44 results about "Active vision" patented technology

The invention provides an intelligent pipeline welding method and system based on data decision, and relates to the technical field of intelligent pipeline welding.The intelligent pipeline welding method comprises the steps that a welding request is responded, a groove image of a to-be-welded pipeline is obtained through an active visual sensor, groove morphology characteristic parameters of the to-be-welded pipeline are determined based on the groove image, and the to-be-welded pipeline is welded according to the groove morphology characteristic parameters. Based on the groove morphology characteristic parameters, corresponding basic layer channel arrangement rules and reference process parameters are determined from a preset layer channel arrangement database, a welding process scheme matched with the current groove is generated in combination with the groove morphology characteristic parameters, and the current groove is welded on the basis of the welding process scheme. And the welding operation is executed in the groove of the to-be-welded pipeline, the technical problems that in the prior art, the global planning capacity is deficient, the deviation correction strategy is locally lagged, and the whole-process accurate control is insufficient are solved, and the effect of improving the stability and reliability of the welding quality of the pipeline is achieved.

The invention discloses a robot grabbing system based on visual perception, and relates to the field of robot grabbing systems, and the robot grabbing system comprises a multi-mode active data perception module, a scene dynamic pre-judgment and decision module, a flexible self-adaptive grabbing execution module and a cross-module cooperative control module. According to the robot grabbing system based on visual perception, the multi-mode active visual perception module effectively solves the problem of inaccurate perception in backlight, shielding, transparent and reflective target scenes through active view angle adjustment and a multi-mode image fusion technology, the target recognition accuracy can be effectively improved, and the robot grabbing system based on visual perception has the advantages that the robot grabbing efficiency is improved. The system can adapt to various complex industrial and life scenes, the cross-module cooperative control module reduces the response delay of the system to a millisecond level through high-precision time sequence synchronization and distributed data interaction, meanwhile, the system has an abnormal self-adaptive adjustment capability, the cooperative lagging and single-point fault risk of an existing linear architecture is avoided, and the system reliability is improved. And the operation stability and reliability of the system are improved.

The embodiment of the invention relates to the technical field of visual feedback, and provides a self-adaptive visual feedback system and method for ensuring material forming quality, the system comprises a front-end framework and a rear-end framework which work cooperatively, the front-end framework comprises a sensing module, a feature extraction module, an error compensation module and an execution control module, the back-end architecture comprises a decision module, a twin simulation module and an evolution module; the system further comprises a quality analysis module. According to the embodiment of the invention, environmental interference is overcome through active vision and event camera collaborative perception, long-term precision is maintained through online calibration compensation, quality quantitative evaluation is realized by combining three-dimensional shape real-time reconstruction and a deep learning method, a digital twin technology is introduced to carry out strategy security verification, and continuous evolution of the system is realized by means of a federated learning architecture. The method is not only suitable for the welding process, but also can effectively meet the high-quality control requirements of various material forming processes such as spraying, cladding and surfacing.

The invention discloses an intelligent anti-bird collision warning method and system and a medium, relates to the technical field of electric power safety protection, is improved on the basis of a mature structure and an installation mode of a traditional reflective warning ball, and intelligently upgrades the prior art on the basis of an active vision enhancement and guidance strategy direction. By integrating a sensing system and a light emitting system and based on bird visual ecology, while the advantages of a passive reflection mode are reserved, the capability of emitting bird sensitive spectrums (such as ultraviolet light) and the capability of randomly switching dynamic flickering in multiple preset flickering modes are added, and an all-weather, anti-adaptation and high-efficiency intelligent bird collision prevention solution is formed; the warning device is upgraded from passive reflection to active sensing and light emitting, the recognizable degree of the wire is fundamentally improved, and birds are guided to safely evade.

The invention belongs to the field of robot perception, and provides an active visual perception method and system based on fusion of a concept scene graph and a large model. The method comprises the following steps: based on an image of a set scene, extracting candidate entities in the image and text description and spatial relationship of the candidate entities; extracting a target entity from a user instruction, calculating a vision-language alignment matching score between text description of the target entity and candidate entities, and constructing a concept scene graph by taking the candidate entity with the highest matching score as a main node; and processing the concept scene graph by using a large model embedded with a ternary prompt project, and generating a strategy code of a current task scene in combination with a successful historical task strategy code, so that a robot actively senses implicit information in the scene, and recognition and capture of a shielded target entity with visual confusion are realized.

