693 results about "Stereo matching" patented technology

Disclosed in the present invention is a salient contour matching-based method for target measurement in a severe imaging environment. The method specifically comprises: (1) acquiring a binocular image of a target; (2) establishing a global-local joint constraint-based background light estimation model, and removing a scattering effect of a medium in an imaging environment to obtain a restored left eye image and a restored right eye image; (3) learning an original image, and on the basis of a residual between a network reconstructed image and the original image, obtaining target localization prediction maps of the left eye image and the right eye image; and (4) respectively extracting contour lines of the target in the left eye image and the right eye image, constructing feature matching descriptors of contour points, performing stereo matching on the two sets of contour lines by minimizing matching cost, and performing three-dimensional reconstruction on the contour lines in light of calibrated intrinsic and extrinsic parameters to complete the measurement of a key size. According to the present invention, the key sizes of different targets in a severe environment can be accurately measured, thereby providing an effective solution for the problem of measuring the sizes of targets in a severe environment.

The invention discloses a distribution network tree barrier real-time analysis method and system based on dynamic vision and SLAM. The method comprises the following steps: generating a dynamic visual baseline by cooperatively controlling the translation and flight displacement of an unmanned aerial vehicle holder, and constructing a bionic binocular model to simulate a time sequence image into a binocular image pair; generating a depth point cloud through epipolar correction and stereo matching; key targets are recognized and extracted through a semantic segmentation network, and semantic point clouds are generated; establishing a dimensionality reduction motion model by utilizing pan-tilt stability augmentation, and fusing a visual inertial odometer and RTK data by adopting a filtering or optimization algorithm to realize centimeter-level pose estimation; and finally, performing optimization processing on the semantic point cloud, completing three-dimensional reconstruction based on multi-modal fusion, and outputting a risk assessment result through tree line spacing calculation and safety margin analysis. According to the invention, accurate, efficient and automatic routing inspection and risk early warning of the distribution network tree obstacles are realized.

The invention discloses a seismic isolation structure support three-dimensional deformation monitoring method and system based on binocular vision. The method comprises the steps that a binocular camera is adopted to collect monitoring scenes under the view angle of a left camera and the view angle of a right camera; performing image distortion removal and three-dimensional correction on the monitored image pair by adopting camera internal and external parameters obtained by camera calibration; carrying out target detection on the corrected image by adopting a target detection algorithm; according to the coordinates of the target detection frame, calculating a cutting frame and cutting target areas in the left image and the right image; carrying out stereo matching on the cut image pair; according to the disparity value of each pixel in the disparity map, depth information of the target is calculated through a triangulation method, and finally a three-dimensional dense point cloud of the target is generated; performing optimization processing on the target three-dimensional dense point cloud obtained in the previous step to obtain an optimized point cloud; solving a rigid body transformation matrix of the two-point clouds by using an iterative nearest point algorithm; according to the invention, the cost of monitoring the three-dimensional pose deformation of the rubber support can be reduced, and the robustness and precision are effectively improved.

The invention discloses a multi-target and long-distance field domain temperature measurement method based on dual-spectrum imaging. A visible light camera module and an infrared camera module are included. Obtaining internal parameters and external parameters in the visible light camera module and the infrared camera module, and optimizing the internal parameters and the external parameters; performing quick image feature search and image feature stereo matching on the acquired visible light image and infrared image to realize identification and positioning during image measurement; registering and fusing the visible light image and the infrared image by adopting a double-spectrum image fusion algorithm to obtain a fused image; carrying out image intersection measurement positioning and temperature measurement area stereo matching on the image; performing systematic edge calculation on the image by adopting a data-based intelligent analysis algorithm, and analyzing a temperature change trend according to the obtained image data to obtain the temperature of the measured object; the current running state of the detected power equipment is evaluated, the equipment fault is diagnosed, and the application end is used for storing, displaying and processing the detection result; and the temperature detection accuracy of the power equipment is effectively improved.

