1048 results about "Monocular camera" 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 invention discloses an end-to-end monocular visual odometer method fusing space-time semantic information. According to the method, continuous image sequence frames are collected through a color monocular camera, and a multi-information fusion end-to-end deep learning framework is constructed; a heterogeneous training domain is adopted to set various data set course sharing parameter fusion training, continuous image sequences are input, and the end-to-end deep learning framework is dynamically coupled with hidden state feature vectors output historically, so that a feature mapping relation of time sequence perception is formed; and interpretable feature decoupling of the static background elements and the dynamic entity objects in the scene is realized. After iterative feature fusion, the system outputs sparse depth and camera motion poses which conform to scene geometric constraints, so that a camera trajectory estimation model with high robustness and strong generalization ability in a complex environment is constructed. According to the method, the positioning precision and stability of the monocular vision odometer are remarkably improved.

The invention provides a multi-source heterogeneous data fusion side slope digital twin modeling method, which comprises the following steps of: acquiring side slope multi-dimensional monitoring data by arranging a GNSS (Global Navigation Satellite System) sensor, a multi-point displacement meter, a distributed optical fiber strain sensor, an accelerometer, an osmometer, a monocular camera and satellite remote sensing image equipment; the collected data is converted into a unified format through time alignment, space registration and standardization processing and serves as modeling input; the method comprises the following steps: constructing an initial digital twinborn model reflecting the real form and physical characteristics of a slope by utilizing a three-dimensional modeling and finite element simulation technology; in combination with real-time sensing data, model evolution is dynamically driven based on a space-time fusion algorithm, boundary conditions and material parameters are automatically corrected through actual measurement deviation feedback, and continuous twin iteration updating of the model is achieved; and finally, extracting a landslide risk index to realize real-time early warning of the side slope. According to the invention, multi-source sensing and digital twinborn fusion is realized, and the accuracy, real-time performance and intelligent level of slope monitoring are improved.

The invention provides a posture recognition algorithm for any object under a monocular camera and an application system, and the algorithm comprises the steps: S1, constructing a target three-dimensional model, carrying out the multi-view annular shooting image collection of a target, and generating a dense grid model through feature extraction, matching, posture calculation and a multi-view geometric method; s2, generating an image depth map, and predicting depth information of a target in a motion process based on a monocular image sequence; s3, extracting a target image mask, and generating a target area mask graph through an image encoder, a prompt encoder and a mask decoder; and S4, executing attitude estimation, performing attitude initialization, correction and screening by combining the three-dimensional model, the depth map and the mask map, and outputting a six-degree-of-freedom attitude result of the target. According to the method, the target is subjected to annular shooting modeling through the method based on multi-view geometry, the three-dimensional model of the target is generated, attitude estimation is achieved in combination with the image mask and the depth map, the generalization ability of an attitude estimation algorithm in an actual scene is improved, and the application range of the attitude estimation algorithm in the actual scene is widened.

Provided in the present invention is a three-dimensional reconstruction method based on LiDAR and vision multi-information fusion. The method comprises: calibrating a coordinate system of a LiDAR detection result and a visual detection coordinate system to unify the coordinate systems; obtaining sparse point cloud data of a LiDAR, and projecting data points onto a visual image; performing feature point extraction and depth estimation on an image collected by a monocular camera, so as to perform feature point extraction and depth estimation on a planar image, and thereby densifying a point cloud; fusing feature points, which are extracted from the image, with LiDAR point cloud data to form a dense point cloud, and mapping the dense point cloud to a three-dimensional space; and performing fast triangulation processing on the processed point cloud data to obtain a three-dimensional plane, performing spatial matching on image pixels and the three-dimensional plane, and restoring colors. In the method of the present invention, the LiDAR and the monocular camera are combined to complement each other, so that device costs are reduced, advantages in both robustness and stability are achieved, and the quality of point cloud data can be effectively improved.