This invention discloses a navigation-following unmanned surface vessel (USV) formation control method based on active vision, comprising the following steps: constructing a kinematic model of the USV based on a geodetic coordinate system; establishing a gimbal monocular camera model for the gimbal camera mounted on the USV; defining servo tracking errors for the navigation-following USV formation system by combining the USV kinematic model and the gimbal monocular camera model; defining image coordinate errors based on the viewing angle constraints of the gimbal monocular camera; designing a motion controller and adaptive parameter update law for the USV using the Lyapunov synthesis method; and designing a gimbal camera steering controller and adaptive parameter update law using the Lyapunov synthesis method. This method ensures the smooth implementation of visual servo control of the USV by defining servo tracking errors and image coordinate errors, and then incorporating these two errors into the Lyapunov synthesis method to design the USV motion controller, the gimbal camera steering controller, and the adaptive parameter update law.

Embodiments of the present application disclose an active vision-based root canal orifice detection and positioning method, device, equipment and medium, belonging to the field of oral medicine and medical image technology, wherein the method comprises: taking an image quality evaluation score as a judgment index of active vision, adjusting the lens according to the image quality scores of the front and rear positions in the process before and after focusing until the imaging position with the highest image quality evaluation score is found, and completing automatic focusing; based on the pulp bottom image collected after automatic focusing, the sub-pixel coordinates of the root canal orifice edge are extracted by using the method of gray gradient interpolation refinement and high-low threshold screening; based on the sub-pixel coordinates of the root canal orifice edge, the root canal orifice is regarded as a circular model, and the least square method is used to fit the center coordinate of each root canal orifice aperture. The present application solves the problems of complicated operation, low accuracy and high repeatability of doctors in the root canal preparation process, saves the time of root canal treatment, and provides a convenient and efficient auxiliary tool for oral medical treatment.

The application discloses a ranging method for coinciding central viewpoints of binocular PTZ cameras, solves the problem that a calibration plate cannot be used for recalibration in an online environment during three-dimensional detection of active vision, and belongs to the technical field of active vision.The application comprises the following steps: left and right cameras of binocular PTZ cameras are used to shoot target images at respective positions in a spherical cavity with various pitch angles and azimuth angles, and an angle-image encoder is obtained; during ranging, the target is shot by the left and right cameras; according to the shot images, the azimuth angle and the pitch angle corresponding to the image pair shot by the left and right cameras are found in the angle-image encoder; then, the included angles alpha0 and beta0 between the left and right cameras and the target point are obtained in combination with the geometric relationship between the positions of the left and right cameras and the target; and finally, the three-dimensional distance of the target point is solved according to alpha0, beta0 and the distance D between the position points of the rotation centers of the left and right cameras.The application can still be used for ranging when the central viewpoints coincide without self-calibration under the condition that the internal and external parameters change after the focusing and the shooting angle and range are adjusted.

The present application belongs to the technical field of intelligent welding, and discloses a multi-layer multi-pass weld tracking method and system based on active and passive vision. The system of the present application comprises an active and passive vision weld tracking sensor, a welding robot and an industrial computer. The active and passive vision weld tracking sensor comprises an active vision device, a passive vision device and a linear laser emitter. The method of the present application generates an original groove model by detecting groove information with a structured light. Then, the active and passive vision are combined to detect the weld surface shape and the weld corner point position during welding, and a groove model of the current weld is established. The groove model of the current weld is compared with the original groove model to determine whether the weld is offset and measure the offset, so as to determine the position of the next weld and plan and generate a preset welding gun posture, thereby avoiding problems caused by heat loss estimation failure, weld offset and poor welding gun posture.

