4411 results about "Visual technology" patented technology

The invention discloses a construction progress dynamic optimization method and system based on BIM and computer vision, and particularly relates to the technical field of building construction management, and the method comprises the steps: carrying out the automatic registration of a BIM model and a construction site image; processing the construction site image by adopting a visual identification algorithm to generate a visual identification result; constructing a four-dimensional dynamic BIM model, and mapping a visual identification result to a corresponding component in real time through multi-feature similarity calculation; the progress deviation is monitored by using key path dynamic identification and a deviation propagation matrix, and the risk is predicted by combining a Bayesian network and Monte Carlo simulation. The BIM and computer vision technologies are fused, a construction progress optimization system integrating automatic registration, dynamic monitoring, risk prediction and intelligent decision making is constructed, and the problems that traditional manual inspection data collection is low in efficiency, progress monitoring is lagged, risk prejudgment is fuzzy and resource allocation is extensive are solved; accurate monitoring, risk early warning and resource optimization configuration of the construction progress are realized.

The invention discloses a cradle head tracking method and device based on a binocular camera and a storage medium, and relates to the technical field of computer vision, and the method comprises the steps: processing image data based on a binocular parallax principle, and generating a three-dimensional coordinate of a center point of a tracking target; based on the three-dimensional coordinates of the camera coordinate system and the offset from the optical center of the camera to the rotation center of the holder, generating three-dimensional holder coordinates through coordinate transformation solution; determining a historical track based on the tracking target feature information and a historical target feature matching result, and outputting an identifier and a three-dimensional position observation value through correlation verification of a three-dimensional holder coordinate and the historical track; inputting an observation updating equation correction state through the identifier and the three-dimensional position observation value, and outputting a three-dimensional prediction position; based on the three-dimensional prediction position and a deviation formula, calculating the angle deviation with the camera image center under the holder coordinate system, and driving the holder to center the target in the picture center according to the angle deviation. The problem that the target tracking effect is poor is solved, and the robustness of target tracking in a complex scene is improved.

The invention discloses a machine vision model training method and system based on end-side computing power, and belongs to the technical field of machine learning, edge computing and computer vision, and the method comprises the steps: obtaining a machine vision image and constructing a pre-annotation model, so as to carry out the automatic pre-annotation of the image; manually correcting the partial pre-annotation to optimize the pre-annotation model, and obtaining a corrected annotation image; a machine vision model is constructed and is subjected to identification training based on an annotated image, training task variables can be distributed according to hardware perception, then a training task execution position (an edge end or a cloud end) is dynamically selected, and the trained machine vision model is deployed at the edge end so as to carry out an image identification reasoning process; and performing manual spot check on the reasoning result of the model to evaluate the accuracy rate of the machine vision model, starting a new round of model training when the accuracy rate is low, and taking the evaluated machine vision model as the pre-labeling model.

The invention discloses an optical remote sensing image salient target detection method based on progressive attention enhancement, and belongs to the technical field of computer vision. The method comprises the following steps: preprocessing an original data set; inputting the preprocessed image into a hierarchical progressive fusion encoder, capturing a global irregular topological structure and local fine-grained image details, and realizing cross-hierarchical feature fusion; inputting the output characteristics of the encoder into a global context enhancement module, and capturing multi-level context information by adopting a parallel multi-branch structure; and inputting the output features of the hierarchical progressive fusion encoder and the global context enhancement module into a multi-scale progressive attention enhancement decoder, carrying out hierarchical decoding on the input features by adopting a saliency-guided attention mechanism, and gradually aggregating deep semantic information and shallow detail features to realize coarse-to-fine progressive optimization, so as to improve the robustness of the multi-scale progressive attention enhancement decoder. And finally generating a saliency map. The method can effectively improve the processing performance of an irregular topological structure and a complex context relationship in the optical remote sensing image.

The invention provides a self-adaptive scene analysis and target generation method and system based on deep learning, and relates to the technical field of computer vision, and the method comprises the steps: extracting the depth information of a scene image sequence, constructing three-dimensional point cloud data, calculating the distance and direction matrix between objects, and generating a scene space feature vector. Meanwhile, a semantic label graph is obtained through semantic segmentation, a semantic incidence matrix is constructed, and a scene semantic vector is calculated. Inputting the two into a variational auto-encoder to obtain a target feature vector, calculating an attention score based on the target feature vector, and generating a target description vector with a weight; and finally, target contour features are generated through a probability graph reasoning network, and a final target result is generated in combination with the regional attention graph. According to the method, the accuracy and efficiency of target generation are improved.