A bridge apparent disease inspection method, inspection system and inspection apparatus, relating to the technical field of bridge maintenance. The method comprises: obtaining, in real time, an inspection image set photographed by a monocular camera in a bridge inspection process; dynamically identifying a target disease in the inspection image set by using a visual inspection algorithm; selecting a two-dimensional code positioning point, determining spatial position coordinates of the two-dimensional code positioning point, and calculating a first relative position between the monocular camera and the two-dimensional code positioning point on the basis of an eight-orientation two-dimensional code positioning method; determining a relative distance and a relative angle between the monocular camera and the target disease, and obtaining a relative position between the target disease and the monocular camera by means of calculation by using the relative distance and the relative angle; and obtaining spatial position coordinates of the target disease by means of calculation by using spatial position coordinates of the monocular camera and the relative position between the target disease and the monocular camera. The technical problems in the prior art that bridge apparent disease inspection is mainly manual inspection, the danger coefficient is high, and the inspection result is affected by subjective factors are solved.

The invention belongs to the field of outdoor AGV obstacle tracking, and particularly relates to an AGV obstacle tracking method based on multi-modal data and interactive Kalman filtering. Comprising the following steps: calibrating a monocular camera and a laser radar, and establishing point cloud data and image data with synchronous timestamps; performing preprocessing and target detection operation on the point cloud data and the target detection frame to obtain preprocessed point cloud data and a generated target detection frame so as to obtain obstacle category information; acquiring an obstacle observation position according to the preprocessed point cloud data and the target detection frame; meanwhile, an improved SORT multi-target tracking algorithm is adopted, and a historical track of the obstacle is established; through a plurality of Kalman filtering fusion methods, a trajectory prediction result is obtained through prediction; finally, the AGV communicates with a dispatching center, global information is obtained, and the track trend is corrected in a targeted mode in combination with obstacle category information; and the historical track of the obstacle and the corrected prediction track are sent to the AGV for obstacle avoidance path planning.

The invention discloses a charging robot automobile charging port pose vision measurement method comprising the following steps: 1, controlling a mechanical arm to drive a monocular camera to collect a charging port image, and constructing a charging port image data set; 2, extracting a charging hole contour in each image by adopting an improved Mask R-CNN instance segmentation model; step 3, performing robust ellipse fitting based on each charging hole contour to obtain center coordinates of each charging hole in the two-dimensional image; step 4, establishing a world coordinate system based on charging hole space distribution defined by the automobile charging port standard model, and determining three-dimensional coordinates of each charging hole; and 5, the multi-view two-dimensional center coordinates and the corresponding three-dimensional coordinates are input into the PnP graph optimization model, and the pose of the charging port in the mechanical arm base coordinate system is solved by fusing the re-projection error constraint, the structure prior constraint and the kinematics chain constraint. The invention further discloses a charging robot automobile charging port pose vision measurement charging method and a charging system.

The invention discloses a multi-modal information fusion odometer construction method for star catalogue positioning, and relates to the technical field of star catalogue patroller positioning. The method comprises the following steps: carrying out space joint calibration on a monocular camera, a laser radar and an inertial measurement unit, reconstructing a laser radar point cloud by using a timestamp of a camera image, and realizing time synchronization of the camera image and the laser radar point cloud; establishing an IMU pre-integration error model; and performing motion compensation distortion removal on the laser point cloud by using an IMU pre-integration result, and extracting geometric features of the distorted laser point cloud based on a neighbor region smoothness calculation method of a fixed measurement distance. By researching a multimodal information fusion odometer method, the accumulative error of motion measurement is reduced, the positioning precision and stability are improved, technical support is provided for design and development of a star catalogue navigation system, and the problems that a single-modal star catalogue positioning method is weak in environment adaptive capacity and poor in algorithm generalization are solved.