The invention discloses an active visual guidance ton bag sealing and shaping method and device. The device comprises a visual acquisition module, double mechanical arms and a main control unit. According to the method, RGB and depth data of a ton bag sealing area are collected and preprocessed through the camera module, after inner bag sealing line extraction, operation point screening and feasibility inspection are completed through the detection module, the decision control module generates an outer bag adjusting / shaping instruction according to a detection result, and after coordinate mapping, the double mechanical arms are controlled to cooperatively execute operation. And an active visual closed loop of visual acquisition, detection positioning, decision control and operation execution is formed. According to the scheme, the problems of inaccurate inner bag positioning, difficult outer bag posture adjustment and easy collision in existing ton bag sealing and shaping are solved, the shaping precision and the operation stability are improved, and the device is suitable for automatic packaging production lines in the fields of chemical engineering, grains and the like.

The invention discloses a road anomaly detection method, which is characterized by comprising the following steps of: firstly, constructing a road anomaly detection model, and training the road anomaly detection model to obtain a trained road anomaly detection model; inputting a to-be-detected road scene image into the trained road anomaly detection model to obtain a detection result; wherein the road anomaly detection model is composed of a backbone network, a neck network and a detection head which are connected in sequence; the backbone network comprises a CBS layer, a C3 layer, a position modulation attention module and an active visual follow-up multi-scale feature extraction module; the method has the advantages that a long-distance dependency relationship is effectively captured through the position modulation attention module to perform accurate positioning and enhance the scene adaptability, and the weak feature target detection capability under the complex road background is improved; the active vision follow-up multi-scale feature extraction module actively adjusts the sight distance to perform multi-scale feature extraction in a multi-receptive-field mode under the condition of fixing the position of a target to be detected, and the limitation of a single-scale method is relieved.

The invention discloses an active vision-based multi-layer and multi-pass welding seam feature identification method, and relates to the technical field of XX. According to the method, laser images are obtained through active vision, a center line extraction and correction technology is adopted, geometric features of welding seams are extracted, a corner recognition and correction technology is combined, welding points are positioned, the welding process is divided into backing welding, filling welding and cosmetic welding, and center line correction and corner recognition and correction methods are selectively adopted for welding of each layer according to welding characteristics. Therefore, the geometric features of the laser center lines of the multiple welding seams are recognized and corrected, automatic welding of the welding robot in multi-layer and multi-pass welding operation can be achieved, manual participation is reduced, and the welding efficiency is improved.

The invention relates to the field of metallurgical equipment state monitoring, and discloses a method and a device for detecting and judging cracks of a refractory layer of a ladle before full-size iron receiving, and the method comprises the steps: obtaining an image of the inner wall of the ladle; performing first-stage processing on the image, and quickly positioning one or more suspected crack regions by using a convolutional neural network; second-stage processing is carried out on each suspected crack area, and three-dimensional physical parameters such as the physical width and the physical depth of the crack in the area are calculated by actively projecting an optical pattern and analyzing geometric deformation of the optical pattern; and carrying out numerical comparison on the calculated physical parameters and a preset safety standard, and carrying out automatic ladle judgment on the ladle. According to the method, through a two-stage processing strategy, the rapid identification capability of deep learning and the accurate quantification capability of active visual three-dimensional measurement are combined, and accurate acquisition of the physical size of the crack is realized, so that reliable data support is provided for automatic and objective packet judgment decision, and the accuracy and reliability of detection are remarkably improved.

The invention relates to a method and a system for identifying an underwater object on a mobile carrier, and belongs to the technical field of underwater target detection and identification. The method comprises the following steps: detecting the water depth of a mobile carrier through a water depth sensor; when the water depth is greater than a preset threshold value, the industrial personal computer starts and adaptively adjusts the underwater active lighting system; acquiring an image by using an optical imaging system; the industrial personal computer preprocesses the image, and inputs the processed image into a pre-trained deep learning neural network model for target recognition; and finally outputting a target identification result. The system comprises a bottom plate, a water depth sensor, an underwater active lighting system, an optical imaging system and an industrial personal computer. According to the invention, active visual illumination and artificial intelligence identification are combined, the defects of low precision of a traditional acoustic method and poor environmental adaptability of an optical method are overcome, high-precision, self-adaptive and automatic identification of an underwater target on a mobile carrier is realized, and the system has the advantages of small size, low power consumption and high integration level.