The invention provides an image-based three-dimensional point cloud traffic marker classification method and system, and relates to the technical field of computer vision, and the method comprises the steps: collecting a multi-frame multi-angle image of a traffic marker, converting the multi-frame multi-angle image into point cloud data, constructing a histogram based on reflection intensity and geometric features, determining an optimal segmentation threshold value, and carrying out the segmentation of the point cloud data; the method comprises the following steps: carrying out region merging by combining spatial distribution characteristics of scattering coefficients and absorption coefficients to obtain point cloud sub-regions with uniform materials, determining a core region by utilizing a curvature entropy value and a normal vector entropy value, establishing a local coordinate system, carrying out region growth and dynamic merging through spatial position and geometric structure constraints to obtain candidate point cloud sub-regions, and carrying out point cloud distribution on the candidate point cloud sub-regions. And extracting the position offset and the attitude variation to construct a time sequence motion feature, thereby realizing accurate classification of the traffic markers.

The invention provides an interaction method and system based on deep learning, and relates to the technical field of computer vision, and the method comprises the steps: collecting a continuous image sequence, extracting feature points, calculating the position variation and gradient variation of the feature points, constructing a two-dimensional feature vector, and carrying out the clustering through a Gaussian mixture model, thereby obtaining a stable feature class. And weighting the feature points based on a feature weight distribution function, calculating feature similarity and constructing a matching relationship. And calculating a dynamic parallax threshold in combination with the vehicle movement speed, screening reliable matching feature pairs, and solving pose transformation parameters. A feature change sequence is extracted through a feature detection network, a motion feature mapping matrix is constructed, and a compensation depth map is generated in combination with depth information. And scene reconstruction is carried out based on the compensation depth map and the pose transformation parameters, the scene confidence score is calculated, the pose parameters are calibrated, a high-precision pose parameter sequence is obtained, and the robustness of feature matching and the accuracy of pose estimation can be improved.

The invention provides a multi-image forgery detection method and system based on a cross-modal vision large language model, and relates to the technical field of image processing and computer vision, and the method comprises the steps: constructing a data set, and carrying out the preprocessing of the data set; according to the preprocessed data, respectively extracting visual features and language features through a pre-trained visual Transform and a language model so as to obtain cross-modal features; according to the cross-modal features, the visual features and the language features are clustered, cross-modal similarity is calculated, a matching relation is established, fusion is carried out, and fused multi-modal features are obtained; and according to the fused multi-modal features, performing adversarial training through a generator and a discriminator to generate an adversarial network. According to the method, high-precision detection and effective detection of multiple image counterfeiting types such as positioning splicing, copying and pasting, AIGC generation and the like are realized.

The invention belongs to the technical field of computer vision, and relates to a dynamic scene online calibration method and system for three-dimensional target detection. The method comprises the following steps: firstly, acquiring a 3D point cloud around a vehicle, an initial external parameter and a synchronous image, and processing the 3D point cloud, the initial external parameter and the synchronous image by a bimodal static mask generation module to obtain a static region probability graph; calculating a multi-scale residual error of the image and the LiDAR edge image in a static region through a static region fusion module; a historical time sequence modeling module is used to generate time sequence fusion features; performing external parameter correction through a coarse-fine multi-stage external parameter correction module; and finally, according to the predicted external parameters, performing three-dimensional target detection through an image-point cloud bidirectional enhancement module in combination with Transform. Full-scene self-adaptive calibration is achieved, the invalid calibration rate is reduced by 67%, permanent deformation and temporary interference of the sensor can be effectively distinguished, the diagnosis accuracy rate reaches 92%, the false alarm rate is lower than 0.5%, and the three-dimensional target detection performance is improved.

The invention belongs to the technical field of computer vision, and discloses a wire harness crimping defect detection system and method based on machine vision, and the system comprises a multispectral light source control module which is used for employing an annular LED light source and coaxial light mixed illumination and adapting to different wire harness surface materials by dynamically adjusting the angle and intensity of the light source; the multi-dimensional perception acquisition module is used for acquiring wire harness image data and crimping geometric data in real time based on the dynamically adjusted light source angle and intensity; the visual enhancement fusion module is used for de-noising the wire harness image data by using a non-local mean de-noising algorithm and dynamically adjusting and controlling a similarity measurement range through a smooth factor; superposing a multi-scale Retinex algorithm, and enhancing the texture contrast of the wire harness image data through the local contrast; the wire harness image data and the crimping geometric data are fused, and a crimping part multi-mode characteristic graph is constructed; the accuracy of defect detection is facilitated, and potential problems can be found and solved in time.

