2266 results about "Visual system" patented technology

Disclosed is a method for constructing a bionic binocular active visual simultaneous localization and mapping (SLAM) system. The method includes: S1, constructing a binocular active visual system, peripheral vision is simulated using a panoramic camera, a panoramic value map is constructed based on the panoramic camera, and a binocular bionic eye gaze control strategy based on the panoramic value map is proposed; S2, designing a SLAM algorithm framework which is suitable for motion of the two bionic eyes, where relative pose optimization of left and right cameras based on a Perspective-n-Point algorithm and depth calculation for binocular matching points based on triangulation are adopted in a SLAM tracking thread; and S3, with the assistance of camera poses estimated by the SLAM system and sparse landmark point coordinates, training a self-supervised depth estimation network, to provide dense depth estimation for a mapping module of the SLAM system, achieving dense mapping.

Disclosed is an area monitoring system including a camera module having configured to generate an output representative of a captured image and / or a video of an area including a object. The system includes a state machine including an inference engine configured to: identify and track an object; establish a state and / or a change in state of an object via machine vision inferencing; and generate an output representative of an object identification, an object state, and / or a change in an object's state. The state machine module includes a mapping engine configured to: process the camera module output to assign coordinates to an object contained in an image and / or a video based on a coordinate system of a computer vision map of an area; apply a timestamp in connection with assigning coordinates an object; and generate a coordinate timeseries log for plural coordinate-timestamp pairs.

An unmanned aerial vehicle (UAV) that includes: a chassis; a plurality of arms that extend outwardly from the chassis; a plurality of propeller assemblies that are supported by the plurality of arms; a canopy that is connected to the chassis so as to provide an outer cover for the UAV that is configured to protect internal components thereof; a frame that is supported by the canopy such that the frame is isolated from the chassis; and a plurality of image capture assemblies that are supported by the frame such that the frame separates the plurality of image capture assemblies from the chassis and the canopy so as to inhibit relative movement between the plurality of image capture assemblies during operation of the UAV.

A display system of an aircraft includes a display device defining a display area, a vision system for generating an image of an operating environment of the aircraft on the display area, data processors operatively coupled to the display device and the vision system, and non-transitory machine-readable memory operatively coupled to the one or more data processors and storing instructions executable by the one or more processors and configured to cause the one or more processors to, when a condition of the aircraft is met, cause an alteration of a display of flight information on the display area to declutter a cutout region in the image generated by the vision system, the cutout region devoid of imaging generated by the vision system and displaying an Approach Lighting System (ALS) of a runway of intended landing for the aircraft.

The invention relates to the technical field of visual automatic calibration, and discloses a joint manipulator automatic calibration method and device based on a visual system. The method comprises the following steps: carrying out multi-view image acquisition on a joint manipulator of the five-axis robot to obtain original image data; performing occlusion region extraction on the original image data to obtain a joint occlusion region set; on the basis of the joint occlusion region set, disordered image splicing and mark point feature extraction are carried out on the original image data, and joint feature image data are obtained; constructing an adaptive calibration equation based on the joint feature image data, and solving a target transformation relation between a global visual coordinate system and a local visual coordinate system; multi-joint collaborative calibration and joint chain constraint optimization of the five-axis robot are executed according to the target transformation relation, and joint error correction parameters are generated, high-precision joint error correction parameter calculation is achieved, and the positioning precision and the movement precision of a joint manipulator of the five-axis robot are greatly improved.

A machine vision system may include a meta-imager including a meta-optic and a polarization-sensitive photodetector. A machine vision system may further include and at least one processor operably coupled to the polarization-sensitive photodetector, and at least one memory operably coupled to the at least one processor. A machine vision system may be configured to: receive, from the polarization-sensitive photodetector, a plurality of feature maps; input, into a trained artificial neural network, the plurality of feature maps; and process, using the trained artificial neural network, the plurality of feature maps to recognize an object.