The invention discloses a monocular vision inertial odometer positioning method based on self-adaptive mixed vision residual errors, and belongs to the technical field of robot navigation positioning. The method comprises the following steps: firstly, respectively carrying out feature point extraction and pre-integration on data of a monocular camera and an inertial measurement unit (IMU), and selecting a strong pixel gradient region according to a feature extraction result; predicting a candidate feature point search window based on IMU (Inertial Measurement Unit) motion information, accelerating feature matching and reducing mismatching, and selecting a key frame at the same time; introducing a direct method model on the basis of feature extraction and matching to construct a luminosity error; a feature reliability evaluation method is designed, and a dynamic factor is adopted to adaptively select and optimize a hybrid visual residual projection error or a luminosity error in real time; and at the back-end optimization stage, a sliding window-based graph optimization method is adopted to optimize and combine with the optimization model to perform pose estimation, so that the purpose of improving the positioning accuracy of the visual inertial odometer in a low-light weak-texture scene is achieved.

The invention belongs to the technical field of three-dimensional reconstruction, and relates to an inspection scene multi-view three-dimensional reconstruction method based on adaptive feature enhancement, which comprises the following steps of: (1) constructing a depth map fusion framework based on laser radar and monocular camera self-calibration, obtaining an inspection scene color image and depth map data, the method comprises the following steps: (1) constructing a routing inspection scene multi-view three-dimensional reconstruction network based on adaptive feature enhancement, (3) carrying out transfer learning fine tuning on the routing inspection scene multi-view three-dimensional reconstruction network, and (4) dividing a routing inspection scene data set and configuring an experimental environment, and carrying out network model training and testing. According to the multi-view three-dimensional reconstruction network provided by the invention, the integrality index on a public data set is superior to that of other network models in a pre-training stage, and each evaluation index on an inspection scene data set is lower than values of a reference network and before fine adjustment in a transfer learning fine adjustment stage, so that the network is more suitable for inspection scene reconstruction through fine adjustment; and an ideal effect is achieved.

The invention relates to a robot environment perception method based on single-view three-dimensional scene generation, and the method comprises the steps: collecting a two-dimensional image containing target environment information through employing a monocular camera, generating multi-view information through combining depth estimation, normal prediction and a two-stage semantic guidance diffusion model, and constructing a high-quality three-dimensional scene. And reconstructing three-dimensional point cloud data through the neural radiation field, and performing texture rendering optimization on the point cloud data. A Point Net + + network is used for carrying out semantic analysis on point clouds, a multi-frame time sequence point cloud registration and Kalman filtering tracking method is introduced, modeling is carried out on a dynamic target, and a dynamic semantic map with a motion state is constructed. And finally, structured output environment information is used for robot navigation, path planning and task execution. The problems of high cost, high complexity and insufficient real-time performance and robustness of a three-dimensional scene generation technology in the field of robot environment perception are solved, and the method is high in structuring degree, standard and unified in output format and high in universality and engineering adaptation capacity.

The invention discloses an aircraft target tracking method and system based on compensation prediction, which are used for improving the target tracking precision in an image transmission delay scene. The method comprises the following steps: firstly, acquiring an image frame sequence of a target aircraft by using an airborne monocular camera, extracting a target center coordinate through a small target detection algorithm, and constructing a position sequence; the method comprises the following steps: extracting current high-frequency I MU data aiming at the condition that an image frame has transmission delay, inputting the current high-frequency I MU data into an LSTM-DKF model constructed by fusing LSTM and a delay Kalman filter, and predicting and generating a process noise and observation noise covariance matrix; and initializing a delay Kalman filter by using the matrix, and recursively predicting the target position during the delay period. And when the delayed image frame is received, backtracking and updating the state of the filter, recurring to the current moment again, and outputting the compensated target position. And finally, pixel deviation is calculated according to the compensation position, an aircraft tracking control instruction is generated, and high-precision target tracking is realized.