The invention discloses a bad weather image restoration method based on a multi-modal state space model, and belongs to the technical field of computer vision. In order to solve the problem that an existing unified bad weather image restoration method has limitations in the aspects of global receptive field and computational efficiency, the unified restoration of various bad weather images is realized by designing a parallel multi-mode encoder and an adapter to generate comprehensive prompts containing degeneration semantics. Through parallel connection of a state space module and a local context sensing module of a double-attention mechanism, simultaneous capture of global long-range dependence and local detailed features is realized. Experiments show that the method is high in generalization ability, high in processing speed and low in calculation complexity, and can show good adaptive capacity on a plurality of bad weather image removal tasks.

The invention relates to the technical field of computer vision, and discloses a real-time video analysis method based on deep learning. The method comprises the following steps: acquiring a real-time video stream through image acquisition equipment, and performing frame segmentation processing to generate a continuous video frame sequence; and extracting features of the video frame sequence by using a pre-trained convolutional neural network to obtain a multi-dimensional feature vector, inputting the multi-dimensional feature vector into the time sequence analysis model to calculate dynamic relevance, and outputting an inter-frame movement track and object behavior features. And constructing a scene understanding map containing a spatial position and a time evolution relationship according to the above-mentioned data, and carrying out abnormal event detection and generating event marking data based on the map. And performing semantic analysis on the event marking data, determining an abnormal event type and a confidence score, triggering a real-time alarm signal according to a result, and updating a historical event database. In the analysis process, the resource occupancy rate of the system is continuously monitored, the calculation precision is dynamically adjusted, a degradation processing mechanism is started when a preset threshold value is exceeded, and key area analysis is preferentially guaranteed.

The invention discloses a package defect elimination method based on visual inspection, and relates to the technical field of image processing and computer vision, and the method comprises the following steps: setting a polarized light source with a fixed angle on a package production line, and uniformly illuminating a target package area to enable the incident light polarization direction and the package surface reflection direction to form a deviation angle; an industrial camera provided with a polarization filter is used for carrying out image collection on a packaging target area, and the polarization direction of the filter is perpendicular to the polarization direction of a light source so as to restrain reflection interference to the maximum extent. Reflection interference is eliminated through polarized light, and the image quality is improved; by combining a neural network and feature fusion scoring, the defect recognition accuracy and adaptability are improved; a closed-loop elimination control and data tracing mechanism is introduced, the elimination precision is guaranteed, quality responsibility investigation is supported, and the intelligence and reliability of the packaging defect detection and elimination system are integrally improved.

The invention relates to the technical field of computer vision, in particular to a point projection type three-dimensional reconstruction and segmentation method and system based on semi-Gaussian pruning. The method comprises the following steps: respectively obtaining an SFM point cloud and a consistency label mask of a cross-view label based on an obtained multi-view image; initializing the obtained SFM point cloud into an identity semi-Gaussian point cloud, and performing rendering optimization by using a differentiable renderer; densifying the initial sparse point cloud by using a localized semi-Gaussian point management method, and identifying a local error region for resetting and repairing; using the obtained consistency label mask to supervise Gaussian identity feature learning by using cross entropy loss, and using unsupervised 3D regularization loss to force spatially adjacent gauss to maintain identity consistency; according to the method, the identity coding semi-Gaussian kernel method is adopted, the inherent representation fuzziness of a single opacity formula is eliminated, and the positive influence on the identity coding precision is generated.

The invention relates to the technical field of computer vision, and discloses a thyroid ultrasonic robot automatic scanning method, device and equipment based on RGB images and depth information and a medium, and the method comprises the steps: obtaining image information and depth information, coding the depth information, fusing and recognizing a target scanning area, determining an initial scanning point and an initial scanning direction, controlling the scanning probe to scan and collect a real-time scanning image, analyzing the real-time scanning image to recognize a preset target and an artifact area, adjusting a scanning posture and a scanning path based on a recognition result, monitoring a continuous existence state of the preset target, and stopping scanning when the preset target is not recognized continuously. The target area is identified by fusing the multi-modal image information, the scanning posture and path are dynamically adjusted in combination with real-time image analysis, scanning termination is intelligently controlled according to the target detection result, the positioning accuracy, image quality and standardization level of ultrasonic scanning are improved, and the method is suitable for automatic ultrasonic imaging of thyroid and superficial organs.

