427 results about "Stereopsis" patented technology

The invention relates to the technical field of three-dimensional imaging, in particular to a four-eye structured light stereoscopic vision imaging method, which comprises the following steps of: arranging four cameras and synchronously acquiring multi-view image data with structured light stripes; carrying out image preprocessing and stripe code identification on the obtained four-view-angle structured light image data, and extracting structured light stripe center line positions and corresponding space projection information under each view angle; generating three-dimensional point cloud data of the high-precision target sample by using parallax calculation and a three-dimensional reconstruction algorithm, and completing spatial registration and filtering optimization of point cloud; fusing the point cloud and the light intensity data by combining the reflection intensity information of the multi-view structured light stripes to generate a composite data set containing space and spectral information; and constructing a dense parallax field and executing three-dimensional consistency verification, and generating a high-fidelity three-dimensional reconstruction model with a complete topological relation and sparse shielding compensation capability. According to the invention, the problems of insufficient view angle coverage, shielding area information loss, difficult edge structure matching and the like of traditional stereoscopic vision imaging can be solved.

The invention relates to the technical field of optical localization, in particular to an optical localization feedback control system in a minimally invasive surgery of bile adipoma. The system comprises the following steps: an image optimization module is used for scanning a full-band optical image in an operation cavity, performing adaptive contrast optimization and constructing a spectrum optimization image; the focus positioning module is used for performing accurate focus positioning on the spectrum optimization image and extracting a focus optical contour; the three-dimensional reconstruction module is used for performing structural feature point identification and three-dimensional reconstruction on the spectrum optimization image to construct a three-dimensional structure model; and the visual error compensation module is used for performing multi-angle optical ranging and stereoscopic visual error compensation on the optical contour of the focus to obtain a three-dimensional compensation positioning coordinate. According to the invention, through real-time accurate position positioning, the instrument response speed and the tissue identification precision are improved.

The invention provides a forest disaster detection method and device, and belongs to the field of forest disaster monitoring, and the method comprises the steps: collecting an infrared image, a stereoscopic vision image and positioning data of a target forest region in real time; an ASSA module is introduced in the feature extraction stage of the YOLOv11, and an infrared image is input into the improved YOLOv11 for flame target recognition; recognizing the stereoscopic vision image through a smoke and fire recognition algorithm, and detecting flame and smoke targets; determining a fire detection result by combining the identification results of the flame target and the smoke target; under the condition that the fire disaster is determined to occur, an HITNet algorithm is adopted to carry out ignition point positioning through a stereoscopic vision image, and the geographic coordinates of a fire disaster point are determined based on positioning data; and sending alarm information based on the fire point geographic coordinates. Therefore, the capability of extracting the flame heat radiation characteristics of the tiny fire points is effectively enhanced, and the detection result of the forest fire is improved.

The invention relates to the technical field of unmanned aerial vehicle obstacle avoidance, and discloses an unmanned aerial vehicle autonomous obstacle avoidance method based on deep learning and binocular vision. According to the method, original data streams of a left view and a right view are acquired through a binocular vision acquisition terminal, and stereoscopic vision feature representation is extracted through parallel convolutional coding branches of a deep neural network model; inputting into a three-dimensional space reconstruction module to generate a dense depth map and an obstacle initial position coordinate set; the dynamic obstacle analysis engine calculates a dynamic threat evaluation index by combining real-time flight attitude parameters of the unmanned aerial vehicle, and the space-time trajectory prediction model calculates future moving path probability distribution of the obstacle according to the dynamic threat evaluation index; and fusing the distribution with preset navigation path planning data to generate a three-dimensional obstacle avoidance course correction vector, converting the three-dimensional obstacle avoidance course correction vector into a flight control instruction set, and downloading the flight control instruction set to an execution module. According to the method, the reliability and adaptability of autonomous obstacle avoidance of the unmanned aerial vehicle in a complex dynamic environment are improved, and a powerful guarantee is provided for safe navigation of the unmanned aerial vehicle.