The invention relates to a three-dimensional reconstruction method based on a visual odometer and deep learning. The method comprises the following steps: obtaining related parameters of a monocular camera through camera calibration; shooting continuous image frames of the target area under the plurality of camera poses through the monocular camera to obtain image frames of the target area after distortion correction under the plurality of camera poses; estimating relative poses of the camera among different image frames by using a visual odometer according to related parameters of the monocular camera; constructing a cost body of the image frame by using a cost body generation model according to the relative pose of the camera; optimizing the cost body generation model by using the geometric consistency loss and the depth consistency loss, and generating a depth map of the image frame through the optimized cost body generation model; performing noise elimination and hole filling on the depth map of the image frame to obtain an optimized three-dimensional map; according to the method, a stable reconstruction effect can be kept in a complex environment, a low-texture region and a scene with a large visual angle span, and compared with an existing single deep learning reconstruction method, the method has higher robustness and reconstruction precision.

According to the panorama camera and 3D laser scanner mobile measurement matching method, distortion correction, camera calibration and multi-lens relative orientation matching of the panorama camera are carried out based on the characteristics of the panorama camera for a monocular camera and the panorama camera; establishing collaborative matching between the laser data and the image data and a matching and fusion basis between the laser data and the image data; based on a pinhole imaging model, all parameters of camera calibration are introduced to complete camera calibration, and the relative orientation relation between the laser scanner and the panoramic camera is deduced according to a cylindrical or spherical projection method; a cooperative calibration method based on plane constraint is adopted, initial values of rotation and translation vectors between a checkerboard plane and a distance are solved by utilizing a constraint relation between the normal vector and the distance of the checkerboard plane in a laser and camera coordinate system, and finally a global optimal solution is obtained by iteratively optimizing a target equation. The method is simpler to operate and does not need too much manual intervention. And the matching precision is high, the speed is high, and the robustness is good.

The invention relates to a multi-modal fusion-based fence crossing behavior detection method, which comprises the following steps of: firstly, judging whether a person in a video is overlapped with a fence or not through a semantic segmentation algorithm module so as to judge whether the person is in contact with the fence or not, and then acquiring distance information between the person and the fence by utilizing a monocular depth estimation algorithm module; the abnormal behavior detection algorithm module is used for analyzing whether the behavior of the current person deviates from a normal mode, and finally, the detection results of the three algorithm modules are fused to comprehensively judge whether the person is crossing the fence; according to the method, multiple information sources including semantics, depth, behaviors and the like are utilized, the spanning behaviors can be judged more comprehensively and accurately, meanwhile, the method can be achieved only through a monocular camera, the deployment and maintenance cost is lower, the method is more suitable for complex scenes such as shielding and angles, some tiny abnormal behaviors can be captured, and the method is suitable for popularization and application. All-weather automatic monitoring for efficiently and stably detecting the fence crossing behavior in a complex environment is realized.

The invention provides a crack three-way measurement research method and system based on multi-coordinate system fusion, and relates to the technical field of earth and rockfill dam monitoring. The method comprises the following steps: shooting concentric circle target images at multiple angles through a monocular camera, extracting feature points through preprocessing, and correcting projection distortion of non-parallel shooting; establishing a mapping relation between pixels and a three-dimensional coordinate system, and optimizing camera pose parameters; reconstructing a three-dimensional coordinate point cloud based on the multi-view data, screening outliers and fitting plane optimization data; and registering the three-dimensional point cloud to a world coordinate system through a coordinate system conversion algorithm, and comparing two measurement results to calculate the three-way displacement of the crack. According to the method, concentric circle target constraint feature precision is utilized, projection correction and plane fitting are combined to compensate monocular vision depth deficiency, high-precision displacement detection is achieved, and engineering monitoring requirements are met.