The application discloses a fruit autonomous picking method and system based on active perception and visual arm cooperative optimization, and relates to the technical field of robot control. The method comprises the following steps: acquiring a color image and a depth image of a working environment, and performing semantic segmentation on the color image by using a preset zero-sample learning model; performing semantic point cloud extraction on the depth image based on a mask corresponding to a target category, and constructing a fruit tree environment map containing spatial information and semantic information; performing candidate target screening and extraction according to the fruit tree environment map, and generating a candidate grasping pose according to the screened candidate target; performing kinematic reachability analysis on all candidate targets and candidate grasping poses, and calculating a joint score; determining a next grasping object according to the joint score; and picking the next grasping object according to a preset trajectory. The application deeply couples active visual perception and mechanical arm grasping action, and realizes efficient fruit autonomous picking of the robot.

The invention relates to the technical field of video conferences, and discloses a video conference target intelligent tracking method based on a panoramic PTZ camera, and the method comprises the steps: starting a system, and loading a panoramic and PTZ lens space coordinate mapping model which is established through polynomial fitting in advance, the method comprises the following steps: acquiring images through a panoramic lens, detecting and identifying all figure information in an application environment and a scene by utilizing a vision and audio algorithm, extracting active vision, passive vision, active audio and passive audio of each target, performing feature scoring, calculating a comprehensive priority score of each target, selecting the target with the highest score for tracking, and performing target tracking. The method comprises the following steps: calculating the horizontal and vertical rotation angles required by a PTZ lens according to the normalized coordinates of a selected target in a panoramic picture through a mapping model, sending the calculated rotation angles to a PTZ holder, driving the lens to rotate, placing the target in the center of the picture, continuously updating the priority of the target and the PTZ position, and accepting user interaction to adjust a tracking strategy.

The application patent is based on the steel coil band loosening anomaly detection and quantification method of active vision imaging, first, the saliency detection model is constructed to obtain the brightness saliency map of the laser stripe, second, the laser stripe image is segmented by using the serialized threshold to obtain the Boolean graph, the weighted sum of different binary images is obtained to obtain the region stability image. The brightness saliency map and the region stability saliency map are fused by wavelet transform, the mean fusion is adopted for the brightness saliency map, the maximum value fusion mode is adopted for the region stability saliency map, and the adaptive maximum entropy segmentation is carried out on the fused saliency map, finally, the stripe normal field is obtained by calculating the stripe gradient vector, and the stripe center line is extracted based on the stripe distribution normal and the fusion gray gravity center method. The quantification of the loosening band is realized by calculating the neighborhood difference value and the curvature radius of the stripe center, and the application prospect is wide.

The invention relates to a multi-mode active vision following method and system. The method comprises the following steps: acquiring an original image comprising an RGB image and a single line point cloud and point cloud data information through a camera and a single line laser radar; performing dimension reduction processing on the original image and the point cloud data information, processing the original image information through a lightweight target detection model to obtain an image key information structure, performing PCA offline learning on the original point cloud data to obtain fixed parameters, and performing weighted fusion on the distance, the intensity and the angle according to the fixed parameter information to obtain point cloud dimension reduction fusion data; constructing a quantitative relation expression of visual entropy and radar entropy according to the image key information structure and the point cloud dimension reduction fusion data; performing multi-source entropy information fusion to obtain an optimal following target; converting the position information into decision information and transmitting the decision information to a control steering engine of a physical execution layer; according to the invention, the target identification, positioning and tracking precision and reliability of the mobile intelligent agent in a multi-target dynamic environment are significantly improved.

The invention discloses a space robot sensing system based on an active visual strategy, which relates to the technical field of robot sensing and comprises an instance segmentation module, a point cloud generation module, a shape clustering module, a shape fitting module, a missing surface visual angle synthesis module, an exploration sampling module and a utility measurement module. The invention further discloses a space robot sensing method based on the active vision strategy. The method comprises the steps that S100, a target part image is collected; s200, extracting a mask of the part instance; s300, generating a three-dimensional point cloud with color information; s400, clustering the shapes of the part instances; s500, fitting parameters of the shape of the part instance; s600, generating an exploratory view angle; s700, synthesizing a target view angle; s800, obtaining an optimal observation visual angle; and S900, finishing active vision strategy planning. According to the method, an optimal visual angle synthesis method is combined, it is ensured that the mechanical arm rapidly and accurately completes part grabbing, and the part recognition accuracy is improved.