The invention discloses a vision-driven multi-mode fusion lightweight semantic map construction method and system. The method comprises the following steps: synchronously acquiring a binocular image pair sequence, IMU data and GNSS data of a target area; based on the acquired multi-modal data, performing multi-sensor joint state estimation through a differential weighted fusion strategy, and outputting camera global pose and scene depth information; based on a current frame and a historical frame in the binocular image pair sequence, combining a camera global pose, extracting geometric prior auxiliary time sequence cross-frame semantic feature fusion through stereoscopic vision, and outputting a two-dimensional semantic segmentation result of the current frame; and back-projecting the two-dimensional semantic segmentation result into a lightweight global three-dimensional semantic map based on camera pose and scene depth information, and carrying out maintenance and updating through a voxelization statistical mechanism. Compared with a traditional dense point cloud map, the method has the light weight effect that the storage space is greatly reduced.

The invention provides an injection molding part surface flaw detection method and device based on double-model collaboration, and the method comprises the steps: carrying out the local abnormal reflection feature analysis of a multi-view optical image collected on the surface of an injection molding part based on a first detection model, and obtaining a candidate flaw region; performing spatial positioning in the multi-view optical image based on the candidate flaw area to obtain a multi-view candidate image segment; performing surface geometric continuity analysis on the multi-view candidate image segments based on a second detection model to obtain a three-dimensional surface geometric consistency result; carrying out authenticity discrimination on the candidate flaw area based on a three-dimensional surface geometric consistency result to obtain a real flaw area; wherein the first detection model is used for capturing an optical response model of local abnormal reflection characteristics; the second detection model is a stereoscopic vision model for analyzing the geometric continuity of the multi-view lower surface. According to the invention, the false alarm rate of surface defect detection of the injection molded part under a complex surface condition is reduced.

The invention discloses a bar count visual identification automatic calculation system and method, the system comprises a stereo visual acquisition unit, a dynamic threshold adjustment unit, a geometric feature analysis unit, a data processing module and a man-machine interaction unit, and the method comprises the following steps: S01, image acquisition and preprocessing; s02, adjusting an environment self-adaptive threshold value; s03, bar geometric feature extraction; s04, the number of bars is calculated; s05, outputting a result, displaying the bar count obtained by calculation on an interface of a man-machine interaction unit, and storing a counting result and related data in a database at the same time; the beneficial effects of the invention are that a conventional visual counting system usually only depends on two-dimensional image information, recognition errors are easy to occur under the conditions of bar adhesion, surface reflection and the like, the scheme constructs a stereoscopic vision system through an industrial camera to obtain three-dimensional point cloud data, image textures are enhanced in combination with coding structured light projected by a structured light emitter, and the recognition accuracy is improved. And the spatial position and surface depth information can be obtained.

The invention relates to a charging pile multi-mode vehicle security monitoring method and system, and the method comprises the steps: collecting vehicle contour point cloud, environment parameters and voltage fluctuation data through a stereoscopic vision sensor, a temperature and humidity sensor and a current sensor, and generating a multi-mode fusion data set through time-space synchronization; comprehensive scores of vehicle deformation, environment sudden change and voltage abnormity are calculated based on a dynamic weight fusion model, low-risk, medium-risk and high-risk three-level safety judgment results are generated in combination with a quantile threshold interval, and the environment adaptability defect of a traditional fixed rule is eliminated through a dynamic weight and an adaptive threshold; triggering a hierarchical control instruction according to a judgment result, and synchronously constructing a credible evidence chain containing a timestamp, an operation log and an encryption check code to ensure that the security event is traceable and the data is tamper-proof; and finally, aggregating multi-node user feedback through a federated learning framework, updating a model weight and a threshold interval in combination with a differential privacy technology, and realizing dynamic optimization of a false alarm rate while protecting user privacy.