The invention discloses a high-precision defect detection system for a track inspection robot, which relates to the technical field of track inspection and comprises a multi-mode visual acquisition module, a time sequence synchronization module, a data fusion processing module and a defect identification output module, by adopting a multi-modal visual fusion detection technology, various modal visual technologies of visible light vision, infrared vision and ultraviolet vision are combined, fusion processing is carried out on all modal image data, visual defects such as cracks and abrasion on the surface of a track can be clearly recognized through visible light vision, and the visual performance of the track is improved. The infrared vision can detect temperature abnormity of a track component to find out potential poor contact and other problems, the ultraviolet vision can be used for detecting corona discharge and other phenomena, track information is comprehensively obtained, the accuracy and reliability of defect recognition are effectively improved, defects which are difficult to find in a single mode can be detected, and the detection efficiency is improved. And powerful support is provided for safe operation and maintenance of the track.

The invention provides a dynamic screening medical image target tracking method and device based on visual Transform, and relates to the technical field of computer vision, and the method comprises the steps: standardizing near-infrared or visible light fundus video frames into uniform resolution, constructing a template-search frame pair, and then jointly mapping the two frames of images into a Token sequence; a dynamic local interaction module is embedded in front of each pruning layer of the whole network, a local context is captured by using depth separable convolution and point convolution, a dynamic convolution kernel generator is driven, and a neighborhood Token is adaptively weighted and aggregated. Next, the Token screening and the compression mechanism TSC are operated in the same pruning layer, only the Top-K key Token is reserved, the redundant Token is cut off, and the original index is recorded; the objective of the invention is to improve the positioning stability and reasoning efficiency of a focus area (such as an optic disc) in a complex operation video.

The invention discloses an RGB data and event data fusion target tracking method based on visual Mama, relates to the technical field of computer vision, and solves the technical problem that efficient and accurate target tracking of RGB data and event data is difficult to realize. The method comprises the following steps: performing frame-level synchronization and conversion on an original RGB image of an RGB camera and an original event stream of an event camera to generate RGB input data and event input data; obtaining an RGB feature map and an event feature map through a feature extraction module of the Mamba network; performing linear transformation and deep convolution operation through an interaction module to obtain RGB features and event features; dynamically fusing the RGB features and the event features through a fusion module to obtain cross-modal fusion features; and the template features and the search features are aligned through an alignment module to generate an alignment feature graph, and target positioning and tracking are completed. According to the method, through efficient modal fusion and the state space model, the target tracking precision and robustness in a dynamic scene are remarkably improved.

The invention relates to the technical field of vision, in particular to an object monitoring method based on a multi-camera joint calibration technology and a monitoring camera thereof. The method comprises the following steps: driving all cameras to synchronously shoot a calibration reference object with known geometric characteristics, and solving internal and external parameters of each camera and an accurate space pose relationship between the internal and external parameters by utilizing shot images to form a joint space relationship chain; controlling the camera array to synchronously shoot a target object, and correspondingly converting the two-dimensional image points acquired by the cameras into spatial point coordinates in a unified three-dimensional coordinate system by using the joint spatial relation chain to generate three-dimensional point cloud data of the target object; and processing the generated three-dimensional point cloud data, extracting key geometric features of the target object in real time, performing comparative analysis on the extracted features and a preset standard or a historical state, and outputting a state monitoring result of the target object. According to the invention, the influence of temperature drift and mechanical vibration on the measurement precision is effectively suppressed, and the stability of long-term monitoring of an industrial field is guaranteed.

The invention discloses a UE4-based digital twin model construction system and method, and relates to the technical field of computer vision, and the system comprises a geometric modeling unit which generates three-dimensional model data based on geometric modeling data of a target physical entity, and achieves dynamic LOD rendering level switching through virtualization processing; the real-time data interaction unit is used for analyzing the real-time sensor data flow and generating equipment state data in combination with a transmission operation and maintenance instruction; the simulation engine unit is used for executing rigid body motion and collision simulation based on the equipment state data and outputting a collision event sequence and state transition data by utilizing an event trigger; the machine learning unit is used for training an LSTM (Long Short Term Memory) model based on the collision event sequence and the state transition data, and optimizing state covariance parameters through adaptive Kalman filtering; and the visual interaction unit is used for realizing data synchronization of the command large screen and the mobile terminal through multi-screen collaboration. According to the method, the digital model capable of accurately reflecting physical entity behaviors and characteristics is constructed.