The invention provides an adaptive welding seam detection and three-dimensional reconstruction method based on deep learning and binocular vision. The adaptive welding seam detection and three-dimensional reconstruction method comprises the steps of S1, collecting samples and making a training data set; s2, the picture of the sample to be welded is processed, a feature region is recognized, the image quality of the region to be welded is analyzed and evaluated through wavelet transform and local variance, and the noise level and the contrast ratio are calculated; s3, dynamically generating edge detection parameters and model fitting parameters according to the image quality; s4, using an edge detection algorithm to extract edge point cloud of the welding seam area; s5, performing RANSAC linear fitting, weighted least square fitting and polynomial curve fitting on the edge point cloud in parallel; s6, selecting an optimal fitting result based on an image quality adaptive dynamic scoring model; and S7, carrying out three-dimensional coordinate conversion in combination with the three-dimensional matching model IGEV-Stereo, and outputting a final welding seam three-dimensional coordinate. According to the invention, automatic detection of the position and size of the welding seam can be efficiently and accurately realized.

A slope monitoring method based on optical flow estimation and binocular vision belongs to the technical field of deformation monitoring and measurement, and adopts the technical scheme that a binocular stereoscopic vision system is built, and camera acquisition parameters are calibrated; transmitting the image to a server in real time through the Internet of Things; distortion correction and stereo matching are carried out to obtain a depth map; monitoring points are manually or automatically arranged on the corrected image, and dense optical flow estimation tracking pixel point movement is carried out; world coordinates of observation points and movement conditions of each frame are obtained, and displacement changes of slope monitoring points are obtained by combining coordinates before and after displacement; and obtaining a displacement rate by combining monitoring time and displacement, judging slope safety, obtaining a displacement track, and performing linear interpolation to obtain a slope deformation cloud picture, thereby realizing the monitoring purpose. The slope displacement monitoring method has the advantages that slope monitoring can be carried out more flexibly and efficiently, the safety of workers is improved, the slope monitoring cost is reduced, real-time slope displacement monitoring is achieved, and the intensive degree of slope monitoring points is greatly improved.

The invention discloses a stereo matching method based on context geometric features and distortion parallax optimization, which comprises the following steps of: firstly, shooting a calibrated left picture and a calibrated right picture through a binocular camera, extracting multi-scale context features of the two pictures, and calculating the similarity between feature points according to the feature points of the left feature graph and the right feature graph under different parallaxes; the matching cost is the matching cost, so that the constructed geometric cube contains rich geometric matching information. Meanwhile, context features are copied and expanded into context feature cubes with the same size according to parallax, and rich context information is contained. The geometric cube contains matching conditions of feature points of left and right views with different parallax errors, cost aggregation is guided through context features of the left view, and context feature information, feature point cost matching information under different parallax errors and geometric information of adjacent feature points are preliminarily fused to improve cost matching reliability. And generating a geometric attention weight cube. And filtering the contextual feature cube.

The invention discloses a three-dimensional vision calibration and positioning method and system based on an event camera, and belongs to the field of positioning. The method comprises the steps of constructing an array containing a plurality of unique frequency coding light sources, constructing a binocular event camera system, collecting light source array light signal event streams, extracting pixel positions, frequencies and timestamp information, determining pixel coordinate centroids of the light sources through frequency and pixel clustering analysis, and establishing frequency and centroid mapping. Auxiliary light sources with the same coding rule are arranged on the surface of a target object, and after a binocular system collects light signals of the auxiliary light sources, the center-of-mass coordinates of the light sources are matched according to the mapping relation. And based on event camera calibration parameters and binocular external parameters, correcting and stereoscopically matching centroid coordinates, and calculating three-dimensional coordinates of the auxiliary light source through triangulation. And in combination with the known geometric topological relation of the auxiliary light source on the surface of the object, the spatial pose of the target object is solved by using a rigid transformation algorithm, and high-precision positioning is completed. According to the embodiment, the calibration precision can be improved in a complex application environment.