This invention relates to the field of metallurgical equipment condition monitoring, and discloses a method and apparatus for detecting and judging cracks in the refractory layer of a full-size molten iron ladle before it receives hot iron. The method includes: acquiring an image of the inner wall of the molten iron ladle; performing a first-stage processing on the image to quickly locate one or more suspected crack areas using a convolutional neural network; performing a second-stage processing on each suspected crack area by actively projecting an optical pattern and analyzing its geometric deformation to calculate the three-dimensional physical parameters of the crack, such as its physical width and depth; and comparing the calculated physical parameters with preset safety standards to automatically judge the molten iron ladle. This invention, through a two-stage processing strategy, combines the rapid recognition capability of deep learning with the precise quantification capability of active visual three-dimensional measurement, achieving accurate acquisition of the physical dimensions of cracks. This provides reliable data support for automated and objective ladle judgment decisions, significantly improving the accuracy and reliability of detection.

The invention discloses a narrow-groove automatic welding pool active vision device which comprises a body middle shell, a body front end cover and a body rear end cover are arranged on the front portion and the rear portion of the body middle shell respectively, an optical filter is arranged on the body front end cover, and a laser pulse light source, a lens, an SOC processor, a controller and an output fracture are arranged in the body middle shell from front to back. The influence of welding arc light on imaging is restrained to a large extent, the smoke dust penetrating imaging capacity is achieved, the whole-domain high definition is achieved, the brightness is uniform, and the picture is free of flicker.

The application provides a welding defect detection method and a robot welding defect detection system, the method comprising the steps of: acquiring a structured light image of a weld surface structure by shooting, processing the structured light image, extracting defects, and identifying and classifying the defects. The robot welding defect detection system comprises a visual detection device and a computer device for processing data of images collected by the visual detection device to determine welding defects. The visual detection device comprises an active visual detection device, which comprises a laser emitter for emitting structured light to a weld and a first camera for acquiring a structured light image of the weld by high-frequency shooting. The computer device is connected to the first camera and configured to process the structured light image of the weld acquired by the first camera through high-frequency shooting to identify defects. In the application, the structured light image of the weld surface is shot and processed to detect various defects such as surface concave-convex, cracks and pores.

This invention relates to the field of computer vision technology, and more particularly to a cross-modal image retrieval method based on a multimodal large language model (VLM) agent. The method includes: S1, initial candidate retrieval using the CLIP model; S2, attribute-structured decomposition and difficulty determination of the query text; S3, identity clustering of candidate images by actively calling DBSCAN clustering, and attribute inference verification of the clustered multi-view images by calling the multimodal large language model; S4, resolving ambiguity by using fine-grained location scoring or direct visual inspection when competing candidates exist; and S5, outputting structured retrieval results. This invention compresses redundant candidate space by introducing DBSCAN unsupervised clustering technology, enhances the inference confidence of the VLM through multi-view features, and accurately distinguishes between multiple highly similar candidates through fine-grained location scoring and active visual inspection.

The application relates to a loop detection method based on active vision and text semantics, which comprises the following steps: changing the pose of a mechanical arm to drive a camera to collect images of a surrounding environment to obtain front-view text images; synchronously controlling a laser radar to collect multiple frames of point clouds to screen key frame point clouds; selecting key text images corresponding to the key frame point clouds from the multiple frames of front-view text images, extracting text information in the key text images and converting the text information to a global coordinate system; calculating the similarity of the text information in the key text images in the global coordinate system and historical text information obtained at a historical moment, determining candidate loop point clouds according to the similarity; and performing geometric consistency calculation on the candidate loop point clouds to obtain distance values to obtain loop detection results. The active vision is used to collect front-view images of text information, and the double loop verification of the point clouds is combined to solve the technical problems that feature degradation, sparseness or similar environment lead to loop detection errors, and the precision and robustness of system positioning and mapping are improved.

