14112 results about "Three dimensional model" patented technology

The invention discloses an unmanned aerial vehicle routing inspection line adaptive obstacle detection method and system based on a monocular camera. The method comprises the steps of obtaining initial flight parameters, evaluating and optimizing the flight parameters based on scene complexity, collecting an original image sequence and carrying out quality evaluation and exposure compensation. Constructing a multi-resolution feature pyramid, extracting feature descriptors, and performing feature matching and verification; performing multi-constraint pose optimization and depth estimation based on a feature matching result, and reconstructing a three-dimensional point cloud; calculating direction enhancement response to extract a power line candidate area, performing multi-view line segment matching and three-dimensional reconstruction, and analyzing dynamic characteristics of the power line; and based on the power line three-dimensional model and the depth map, carrying out obstacle segmentation, calculating an obstacle distance, evaluating a risk level, and generating early warning information and a monitoring scheme. According to the invention, different scenes can be adaptively processed, the obstacle risk can be accurately identified and evaluated, and the inspection efficiency and reliability are improved.

The present invention provides a three-dimensional reconstruction method integrating a laser radar and oblique photography, comprising: S1, acquiring an oblique image and image data and preprocessing same to obtain a first oblique image, wherein the image data comprises POS data; S2, acquiring coordinates of a ground control point and high-precision POS data; S3, correcting the oblique image to obtain a second oblique image; S4, performing aerotriangulation on the second oblique image, and obtaining an effective matching point pair by means of improved SIFT; S5, performing combined adjustment on the basis of the effective matching point pair and performing dense matching to obtain a dense point cloud; S6, acquiring a laser point cloud and preprocessing same; S7, unifying the coordinates of the laser point cloud and the dense point cloud, and using improved ICP to obtain a fused point cloud; and S8, constructing a triangulated irregular network and performing texture mapping to generate a three-dimensional model. In the present invention, on the basis of the advantages of high resolution and strong anti-interference capability of a laser radar, defects in an oblique photography three-dimensional model are mitigated, and meanwhile, an oblique image is corrected and an algorithm used in feature matching is improved, thereby improving the precision, integrity and efficiency of the three-dimensional model.

The embodiment of the invention provides a scene space three-dimensional model dynamic modeling method based on multi-modal data, and the method comprises the steps: fusing the feature information of a video sequence, point cloud data and IMU data, and building a space-time corresponding relation through employing a particle filtering algorithm. Scene semantic understanding is realized based on a Transform multi-modal scene analysis network, scene scores are calculated in combination with spatial density, depth occlusion and shape change features, and an optimal modeling algorithm is selected through a strategy network of deep reinforcement learning training. Meanwhile, scene dynamic features are analyzed by using a deep neural network and a graph neural network, adaptive grid subdivision and geometric constraint reconstruction of a dynamic region are realized, and a reconstruction threshold is dynamically adjusted according to a scene semantic type. According to the method, the three-dimensional reconstruction quality and efficiency of a large-scale complex scene are effectively improved.

The invention discloses a smart park full life cycle management system and method based on digital twinning and Internet of Things, and relates to the technical field of smart park management, and the system comprises a sensing edge module, a data governance module, an intelligent analysis module, a life cycle module and a twinning modeling module. According to the invention, multi-protocol access and edge computing capability are supported, and the data transmission efficiency and stability are greatly improved; the intelligent analysis module outputs an accurate analysis result by constructing a multi-class feature matrix and deep multi-task joint modeling mechanism, and provides data support and model guidance for dynamic management and intelligent decision making of the park; the life cycle module integrates a Kepler optimization algorithm and a multi-agent reinforcement learning and simulated annealing algorithm, establishes a collaborative optimization mechanism, realizes combination of global search and local fine tuning of resource scheduling, and effectively optimizes energy consumption, response time, space utilization and safety risks; and the twin modeling module constructs a park three-dimensional model, so that the interactivity and operability of the system are improved.