The invention particularly relates to a binocular vision-based three-dimensional reconstruction method, which comprises the following steps of: calibrating a binocular camera based on an improved Zhang Zhengyou calibration method to obtain internal and external parameters and a distortion coefficient of the camera; performing stereo correction on the image by using the internal and external parameters of the camera and the distortion coefficient obtained by calibration, so that the binocular image meets an epipolar constraint condition; a multi-strategy optimized semi-global stereo matching algorithm is adopted to process the image after stereo correction, and a disparity map is generated; based on the generated disparity map, generating a three-dimensional point cloud through a triangulation principle; carrying out anti-interference processing and registration optimization on the three-dimensional point cloud; and performing global splicing on the three-dimensional point clouds subjected to anti-interference processing and registration optimization based on a sequential registration error sharing strategy to complete three-dimensional reconstruction. According to the method, the key problems of large calibration error, weak texture matching failure, point cloud noise sensitivity and registration accumulative error in a traditional method are solved, and the reconstruction precision and stability are remarkably improved.

The invention discloses a monocular and binocular cooperative localization and mapping method and device for underwater refraction compensation, and the method comprises the steps: extracting checkerboard angular points, compensating an underwater light path through employing a two-time vector correction model based on a Snell law, and precisely calibrating the internal and external parameters of a binocular camera. According to the method, an underwater real light path is effectively restored, and calibration errors caused by medium refraction are remarkably reduced. Then epipolar correction is performed on the corrected left and right images, so that the geometric consistency of stereo matching is ensured; and finally, monocular and binocular poses are fused, and seven-degree-of-freedom similarity transformation global alignment is applied, so that the problem of monocular scale uncertainty is effectively solved, and high-precision and high-robustness three-dimensional mapping is realized by means of binocular real scale introduction. Key links such as camera pre-calibration, refraction compensation, image correction, monocular tracking, binocular depth measurement, scale alignment and the like are connected in series to form an integrated process, so that the underwater vision positioning and mapping precision is improved.

The invention discloses a multi-view underwater three-dimensional point cloud splicing method based on sparse and dense point cloud fusion, and relates to the technical field of structure underwater detection. The method comprises the steps that a binocular camera surface image of an underwater structure is received, the binocular camera comprises a left camera and a right camera, dense point clouds are generated based on stereo matching and triangulation, and the corresponding relation between the dense point clouds and image pixels of the left camera is output; and based on the received left camera image, a sparse point cloud is generated by adopting a motion structure recovery algorithm and utilizing multi-view triangulation, and a corresponding relation between the sparse point cloud and the pixels of the left camera image is output. According to the method, when the point cloud of multi-view reconstruction is spliced, the surface features of the underwater structure can be well restored, feature extraction is not needed, the method has high accuracy and robustness, and high-precision point cloud splicing can be achieved in the underwater complex environment, so that the method has good application prospects in actual underwater structure detection and image splicing tasks.

The invention relates to the technical field of image data processing and intelligent control, and discloses a patch board spot welding track intelligent control method and system based on visual perception. According to the method, synchronous image streams are collected through a binocular vision sensor, and epipolar correction image pairs are generated through timestamp synchronization, ROI extraction and epipolar correction processing; calculating a disparity map by adopting a stereo matching algorithm, and constructing a compensated three-dimensional point cloud model based on deformation vector field fusion multi-frame point cloud data of a radial basis kernel function; welding spot position error vectors are generated by extracting welding spot feature points and performing spatial filtering optimization; and carrying out inverse kinematics calculation on the mechanical arm by adopting a damping least square method, carrying out safety constraint optimization in combination with prospective collision risk assessment and real-time pose data, and generating a trajectory compensation instruction. According to the method, the problems of dynamic deformation compensation and motion safety in patch plate welding are solved, the submillimeter welding spot positioning precision is achieved, and the welding quality stability and the system robustness are improved.

The invention provides a robust stereo matching method fusing monocular semantic prior and multi-dimensional expert aggregation, relates to the technical field of computer vision and stereo matching, establishes a robust stereo matching framework fusing monocular semantic prior and multi-dimensional expert aggregation, and comprises a monocular branch and a binocular branch, inputting a stereo image into a robust stereo matching framework based on fusion of monocular semantic prior and multi-dimensional expert aggregation for matching calculation, wherein the matching calculation comprises the following steps: extracting semantic prior features of the input image by using a monocular branch; the method comprises the following steps: extracting a geometric enhancement feature map of a stereo image by using binocular branches, generating a matching cost body based on the geometric enhancement feature map, and carrying out refined iterative updating on the matching cost body to obtain a matched disparity map. According to the method, monocular and binocular depth estimation is cooperatively supported in a unified network architecture, and a parallel monocular depth prior path is utilized to actively guide and strengthen a core binocular matching process.