The invention provides double-arm picking equipment for shelf-type planted shiitake mushrooms and an operation method thereof. The double-arm picking equipment comprises a moving platform, the upper portion of the moving platform is connected with a chassis, and a lifting device, a shiitake mushroom storage device and a shiitake mushroom picking device are arranged above the chassis. Through mechanical automatic mushroom stick fixing and mushroom picking, intelligent identification, picking and placing of mushroom sticks on a shelf and identification and picking of mushrooms on the mushroom sticks can be carried out at the same time, planting areas can be traversed automatically, the mushroom picking speed is increased, and labor and time cost is saved. Three-dimensional scanning is carried out through the depth camera vision system, the position and size of the mushroom sticks and the maturity of mushrooms can be accurately recognized, and it is ensured that the quality of picked mushrooms is high. The lifting device adjusts the height of the mechanical arm and cooperates with a visual system, picking and mushroom stick processing are more accurate, damage to mushrooms and mushroom sticks is reduced, mushroom picking automation is achieved, operators do not need to put physical labor heavily, and the workload is relieved.

Systems and methods are provided for assisting individuals with physical and / or cognitive ailments, such as dementia, Alzheimer's disease, and / or visual impairment. The quality of life, safety, and / or autonomy of such individuals can be enhanced by utilizing a combination of sensors, actuators, augmented reality (AR) technology, and machine learning (ML) algorithms. The sensors and actuators can include (but are not limited to) cameras, global positioning system (GPS) sensors, accelerometers, motion sensors, AR visual systems, headphones, microphones, haptic feedback systems, and / or connectivity capabilities (wired and / or wireless) to provide information about the surrounding environment and events. By leveraging advanced sensor technology, AR, and real-time data processing, a comprehensive support system can be provided that addresses the unique challenges faced by these individuals on a daily basis.

A dynamic vision system includes a variable focus liquid lens optical assembly. The dynamic vision system includes a variable lighting assembly. The dynamic vision system includes a control system configured to adjust one or more optical parameters and data collected from the variable focus liquid lens optical assembly in real time. The dynamic vision system includes a control system configured to adjust the variable lighting assembly. The dynamic vision system includes a processing system that dynamically learns on a training set of outcomes, parameters, and data collected from the variable focus liquid lens optical assembly to train a set of machine learning models to control the variable focus liquid lens optical assembly to optimize collection of data for processing by the set of machine learning models.

The invention provides a moving target capturing method and system based on AI multi-view vision, and relates to the technical field of data processing.The method comprises the steps that camera parameters in all vision systems are calibrated, and the camera parameters comprise a camera internal parameter matrix and a camera external parameter matrix; extracting 2D feature points of the moving target through each calibrated visual system; reconstructing each 2D feature point by combining a multi-view geometric reconstruction algorithm and a least square method according to the calibrated camera parameters to obtain 3D feature points; constraints are added to the 3D feature points, and the constraints comprise spatial constraints, direction vector constraints and time sequence constraints; reconstructing the 3D feature points added with the constraints through a multi-view geometric analysis algorithm to obtain three-dimensional motion information of the moving target; and outputting the three-dimensional motion information of the moving target to complete the capture of the moving target. And non-contact high-precision capture of the moving target can be realized.

The invention discloses an intelligent power grid new energy consumption management system, and relates to the technical field of intelligent power grids, and the system comprises the following components: bidirectional interaction mechanism construction, an intelligent decision engine, a collaborative optimization scheduling strategy and user participation and incentive mechanism, and visual monitoring and operation and maintenance management. According to the invention, a two-way interaction mechanism between the micro-grid and the main network is constructed, rapid transmission and sharing of data are realized, a scheduling strategy can be rapidly adjusted under emergency and extreme weather conditions through collaborative optimization of the scheduling strategy, continuity and stability of power supply are guaranteed, and meanwhile, the system is convenient to use. The visual monitoring and operation and maintenance management module provides visual system operation state display and intelligent operation and maintenance management, and improves the operation and maintenance efficiency and safety, thereby remarkably enhancing the safety and stability of the power grid and the capability of coping with emergencies.