The invention discloses a multi-modal perception humanoid robot action adaptive control method and system, and belongs to the technical field of computers, and the method comprises the steps: obtaining first multi-modal data; the first multi-modal data comprises first image data, first point cloud data and first force sense data; performing data fusion on the first image data and the first point cloud data, and constructing a three-dimensional model of the scene in real time; generating an optimal path according to the real-time environment data of the three-dimensional model, converting the optimal path into a joint instruction of the humanoid robot, and generating an action control strategy; according to the action control strategy, arm movement of the robot is adjusted through visual servo control, and first operation data are obtained; the path and action of the robot are adjusted in a closed-loop control mode, and second operation data are obtained; and reinforcement learning and regular evaluation are carried out through a DDPG algorithm, and an action control strategy is continuously optimized. According to the invention, multi-modal sensing robot motion adaptive control with strong adaptive capability, high operation precision and good robustness can be realized.

The embodiment of the invention discloses a visual inspection and quality evaluation method for a small structural component of an airplane, and the method comprises the steps: S1, obtaining three-dimensional point cloud data, and carrying out the preprocessing of the three-dimensional point cloud data; s2, carrying out multi-angle two-dimensional image acquisition; s3, mapping the two-dimensional image texture obtained in the S2 to the surface of a three-dimensional model, and generating the three-dimensional model comprising surface information; s4, extracting geometric features and surface texture features from the three-dimensional model; s5, fusion and comparative analysis are carried out to identify potential anomalies and defects; s6, outputting specific abnormal types and positions; and S7, performing comprehensive quality evaluation according to an anomaly detection result in the step S6, and generating a quality evaluation report. According to the technical scheme provided by the embodiment of the invention, by fusing three-dimensional scanning and high-resolution two-dimensional image technologies and adopting the classification model based on machine learning, the comprehensiveness, accuracy and efficiency of detection are remarkably improved, so that the production quality and safety are effectively improved.

The invention relates to the technical field of geological risk monitoring and early warning in tunnel construction, in particular to a karst tunnel risk intelligent early warning method, device and equipment based on deep learning, and the method comprises the steps: obtaining multi-source coupling data; performing high-dimensional fusion on the processed multi-source coupling data by using a generative adversarial network, and reconstructing a high-precision three-dimensional model of the detection area; performing geological risk grade classification on the detection area to generate a corresponding risk distribution map; generating a dynamic risk evaluation index, and triggering an early warning signal when the risk level exceeds a preset threshold value; the method comprises the steps of multi-source data acquisition, data preprocessing, high-precision three-dimensional model reconstruction, risk grade classification, dynamic risk monitoring and early warning and the like, and can solve the problems of single risk detection means, insufficient evaluation precision and poor real-time performance in existing karst tunnel construction.

The embodiment of the invention provides a three-dimensional model adaptive generation method based on video and point cloud data, scene data is collected through a multi-view camera array and a depth scanning array, and key features are extracted and screened by using a hierarchical feature extraction network and an attention mechanism. And organizing local data blocks by adopting a hierarchical index tree based on a union-check set, and realizing self-adaptive parameter optimization by combining integrity and accuracy scores. Meanwhile, efficient reconstruction and material mapping of a local three-dimensional grid are realized through a parallel operation channel and a dynamic calculation resource scheduling mechanism. According to the method, the precision and efficiency problems of a traditional three-dimensional reconstruction scheme in processing a large-scale scene are effectively solved, and the model generation quality is improved.