The invention discloses a tunnel surrounding rock multi-source data dynamic fine rapid sensing method, which comprises the following steps: based on a mobile terminal multi-view image sequence, arranging shooting positions according to a preset space interval and an orthogonal angle, generating a sparse point cloud through a motion recovery structure algorithm, and generating a dense point cloud model in combination with multi-view stereo matching optimization; and for the dense point cloud model, delimiting a local neighborhood based on k-nearest neighbor search, resolving a neighborhood point covariance matrix through principal component analysis, extracting a feature vector corresponding to a minimum feature value as a normal vector, and constructing a rock mass surface microscopic geometric feature field. The invention provides a dynamic fine rapid sensing method based on multi-source data, and aims to improve the efficiency, precision and timeliness of tunnel surrounding rock information acquisition and provide accurate surrounding rock information support for tunnel construction and support design.

The invention discloses a dual-light camera image registration fusion method, system and device based on binocular distance measurement and a storage medium, which are applied to the technical field of image processing, and the method comprises the steps: carrying out the distortion correction of a dual-light image, obtaining the internal and external parameters of a dual-light camera, and carrying out the stereo correction and mapping transformation of the dual-light image based on the internal and external parameters; performing parallax estimation calculation on the corrected image by using an SGBM stereo matching method, calculating a distance from a target to the dual-light camera according to a value in a center range in the image, and extracting corresponding preliminary registration parameters and an affine transformation homography matrix according to a registration parameter index table to perform image registration; and performing pixel value proportion conversion on the dual-light image after image registration, extracting high-frequency information and low-frequency information of the infrared image, and performing fusion of the infrared image and the visible light image based on the high-frequency information and the low-frequency information. According to the method, the real-time performance and practicability of image registration fusion are effectively improved, and the image registration fusion precision is further improved.

The invention relates to the field of power transmission line channel risk hidden danger monitoring, and provides a data fusion power transmission line channel risk hidden danger monitoring method and system, and the method comprises the steps: collecting the multi-modal sensing data of a power transmission line channel, and generating a multi-modal data flow of a unified time-space coordinate; constructing a three-dimensional space point cloud through a phase unwrapping and stereo matching fusion algorithm, and fusing multi-modal data to generate a space probability tensor; extracting risk semantic latent variables, constructing a Bayesian network and identifying potential risks; performing tensor product on the potential risk and the environmental data to generate a dynamic risk enhancement feature matrix, and constructing a nonlinear dynamic threshold curved surface through quantum annealing and Gaussian process regression; a mechanical equation is constructed, Gaussian kernel density estimation and numerical simulation are combined, the evolution trajectory of the risk in the space-time dimension is predicted, and a risk thermodynamic diagram and early warning information are generated; and generating a structured risk early warning report by adopting a natural language processing method. And the accuracy of power transmission line channel risk hidden danger monitoring is improved.

The invention discloses a photoelectric fusion beam control method and device based on a reconfigurable intelligent reflecting surface. The method comprises the following steps: acquiring internal and external parameter matrixes of a binocular camera and a binocular-RIS coordinate transformation matrix; capturing a binocular image stream of a detection target by using a binocular camera and executing stereo matching to obtain a disparity map and depth data of a binocular image; detecting a target image in the binocular image and outputting bounding box information of a center point of the target image; calculating target image coordinates through the depth data and the bounding box information, and converting the target image coordinates into three-dimensional space coordinates under an RIS coordinate system so as to determine the spatial position of the target; generating an RIS phase codebook according to the target spatial position, wherein the RIS phase codebook comprises a near-field phase compensation item; and loading the phase codebook to the RIS unit to form a target beam so as to perform directional tracking on a target. According to the technical scheme of the invention, high-precision beam control and stable positioning and tracking of the target can be realized in a complex multi-target environment.

