11 results about "Contour mapping" patented technology

The invention discloses a counterweight type robot polishing automatic clamping system which comprises an intelligent workpiece adaptability automatic clamping module and a control system. The intelligent workpiece adaptability automatic clamping module comprises a visual recognition unit, an execution mechanism, a clamping assembly and a force feedback unit. The visual identification unit is used for collecting image information of a to-be-polished workpiece and analyzing and determining the shape, size and surface feature parameters of the workpiece; the control system is based on the shape, size and surface characteristic parameters of a workpiece. The invention relates to the technical field of robot automation. According to the counterweight type robot polishing automatic clamping system, through multi-angle image collection and processing of the visual recognition unit, the system can accurately obtain the shape, size and surface characteristics of a workpiece, automatically analyze and generate a three-dimensional contour mapping graph, provide a basis for subsequent clamping path planning and clamping point optimization, have high adaptability and are suitable for large-scale popularization and application. And diversified workpieces can be handled.

The invention provides an unmanned aerial vehicle naked eye 3D two-dimensional and three-dimensional digital mapping method, and provides three-dimensional contour mapping based on point cloud, multi-layer building digital mapping based on a live-action model and ground object and landform digital mapping based on a digital orthoimage, so that rapid mapping of landforms, ground objects and landforms in a construction area is realized, the mapping precision is better than or equal to 5 cm, and the mapping efficiency is greatly improved. The large-scale digital mapping requirement is met, the construction deployment efficiency of an engineering project is improved, and the method is suitable for three-dimensional topographic mapping of all construction areas such as railways, highways and municipal administration. Belongs to the surveying field.

The invention discloses a contour map feature extraction method for sleeping posture recognition based on a clustering algorithm, and relates to the technical field of sleeping posture recognition. The pressure sensor is used for collecting body pressure distribution, so that the patient is not restrained, the privacy of the patient is not leaked, and the influence of shielding and light intensity is avoided. Aiming at the problem of high data dimension caused by more pressure detection units, the invention provides a contour map feature extraction method based on a K-means clustering algorithm, and features are extracted from original high-dimensional data for sleeping posture recognition. The method comprises the following steps: S1, preprocessing original body pressure distribution data; s2, clustering the preprocessed body pressure distribution data through a K-means clustering algorithm; and S3, according to the cluster center value in each cluster, drawing a body pressure distribution contour map corresponding to each contour line from top to bottom in the contour map in a descending order.

The application provides a UAV naked-eye 3D two-three-dimensional digital mapping method, proposes three-dimensional contour mapping based on point clouds, multi-storey building digital mapping based on real scene models, and digital orthographic image-based digital mapping of ground objects and landforms, realizes rapid mapping of the terrain, ground objects and landforms in the construction area, and the mapping accuracy is better than 5cm, meets the large-scale digital mapping requirements, improves the engineering project construction deployment efficiency, and is suitable for three-dimensional terrain mapping of all construction areas such as railways, highways and municipal engineering. It belongs to the field of surveying and mapping.

The present invention discloses a truck compartment contour mapping and positioning method based on laser radar, comprising the following steps: (1) selecting a truck compartment contour mapping location; (2) repeatedly scanning the truck compartment contour by laser radar to collect multiple frames of valid point clouds; (3) constructing a truck compartment point cloud contour map; (4) constructing a local reference coordinate system for the truck compartment contour; (5) matching the laser frame contour coordinate system with the local reference coordinate system for the truck compartment contour to achieve coarse positioning of a robot; and (6) constructing a nonlinear optimization objective function to achieve precise positioning of the robot. This method can accurately and reliably achieve precise positioning of the robot within the truck compartment area.

The invention relates to the technical field of rock wool board production and detection, and discloses rock wool board size detection equipment which comprises a movable frame body structure, driving mechanisms are arranged on the frame body structure, a sliding structure is in transmission connection between the driving mechanisms, an elastic attaching mechanism is arranged at the lower end of the sliding structure, and the elastic attaching mechanism is connected with the driving mechanisms. The lower portion of the elastic attaching mechanism is slidably connected with a thickness testing mechanism, the lower end of the thickness testing mechanism is provided with an outline drawing mechanism, the outline drawing mechanism draws the outer outline of the rock wool board through the elastic attaching mechanism, and the thickness testing mechanism can be attached to the upper edge of the rock wool board to measure the thickness of the rock wool board. The rock wool board thickness detection device can move to the position above a tested rock wool board to avoid carrying injury accidents, the thickness of the rock wool board can be detected through the thickness testing mechanism, and the outer contours of rock wool boards of all shapes can be drawn through a displacement sensor of the contour drawing mechanism.