The invention discloses an electric power capital construction process early warning method and system based on a high-precision three-dimensional model, and relates to the technical field of electric power capital construction, and the method comprises the steps: collecting multi-source data of an electric power capital construction site, and constructing a high-precision three-dimensional model comprising equipment space coordinates, construction time sequence labels and environmental parameters; inputting the space-time diagram into a space-time diagram convolutional network, outputting a risk propagation probability matrix of a three-dimensional space and a construction time sequence dimension, and identifying high-risk nodes and associated construction stages; and dynamically marking root cause coordinates and a risk diffusion range in the high-precision three-dimensional model, generating an early warning instruction containing a visual path, a root cause report and a disposal priority, and pushing the early warning instruction to a terminal in real time through an edge node. Based on the hierarchical space-time attention-physical constraint graph convolutional network, the inter-node cross-stage association weight is dynamically allocated, the convolution kernel parameters are optimized in combination with the physical connection strength, and the space-time association of high-risk node identification is significantly improved.

The invention provides a management method and system for hazardous chemicals in a laboratory, and relates to the technical field of hazardous articles, and the method comprises the steps: obtaining hazardous article operation video data of an operator through an image collection device, extracting key node coordinates and skeleton movement track information, and mapping the information into standardized operation behavior sequence data; and a time sequence behavior evaluation model constructed based on a long and short term memory network is used to identify the non-standard operation fragments, a risk level coefficient is calculated in combination with a dangerous goods attribute information base, and a multi-dimensional evaluation report is generated. And according to the evaluation report, calling a correction strategy library to generate an improvement suggestion containing characters, videos and three-dimensional models, and pushing the improvement suggestion to an operator. By monitoring the behavior of the operator after executing the improvement suggestion and dynamically updating the risk score and the time sequence behavior evaluation model, the dynamic optimization of the evaluation standard is realized, and the safety of the laboratory hazardous chemical substance operation is effectively improved.

The invention provides a real-time map updating method and system based on multi-source geographic information data fusion. Wherein a three-dimensional model is constructed by integrating traffic flow data, remote sensing images and road network topology, the road traffic pressure is quantified, and a feature correlation index is established. And obtaining a vehicle displacement vector and a speed gradient in combination with laser point cloud and video monitoring data, generating a dynamic road feature data set, and establishing a mapping relationship with the index table. On the basis, a road state prediction model is constructed, and through correlation analysis of traffic pressure data and a real-time feature data set, deep correlation between vehicle motion features and road states is established. And dynamically adjusting a road network connection structure, synchronously updating mapping parameters and predicting model precision, and realizing real-time adaptive adjustment of the road network topology. According to the technical scheme provided by the invention, the real-time performance of map updating and the road condition prediction accuracy are remarkably improved, and the urban traffic congestion index can be reduced.

The invention discloses an intelligent supervision management and control method and system, and the method comprises the steps: generating a violation operation recognition result and an equipment abnormality early warning signal according to construction scene multi-mode data collected by an intelligent camera and real-time monitoring information of an Internet of Things sensor; generating a non-tampering block chain evidence storage data chain based on the violation operation identification result and the equipment abnormity early warning signal; according to the engineering change information in the block chain evidence storage data chain, updating a BIM collaboration platform through an incremental three-dimensional model synchronization technology, and generating a multi-party real-time interaction enhanced three-dimensional model; based on an enhanced three-dimensional model and Internet of Things environment monitoring data, a digital twin mirror image space is constructed, a construction process is simulated through a multi-source data fusion deduction engine, and a risk pre-judgment map and a resource configuration optimization strategy are generated. According to the embodiment of the invention, visual supervision, credible tracing and prospective decision making of the construction process can be realized.

The invention discloses a coal mine hole fracture seepage analysis and prediction system based on a deep learning model, and relates to the technical field of coal mine safety. Comprising a geological data acquisition and three-dimensional model construction module, a data preprocessing and cleaning module, a deep learning feature extraction and training module, a graph neural network optimization and topology modeling module and a seepage prediction and risk assessment module, geological exploration data and hydrogeological information of a coal mine area are obtained, and a three-dimensional geological model containing pores and fractures is constructed. Through the deep learning method combining the convolutional neural network and the graph neural network, the accuracy of coal mine fracture seepage analysis is remarkably improved. The CNN extracts local features, the GNN optimizes a global topological structure, and the seepage prediction precision is improved. The model not only can accurately predict a seepage path, but also can evaluate safety risks, help mine managers to identify hidden dangers in advance, reduce water damage and gas accumulation accidents, and optimize mine safety management.