The present application provides an iris recognition method and apparatus, an electronic device, and a storage medium. The method comprises: obtaining an iris image group acquired for an object to be recognized, wherein the iris image group comprises at least two iris images acquired at different acquisition angles for a same eye region of said object; performing stereo matching on the at least two iris images to obtain a disparity map between the at least two iris images, wherein the disparity map comprises disparity elements, and each disparity element represents a displacement amount in a specified direction between two pixel points corresponding to a same eye region element in the at least two iris images; in one iris image among the at least two iris images, determining a pupil edge on the basis of the displacement amounts; determining an iris region in the iris image on the basis of the pupil edge, and performing feature extraction on the iris region to obtain an iris feature; and performing identity recognition on said object on the basis of the iris feature.

The invention relates to the technical field of three-dimensional reconstruction, and provides a local feature adaptive fusion and high-fidelity new view angle synthesis method based on three-dimensional reconstruction. According to the method, a local feature module is introduced into a point cloud network, robust matching of local features is realized, a self-adaptive matching strategy is combined, high-resolution images can be efficiently processed, and the precision and stability of three-dimensional reconstruction are remarkably improved. The method is suitable for application scenes such as three-dimensional rendering, three-dimensional reconstruction and new view angle synthesis. The specific process comprises dense reconstruction and initial rendering; and carrying out missing region repairing and iterative optimization. Generating a high-precision point cloud based on an improved stereo matching algorithm, and rendering an initial image of a target view angle in combination with camera parameters; a conditional video generation model is adopted, and holes, distortion and artifacts in initial rendering are repaired; and carrying out progressive complementation on the occlusion region by using the view angle consistency constraint, and improving the reconstruction integrity by jointly optimizing the point cloud geometry and texture. According to the method, the final picture quality is remarkably improved through feature matching optimization and efficient processing capacity. Local feature branches and global context information are coordinated, and the problem of matching ambiguity in a complex scene is solved; the adaptive fusion strategy gives consideration to high-resolution image processing efficiency and detail retention; and the detail fidelity and the overall quality of the rendering result are effectively improved.

The invention relates to the technical field of structure health monitoring, and provides a structure unmarked three-way displacement real-time measurement method based on deep learning stereoscopic vision, which comprises the following steps: erecting and calibrating a binocular camera; obtaining a structure monitoring image sequence, and automatically extracting a to-be-monitored region of the structure through the semantic segmentation model; tracking coordinates of feature points of the to-be-monitored region of the structure through a deep learning feature point detection model; obtaining feature point coordinates of sub-pixel precision through sub-pixel refinement; three-dimensional coordinates of the feature points at all moments are obtained through stereo matching and three-dimensional reconstruction; and obtaining three-direction displacement information of the structure according to the three-dimensional coordinates and the three-dimensional point displacement. According to the invention, three-dimensional displacement measurement of the structure can be carried out without manually selecting a to-be-monitored area of the structure and designing a mark, the cost is low, the precision is high, and the engineering practicability is high.

The invention belongs to the technical field of data measurement, and discloses a high-speed railway ballastless track construction measurement method, which comprises the following steps of: carrying out internal reference and external reference calibration on a binocular camera of a track detection trolley; obtaining related data containing a binocular image, and carrying out space-time alignment processing on the related data so as to construct a prediction state vector; carrying out stereo matching on the binocular image to generate a prediction disparity map, and carrying out three-dimensional reconstruction on the prediction disparity map to obtain a track point cloud map; and extracting a track inner side point coordinate sequence and a track height program sequence from the track point cloud picture, and generating a corresponding track gauge table and a corresponding smoothness table. According to the invention, vision, three-dimensional reconstruction and inertial navigation technologies are integrated, and integrated detection of gauge, elevation and smoothness parameters is realized.