The invention is suitable for the technical field of laser welding, and particularly relates to a laser welding control method which comprises the steps that an initial polarization image and an initial thermal imaging image of a to-be-welded workpiece are obtained; according to the initial polarization image and the initial thermal imaging graph, the welding path and initial laser parameters of the to-be-welded workpiece are determined; based on the welding path and the initial laser parameters, a laser welding device is controlled to weld the to-be-welded workpiece, and meanwhile a real-time polarization image and a real-time thermal image of the to-be-welded workpiece are obtained; according to the real-time polarization image, the real-time thermal image and the welding path, real-time state information of a molten pool of the to-be-welded workpiece is determined; and real-time laser parameters are determined according to the real-time state information of the molten pool, and the laser welding device is controlled to weld the workpiece to be welded according to the real-time laser parameters. According to the method, accurate welding process control is achieved, and the defects of a traditional visual system are overcome.

The invention relates to the technical field of automatic control of a dispensing process, and discloses an intelligent dispensing control method based on a self-adaptive track and dynamic fault-tolerant control. The method comprises the following steps: acquiring spatial pose data of a workpiece to be dispensed through a visual system, identifying a Mark point, and generating positioning reference information; the information is fused with height measurement data, and a dispensing track and dispensing height control parameters are obtained through calculation; driving an injection mechanism to dispense glue according to the control parameters, collecting a glue dispensing image, and comparing the image template to realize real-time judgment and alarm control of the offset and blockage state of the nozzle; after dispensing is completed, coverage rate analysis is conducted on the dispensing image, and a qualification result used for workpiece circulation judgment is generated; all control parameters, image data and judgment results are uploaded to a manufacturing execution system, the time interval between dispensing and the previous process is recorded, and process tracing and time card control are achieved. According to the invention, the dispensing precision and consistency are improved, and the automatic and fault-tolerant performance is good.

The invention provides a target cooperative control method and system based on deep learning, and the method comprises the steps: obtaining the real-time visual data of a target region through a multi-view image collection device, carrying out the recognition and classification of object features through a deep learning model, and generating feature distribution data containing a class identifier and boundary coordinates, a space calibration algorithm is adopted to map the two-dimensional features into three-dimensional coordinate system data, a space position and posture angle parameters are output in combination with posture calculation, a control instruction including path planning, tail end executor posture adjustment and self-adaptive clamping force is generated according to physical attribute parameters of an object, an operation device is driven to complete the precise grabbing action, and the grabbing precision is improved. Therefore, through dynamic multi-view perception, three-dimensional space modeling and physical attribute driven closed-loop control, the problems of feature deficiency, positioning errors, operation rigidity and the like of a traditional visual system in a complex scene are effectively solved, and the precision and adaptability of target operation in an unstructured environment are remarkably improved.

The invention provides a movable flexible docking platform, method and equipment based on visual guidance and a storage medium, and relates to the field of automatic auxiliary assembly. The docking platform comprises an AGV trolley, a six-degree-of-freedom platform erected in the middle of the upper portion of the AGV trolley, and a binocular vision system erected on the rear side of the upper portion of the AGV trolley. The binocular vision system provides real-time vision guidance in the advancing process of the AGV; a butt joint workpiece A is placed on a six-degree-of-freedom platform, and a butt joint workpiece B which has an assembly relation with the butt joint workpiece A is placed on a working table at a target position; and the AGV transports the butt joint workpiece A to the target position, and butt joint of the butt joint workpiece A and the butt joint workpiece B is completed. According to the invention, the six-degree-of-freedom parallel platform is arranged on the AGV trolley, so that the pose fine adjustment of the assembled workpiece is realized, the pose of the workpiece can be continuously adjusted in the transportation process to keep balance, and the workpiece transportation and high-precision butt joint can be realized.

This invention provides a system and method inspecting transparent or translucent features on a substrate of an object. A vision system camera, having an image sensor that provides image data to a vision system processor, receives light from a field of view that includes the object through a light-polarizing filter assembly. An illumination source projects polarized light onto the substrate within the field of view. A vision system process locates and registers the substrate, and locates thereon, based upon registration, the transparent or translucent features. A vision system process then performs inspection on the features using predetermined thresholds. The substrate can be a shipping box on a conveyor, having flaps sealed at a seam by transparent tape. Alternatively, a plurality of illuminators or cameras can project and receive polarized light oriented in a plurality of polarization angles, which generates a plurality of images that are combined into a result image.

