1224 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 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 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 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.

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 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 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 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 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 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.

The invention relates to the technical field of flight path planning, in particular to an autonomous flight path planning method and system for a road slope inspection unmanned aerial vehicle, and the method comprises the following steps: controlling the unmanned aerial vehicle to fly in a road slope region according to a preset flight path, collecting the aerial image data of the unmanned aerial vehicle, analyzing the aerial inspection adaptive feature value of the unmanned aerial vehicle, and determining the flight path of the unmanned aerial vehicle. Judging whether the feature value is lower than a preset inspection adaptation feature threshold value or not; analyzing a field-of-view missing feature set of the unmanned aerial vehicle based on the aerial image data of the unmanned aerial vehicle if the field-of-view missing feature set is lower than the preset routing inspection adaptive feature value threshold; performing flight path planning processing on the unmanned aerial vehicle based on the field-of-view missing feature set; according to the method, the unmanned aerial vehicle is subjected to flight path planning processing on the basis of the field-of-view missing feature set, so that when an aerial picture is deviated, fuzzy or blind areas, judgment and automatic adjustment are performed in real time, self-adaptive optimization of the flight path is realized, and the road slope inspection efficiency is remarkably improved.

The invention discloses an air-ground image matching method, device and equipment and a storage medium, and the method comprises the steps: carrying out the sparse reconstruction of an aerial image, obtaining a sparse point cloud model of an aerial image pose and a scene, generating the camera visual angle information of a new camera according to the sparse point cloud model, and enabling the camera visual angle information to serve as a middle visual angle of the scene, the visual angle difference between the space images and the ground images can be reduced, three-dimensional reconstruction is carried out through camera visual angle information, the rendered image of the middle visual angle after rendering is obtained, feature points corresponding to the rendered image are further removed after feature point matching is carried out on the ground image, the aerial image and the rendered image, and the image quality is improved. At the moment, a matching relationship between the ground image and the aerial image can be obtained, and an air-ground joint scene graph is generated according to the matching relationship to complete matching of the air-ground images. According to the embodiment of the invention, the homonymous connection points between the air-ground images can be automatically generated, and automatic air-ground joint model construction is realized.

The invention discloses a lightweight unmanned aerial vehicle image detection method fusing context and multi-scale features. The method comprises the following steps: designing a context guidance module and a multi-scale attention mechanism module in a Backbone network; the method comprises the following steps of: constructing an MFFPN (Multi-Feature Fusion Pyramid Network) feature fusion network structure, so that the detection capability of a model on a small target is improved; by constructing a P2 small target detection layer, the detection precision of the model on a small target is enhanced, and the parameter quantity is reduced; by designing a bounding box regression loss function Inner-CIoU, the convergence speed of the model is accelerated, and the accuracy of a prediction box is improved; and finally, training the model to obtain a detection result and evaluating the detection result. According to the method, by enhancing YOLOv8 feature extraction and multi-scale feature fusion capabilities, the problem of loss of deep details and context information of the feature map is relieved, the problems of missing detection, low precision and the like in unmanned aerial vehicle aerial image detection are effectively solved, the detection efficiency and precision are remarkably improved, and a more efficient and accurate solution is provided for the field.

The invention discloses a re-parameterization unmanned aerial vehicle target detection method based on multi-core fusion and omnidirectional connection, and the method comprises the following steps: obtaining an unmanned aerial vehicle aerial image data set, and constructing a detection model comprising a backbone network, a feature fusion network and a deformable task decoupling detection head; the backbone network extracts multi-scale and multi-direction features by six parallel paths in a training stage through a wide-branch re-parameterization convolution module, and re-parameterization is carried out in a reasoning stage to obtain single-path convolution; the feature fusion network performs down-sampling through a spatial deep convolution module and reduces spatial information loss, and realizes cross-stage omnidirectional feature interaction and double attention enhancement in combination with an omnidirectional kernel cross-stage partial connection module; and the deformable task decoupling detection head decouples the classification and regression features, optimizes feature expression, weights the classification features and then performs aggregation decoding. The model is trained to be used for a test set to output a detection result, detection precision and reasoning efficiency can be balanced, and the model adapts to a complex aerial photography scene of an unmanned aerial vehicle.

The invention relates to the technical field of computer vision and target detection, in particular to an unmanned aerial vehicle small target aerial photography detection method based on improved YOL0v8, and the method comprises the steps: carrying out the preprocessing of aerial photography image data, and generating standardized input data; optimizing bounding box regression by adopting WIoU v3; the feature extraction efficiency is improved through a lightweight adaptive partial convolution module; using an SAMA attention mechanism to optimize feature attention; introducing an ADWN down-sampling module to enhance feature expression; and a P2 detection layer is newly added to realize multi-scale feature fusion. Through the above technical means, the method effectively improves the precision and robustness of small target detection, solves a problem that the target is difficult to detect in a complex scene, and achieves the more efficient and more accurate detection of the aerial photography small target.