The invention discloses an underwater image enhancement method and system based on binocular vision and polarization imaging. The method comprises the following steps: firstly, synchronously acquiring an orthogonal polarization image pair through a binocular polarization imaging unit, recovering scene depth information from the polarization image pair by using a binocular stereoscopic vision technology, and calculating a transmissivity graph; analyzing the polarization characteristic difference between target reflected light and backscattered light, establishing a polarization difference model, and separating background scattered light; solving an underwater imaging equation in combination with the depth information and a polarization difference model to obtain a preliminary restoration result of the target reflected light; and finally, further improving the image quality through the steps of multi-scale spectrum analysis, adaptive filter construction and post-processing by adopting a spectrum adaptive image enhancement technology. The system comprises a binocular polarization imaging unit, a calculation processing unit and an active illumination unit. According to the method, the problems of color distortion, low contrast and fuzzy details of the underwater image are solved, and the visual quality and usability of the underwater image are improved.

The invention discloses a multi-source information fusion-based tunneling equipment vision-assisted pose detection system and method, and relates to the technical field of exploration and engineering surveying, and the system comprises a binocular vision module, a strapdown inertial navigation module, a laser orientation instrument module and a data fusion processing module. The binocular vision module solves three-dimensional coordinates through stereoscopic vision; the strapdown inertial navigation module provides high-frequency dynamic attitude data; the laser orientation instrument modules are triangularly arranged to provide a global absolute position reference; and the data fusion processing module adopts an extended Kalman filtering dynamic fusion algorithm and combines dynamic weight adjustment to fuse multi-source data. The method comprises the steps of data acquisition, preprocessing and time-space synchronization, fusion calculation and pose information output. Through the multi-source collaborative fusion and dynamic environment adaptation technology, the detection precision and stability are improved, the automatic tracking and remote monitoring requirements of the tunneling equipment are met, and intelligent upgrading of coal mine tunneling is promoted.

The invention relates to the field of pipeline three-dimensional reconstruction and intelligent detection, in particular to a pipeline three-dimensional reconstruction and intelligent detection method based on panoramic stereoscopic vision. According to the technical scheme, a panoramic camera and a radar sensor are used for collecting panoramic images and depth data in a pipeline; carrying out denoising, distortion correction and illumination compensation processing on the collected panoramic image, and carrying out image optimization; stable feature points in the panoramic image are extracted, and feature point matching is carried out; a multi-view stereoscopic vision algorithm and a synchronous positioning and mapping technology are combined, and pose estimation and three-dimensional point cloud generation are carried out by using a panoramic image and depth data; optimizing the generated point cloud data, and reconstructing a smooth and continuous three-dimensional surface; and based on the processed three-dimensional data, intelligently detecting cracks and corrosion defects in the pipeline by using a convolutional neural network, and automatically generating defect types, positions and repair suggestions. The method is suitable for pipeline detection.

The invention discloses a safe driving map construction method based on binocular vision and IMU information fusion, and the method comprises the following steps: obtaining image data information through a binocular image collection unit, obtaining IMU data information through an inertial measurement unit, and carrying out the data preprocessing of the image data information and the IMU data information; performing joint initialization processing on a data preprocessing result based on binocular vision initialization and inertial measurement unit initialization; carrying out global pose nonlinear optimization processing on a joint initialization processing result based on a tight coupling optimization strategy of a sliding window; based on a BVAI-Fusion algorithm, generating a safe driving track map for a global pose nonlinear optimization processing result; initial pose estimation is improved through binocular vision and IMU multi-source heterogeneous information fusion, so that pose estimation precision and stereoscopic vision matching under multi-source heterogeneous fusion are improved, SLAM environment positioning accuracy is enhanced, and safety of driving map construction is guaranteed.

The invention discloses a three-dimensional wave liquid level visual intelligent reconstruction evaluation system, and belongs to the field of ocean engineering monitoring. The system comprises an image acquisition module, an image stereoscopic vision processing module, an attention-enhanced reconstruction neural network module, a camera attitude evaluation module, a visualization and output module and a hydrodynamic parameter analysis module. An image is collected through a fixed baseline binocular camera system, after preprocessing, a neural network fused with a multi-scale attention mechanism is utilized to reconstruct a three-dimensional wave structure, coordinate system conversion is achieved in combination with self-supervised attitude evaluation, and finally hydrodynamic parameters such as significant wave height and a three-dimensional velocity field are extracted and visualized. The system does not need explicit calibration and large-scale data, has high automation, real-time performance and strong environmental adaptability, can be deployed on various platforms, and significantly improves the precision and engineering applicability of non-contact wave observation.

