DoF pose information with high frequency and higher precision is output; based on high-frequency and higher-precision 6-DoF pose information, after the unmanned aerial vehicle is recovered, all aerial images and satellite images are fused and updated. The system comprises a calculation module and a fusion updating module. According to the invention, more accurate and high-frequency pose output can be provided.

The invention relates to the technical field of image processing, in particular to a road area intelligent extraction system for unmanned aerial vehicle aerial images, and the system comprises an image noise filtering module which analyzes and inputs frequency domain information of the unmanned aerial vehicle aerial images, recognizes a target noise mode, and filters the unmanned aerial vehicle aerial images. According to the method, frequency domain noise analysis and notch filtering are combined, the sharpness of the road edge is reserved, meanwhile, high-frequency noise and periodic interference are restrained, and the visibility of a low-contrast road area is improved; local pixel spectrum dispersion calculation is introduced and is dynamically fused with morphological parameters, and direction consistency and length-width ratio constraints are combined, so that the connectivity perception capability of the fragmented non-paved road is enhanced, and the fracture misjudgment under the form-changeable terrain is reduced; a normalized vegetation index and multi-band reflectivity are combined for modeling, water reflection interference is eliminated through a blue light band standard deviation, and a space mask of a non-road surface feature interference area is constructed.

The invention relates to a dangerous rock body risk assessment method based on fracture density analysis. The method comprises the following steps: firstly, acquiring an aerial image of a dangerous rock mass, and preprocessing the aerial image; then, carrying out target detection on the preprocessed dangerous rock mass aerial image by adopting a YOLOv8 neural network to obtain a dangerous rock mass detection frame; on the basis of the dangerous rock mass detection frame, a Fast-SCNN convolutional neural network is adopted to carry out dangerous rock mass crack segmentation and density calculation, and dangerous rock mass crack density is obtained; and finally, determining the risk grade of the dangerous rock mass based on the crack density of the dangerous rock mass in combination with the crack direction and the cross point. That is to say, a dangerous rock mass risk assessment method based on deep learning is provided, the dangerous rock mass crack density, namely the proportion of the effective area of cracks in a dangerous rock mass target frame in the whole frame, is used as a main assessment index, and an intersection point generated due to different crack directions is used as an auxiliary judgment basis. The risk of the dangerous rock mass can be efficiently evaluated, so that the disaster risk of the rock mass is judged.

The invention discloses an unmanned aerial vehicle aerial image small target detection method based on RT-DETR, an end-to-end target detection algorithm RT-DETR is improved, an FCW-RTDETR algorithm is provided, and the method comprises the following steps: improving a backbone network by using adaptive frequency cavity convolution, and extracting unmanned aerial vehicle aerial image features; performing intra-scale feature interaction AIFI on a P5 layer in the backbone network, and inputting obtained features into a cross-scale feature fusion network CCFM; the method comprises the following steps: respectively improving a CCFM structure and an up-and-down sampling method by using a context and spatial feature calibration network CSFCN and wavelet pooling, and fusing features of different scales in the CCFM to obtain an image feature sequence; selecting a feature from a feature sequence output by an encoder as an initial target query of a decoder by using IoU-perceived query selection; and the decoder iteratively optimizes the target query through the auxiliary prediction header, generates a bounding box and a confidence score, and completes small target detection. According to the scheme, the small target detection efficiency and accuracy are improved.

The invention discloses a semantic detail fusion and context enhancement remote sensing image segmentation method based on DeepLabv3 +. On the basis of Deeplabv3 +, a multi-scale context feature fusion module adopting a multi-scale pooling, convolution and feature fusion structure is constructed, context information in a complex scene is effectively captured, and the capabilities of detail processing and global structure understanding are improved. A global context enhanced self-attention module is introduced to improve the capture capability of the model on remote information, and the module combines a self-attention mechanism and global average pooling and introduces residual connection to balance local details and global features. In order to effectively fuse semantic information and spatial details, a semantic detail information fusion module is provided to effectively capture and fuse multi-level features. In order to verify the effectiveness of the proposed model, an experiment is carried out on an unmanned aerial vehicle aerial image data set, and the result shows that the improved model obtains 67.2% mIoU on the data set, and the performance is remarkably improved compared with a traditional Deeplabv3 + model.