A kind of binocular active vision semi-dense depth estimation method based on bionic eye, characterized in that, comprising the following steps: S100, image is collected using binocular bionic eye camera;S200, the image collected in S100 is input into SLAM system, and camera pose and road marker point coordinates are estimated using SLAM system to track key frame;S300, using the key frame pose and its corresponding key frame image data obtained in S200, through polar search, block matching is combined with triangulation, then semi-dense depth estimation is realized using depth filter.The application filters some image block gradient that does not meet the requirements of block matching NCC calculation in the process of block matching, so compared with dense direct method, the application can better achieve the requirement of real-time.

The application discloses a kind of space robot perception systems based on active vision strategy, it is related to robot perception technical field, including instance segmentation module, point cloud generation module, shape clustering module, shape fitting module, missing surface view angle synthesis module, exploration sampling module and utility measurement module.It also discloses a kind of space robot perception methods based on active vision strategy, comprising: S100, acquisition target part image;S200, extract the mask of part instance;S300, generate three-dimensional point cloud with color information;S400, clustering of part instance shape;S500, parameter fitting of part instance shape;S600, generate exploratory view angle;S700, synthesis target view angle;S800, obtain optimal observation view angle;S900, complete active vision strategy planning.The application combines optimal view angle synthesis method, ensures that mechanical arm is quickly, accurately completes part grabbing, improves part identification accuracy.

The application belongs to the technical field of unmanned aerial vehicle cooperative positioning, and provides a kind of unmanned aerial vehicle cluster distributed cooperative positioning method based on active vision, including distributed state propagation and cross covariance maintenance, active relative observation target selection, active relative observation target selection proposed in the application, so that each unmanned aerial vehicle can autonomously decide optimal observation target based on local information, through the introduction of flight safety constraints, while ensuring flight safety, the observation information gain is maximized, unlike the passive evaluation mechanism of traditional single machine navigation error boundary calculation and alarm, the application can not only perceive the uncertainty of relative measurement online through active vision mechanism and ERMU effectiveness check, but also actively plan maneuvering observation to obtain high-quality observation data, realize active inhibition of cumulative drift and high-precision relative state estimation in GNSS-free environment.

The application discloses a kind of active-passive hybrid stereo target for visual measurement, it is related to visual measurement technical field.Active visual mark uses three LED infrared light sources, wavelength is 940nm, passive visual mark uses three reflective target balls, reflective waveband 720nm~1000nm, shell uses six arm configuration, including three active end arms and three passive end arms, the outside end of active end arm is provided with annular mounting cavity for the accommodation of LED infrared light source Positioning and setting light transmission piece, the surface of the outside end of passive end arm is provided with stud for the threaded fixing of reflective target ball, bottom cover is fixed at the bottom of shell, electrical system is fixed inside target structure, the light intensity of three LED infrared light sources can be remotely controlled adjustment.Through the design of active adjustable light intensity LED infrared light source combined with reflective target ball, the continuous identification of target under complex environment and dynamic environment is maintained, with stronger environmental adaptability.

This invention proposes a mobile robot active recognition method and apparatus, belonging to the field of target recognition technology. The method includes: having the mobile robot acquire an RGB image and a depth image of its current position and orientation within a workspace containing a pre-defined target object; inputting the RGB image and depth image into a pre-defined robot active recognition network, which outputs the mobile robot's action at the next moment, including the direction and distance of the action; the mobile robot moving accordingly according to the network's output until it reaches the position closest to the pre-defined target object, at which point recognition is complete. This invention, based on reinforcement learning technology and combining depth and target object information, predicts the optimal field of view using an active vision algorithm. This overcomes the problem of insufficient target object information in the camera's field of view and significantly improves recognition reliability caused by target object occlusion or excessive distance.

An exemplary system and method are disclosed for providing visual communication between agents in a multi-agent system. In some implementations, the exemplary system and method are configured to (i) receive and decode a data packet or message visually received from other communication targets and (ii) visually transmit a reply data packet or message, in response to the communication targets, using a digital or electromechanical display.