The invention relates to the technical field of cabin loading and unloading automation, and provides a multi-sensor fusion cabin loading and unloading equipment cooperative positioning method and system. A laser radar is used for scanning a cabin, laser radar point cloud data are generated, and a laser radar coordinate system and a point cloud map are constructed; a monocular camera is adopted to obtain an operation area image of loading and unloading equipment, camera image data is generated, and a camera coordinate system is constructed; performing anti-interference processing on the laser radar point cloud data; performing illumination change influence resistance processing on the camera image data; fusing the laser radar point cloud data with the camera image data to obtain fused positioning information; calculating the distance and angle of loading and unloading equipment according to the fused positioning information, generating a moving instruction, and driving the loading and unloading equipment to perform cooperative positioning; a dynamic object in a cabin is detected through a laser radar and a monocular camera, multi-modal verification is carried out on a detection result, the dynamic object is filtered, and a point cloud map is updated in real time. The problems of insufficient positioning precision, GPS failure and large dynamic environment interference of a single sensor are solved.

The invention discloses a visual language navigation method and system based on a monocular camera and a language instruction, and the method comprises the steps: S1, feature field construction: employing a 3DGS to construct a 3DGS feature field by employing visual information obtained by a monocular RGB-D camera; s2, implicit partial complementation: inferring the representation of a missing region through a context feature relationship to generate a complete feature map; s3, waypoint prediction: generating an aerial view feature map based on the 3DGS feature field, and predicting nearby navigable waypoints through a waypoint predictor; s4, based on active perception of uncertainty, when the robot is uncertain in navigation decision, more visual information is acquired and missing visual information is supplemented by rotating a camera; and S5, constructing a topological map based on the constructed panoramic feature map and the predicted path points, and carrying out navigation decision making by using a panoramic visual language navigation planning model in combination with a language instruction. The problem that monocular vision information is incomplete is effectively solved, and the success rate and efficiency of navigation are remarkably improved.

The invention discloses a three-dimensional reconstruction method based on an unmanned aerial vehicle and edge calculation, and relates to the field of unmanned aerial vehicle technology, edge calculation and three-dimensional scene reconstruction. The method comprises the following steps: an unmanned aerial vehicle carries a monocular camera to collect a standard-definition video stream from a target scene; the edge computing device receives and processes the standard-definition video stream in real time to generate preliminary low-precision three-dimensional reconstruction; the edge computing device identifies a defect area by analyzing a low-precision reconstruction result, and optimizes the flight path of the unmanned aerial vehicle; the unmanned aerial vehicle collects a high-definition video stream from the target scene according to the optimized path; and the edge computing device performs off-line high-precision three-dimensional reconstruction on the high-definition video stream so as to improve the accuracy and detail expression capability of a reconstructed scene. Through cooperative work of the unmanned aerial vehicle and the edge calculation end, real-time data processing, feedback and decision making in the collection process and high-precision three-dimensional reconstruction result storage in the off-line stage are achieved, and the real-time performance and accuracy of three-dimensional reconstruction are effectively improved.

The invention discloses a container corner fitting positioning method, device and equipment and a storage medium, a target area on a container is shot through a monocular camera and a three-dimensional structured light camera, an RGB image and a depth image corresponding to the target area are obtained, and the target area comprises corner fittings; performing target detection processing on the RGB image to obtain a side hole detection frame corresponding to a side hole in the corner fitting; determining a target image area corresponding to the side hole detection frame in the depth image; converting the target image area into a first three-dimensional point cloud; extracting edge point clouds of the side holes from the first three-dimensional point clouds; determining first attitude information of the side hole relative to the three-dimensional structured light camera based on the edge point cloud; and determining second attitude information of the lockhole in the corner piece relative to the three-dimensional structured light camera based on the first attitude information. According to the embodiment of the invention, the problems of low robustness, weak real-time performance, large scene dependence and high construction cost existing in current corner fitting positioning can be solved.