The invention is suitable for the technical field of electrical equipment corrosion detection, and provides an electrical equipment corrosion detection method based on double-spectrum collaborative analysis and space-time prediction, and the method comprises the following steps: carrying out fusion analysis on a visible light image and an infrared image of acquired electrical equipment, and outputting position information and morphological category of a corrosion region; a binocular vision system and an IMU sensor are combined to calculate three-dimensional space coordinates, main direction angles and environmental parameters of the corrosion area, and pose data are generated; inputting the pose data into a preset corrosion extension path prediction model for processing, and outputting physical characteristic parameters; fusing the dual-frequency RTK GPS positioning data, the IMU attitude data and the physical characteristic parameters to construct a space-time association data set, and inputting the space-time association data set into a preset space-time diagram neural network model to analyze a space-time evolution relationship of diagram structure modeling corrosion nodes; and outputting microscopic and macroscopic prediction results. The identification accuracy of corrosion characteristics and the corrosion detection efficiency can be improved, and the labor cost and the operation risk are reduced.

A vehicular vision system includes a camera disposed at a vehicle with an extender platform that is movable between an extended state and a retracted state. With the extender platform in the extended state, the camera is at an extended position that is at a distance above the roof of the equipped vehicle. With the extender platform in the retracted state, the camera is at a retracted position that is closer to the roof of the equipped vehicle than when the camera is at the extended position. The camera, when in the extended position, has a field of view that encompasses the equipped vehicle and a region at least partially surrounding the equipped vehicle. The system displays, when the extender platform is in the extended state, at a display disposed within the equipped vehicle, a bird's-eye view of the equipped vehicle derived from the image data captured by the camera.

The invention discloses a full-automatic grinding method and system for steering knuckle flash based on 3D vision, and relates to the technical field of casting post-processing automation. A trimming-free direct polishing process is innovatively provided, a casting is scanned in real time through a high-precision 3D visual system, a flash space molded line is accurately captured, and one-key calibration of a camera, automatic generation and dynamic simulation of a flash path, multi-tool intelligent switching and self-adaptive adjustment of residual height are achieved in combination with self-developed off-line programming software. According to the system, a robot is adopted for grabbing workpieces to cooperate with a fixed type multi-cutter work station, a disordered feeding technology is integrated for 2D / 3D visual guiding grabbing, workpiece deformation and die difference are effectively dealt with, and flash residues are thoroughly eliminated. The method is characterized in that visual scanning, path planning and execution control are seamlessly fused, the dependence of traditional automatic grinding on the trimming process is broken through, the yield and efficiency are remarkably improved, and the industrial problem of full-process automation of casting flash is solved.

The invention relates to the technical field of visual SLAM (Simultaneous Localization and Mapping), in particular to a YOLOv8-based point-line fusion visual SLAM method in an indoor dynamic scene, which comprises the following steps of: respectively transmitting an RGB (Red, Green, Blue) image and a depth image acquired by an RGB-D (Red, Green and Blue) camera to a front-end tracking thread and a dynamic object detection thread, extracting ORB (Object Request) feature point and line features, and obtaining a visual SLAM result; meanwhile, the dynamic object detection thread adopts YOLOv8-Seg to complete instance segmentation of the dynamic object, a dynamic mask is generated, ORB point features and line features falling in the dynamic mask are judged as dynamic features, and then the dynamic features are removed; then, multi-view geometric constraint is adopted to further filter and segment missing dynamic feature points outside the mask; after the dynamic point-line features are removed, key frames are finally output and transmitted to a mapping thread, and positioning and mapping are carried out. According to the invention, the positioning precision of the visual SLAM system can be effectively improved.

The invention relates to a stereoscopic warehouse checking method and system based on an unmanned aerial vehicle, and the method comprises the steps: receiving a cargo checking task, and obtaining the layout information of a stereoscopic warehouse; determining the position of a to-be-checked goods location in the goods checking task in the stereoscopic warehouse; according to the layout information of the stereoscopic warehouse and the position of the to-be-checked goods location in the stereoscopic warehouse, determining a safe flight area and a hovering point of the unmanned aerial vehicle based on a path planning algorithm, and generating a flight task; issuing a flight task to the unmanned aerial vehicle, and calculating pose information of the unmanned aerial vehicle during execution of the flight task in real time through an inertial measurement unit; based on the observation image of the unmanned aerial vehicle visual system, correcting the accumulated error of the pose information through a Kalman filtering algorithm; rGB color images and depth images collected by the unmanned aerial vehicle at the to-be-checked goods locations are obtained in real time; determining whether the box stack is in an abnormal condition according to the depth image; and for the box stack which is not in the abnormal working condition, the number of goods in the box stack is determined according to the RGB color image.