The invention discloses a stereoscopic vision optimization method and system for a naked-eye 3D large screen, and particularly relates to the technical field of naked-eye 3D vision optimizing.The method comprises the steps that environment illumination and audience positions are sensed in real time through multi-sensor fusion, and an environment light field model is established; glare crosstalk noise is predicted based on physical simulation, and self-adaptive suppression and compensation are carried out in combination with human eye visual sensitivity and image content features; a virtual camera is dynamically generated according to the real-time positions of the eyes of the audience, and a lightweight neural radiation field renderer is used for real-time re-rendering, so that motion parallax is realized; and finally, intelligently fusing the glare compensation layer and the perspective correction layer, coding and outputting to a screen. The system correspondingly comprises an environment perception module, a glare compensation module, a perspective rendering module and a fusion coding module. The naked-eye 3D large-screen display method effectively inhibits ambient light interference, improves the quality and immersion of a stereoscopic picture under different visual angles, and is suitable for naked-eye 3D large-screen display under outdoor and complex illumination environments.

The invention provides a three-dimensional calibration method using a speckle image as a calibration object and combining weight parameters. The method comprises the following steps: acquiring a standard grid image and the speckle image through a camera; determining angular point positions in the standard grid image through an angular point detection algorithm; calibrating the stereoscopic vision system by using the obtained angular points to obtain an initial calibration result; calculating 3D coordinates of the camera calibration board in the world by using the disparity map; determining a subset of the reference image corresponding to the checkerboard angular points in the target image through a digital image cross-correlation algorithm; calculating a weight parameter of each calibration point based on the re-projection error; solving internal and external parameters of the camera by using the weight parameters, radial alignment constraint and a least square method; optimizing the main point of the camera by using the weight parameters; and if the re-projection error or the number of iterations does not meet the requirement, repeatedly calculating the weight parameter of the calibration point and the subsequent steps, otherwise, obtaining a camera calibration result, and obtaining the distortion curved surface of the camera lens.

The invention relates to the technical field of computer vision, and discloses a flame recognition and fire extinguishing system for indoor fire fighting, comprising: a binocular infrared imaging module for acquiring a thermal imaging image; the monocular visible light imaging module is used for acquiring a visible light image; the infrared stereoscopic vision module is used for carrying out processing and feature extraction on the thermal imaging images, carrying out feature aggregation to obtain an aggregated feature map, carrying out parallax optimization on the aggregated feature map, and carrying out calculation according to a parallax optimization result to obtain depth information of the thermal imaging images at different visual angles; the multi-mode stereoscopic vision fusion module is used for fusing the thermal imaging image with the depth information and the visible light image; the flame identification and fire behavior prediction module is used for performing flame detection according to the fused image, predicting a bounding box, confidence and a category label of the flame in the fused image, and predicting a future diffusion position of the flame, a diffusion scale of the flame and a diffusion trend of the flame; and the self-adaptive water gun spraying control module controls the water gun to execute spraying operation.

The invention provides a sweet menu embryo pelleting seed breakage rate detection method based on machine vision, and relates to the technical field of seed detection.The method comprises the steps that firstly, visible light and near-infrared images of seeds under standard illumination and non-standard visible light images under multiple light sources are collected, and reflectivity calibration and polarization difference correction processing are carried out; features such as colors and textures are extracted based on visible light and near-infrared reflectivity images, and multispectral feature vectors are constructed; a surface normal vector is calculated by using a photometric stereoscopic vision technology and combining a plurality of non-standard visible light images, an initial three-dimensional height map is generated through integration, and a plurality of abnormal judgment features are extracted to mark a suspected damaged area. And further calculating an edge sharpness index, removing a mechanical damage area, and determining a final damage area and a three-dimensional contour thereof. And finally, combining the volume loss rate, the projection area ratio and other multi-dimensional features with the multispectral feature vector to construct a damage probability prediction model, thereby realizing accurate and efficient detection of the seed damage rate.