According to the invention, based on deep learning target detection, automatic identification of power transmission line insulator defects is realized. The method comprises the following steps: firstly, acquiring and preprocessing an aerial image, and then inputting the aerial image into a backbone network integrated with S-CBAM attention, an FPN + PAN + GSConv lightweight neck network and a detection head additionally provided with a small target detection branch; the model adopts an SGD optimizer, is matched with cosine annealing learning rate scheduling, and is trained by using CIoU loss, Focal Loss and binary cross entropy. After training is completed, the model is exported as a TensorRT engine and deployed in embedded equipment, and forward reasoning is accelerated. And for the new image, the network outputs the candidate box and the category confidence, and after NMS screening, a final defect detection result is obtained.

The invention discloses an unmanned aerial vehicle aerial image target detection method based on improved YOLOv8, and relates to the technical field of target detection, and the method comprises the steps: collecting image data of unmanned aerial vehicle aerial photography, inputting the image data into a backbone network of an improved YOLOv8 model, and extracting features; transmitting the feature map to a neck network for feature processing and fusion, and carrying out target classification and bounding box prediction in a head network by using a detection head specially aiming at a small target and a multi-scale feature map; and calculating the difference between a predicted bounding box and a real bounding box by using an MPDIOU loss function, updating model parameters in a back propagation manner, and outputting a detected target category and bounding box coordinates. According to the method, a C2f module is replaced by RepNCSPELAN4, a GAM attention mechanism is introduced, a small-scale feature detection head is added, an MPDIOU loss function is adopted, feature extraction and fusion are optimized, and the detection precision and bounding box regression accuracy are improved.

The invention relates to an aerial photography target detection method and device, computer equipment and a storage medium. The method comprises the following steps: acquiring an aerial image acquired by an image sensor; inputting the aerial image into a pre-trained target detection model to obtain a target detection result; the target detection model comprises a backbone network, a neck network and a head network; wherein the backbone network is used for performing feature extraction on an aerial image to obtain image extraction features; the neck network is used for performing multi-scale fusion on the image extraction features to obtain a plurality of fusion features of different scales; the neck network comprises a bidirectional information transfer network, the bidirectional information transfer network comprises a separation and attention enhancement module, and the separation and attention enhancement module is used for generating fusion features corresponding to tiny targets; and the head network comprises a detection head corresponding to the tiny target and is used for obtaining a target detection result corresponding to the fusion feature based on the fusion feature. By adopting the method, the detection accuracy under the complex aerial image can be improved.

The invention discloses a conductor instance detection method and system based on key point matching, and the method comprises the steps: obtaining original aerial image data, generating a four-dimensional feature matrix through dynamic feature mapping and enhancement processing, obtaining three-dimensional projection feature data through projection transformation, and carrying out the feature enhancement to obtain enhanced feature data. Generating enhanced feature map data through multi-scale feature fusion; performing feature recombination on the enhanced feature map data to obtain feature matrix data, generating response matrix data through recursive decomposition and mapping, and calculating adaptive threshold data to screen key points; generating thermodynamic diagram data for the screened key point set through a multi-layer thermodynamic diagram, obtaining a spatial incidence matrix by combining spatial feature enhancement, and performing endpoint matching based on multi-dimensional constraints; and finally, through boundary feature analysis and cross-block instance matching, a global traverse map is constructed, and accurate detection of traverse instances is realized. According to the invention, the accuracy and robustness of wire detection can be effectively improved.

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 discloses an unmanned aerial vehicle target detection method based on feature fusion and detection head collaborative optimization, and the method comprises the steps: obtaining a multi-scale feature map through employing a backbone network formed by stacking a convolution module CBS and a residual module C2f, sequentially transmitting the feature maps of various scales to a multi-level feature fusion module (ML-FFM), carrying out the feature fusion from a deep layer to a shallow layer, and carrying out the detection of a target target. Semantic information of shallow layer features is enhanced; inputting the fused feature map into a refined feature enhancement module (RFEM), eliminating position conflicts and redundant information based on a space-time attention mechanism, and optimizing details and positioning capability of deep features; and the enhanced feature map is sent to a prediction head based on Transform for detection, and target classification and positioning of the aerial image of the unmanned aerial vehicle are completed. According to the method, the problems that the unmanned aerial vehicle aerial image is large in scale change, and small targets are high in proportion and dense in distribution are solved, so that the precision of target detection in the unmanned aerial vehicle aerial image is improved. The modules proposed in the method can significantly reduce the calculation amount, and are suitable for the real-time detection scene of the unmanned aerial vehicle.