The present invention discloses a method for calculating and displaying radar blind spots based on terrain analysis, which relates to the field of military equipment capability analysis and mapping display of command automation systems. Based on line-of-sight analysis, the method first calculates the obstruction points and tangent segments between the radar and the terrain in the scanning direction through binary search, then uses the equation of the tangent segment in the scanning direction and the equation of the geodetic ellipsoid at different altitudes to directly calculate the intersection point, and obtains the maximum detection range of the radar at the same altitude by connecting the intersection points of the tangent segments of all scanning directions on the ellipsoid at the same altitude. The obstruction lines and contour lines at different altitudes are drawn and displayed using different colors and line widths. The present invention has the characteristics of a simple, efficient and reliable calculation method, adaptability to various geographic computing platforms, concise and intuitive expression on a two-dimensional map, adaptability to parallel computing platforms, and the ability to adjust the calculation accuracy as needed and adaptability to various radar parameter types.

This invention provides a method, server, and storage medium for predicting latent anomalies in copper-clad laminate (CCL) lamination based on contour pattern recognition. By acquiring sampling sequences of CCL edge contours and a reference contour sequence, a cross-scale contour mapping state field is generated through dual-dimensional curvature and fractal encoding. Gradient flow decomposition is performed on the mapping field to separate geometric constraint components and instability components, forming an intrinsic contour pattern distribution manifold. Trend mutual induction measurement is performed on the perturbation components corresponding to latent anomalies such as interlayer slip and uneven resin wetting in multiple batches of manifolds, generating an evolution trend map. When the two types of perturbation components in the map synchronously and monotonically increase within the anomaly response time window and the slope convergence exponent exceeds a threshold, a prediction signal is generated. This invention achieves early coupled prediction of latent anomalies in CCL lamination, improving quality monitoring capabilities and the timeliness of process control response.

The invention relates to the technical field of intelligent traffic facility monitoring, in particular to a barrier gate fault detection method and system based on image recognition and instruction verification, and the method comprises the following steps: obtaining image frame sequence equalization image brightness, extracting a contour mapping coordinate judgment state, comparing control time, extracting an abnormal frame, tracking a serial number analysis direction, and eliminating interference. And position change is extracted to compare control missing, and a barrier gate abnormal action fault section number set is obtained. According to the method, through corresponding analysis of image frame states and control instruction time periods, state changes caused by non-instructions are separated, frame sequence numbers and direction consistency judgment are combined, interference frame segments are eliminated, position offset starting nodes are extracted, image sections with concentrated action deviation are positioned, and the image frame states and the control instruction time periods are compared in an image mapping and state direction comparison mode. Abnormal action recognition which is continuous in offset and lacks control signal support is completed, the tracking capability of the state change process is enhanced, and the mining range of action abnormity and the positioning precision of a fault section are improved.

The application discloses a contour mapping method, device, equipment and storage medium, selects an initial vertex from each first vertex of a three-dimensional closed contour, breaks at the initial vertex to obtain an initial open contour, and the initial vertex becomes two first vertices; maps the first vertex on a plane to obtain each second vertex, groups each second vertex to form a first open contour, and the two second vertices obtained after the initial vertex is changed are not connected; closes the first open contour to obtain a first closed contour; judges whether the first closed contour contains at least two closed areas; if yes, a target area is determined, intersection elimination processing is performed on the target area, a new first closed contour is obtained, and the step of judging whether the first closed contour contains at least two closed areas is returned to be executed; and if no, the first closed contour is taken as a target mapping contour. The target mapping contour obtained by the scheme is more accurate and closer to the real shape of the three-dimensional closed contour.

The invention discloses a medical image lesion area identification method and system based on deep learning, and the method comprises the steps: extracting a multi-modal feature sequence from original image data collected by lung CT equipment, and obtaining a denoised feature image set; determining initial edge contour mapping according to the pixel distribution difference in the de-noised feature map set; obtaining regional connected components in the preliminary edge contour mapping, and obtaining an enhanced connected region set by calculating a gray similarity matrix between the components; for the enhanced connected region set, performing semantic segmentation operation on each region by adopting a U-Net model to obtain a fine segmentation mask; extracting a boundary coordinate sequence from the fine segmentation mask, and obtaining a positioning coordinate group; obtaining a heterogeneity index in the positioning coordinate group to obtain a robust positioning result; and according to lesion boundary information in the robust positioning result, generating a diagnosis report superposition layer, and superposing the diagnosis report superposition layer on the original image data to obtain final visual lesion identification output.