The invention discloses a three-dimensional environment reconstruction optimization method based on multi-sensor fusion data, and relates to the field of three-dimensional environment reconstruction optimization, and the three-dimensional environment reconstruction optimization method based on the multi-sensor fusion data comprises the following steps: S1, collecting multi-source sensor data, and constructing a data set under a unified coordinate system; s2, generating dense visual point cloud, and extracting laser point cloud features to construct a model; s3, establishing a local three-dimensional model, and generating a local environment image; s4, shadow parameters are extracted through shadow geometric analysis, and time sequence optimization is carried out; s5, consistency verification and correction are carried out, and three-dimensional reconstruction data are output; and S6, comparing the reconstruction data with the navigation map database, and carrying out map optimization updating. According to the method, time synchronization and space calibration are carried out on data acquired by the depth camera and the laser radar, complete and accurate three-dimensional information modeling of the target environment is realized, and the geometric precision of environment reconstruction and the image detail reduction capability are improved.

The invention relates to the technical field of three-dimensional model adjustment, in particular to a three-dimensional model adjustment method and system and a medium. The method comprises the following steps: obtaining a multi-angle image of an original model, carrying out multi-view normalization on the multi-angle image, generating a normalized view image set, extracting feature points of the original model, carrying out parallax correction on the feature points, reconstructing a simulation three-dimensional model, collecting basic purpose data of the model, carrying out ideal demand mapping through the data, and carrying out ideal demand mapping. The method comprises the following steps: determining an ideal three-dimensional model structure, carrying out core region segmentation on a reconstruction model according to basic purpose data to obtain key region model slices, carrying out highlight region comparison with the ideal model structure, analyzing model differences, determining a structure adjustment amplitude interval according to a comparison result, and carrying out cyclic fine adjustment correction on the key region model slices to obtain a three-dimensional model. And the three-dimensional model is consistent with the ideal three-dimensional model in structure, so that the optimized three-dimensional model is generated. According to the invention, efficient and accurate three-dimensional model adjustment and optimization are realized.

The invention relates to an electric power working environment intelligent positioning and risk management and control method based on a three-dimensional model, and the method comprises the following steps: S1, constructing a three-dimensional model of an electric power working environment, and carrying out the spatial layering and equipment marking of the three-dimensional model, and forming a three-dimensional panoramic working image with attributes; s2, marking positions of operating personnel and equipment in real time, and generating dynamic positioning in a power operation scene; s3, recording and predicting the track of the operator, and pre-warning whether the operator enters a safe area or a dangerous area; a camera is used for collecting images of the operating personnel, non-standard behaviors of the operating personnel are recognized in real time through an AI algorithm, and behavior risk indexes of the operating personnel are generated based on detection results; s4, training a risk prediction model based on sensor data, detecting possible risk events, and obtaining an operation risk index of the equipment; and S5, calculating a comprehensive risk index in real time. According to the invention, the comprehensive safety of personnel, equipment and environment is guaranteed while the safety management efficiency is effectively improved.

The invention relates to the technical field of construction data modeling, in particular to a BIM (Building Information Modeling)-based construction progress dynamic optimization system, which comprises an abnormal event identification module used for detecting local abnormity based on multi-source data and marking an abnormal area; the local component reconstruction and dynamic fusion module is used for reconstructing an abnormal region component model and performing space mapping to obtain a global BIM model; the component-semantic double-layer graph modeling module is used for constructing a component topological dependency graph and a component semantic relation graph and jointly reasoning a state transition sequence of the component; the state recognition and self-adaptive classification module is used for fusing the geometric features of the component and the graph structure features and executing state recognition and judgment boundary adjustment; and the graph evolution calculation module is used for performing multi-round state propagation deduction to generate a component state evolution result. According to the method, in a heterogeneous information modeling environment, dynamic processing and state intelligent identification of component-level information are realized, and the data calculation precision and response capability under three-dimensional model driving are improved.