The invention discloses a light unmanned aerial vehicle aerial image small target detection method and system based on TFA-YOLO11, and relates to the field of target detection. The method comprises the steps of data set acquisition and data enhancement; a TFA-YOLO11 network model is constructed; training the neural network by using the data set after data enhancement to obtain a trained neural network; and detecting an image to be detected by using the trained model. According to the method, on the basis of a multi-head self-attention mechanism, complementary information between adjacent features is emphatically mined, a feature aggregation module is designed, and space and semantic information of feature maps with different scales can be better fused; according to the invention, a minimum target detection module is designed based on a high-resolution feature map, so that the sensing capability of a network to a minimum target can be enhanced; according to the method, lightweight trunk and neck networks are designed based on SPD convolution, and downsampling can be realized with smaller parameter quantity and calculation quantity under the condition of no information loss.

The invention relates to the field of urban road modeling, in particular to an aerial photography data processing method and system for urban road modeling. The invention discloses an aerial photography data processing system for urban road modeling. The aerial photography data processing system comprises an aerial photography video acquisition module, a road modeling module, a photogrammetry module, a spatial registration module and a rendering synthesis module. According to the invention, on the basis of a spatial calibration mechanism of the original aerial image and the BIM model, redundant processes such as point cloud acquisition, data fusion and manual correction are eliminated, and linear processing from data acquisition to dynamic result output is realized; according to the architecture, the hardware resource dependency and the operation complexity are greatly reduced, and designers are endowed with the capability of completing large-scale scene visualization processing in a conventional engineering environment; according to the method, a transmission chain from original data to decision support is remarkably shortened, and important engineering scheme deduction and dynamic review have unprecedented timeliness.

The invention relates to the technical field of computer vision and deep learning, in particular to an aerial image target detection method based on frequency domain decoupling multi-scale feature fusion, which comprises the following steps of: acquiring an aerial image of an unmanned aerial vehicle, establishing a data set, and performing preprocessing and data division; an aerial image target detection network is constructed, and the aerial image target detection network receives an input image and outputs a target category and a bounding box position; loss functions are determined, wherein the loss functions comprise classification loss representing matching quality, coordinate loss representing prediction coordinate relevancy and bounding box regression loss representing bounding box positioning accuracy; training the aerial image target detection network based on the data set and the loss function; inputting a to-be-detected aerial image into the trained aerial image target detection network to obtain a to-be-detected target category and a bounding box position; the method can improve the feature fusion degree, retains high-frequency details, and enhances the small target recognition rate.

The invention discloses a dense small target detection method and device, a medium and equipment in the technical field of computer vision and target detection, and aims to solve the optimization problem of aerial photography dense small target detection. The method comprises the following steps: carrying out image preprocessing on acquired aerial image data to obtain an input image, and inputting the input image into a trained dense small target detection model: carrying out feature extraction through a trunk feature extraction network to obtain a multi-scale feature map; through a neck multi-scale feature fusion network, feature fusion and feature refinement are carried out on the multi-scale feature map, and multi-scale fusion features are obtained; and the small target detection layer is used for carrying out target detection on the multi-scale feature map to obtain a detection result. According to the invention, the detection performance of small target detection of the aerial image of the unmanned aerial vehicle can be improved, and robustness is increased for an algorithm.

The invention belongs to the field of computer vision, and particularly relates to an unmanned aerial vehicle aerial photography night image enhancement method based on long and short exposure fusion. Comprising the following steps: firstly, constructing a low-illumination synthetic data set; secondly, carrying out data preprocessing on the long-exposure image and the short-exposure image in the low-illumination synthesized data set to obtain a long-exposure image and a short-exposure image containing blurring and noise; then, a lightweight denoising and deblurring network is constructed, the first stage comprises a multi-scale input module, a dual-core feature fusion module and a feature reconstruction module, and the second stage adopts a channel attention mechanism module for fine tuning; designing a multi-scale perception loss function, and guiding network training; and finally, using the trained final model to enhance the to-be-tested unmanned aerial vehicle aerial night image. According to the method, the visual effect and fidelity of the aerial image of the low-illumination unmanned aerial vehicle are effectively improved by utilizing complementary information of continuous short exposure and long exposure.