The invention discloses a city-level live-action three-dimensional modeling method based on air-ground multi-source data, and the method comprises the following steps: 1) respectively constructing a ground point cloud and an air point cloud, respectively extracting semantic tags, local geometric features and color features of a ground image and an air image, and carrying out the back projection of the semantic tags, local geometric features and color features to the corresponding ground point cloud and air point cloud; the method comprises the steps of (1) obtaining a ground point cloud and an aerial point cloud, (2) respectively calculating fusion multi-modal features of the ground point cloud and the aerial point cloud, (3) realizing cross-point-cloud feature interaction through a Transform point cloud registration model, and (4) selecting a three-dimensional reconstruction area. According to the invention, accurate registration of different-source point clouds with large air-ground view angle difference and low overlapping degree can be realized; the problems that an existing oblique photography urban three-dimensional model is lack of details and insufficient in precision in ground and building facade areas, and a live-action three-dimensional model is complex in updating process, long in period and low in automation degree are solved.

The invention relates to a robust unmanned aerial vehicle detection method based on dynamic feature fusion and context attention, and belongs to the technical field of image processing. Aiming at the problems of small target feature loss, semantic gap, background noise interference and the like caused by a fixed convolution kernel scale, one-way feature fusion and a static attention mechanism in an existing unmanned aerial vehicle aerial image target detection method, the method comprises the following steps: constructing a detection model comprising a backbone network, a neck network and a detection head network; a feature rearrangement and extraction module is designed in the backbone network to enhance feature learning, an enhanced double-flow feature fusion pyramid is designed in the neck network to optimize multi-scale feature fusion, and a dynamic multi-scale context attention mechanism is designed in the detection head network to suppress irrelevant background noise. The method effectively improves the accuracy and robustness of small target detection, and achieves a clearer and more stable detection effect in a complex environment.

The invention relates to a computer vision and target detection technology, in particular to an enhanced target detection method and device based on feature fusion and a medium. The method comprises the following steps: acquiring aerial image data; extracting a multi-level initial feature map through an initial feature extraction network; respectively extracting local features and global features of the initial feature map of each level through a local feature extraction network and a global feature extraction network which are deployed in parallel; self-adaptively fusing the local features and the global features through a gating fusion module to generate a fused feature map, and calculating a feature competition map and generating a spatial dimension gating weight map by adopting a local-global feature competition mechanism oriented to an aerial photography scene to realize feature weighted fusion of spatial positions one by one; and performing multi-scale fusion on the multi-level fusion feature map, and finally outputting a target detection result. According to the invention, the detection precision and robustness of the multi-scale target in the aerial image are effectively improved.

The invention relates to the technical field of topographic change analysis, in particular to an unmanned aerial vehicle-based topographic change detection system, which comprises a slope direction sensing track control module, a texture structure extraction module, a crack evolution track construction module, a direction trend comparison module and a patrol recheck positioning module. According to the method, a continuous elevation point column of an unmanned aerial vehicle scanning area is extracted, laser reflection point coordinates are fused, a space relation of transition point distribution is constructed, dynamic adjustment of a ground-imitated flight path is achieved, and a texture structure area with continuous directivity is recognized in combination with a high-angle image boundary communication relation; texture boundary evolution is compared at different time nodes to form a crack path, the stability of the path and the slope direction is judged through an included angle sequence, recognition and sorting of areas with the consistent direction are completed, a space comparison result is registered in a three-dimensional coordinate system, terrain change areas are accurately marked, and rapid positioning and continuous tracking of high-risk areas are achieved.

A method for detecting printing defects in a photolithography mask that will print on a wafer when using the photolithography mask in a specific photolithography system to print semiconductor structures on the wafer, the method comprising: acquiring a first aerial image of the photolithography mask using a mask inspection system; generating a second aerial image of the photolithography mask by applying a machine learning model (26) to the first aerial image, wherein the machine learning model is trained to map a first aerial image acquired by a mask inspection system to a second aerial image that emulates the application of the specific photolithography system to the photolithography mask; and detecting printing defects in the photolithography mask by comparing the second aerial image to a reference image.

The invention discloses a building height detection method and device and a storage medium, and relates to the technical field of image data processing, and the method comprises the steps: generating a digital elevation model of a target region and a digital orthoimage of the target region based on an aerial image; inputting the digital orthographic image into a building detection model for building identification to obtain an identification result of the building in the target area; according to an identification result of the building, establishing world coordinates of the building in the digital orthographic image; and determining a building elevation value and a ground elevation value of the building in the digital elevation model according to the world coordinates, and determining the height of the building according to the building elevation value and the ground elevation value. The technical problems of poor precision and low efficiency of building height detection in urban wide-area complex areas are solved, and the precision and efficiency of building height measurement are improved.