The invention relates to an injection molding product defect detection method based on machine vision, which comprises the following steps: collecting material information of a to-be-detected injection molding product in real time, and dynamically matching and adjusting light source parameters according to spectral reflection characteristics of materials to ensure image collection quality; secondly, the collected images are preprocessed, edge features and texture features are extracted, a three-dimensional model is constructed through multi-view image splicing, and three-dimensional defect features are extracted; thirdly, the multi-dimensional features are input into a deep learning model, the defect probability is calculated through feature fusion and forward propagation, and whether the product has defects or not is judged; if the defect exists, further identifying the defect category, and calculating the number and size of the defect; and generating a standardized detection report based on the defect information. According to the method, the image adaptability of products made of different materials is improved through dynamic light source adjustment, the two-dimensional and three-dimensional features are fused, the defect recognition accuracy is improved, and full-process automation from qualitative judgment to quantitative analysis of the defects is achieved.

The invention belongs to the technical field of three-dimensional airspace management, and provides a three-dimensional airspace rasterizing management method and system based on multistage dynamic division, and the method comprises the steps: dividing height layers for a three-dimensional airspace, carrying out the rasterizing division of the three-dimensional airspace in each height layer, and carrying out the rasterizing division of the three-dimensional airspace; a live-action three-dimensional model of a three-dimensional airspace of a target area is constructed through multi-source data fusion, passable grids are marked, selection priority values of the passable grids are calculated through multi-source data fusion, when an aircraft is about to enter the three-dimensional airspace, route dynamic priority values of the grids are initialized, and a basic planning route of the aircraft is generated. Detecting the collision risk of the aircraft on the grids in the basic planning route, dynamically adjusting the route dynamic priority value, updating to obtain the actual planning route of the aircraft, monitoring the space between the aircrafts in the three-dimensional airspace in real time, and judging whether the collision risk exists between the aircrafts; and if there is a collision risk signal, triggering an aircraft obstacle avoidance and path re-planning strategy, and dynamically adjusting and updating the grid side length.

The invention discloses a power transformer partial discharge positioning method based on multi-sensor array fusion, and the method comprises the following steps: S1, selecting a sensor installation point, and laying a multi-sensor array structure; s2, partial discharge signals of the three types of sensors are collected, and primary signal processing is carried out; s3, calculating propagation time differences between the reference channel and other channels by adopting a generalized cross-correlation weighting algorithm, and generating a time difference matrix; s4, constructing a TDOA model in combination with the layout coordinates and the time difference matrix, and solving three-dimensional initial coordinates of a power supply; s5, establishing a structure correction model, compensating the path deviation, and outputting corrected positioning coordinates; s6, calculating an error and generating a confidence score; s7, mapping a positioning result to the three-dimensional model and generating an image; and S8, writing the positioning information into a database for filing management. According to the method, the multi-frequency sensor and the path correction model are fused, and high-precision three-dimensional positioning of partial discharge of the transformer is realized.

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

The embodiment of the invention provides a welding path planning method and device, and the method comprises the steps: carrying out the three-dimensional scanning of a to-be-processed object through a machine vision algorithm, obtaining three-dimensional point cloud data, carrying out the data analysis of the three-dimensional point cloud data through a segmentation algorithm based on deep learning, and carrying out the recognition to obtain geometric features; the geometric features are constructed into an undirected graph, an optimal communication path of a weld joint center line is searched on the graph through a minimum spanning tree algorithm, and a fine three-dimensional model of the machined object is constructed through the optimal communication path; according to the three-dimensional coordinates of the welding seam locating point, the current welding gun posture and the target welding seam length, the optimal locating height, locating depth, inclination angle and offset of a welding gun are determined, and a corresponding welding path plan is generated through a welding process simulation algorithm based on the locating height, the locating depth, the inclination angle and the offset; the precision and efficiency of welding path planning can be effectively improved.