The invention discloses a target detection method based on a YOLO model, electronic equipment and a storage medium, and relates to the technical field of target detection. Comprising the following steps: inputting an aerial image of an unmanned aerial vehicle into a trained target YOLO model; the target YOLO model comprises a backbone network, a neck network and a head network, and a feature extraction module in the backbone network performs multi-scale feature extraction by adopting a double-branch architecture attention mechanism; performing multi-scale feature extraction on the aerial image by adopting a dual-branch architecture attention mechanism through a feature extraction module in the backbone network, and constructing to obtain a plurality of layers of first comprehensive image features of the aerial image; performing feature fusion on the first comprehensive image features of different levels through a neck network to obtain second comprehensive image features of multiple levels; and inputting the multiple levels of second comprehensive image features into a head network to obtain a target detection result of the aerial image. According to the invention, the accuracy of small target detection can be improved.

The invention provides an aerial image small target detection method based on an improved YOLOv8 algorithm, and the method comprises the steps: S1, obtaining an aerial image data set, converting the aerial image data set into a YOLO format, and dividing the data set into a training data set and a test data set; s2, an improved YOLOv8 detection model is built; s3, inputting the training data set into the improved YOLOv8 detection model, and training to convergence to obtain a final improved YOLOv8 detection model; s4, a test data set is input into the improved YOLOv8 detection model for testing, and the mAP value of the improved YOLOv8 detection model on the data set is recorded; and S5, inputting the aerial image into the tested improved YOLOv8 detection model, and carrying out small target detection. Therefore, the accuracy of target detection is improved, the global to local effective features of the small target image are effectively extracted, and the detection precision of the small target is improved.

The invention discloses an unmanned aerial vehicle visual retrieval and instruction feedback method based on a multi-mode large model, and relates to the technical field of unmanned aerial vehicle combined large model intelligent sensing. The method comprises the steps that an aerial image obtained through preprocessing is input into a visual encoder of a CLIP (Control Language-Image Prediction) model to generate a high-dimensional feature vector, and meanwhile, a text feature vector is generated based on task-related text description; image and text features are matched through a CLIP multi-mode alignment technology, and the correlation is evaluated; performing context analysis and semantic understanding on the data by adopting a Transform architecture in combination with a time image sequence and a task context; an initial text instruction is generated according to a task target through a T5 (Text-to-Text Transfer Transform) model, and an accurate operation instruction is generated based on image semantic information and context dynamic optimization. The method realizes efficient understanding of the visual data of the complex environment by the unmanned aerial vehicle and automatic generation of the accurate operation instruction, has the advantages of intelligence, dynamic adaptability and high precision, and is suitable for task planning and execution of the unmanned aerial vehicle.

The invention discloses an unmanned aerial vehicle aerial image deblurring method based on an improved DeblurGAN. The method comprises the following steps: firstly, simulating and synthesizing a blurred image data set of unmanned aerial vehicle aerial photography through an Albumentations image data enhancement library; secondly, the data set is preprocessed, image feature information is extracted from a spatial domain and a frequency domain, and feature fusion is carried out; secondly, constructing a dual-branch generative network structure, and enhancing the feature extraction capability of the image and the global attention capability of the image by utilizing the combination of an FPN MobileNet network and a Swin Transformer network; and finally, the training effect is further optimized through the improved loss function, and the recovery effect of the improved algorithm on the aerial blurred image of the unmanned aerial vehicle is evaluated by using the PSNR and the SSIM.

The invention discloses a laser point cloud shielded vehicle completion method and device based on a thunder-vision fusion deep learning framework in the technical field of automatic driving environment perception. The method comprises the following steps: acquiring original laser point cloud data and an aerial image; constructing a fan-shaped shielding area based on the original laser point cloud data; identifying two-dimensional bounding boxes, orientations and category labels of all vehicle targets in the image; inputting the fan-shaped occlusion area and the two-dimensional bounding boxes, orientation and category labels of all the vehicle targets into a pre-trained double-branch deep learning network, and predicting whether an occluded vehicle exists or not and the position, orientation and category information of the occluded vehicle; and selecting a vehicle point cloud template based on the category information of the shielded vehicle, and then generating a scene point cloud after vehicle point cloud completion as a final result of vehicle completion in the shielded region. According to the invention, an image-point cloud space mapping mechanism based on the Leiyu fusion deep learning framework is introduced, so that the capability of automatically identifying and positioning the shielded vehicle is remarkably improved.