The invention provides a road vehicle real-time twinning method and system based on a monocular camera, which can sense the change of a multi-target vehicle in a dynamic scene in real time by combining target detection, target tracking and depth estimation technologies, and accurately map the position and the feature of the multi-target vehicle to a virtual twinning scene, so that the real-time twinning of the multi-target vehicle is realized. And the defect of dynamic vehicle twinning support in the existing road twinning scene is overcome. The vehicle features are extracted by adopting a visual question and answer large model, dependence on specific task data and scenes is reduced, generative reasoning and multi-round interaction are supported, problems can be dynamically adjusted, and a new scene can be quickly adapted.

The invention discloses a lightweight unmanned aerial vehicle visual obstacle avoidance method and device based on improved YOLOv8. The method comprises the steps that an improved YOLOv8n network structure is used for obstacle detection; fusing the detected obstacle information and the original depth information, and processing the original depth information based on adaptive distance detection of obstacle attributes to obtain the distance between the unmanned aerial vehicle and the obstacle; designing an unmanned aerial vehicle visual obstacle avoidance algorithm according to the relative position relationship between the obstacle and the unmanned aerial vehicle and the safe flight area without the obstacle, and obtaining the safe flight area; and outputting the next obstacle avoidance instruction, the obstacle information, the standard detection frame, the obstacle type, the confidence coefficient, the distance value between the unmanned aerial vehicle and the obstacle and the maximum obstacle-free area. The device comprises a processor and a memory. According to the method, the problems of large depth estimation error of a monocular camera and high cost of an RGB-D camera are solved, and the binocular camera is adopted for detection and depth information acquisition; the method gives consideration to the detection performance of irregular obstacles and small targets.

The invention discloses a running train mileage positioning method and device based on scene reconstruction. The method comprises the following steps: detecting a semantic target with obvious features from a train running video shot by a monocular camera; target tracking is carried out on semantic targets in the train operation video containing the semantic target detection frame, and the semantic targets in each frame of image are marked with numbers; according to a train operation video containing a semantic target detection frame and a serial number and the optimized monocular SLAM model, camera poses and motion trails obtained after camera pose screening is carried out on prior information of train operation are obtained, and a three-dimensional point cloud map obtained after semantic segmentation is obtained through reconstruction; mileage calibration is carried out according to the camera pose, the movement track and the three-dimensional point cloud map, and running train mileage positioning information and a three-dimensional point cloud semantic map are obtained. According to the method, the train running mileage can be efficiently and accurately positioned.

The invention provides a target position detection method, system and device and a storage medium, and the method comprises the steps: obtaining a conversion relation between a camera coordinate system of a monocular camera and a calibration plate coordinate system, the calibration plate coordinate system being a three-dimensional coordinate system established based on a calibration plate placed on a specified plane; acquiring a detection image shot by the monocular camera; performing depth estimation and target identification on the detection image, and obtaining a three-dimensional coordinate of a target in a camera coordinate system; converting the three-dimensional coordinate of the target in the camera coordinate system into the three-dimensional coordinate of the target in the calibration plate coordinate system based on the conversion relation between the camera coordinate system and the calibration plate coordinate system; and performing target position detection according to the three-dimensional coordinates of the target in the calibration plate coordinate system. According to the method and the device, the target three-dimensional coordinates in the camera coordinate system are converted into the calibration plate coordinate system, so that the visual angle can be converted into the visual angle perpendicular to the specified plane, errors caused by the visual angle are reduced, and the target position detection accuracy is improved.

The present discloses a lightweight attention mechanism distance estimation method for assisting visual navigation of a vehicle at a container terminal. Firstly, using a depth monocular camera calibrated with the imaging parameters of the planar checkerboard tool to collect RGB-Depth image pairs in the working scenario of the automatic guided vehicle. Secondly, performing depth completion and manual annotation processing on the collected depth image. Thirdly, inputting image pairs into a lightweight monocular metric depth estimation framework which uses an improved lightweight attention mechanism Squeeze Former as the token mixer for training. Finally, fusing the results of relative depth estimation and absolute depth estimation to obtain a prediction of an actual distance between an object in the RGB image and the camera in the real world. The method and model provided by the present invention feature simple equipment, low cost, high timeliness of prediction and accurate results.