The invention discloses a pavement disease size accurate quantification method based on binocular vision, and the method specifically comprises the steps: collecting pavement image data through a vehicle-mounted binocular camera, achieving the real-time detection of a disease target through an improved RT-DETR model, and outputting the disease type and detection frame information; after a pavement disease target is detected, pixel-level segmentation is carried out on a disease area in the detection frame based on a semantic segmentation model, and a disease contour mask is extracted; an improved IGEV-Stereo stereo matching algorithm is used for calculating a disparity map, depth information is output based on camera calibration parameters and a disparity estimation result, and conversion from pixel coordinates to three-dimensional coordinates is achieved; and the binocular depth information and a disease detection segmentation result are combined to realize accurate size quantification of typical road surface diseases with different characters. Through the binocular stereoscopic vision technology, automatic detection and precise quantification of pavement diseases can be realized, the cost can be effectively reduced while the disease quantitative evaluation precision is improved, and decision support is provided for road management and maintenance.

The invention discloses a dynamic tracking optical scanning system and method, and relates to the technical field of three-dimensional scanning, tracking equipment and scanning equipment are controlled through a central control subsystem to complete three-dimensional model reconstruction, and the central control subsystem comprises a calibration correction module, an image splicing module, a three-dimensional reconstruction module and a display module; the calibration correction module feeds back a correction instruction through a double-target calibration correction method; the image splicing module is used for splicing all binocular correction image sequences based on a transformation splicing algorithm to generate a global image; the three-dimensional reconstruction module generates an object disparity map and a target disparity map in combination with a stereo matching algorithm and a semantic segmentation labeling method, obtains a sparse three-dimensional model of a single time frame by adopting a re-projection transformation method, and generates a local dense three-dimensional model of the single time frame in combination with a dual-feature pose estimation network and a coordinate transformation method; and the display module performs real-time display based on a pose detection updating display strategy, so that synchronous dynamic tracking of pose transformation of the detected object and the scanning equipment in the scanning process is realized.

The invention discloses a fabric apparent morphology three-dimensional reconstruction method under multi-view progressive matching. The method comprises the following steps: (1) building a closed shooting device; (2) acquiring a fabric image; (3) image preprocessing; (4) camera calibration and image correction: carrying out parameter calibration on the camera of the shooting device by adopting a checkerboard calibration method to obtain internal and external parameters of the camera, and carrying out correction operation on the acquired fabric image; (5) feature extraction and stereo matching: extracting appropriate feature points from the obtained left and right views of the fabric, and carrying out feature matching on the two images to obtain an optimal matching pixel pair; (6) generating a disparity map: calculating the fabric disparity map by utilizing the position difference between the left and right view pixels in the optimal matching pixel pair set; and (7) point cloud generation: calculating a depth map of each pixel value according to the view difference obtained in the step (6) so as to obtain a depth map of the fabric surface morphology and form a three-dimensional point cloud of the fabric apparent morphology.

The invention provides an OPM-MEG and MRI registration method and system based on a binocular camera, and the method comprises the steps: firstly carrying out the calibration of the binocular camera, and obtaining a calibration parameter through a checkerboard calibration method; then, binocular images of the testee wearing the brain magnetic measurement device are collected; performing three-dimensional reconstruction, obtaining an initial disparity map through epipolar correction and an SGBM binocular stereo matching algorithm, optimizing the disparity map to generate a three-dimensional point cloud, and performing noise reduction and smoothing processing by using MLS and Poisson surface reconstruction technologies; in the face point cloud registration stage, point cloud is cut by taking a nose tip point as a sphere center, ICP registration is carried out, and a transformation matrix from a camera coordinate system to an MRI coordinate system is obtained; and finally, in a helmet positioning stage, detecting a three-dimensional coordinate of a ball target on the helmet, calculating a transformation matrix from a helmet coordinate system to a camera coordinate system, and unifying the helmet coordinate system to an MRI coordinate system. According to the invention, a one-button registration process can be realized, the registration time is shortened, the registration precision is improved, and a basis is provided for improving the source positioning precision.