The invention belongs to the technical field of digital modeling, and relates to a three-dimensional automatic modeling method and system based on an AI large model technology, and the method comprises the steps: obtaining multi-modal data inputted by a user, and converting the multi-modal data into feature vectors in a unified coding format; generating a conflict arbitration result based on a priority rule preset by the user or a default physical reasonability rule; according to a conflict arbitration result, generating an initial three-dimensional model through a large language model and a neural radiation field which are jointly trained; inputting the initial three-dimensional model into a physical simulation engine for physical verification, and outputting a correction model containing a structure defect mark; the correction instruction is analyzed into a model parameterization adjustment command; and based on a parameterized adjustment command, correcting an associated region in the model to perform incremental solution and re-rendering to generate a final three-dimensional model, and updating a verification result of the physical simulation engine. According to the method, the problem that a fuzzy semantic instruction is difficult to accurately convert into a model modification action due to the fact that a multi-modal interaction technology stays at a basic parameter adjustment level is solved.

An example operation may include at least one of receiving, via a user interface of a device, an activation input from a user to initiate a session, capturing, by a camera of the device, a scan of a body of the user, wherein the capturing comprises recording at least one image and / or at least one video of the user, processing the at least one image and / or video to generate a three- dimensional model of the user comprising measurements and contours of the body, retrieving, from a database, at least one clothing item associated with the user, the at least one clothing item comprising dimensional attributes and texture attributes, rendering, by a graphics processing unit, the at least one clothing item onto the three-dimensional model to generate a visual representation, wherein the rendering simulates draping behavior, movement, and light interaction of the at least one clothing item relative to the three-dimensional model, and displaying, on the user interface, an interactive visualization comprising the visual representation of the three-dimensional model with the at least one clothing item from multiple viewing angles.

The invention discloses a surgical robot resection navigation method based on multi-modal image registration. The method comprises the following steps: acquiring a preoperative multi-modal medical image of a patient and reconstructing a three-dimensional model containing a focus and a dangerous structure; acquiring an intraoperative real-time image; extracting cross-modal invariant features from the pre-operative and intra-operative images by using a first deep learning network, and establishing an initial corresponding relation; predicting an initial non-rigid deformation field by using a second deep learning network, based on the pre-operative / intra-operative image and the initial corresponding relation and in combination with physical model constraints; tracking a surgical tool in real time, and updating a non-rigid deformation field in real time by adopting an incremental calculation strategy; applying the updated deformation field to the preoperative three-dimensional model to generate a deformed model; and mapping the tool position to the deformed model, and generating a navigation instruction. According to the method, soft tissue deformation can be accurately compensated, multi-modal image information is effectively fused, navigation information is dynamically updated in real time, and the precision and safety of robot surgery are remarkably improved.

The invention relates to the technical field of building model design, in particular to a building three-dimensional model lightweight design method and system based on artificial intelligence, and the method comprises the steps: extracting geometric-semantic features of a building model through a multi-scale curvature filtering and semantic segmentation network, constructing a fusion feature vector matrix, and obtaining a fusion feature vector matrix; by utilizing an integrated graph convolutional network and a self-adaptive neural simplification network of a double-branch attention mechanism, differential resampling is executed based on vertex importance weight, a simplified intermediate model is generated, a surface microstructure is recovered by means of a generative adversarial network, grid holes are corrected by combining Delou inner triangulation, and a surface microstructure is obtained. A non-uniform rational B-spline curved surface is adopted to reconstruct a key decoration component and output a lightweight model, so that the problems of insufficient geometric feature retention and semantic information splitting in the traditional technology are solved, intelligent, efficient and lightweight of a building three-dimensional model is realized, visual fidelity is ensured while data compression is performed, and the construction quality is improved. And the digital management requirement of the whole life cycle of the building is met.