The invention belongs to the technical field of medical robots and minimally invasive surgery, and provides a robot control system and method for blue laser vaporization surgery of prostatic hyperplasia. Mapping the nuclear magnetic image volume data to a deformation field under an ultrasonic acquisition coordinate system, and deforming the preoperative nuclear magnetic image to an intra-operative ultrasonic space by using the deformation field to complete image registration; fusing the registered image with a stereoscopic vision system; the spatial depth of the surface of the target tissue is obtained from the endoscopic image so as to supplement navigation information; according to the utility model, the prostate deformation and probe posture change adaptive capacity in an operation is improved, the real-time visual closed-loop regulation and control capacity is improved, and the characteristics of small light spots, shallow heat diffusion, excellent hemostasis and the like of blue laser are combined, so that the vaporization and hemostasis precision in a tiny blood vessel dense area is ensured, and the problems of large tissue trauma and the like are avoided.

The invention discloses a stereoscopic vision-based model attitude measurement method, belongs to the technical field of attitude measurement, aims to solve the problems of insufficient precision and expensive depth camera of traditional attitude estimation, realizes accurate measurement of model attitude, and has the core of fusing stereoscopic vision geometric characteristics and deep learning advantages. The method specifically comprises the steps of 1, forming a three-dimensional system by using at least two industrial cameras, calibrating internal and external parameters, and calculating a scene depth map through an improved SGBM algorithm to obtain object depth information, and 2, predicting coordinates of pixels in a target 3D coordinate system by using a pre-trained double-branch fusion network in combination with the depth map and left and right eye RGB images, the method comprises the steps of 1, generating a plurality of groups of candidate poses on the basis of association and spatial association, 2, restoring the candidate poses into virtual objects, comparing the virtual objects with real objects through geometric consistency verification to quantify scores, and 3, selecting the candidate pose with the highest score as a final result through a PoseSelection module. And large view field coverage and high-precision positioning are both considered.

The invention relates to the technical field of optical three-dimensional measurement and metering, and discloses a single-camera three-dimensional coordinate measurement method and system based on full-field distance measurement, and the method comprises the following steps: S1, building a calibration field containing a control point, shooting an image of the calibration field through a monocular camera, extracting the image coordinate of the control point, carrying out the camera calibration, and obtaining a calibration field; obtaining internal parameters and external orientation parameters of the camera; and S2, controlling the laser range finder to aim at the control point in the calibration field through the non-orthogonal double-axis turntable. Submillimeter-level high-precision measurement can still be realized under the condition of a short baseline, and the problems that the precision in the depth direction is insufficient, the baseline requirement is long, parameters are easy to drift and the like in traditional stereoscopic vision measurement are effectively solved. By introducing a non-orthogonal double-shaft turntable and a visual guidance laser aiming mechanism, autonomous, rapid and accurate measurement of multiple measurement points in a large field of view is realized, and the automation degree and reliability of measurement in a complex environment are remarkably improved.

The invention discloses a binocular vision-based fish body quality non-contact estimation method, which comprises the following steps of: in a land-based facility circulating water high-density culture environment in which a fish body freely swims and is easy to block, firstly, obtaining a fish head local image through fish head region segmentation, and carrying out position alignment and scale normalization processing on the fish head local image; constructing a stable fish head geometric anchor point; then, the fish head anchor point is used as condition input, and a generative model is used for inferring and complementing the overall shape of the fish body so as to recover a continuous geometric structure of the fish body; on the basis, binocular stereo vision is combined to carry out stereo matching on key points of the head and the tail of the fish body, and the length of the fish body is obtained through triangulation; finally, based on the mapping relation between the body length and the quality, automatic estimation of the fish body quality is achieved, manual contact with the fish body is not needed, stable and automatic measurement of the fish body quality can be achieved in a complex breeding scene, and the method has the advantages of being high in adaptability, low in stress risk and high in application value.