The invention discloses a water level measurement method based on binocular stereoscopic vision. The method comprises the following steps that model selection is conducted on a binocular camera according to an actual measurement scene; horizontally erecting a binocular camera on a river bank, adjusting a pitch angle to enable left and right views to cover a water surface area under a water level full range, and synchronously acquiring water surface images of the left and right views by using the binocular camera; acquiring a corresponding disparity map pixel by pixel by adopting an iterative optimization semi-global stereo matching method; based on a calibration result of the binocular camera, obtaining a three-dimensional point cloud coordinate value of the water surface image by using a relationship between parallax and depth; performing plane fitting on the point cloud by using an RANSAC algorithm to generate a horizontal plane equation; calculating the distance from the optical center of the left camera to the horizontal plane, and converting the elevation of the camera to obtain the water level. According to the invention, the shore-based arrangement of the instrument can be realized, the water level can be measured remotely in an inclined manner, the measurement does not depend on any reference object in contact with a water body such as a water gauge, the frame-while-frame measurement can be realized in severe environments such as a high flood period, and the device is particularly suitable for water level emergency monitoring.

The invention discloses a remote sensing image stereo matching method and system based on a Mama model interpretation cost body, and the method comprises the steps: S1, extracting multi-level and multi-scale semantic features from input left and right remote sensing stereo image pairs, and outputting a stereo image pair feature map; s2, aligning the left and right stereo image pair feature maps on the parallax dimension to construct a three-dimensional cost body; s3, introducing a Mama framework based on a selective state space model, interpreting the cost body in combination with a multi-scale feature interaction mechanism, and generating a parallax probability distribution diagram; s4, mapping the probability distribution into a continuous disparity value, and outputting a disparity map; according to the method, adaptive feature screening and global modeling are realized through a dynamic weight selection mechanism of the state space model, the local receptive field limitation of 3D convolution is overcome, and the multi-granularity feature representation capability of the model is improved by promoting interaction and fusion among features of different scales by virtue of a cross-scale information interaction mechanism, so that the robustness of the model is improved. And the overall interpretation accuracy and efficiency are improved.

The invention discloses a cable defect detection method of a binocular intelligent inspection robot, and relates to the field of image processing, and the method comprises the steps: firstly obtaining the state information of a cable, then carrying out the preprocessing and stereo matching of the image information of the cable, and then constructing a target detection and segmentation model; the target detection and segmentation model is adopted to detect and segment cable defects, and model sample data is updated in real time in an incremental learning mode; fusing the cable state information acquired by the infrared camera, the thermal imager and the wireless sensor network by using a multi-sensor data information fusion model; the cable is maintained and managed based on a detection result; according to the invention, the detection precision and accuracy can be improved, cables of different types and specifications can be handled, the adaptability and stability are ensured, and the real-time performance and accuracy of detection can be improved.

The invention discloses a double-vision video processing method and system, and relates to the field of double-vision videos, and the method comprises the steps: obtaining an image on a billiard table through a binocular camera, carrying out the preprocessing of the image, carrying out the double-vision three-dimensional reconstruction, obtaining the movement direction and speed of a billiard ball, predicting a movement track, and carrying out the collision auxiliary judgment. According to the invention, through processing of the double-vision video and combination of billiard motion trail prediction, auxiliary judgment is provided for billiard collision, and a referee is helped to carry out judgment and cutting; moreover, in the process of processing the video image, the image is deblurred based on a dynamic position estimation model, so that the deblurring effect is enhanced; according to the method, stereo matching is optimized, and an energy function combining Census transformation and mutual information is adopted, so that the robustness under illumination change is improved, and the result accuracy is improved.

The invention discloses a road contour estimation method and system of a vehicle-mounted binocular system based on deep learning. The method comprises the steps of obtaining an original image, obtaining a feature map through the original image, performing post-processing on the feature map, and obtaining road obstacle pixel coordinates and confidence information; processing the pixel coordinates and the confidence information of the road surface obstacle to obtain a local image of an obstacle area of the original image; inputting the local image of the barrier region of the original image into a deep learning stereo matching model based on a gate-type cycle unit for processing to obtain a local disparity map corresponding to the barrier region; millimeter-level pavement contour estimation is realized by fusing a real-time pitch angle provided by an inertial navigation unit through a local disparity map of a corresponding obstacle area. According to the method, important features of a road surface obstacle area can be concerned, irrelevant features are inhibited, and high-precision, high-efficiency, high-continuity and high-robustness road surface contour estimation is realized.