The invention relates to the field of mobile robot positioning, in particular to a LiDAR-inertia-vision fusion SLAM switching positioning method applied to a degraded environment. According to the method, through a multi-sensor system composed of a monocular camera, an inertial measurement unit and a laser radar which are installed on a mobile robot, original measurement data such as images, angular velocity, linear acceleration and dense space point cloud are acquired, a system state initialization model is constructed, and the multi-sensor system is used to acquire the measurement data. According to the method, the vision-inertia poses and the laser radar poses or linear interpolation of the vision-inertia poses and the laser radar poses are fused, the degradation state can be effectively recognized in real time, and multi-sensor system information switching can be carried out, so that the problem of positioning failure of the laser radar and the vision-inertia odometer module in the degradation environment can be effectively solved; the overall visual SLAM positioning precision and robustness are improved, and the long-term stable operation requirements under the severe conditions of single structure, complex illumination, sparse texture and the like are met.

The invention discloses an intelligent building decoration design method based on deep learning, and the method comprises the following steps: S1), employing a monocular camera to shoot pictures of a building at all angles, and obtaining an original picture; s2) extracting keywords, and making cue words; s3) building decoration data collection; s4) performing data screening and preprocessing; s5) customizing a loss function, and training the diffusion model to obtain a trained diffusion model; s6) inputting the original image into the trained diffusion model to generate a preliminary image; inputting the preliminary image and the cue word into a pre-trained conditional generative adversarial network to obtain a preliminary design drawing; and S7) inputting the preliminary design drawing into a pre-trained variational auto-encoder to obtain an architectural decoration design reference draw.According to the intelligent architectural decoration design method based on deep learning, through the design of the self-defined loss function and the dynamic weight, in combination with the deep learning model, the image is automatically processed, and the design reference drawing is generated, so that the working efficiency is improved.

The invention discloses a 3D attitude estimation method based on a monocular camera, electronic equipment and a storage medium, and the method comprises the steps: firstly constructing a data set composed of real data and synthetic data, then detecting the positions of 2D skeleton key points for the data set through YoloPose, obtaining estimated 3D data through GHUM fitting, obtaining a 3D attitude data set of 3D attitude estimation, and carrying out the 3D attitude estimation of the 3D attitude data set. 3D dimension improvement is added into YoloPose to obtain a 3D attitude estimation model, and the 3D attitude estimation model is trained by the three-dimensional data set and is used for 3D attitude estimation of a two-dimensional image obtained by the monocular camera. The method is low in cost and easy to deploy, does not depend on additional sensors or equipment, can stably work in various illumination and complex environments, and can realize detection and attitude estimation through one deep learning model.

The invention discloses a two-stage object six-degree-of-freedom pose estimation method based on a monocular camera, solves the problem of how to carry out high-precision and high-efficiency pose estimation on weak-texture industrial parts, and belongs to the technical field of monocular camera pose estimation. The invention relates to a coarse-to-fine two-stage pose estimation method. The coarse pose estimation network roughly estimates the pose value of six degrees of freedom based on mask feature and 2D position feature fusion, and the coarse pose estimation stage provides an initial value for precise pose estimation, so that the optimization process of the precise pose estimation algorithm is prevented from falling into a local optimal value. A dynamic snakelike convolutional layer is introduced into an accurate pose estimation network to construct an object contour feature extraction network, the contour feature generation quality is improved, and a background contour is filtered out. Through a differentiable Gaussian-Newton optimization method, iterative optimization is carried out on multiple scales, the re-projection error of the projection contour and the object contour is minimized, and accurate pose estimation is realized.