The present invention relates to the technical field of industrial process control and detection. Disclosed is a method for real-time measurement of length-width size distribution of a crystal population in a crystallization reactor using binocular telecentric cameras. In-situ environment calibration is performed for a binocular telecentric stereo vision system, a calibration rod capable of probing into an in-situ environment (a reactor / glass tube) is designed, a binocular telecentric stereo vision imaging model is established, and a simple calibration method of rotating the calibration rod suitable for an in-situ limited space and a calibration plate design scheme are proposed. In order to improve binocular matching efficiency, a simplified telecentric stereo epipolar rectification method is further provided, so as to ensure accurate matching of on-site snapshot image pairs. On the basis of the matched image pairs, a three-dimensional reconstruction method using analytical solution-based ray intersection is provided to measure the three-dimensional pose of particles within a crystallizer. Finally, the three-dimensional length and width of a crystal are quantitatively evaluated by means of statistical data of Euclidean distances of pairs of length and width feature points of the crystal. The present invention has high operability, and can achieve the effect of automatically measuring the three-dimensional size of crystals.

The invention discloses a pair of binocular visual function training glasses used after strabismus operation, and relates to the technical field of visual rehabilitation training. The interpupillary distance adjusting mechanism is mounted on the upper side of the inner wall of the glasses frame; the two angle adjusting mechanisms are mounted on the two sides of the bottom end of the interpupillary distance adjusting mechanism respectively; the angle adjusting mechanism comprises an adjusting frame, a pitch angle driving assembly, a rotating frame, a lens cone and a miniature display screen. The angle and the position of the visual training pattern displayed by the micro display screen can be dynamically adjusted according to the specific strabismus type and the postoperative recovery condition of a patient, so that the capacity of coordinating the two eyes of the brain visual center is actively exercised and awakened, recovery of the visual functions of the two eyes is promoted, and the recovery effect is improved. By means of the design, the defect of a fixed parameter training lens in the aspect of adaptability is overcome, the binocular cooperation function of the visual center is actively activated through personalized parameter adjustment, and the postoperative binocular fusion capacity and the recovery efficiency of stereoscopic vision are remarkably improved.

The invention relates to the field of naked-eye 3D display, and discloses a self-adaptive naked-eye 3D stereo display parameter dynamic adjustment system, which comprises a sensing module used for collecting user eyeball position, environment illumination and display content depth information in real time, and encrypting user biological characteristic data; the core processing module is used for dynamically calculating parallax mapping, viewpoint synthesis and depth-of-field adjustment parameters according to the sensing data and setting an access control mechanism; and a hardware driving module. Through cooperation of multi-dimensional data fusion and an intelligent algorithm, dynamic and accurate adaptation of naked-eye 3D display parameters is achieved, a sensing module collects eyeball positions, environment illumination and content depth information of a user in real time, and a core processing module predicts the moving trend of the user and pre-calculates parallax parameters based on an LSTM model. And a multi-view interlaced signal is generated in combination with a light field compression technology, so that different users can obtain a crosstalk-free and low-delay stereoscopic visual effect at different viewing positions.

The invention relates to a device for stereovision of a hot translucent container.The stereovision device (2) comprises a colour distance sensor (4), an infrared optical sensor (6), a control unit (8) comprising a storage module (24) including a database and a control method implementing the following steps:i. the control unit (8) measuring, via the colour distance sensor (4), a distance (E′) between the sensor (4) and a container (34);ii. the computing module (26) measuring, via the infrared optical sensor (6), the position (P′) and a dimensional measurement (D′) of the container (34);iii. identifying, from the database, a value (R) correlated with the measurements (E′, P′, D′) carried out in steps i and ii;iv. identifying a defect in the container (34).