The invention belongs to the technical field of artificial intelligence and computer vision, particularly relates to a visual processing edge adaptive decision-making method for resource and task perception, and aims to solve the problem of scheduling mismatch caused by resource dynamic change and task demand diversity in visual task processing in an edge computing environment. The method comprises the following steps: collecting multi-dimensional resource state data of edge nodes in real time to form a resource state vector with high time resolution; analyzing the visual task request, and constructing a quantifiable task feature vector; and establishing a resource-task association mapping model based on a dynamic weight distribution mechanism. The method also supports cross-edge domain collaborative decision, and processes a pipeline dynamic reconstruction and security isolation mechanism. According to the technical scheme, the fluctuation of the resource utilization rate is reduced to 15% or below, the average task processing delay is reduced to 60%, the scheduling satisfaction degree is improved by 40% or above, and the self-adaptability and the service quality guarantee capability of the edge vision system are remarkably enhanced.

An automatic sample cutting method for shoe sole detection relates to the field of shoe sole detection equipment, and comprises a machine table, a shoe sole clamping mechanism, a numerical control milling cutter mechanism, a manipulator and a visual system, the shoe sole clamping mechanism comprises a shoe tree, a clamping cylinder and a clamping plate, and the shoe tree is inversely arranged on a working platform; the clamping cylinder pushes the clamping plate to move and clamps the shoe on the shoe tree; the numerical control milling cutter mechanism is arranged on the machine table and can cut shoe soles. The manipulator is arranged in the machine table to clamp the cut sample; the visual system comprises a visual camera and a controller, the visual camera is arranged at the end of the mechanical arm and can move along with the mechanical arm to collect marks on the shoe sole and feed back images to the controller, the controller controls the numerical control milling cutter mechanism to switch cutters and cut the shoe sole, and cutting and sampling of shoe sole samples can be rapidly completed. According to the shoe sole cutting device, only a small number of persons are needed to install and observe shoe soles beside the device, labor force is saved, some shoe soles made of firm materials can be rapidly cut, and working efficiency is improved.

The invention discloses an automatic three-dimensional laser scanning method and system based on a robot, and relates to the technical field of robot automation, and the robot comprises a mobile system, a sensor system, a mechanical arm and operation system, a visual system, a control system and a communication system. The robot performs system self-inspection and initializes each system, and receives a scanning task from the server; a sensor is used for sensing the environment, a map is constructed, an optimal scanning path is planned, a scanning task is carried out according to the planned path, control points are recognized through a visual system, and scanning dotting is executed. According to the method, the main clock signal is used in the main control system to trigger the activation process of all the subsystems, synchronous starting of all the subsystems is ensured, accurate environment data can still be obtained in a complex environment through combination of the laser radar, the ultrasonic sensor and the infrared sensor, and the accuracy of the environment data is improved. An environment map is dynamically constructed and updated by dividing a three-dimensional space and cooperating with an SLAM algorithm.

The invention discloses a stereo image quality evaluation method of a multi-task coder-decoder based on Transform, and belongs to the technical field of image quality evaluation. The invention provides a multi-task coding-decoding device network architecture based on Transform. The multi-task coding-decoding device network architecture takes image depth estimation as an auxiliary task; a feedback mechanism of a human visual system can be better simulated by adopting a coder-decoder network, the problems existing in left and right view feature extraction are solved, and an information processing process from a global state to a local state is realized; in addition, the global interaction capability of Transform and the local attention capability of CNN are combined to enhance image features, depth estimation is introduced as an auxiliary task, depth prior information with higher quality can be provided, cross-view parallax fusion based on Transform is guided, the problem existing in left and right view feature fusion is solved, and more accurate binocular features are obtained. And feedback compensation is carried out on multi-level binocular feature fusion by using an attention mechanism to obtain a final pixel-level quality map, so that the image quality is accurately evaluated, a research scheme is provided for a deep learning method for non-reference stereo image quality evaluation, and the development of a visual task is effectively promoted.