The invention provides a virtual reality building roaming method and system based on scene rendering, and relates to the technical field of virtual reality, and the method comprises the steps: dividing building three-dimensional model data through an octree space segmentation algorithm, and determining a detail level threshold according to a viewpoint distance, and constructing a multi-level detail model. And baking the basic illumination chartlet by using a pre-calculation irradiance transmission technology, and performing real-time synthesis in combination with the multi-resolution shadow chartlet. And calculating illumination by adopting a physics-based rendering method, and fusing the illumination with the scene light and shadow synthesis data to generate dynamic illumination rendering data. And determining a current viewpoint based on the spatial position and attitude data of the virtual reality head-mounted display, performing view cone elimination and shielding elimination, and performing GPU instantiation rendering on visible building data. And the rendered image is optimized through high dynamic range processing and a time domain anti-aliasing technology, and a final scene image is generated in combination with an atmospheric scattering algorithm. According to the invention, the rendering efficiency and scene reality sense of virtual reality building roaming can be effectively improved.

The invention relates to an intelligent detection and evaluation method and system for underground pipeline defects, and the method comprises the following steps: S1, collecting multi-modal data of an underground pipeline based on an inspection robot, and transmitting the multi-modal data to an intelligent detection and evaluation platform; s2, the intelligent detection and evaluation platform preprocesses the collected multi-modal data; s3, performing defect detection on the underground pipeline based on the preprocessed multi-modal data; s4, constructing a three-dimensional model of the underground pipeline, and superposing a defect detection result into the three-dimensional model; and S5, based on the three-dimensional model of the underground pipeline, performing intelligent evaluation on the underground pipeline, and generating a pipeline condition evaluation report. According to the invention, the efficiency and reliability of underground pipeline defect detection are effectively improved.

The invention relates to the technical field of industrial nondestructive testing and computer vision, and particularly discloses an intelligent detection method for surface defects of a container bottom plate based on deep learning. The method comprises the following steps: synchronously acquiring data through a laser radar and a line scanning camera, complementing a shielding area of a point cloud, and executing coordinate normalization to generate preprocessed data; constructing a lightweight feature alignment network to realize cross-modal feature mapping and pixel-level error correction; segmenting a defect area by adopting an improved PointNet + + network and reconstructing a three-dimensional model; extracting defect geometric parameters and combining with material attributes to perform stress simulation and life prediction; aggregating multi-port safety life data to construct a federated framework to update model parameters; and synthesizing physically real defect samples based on false detection cases, and injecting the defect samples into the network for training. According to the method, the bottleneck of missing detection of internal defects in traditional two-dimensional detection is overcome, the defect detection rate and quantification precision are remarkably improved, full-life-cycle safety evaluation of the container is supported, and the efficient requirement of automatic port inspection is met.

The invention discloses a bridge structure detection method based on an unmanned aerial vehicle, a product, a medium and equipment, and relates to the technical field of bridge structure detection, and the method comprises the steps: obtaining the basic data of a bridge structure, and constructing a three-dimensional model according to the basic data; layering the three-dimensional model of the bridge structure according to a preset interval, and calculating an initial path of the unmanned aerial vehicle; optimizing the initial path by adopting a gradient descent method to obtain an optimal path of the unmanned aerial vehicle; acquiring to-be-detected data of the bridge structure based on the optimal path of the unmanned aerial vehicle; preprocessing the to-be-detected data to obtain preprocessed to-be-detected data; and performing crack detection and deformation detection based on the preprocessed to-be-detected data to realize bridge structure detection. According to the bridge structure detection method based on the unmanned aerial vehicle, the product, the medium and the equipment, efficient and accurate detection of the bridge structure can be realized.