The invention relates to the technical field of stereoscopic vision, and discloses a synchronous speed visual matching method and system, electronic equipment and a storage medium, and the method comprises the steps: synchronously collecting a stereoscopic video sequence with a predefined frame rate; executing multi-target hybrid tracking and motion induction detection, and outputting target motion information including position and velocity vectors; extracting hierarchical motion features of the target from continuous multiple frames of the stereoscopic video sequence, and performing unified space-time coding; under geometric constraints of stereoscopic vision, scale cosine similarity, direction similarity and trajectory consistency measurement are calculated and serve as observation evidences to be input into the probabilistic reasoning model for fusion, and a posterior probability representing matching reliability is output; the weight distribution of the speed similarity and the direction similarity is adjusted according to the motion characteristics of the targets in the scene, and the stable corresponding matching relation between the left view target and the right view target is established. According to the method, high-time-resolution information can be utilized, motion features and geometric constraints can be effectively fused, and the method has self-adaptive capacity.

The invention belongs to the technical field of clothing defect detection, and particularly relates to a suspension type clothing defect detection method, which comprises the following steps of: 1, synchronously acquiring multi-view images of clothing in a suspension state through a plurality of groups of industrial cameras which are annularly arranged; step 2, reconstructing a 3D point cloud model of the garment based on the multi-view image, and calculating a fabric tensile deformation rate; step 3, executing deformation compensation: registering each view angle image to a standard plane template by using thin plate spline transformation (TPS), and eliminating geometric distortion; the millimeter-level three-dimensional reconstruction of the hanging clothes is realized through a multi-view stereoscopic vision and coding mark point system, and the vision reconstruction error is obviously reduced. And a dynamic TPS registration technology (control point density self-adaptive deformation rate) is combined, so that the geometric distortion compensation success rate is obviously improved. The synchronously calculated fabric tensile deformation rate eta provides accurate physical parameter support for subsequent processing, and solves the core pain point of deformation interference in flexible textile detection.

The invention relates to the technical field of vehicle safety, and discloses an integrated motorcycle auxiliary driving system and method. The method comprises the following steps: synchronously acquiring vehicle kinematics parameters and environment three-dimensional point cloud data through a multi-source sensing system of a motorcycle; executing a dynamic risk mapping operation by using the collected data to generate a risk probability distribution diagram; acquiring a color image and a depth image of a road scene through a stereoscopic vision camera, inputting the color image and the depth image into the multi-scale feature extraction network for analysis in combination with the risk probability distribution map, and outputting a comprehensive risk score; performing obstacle trajectory prediction according to the comprehensive risk score to obtain an obstacle prediction trajectory, scanning a target area by using an infrared sensor, processing point cloud data by using a point cloud segmentation algorithm based on the prediction trajectory, and extracting actual obstacle attributes; and inputting the comprehensive risk score and the actual obstacle attribute into a fuzzy logic controller for data fusion, and finally generating an integrated motorcycle aided driving control instruction.

The invention relates to the field of computer vision and image processing, in particular to a three-dimensional matching method based on wavelet transform and superpixel segmentation, which can improve the problems of poor depth estimation precision, blurred depth map contour and failure in multi-texture area depth estimation of the existing three-dimensional matching method. According to the method, different frequency domain sub-band information of the image is obtained by introducing a wavelet transform image processing method, the guiding effect of a low-frequency structure is enhanced, and the cost body aggregation process is participated; and meanwhile, fine modeling is carried out on the boundary of an image structure by combining a superpixel segmentation technology so as to enhance the integrity of an object contour in a depth image and the accuracy of parallax estimation. The method shows higher matching precision and boundary definition in a complex scene, has good calculation efficiency and deployment feasibility, and can provide reliable support for an embedded stereoscopic vision system with high precision and low resource consumption.

A robot sized and shaped for reception in a pipe, the robot including a chassis configured for movement of the robot in the pipe, a plurality of sensors including an inertial measurement unit (IMU), an encoder and a stereo vison camera associated with the robot, and a sensor fusion system operable to combine readings from the IMU, the encoder and the stereo vision camera to determine a position of the robot within the pipe, and wherein the sensor fusion system is operable to use machine learning in creating a digital twin of the pipe.