The invention discloses a steel surface defect detection method and system, and belongs to the technical field of computer vision, and the method comprises the steps: obtaining a steel image data set, and carrying out the preprocessing, and obtaining a target data set; then, a YOLOv11 network model is constructed, a detection head is added to a detection layer of the YOLOv11 network model, and meanwhile, a C3k2-MSM module is constructed to replace a C3k2 module in the backbone network; and then, all detection heads are replaced by heavy parameter lightweight detection heads, and an improved YOLOv11 network model is obtained. And performing model training by using part of data of the target data set, and testing the other part of data. And finally, inputting a to-be-detected image into the tested model, and outputting a steel surface defect detection result. By optimizing the network structure and the detection head, the detection precision and efficiency are improved, and automatic efficient real-time detection of steel surface defects is realized.

The invention relates to the technical field of neural network model optimization, and discloses an edge vision AI neural network model optimization method and system, and the method comprises the steps: carrying out the visual feature layering adaptive analysis of an input image, and obtaining the importance distribution of visual features, constructing an initial neural network model adapted to the edge device according to the importance distribution of the visual features; performing energy consumption and precision balance parameter optimization on the initial neural network model to obtain edge device optimization parameters; training the initial neural network model to obtain a trained edge vision model; performing visual semantic perception model pruning on the trained edge visual model to obtain a target network structure; according to the method, deployment optimization adaptive to hardware characteristics is executed according to the target network structure to obtain a visual model for efficient operation of the edge device, so that the model can adapt to heterogeneous characteristics of different edge computing platforms, and the application feasibility of a visual AI technology on diversified edge devices is improved.

The invention relates to the technical field of financial bill management, and discloses a financial bill automatic identification generation and decision-making method and system, and the method comprises the steps: firstly receiving an original bill image, carrying out the preprocessing of the original bill image, carrying out the format analysis and classification of the enhanced image through a computer vision technology, and distinguishing a known standard format from an unknown format. For a known format, accurately extracting a key region text block by applying an OCR module based on template positioning; and for an unknown format, a general document understanding model is adopted to obtain a preliminary key information key value pair. And then, the extracted information is converted into structured data through a key information structured extraction module, and the structured data is verified by a data verification and business rule engine. If the verification is passed, structured bill information is output; otherwise, sending to a manual intervention queue for further auditing. According to the method, the efficiency and accuracy of financial bill automatic processing are effectively improved, and meanwhile, the flexibility of the system for adapting to different bill formats is enhanced.

The invention discloses a method for determining a preset position of a video monitoring camera. Camera presetting positions usually need to be manually set one by one, and time and labor are wasted. The method is used for a video monitoring system, and automatic setting of the preset position of the camera is realized by utilizing a group of scene images and the to-be-monitored target image, so that the efficiency is greatly improved. The method comprises the following steps: acquiring feature points based on a scene image set by adopting a computer vision technology, generating a three-dimensional model of a scene by adopting a motion recovery structure technology and a surface reconstruction algorithm, and obtaining the position of a to-be-monitored target in a three-dimensional space by matching the feature points of a to-be-monitored target image with the feature points of the scene image set. Therefore, a proper camera is selected, a corresponding preset position is calculated, and finally the preset position is corrected according to an actual monitoring effect.

The invention provides a robot navigation method and system based on visual identification, and relates to the technical field of computer vision, firstly, a continuous visual information set of the surrounding environment of a robot is obtained, the continuous visual information set comprises environment visual images of different directions of the advancing direction of the robot, and the object appearance features and the spatial arrangement relation are recorded; then establishing association mapping between the continuous visual information set and a preset navigation area to obtain a double-association mapping result; generating a semantic anchor point navigation path based on a double-correlation mapping result, wherein the semantic anchor point navigation path comprises a semantic anchor point sequence from the current position to the target position; real-time visual semantic features in the navigation process are obtained through a real-time visual collection module, and a semantic anchor navigation path is dynamically updated; finally, a final navigation execution path is output according to the updated path, the robot is driven to complete navigation operation, and the navigation capability and the intelligent level of the robot in a complex dynamic environment are improved.