The invention discloses an interferometer error calibration method and device and a computer readable storage medium, and belongs to the technical field of measurement equipment.The method comprises the steps that measurement data collected when an interferometer moves on the x / y axis of a motion table is obtained, and x / y axis compensation amount is generated through calculation processing; meanwhile, a coordinate system transformation matrix is established and optimized by using data of a marking plate arranged on a motion table, and x-axis and y-axis non-orthogonal parameters are obtained. And then the compensation amount and the non-orthogonal parameters are input into a preset kinematic model, and the accurate position of the motion platform under the global coordinate system is calculated in real time. And when a preset updating condition is met, dynamically updating the error compensation value of the interferometer through the deviation between the actual position detected by the visual system and the calculated position. Reflector surface type error compensation and non-orthogonal parameter correction are fused, and high-precision error compensation calibration of the interferometer is achieved.

The invention relates to the technical field of constructional engineering, and discloses a bridge hanging basket installation accurate positioning automatic operation method, which comprises the following steps of: acquiring image data of the position, the speed, the posture and the construction environment of a bridge hanging basket in real time by installing a high-precision sensor and a visual system; analyzing the acquired image data by using a deep learning model, and identifying and estimating the spatial position and angle of the hanging basket; the system comprises a high-precision sensor and an image acquisition system which are used for acquiring the position, attitude and environmental data of the hanging basket in real time; the deep learning module comprises a convolutional neural network and a recurrent neural network and is used for recognizing the position of the hanging basket in real time and predicting future deviation; and the nonlinear optimization module is used for calculating optimal control input in real time according to the optimization objective function and correcting the optimal control input. By adopting the technical scheme of the high-precision sensor and the image acquisition system, real-time accurate monitoring of the position, the posture and the construction environment of the hanging basket is achieved.

This invention overcomes disadvantages of the prior art by providing a vision system and method of use, and graphical user interface (GUI), which employs a camera assembly having an on-board processor of low to modest processing power. At least one vision system tool analyzes image data, and generates results therefrom, based upon a deep learning process. A training process provides training image data to a processor (optionally) remote from the on-board processor to cause generation of the vision system tool therefrom, and provides a stored version of the vision system tool for runtime operation on the on-board processor. The GUI allows manipulation of thresholds applicable to the vision system tool and refinement of training of the vision system tool by the training process.

The invention, which relates to the technical field of engineering automation, discloses a BIM-based cable bridge routing autonomous optimization visualization system comprising a heterogeneous professional semantic modeling module, a path identification module, a path generation module, a path conflict evolution module, a path reconstruction module and an output module. The system identifies a cable thermosensitive area by constructing a cross-professional semantic map, constructs a multi-target path evaluation function and generates a high-quality candidate path by adopting a variable weight genetic algorithm; introducing a construction stage dynamic compression prediction mechanism to improve the path robustness; visual expression of the whole process of path design is realized through versioned path reconstruction and a three-dimensional animation evolution function; and finally outputting structured path data and an obstacle avoidance strategy which can be used for construction guidance. According to the invention, the intelligent level of cable bridge path planning, the engineering implementability and the system integration efficiency are significantly improved.

The invention belongs to the field of road condition recognition, and discloses a road condition recognition method and device and electronic equipment, and the method comprises the steps: calculating a yaw velocity actual value according to a lateral sensor signal; calculating a yaw velocity reference value according to the vehicle information and the driving information; calculating an accumulated deviation value, and judging whether the accumulated deviation value is greater than a first threshold value or not; and if yes, setting first identification information for the current road section. According to the method, the actual value and the reference value of the yaw velocity are calculated in real time by using the lateral sensor signal and the vehicle driving data, and the road condition is accurately judged by comparing the deviation value of the two values, so that the road condition information is provided for a driver or an automatic driving system in time. When the bad road is detected, the identification information is set, so that early warning is facilitated, a driver can take proper measures, potential driving risks are avoided, and driving safety and comfort are ensured. And meanwhile, the method does not depend on a visual system which is easily influenced by the environment, so that the robustness and adaptability of the road condition recognition system are enhanced.