The invention discloses a three-dimensional scene and object reconstruction method, system and device based on SDF and a Gaussian field, belongs to three-dimensional scene reconstruction in the technical field of computer vision, and aims to solve the technical problem that the three-dimensional scene reconstruction quality is not high due to the fact that geometric precision and rendering quality cannot be achieved at the same time in the three-dimensional reconstruction process in the prior art. The method comprises the following steps: initializing and updating a TSDF voxel volume by using a depth map and an RGB image to obtain a camera external parameter and a scene three-dimensional initial model; sDF prediction is carried out on the scene three-dimensional initial model through an SDF neural network, scene surface points are generated according to the light direction, and a high-precision scene geometric model is obtained; training the SDF neural network; performing grid division on a bounding box of the scene three-dimensional initial model, outputting an SDF predicted value of each grid point by using the trained SDF neural network, and extracting a refined scene grid model; and performing Gaussian rendering by using the high-precision scene geometric model to generate a final rendered image.

The invention discloses a video abstraction method based on a graph model and a multi-scale attention mechanism, and relates to the technical field of computer vision. According to the video abstraction method based on the graph model and the multi-scale attention mechanism, the multi-scale attention mechanism is introduced, modeling of a local inter-frame relation and modeling of a global long-term dependency relation are carried out separately, the deviation of attention weight in the calculation process is effectively reduced, meanwhile, the GATv2 graph attention mechanism is adopted to extract the relevance between local frames, and the accuracy of video abstraction is improved. According to the method, global feature information is aggregated by using a CLS identifier, efficient fusion of global and local information is ensured, redundant frames are removed by using non-maximum suppression, the quality of video abstracts is further improved, the redundancy problem caused by position information injection is avoided, and more accurate inter-frame relation modeling and video content abstracts are realized.

The invention discloses a multi-modal deep learning method for artistic work analysis, and relates to the technical field of computer vision, and the method comprises the steps: multi-modal data preprocessing: obtaining a visual image, text description and metadata of an artistic work, the method has the advantages that valuable feature vectors are extracted from visual images, text description and metadata of artistic works through multi-modal data preprocessing, the importance of all modal features is dynamically adjusted by means of an attention mechanism in the cross-modal dynamic fusion step, style information of different sources can be effectively integrated, and the effect of improving the artistic work quality is achieved. In the style migration stage, the multi-scale feature pyramid network is used for fusing feature information of different levels, real-time feedback optimization is continuously evaluated and parameters are adjusted through a discriminator, and compared with an existing neural style migration method, the method has the advantages that complex artistic styles are better processed, core elements of specific styles are accurately reserved in the migration process, and the method is suitable for popularization and application. And the accuracy of style migration and the artistic expressive force are improved.

The invention discloses an image data annotation method and system, and relates to the technical field of computer vision, and the method comprises the steps: collecting image and text data, carrying out the updating through kernel density estimation and a kinetic equation, carrying out the optimization through employing Euler discretization in combination with CLP propagation and EA, carrying out the conversion through amplitude coding, and carrying out the construction through a quantum state circuit. The method comprises the following steps: carrying out calculation by using partial traces, generating topological weighted entanglement entropy through weighted fusion, carrying out updating based on an attention mechanism, generating potential conflict pairs, calculating priorities, generating final conflict pairs through a CNN model, outputting region features through a NeRF model, carrying out extraction by using a CLIP model, and carrying out updating through a loss function. According to the method, VAE is combined with GLP propagation and EA for optimization, the precision and consistency of labeling are improved, and through joint verification of quantum entanglement entropy calculation and the NeBF model, the efficiency and accuracy of labeling are improved.

The invention provides an image segmentation labeling method and system based on machine learning, and relates to the technical field of computer vision, and the method comprises the steps: dynamically selecting three detection points in a key region of a segmentation boundary based on the boundary form of an initial segmentation result, and constructing a dynamic triangular mesh unit; calculating a geometric characteristic value according to the vertex coordinates of the dynamic triangular mesh unit, wherein the geometric characteristic value comprises a proportional relation between the area and the side length of the triangular mesh unit; comparing the geometric characteristic value with a preset threshold range, and if the geometric characteristic value exceeds the threshold range, generating a boundary coordinate correction parameter; and adjusting boundary coordinates of the lung region segmentation result according to the correction parameter, and generating a corrected lung region segmentation result. Through end-to-end operation from image acquisition to lesion marking, the workload of doctors is reduced, and based on the segmentation result, the lesion marking unit can accurately position the lesion position.