26 results about "Vision enhancement" patented technology

The invention relates to the technical field of robots, and discloses a collision early warning method and device in robot teleoperation, and the method comprises the steps: obtaining the visual information of the working environment of a remote robot and the motion state data of an end effector; environment three-dimensional modeling is carried out based on visual information, a three-dimensional environment model containing obstacles is generated, and a virtual protection area surrounding an end effector is dynamically generated based on end speed parameters in motion state data and system communication processing time delay; wherein the spatial range of the virtual protection area is expanded along with the increase of the terminal speed and the system communication processing time delay; performing collision risk assessment on the virtual protection area and an obstacle in the three-dimensional environment model to obtain a current collision risk level; and generating early warning information based on the collision risk level, and outputting a vision enhancement image through an operator terminal to guide an operator to avoid the collision risk based on the early warning information. According to the invention, the teleoperation safety can be improved.

The invention relates to the technical field of intelligent ship driving, and discloses an intelligent ship driving assistance system based on multi-source information fusion and AI decision, which comprises a multi-source information acquisition module, an information data processing module, an information fusion processing module, an AI decision calculation module and a man-machine interaction module, according to the ship intelligent driving assistance system based on multi-source information fusion and AI decision making, perception blind areas of scenes with low visibility and the like are eliminated through a multi-source information collection and vision enhancement technology, and a target is accurately recognized; through data processing and three-level fusion, multi-source information is efficiently integrated, and the manual analysis pressure is reduced; the AI decision calculation module provides accurate guarantee for berthing, stranding, collision avoidance and other scenes, and accident risks are reduced; the system can also optimize the route and speed in combination with data such as weather, reduce energy consumption, shorten the navigation time, take into account the safety and economy of ship driving, and greatly improve the intelligent level of shipping.

The invention discloses a multi-target vision enhancement tracking method and system for an airport flying area, and relates to the technical field of computer vision and artificial intelligence. The method comprises the following steps: performing target detection on a current frame to obtain detection features; accessing a historical track feature library, fusing the appearance of the historical target and the historical motion sequence, and generating enhanced track context features; using a decoder based on an attention mechanism to perform cross-frame association decoding on the current detection features as queries and the enhanced track context features to generate an identity association scoring matrix; and carrying out optimal matching and decision making based on the scoring matrix, completing track updating, new registration and loss elimination, and obtaining a tracking result. According to the method, through end-to-end depth correlation and time sequence feature modeling, the problems of tracking interruption and identity switching caused by target shielding, similar appearance and complex motion in a complex scene of an airport are effectively solved, and the tracking robustness and identity consistency are remarkably improved.

The invention relates to the technical field of vision enhancement, in particular to an underwater robot adaptive vision enhancement system based on multi-modal feature fusion. Analyzing the sludge image to obtain sludge brightness distribution, and performing illumination equalization optimization to obtain an equalization image; performing texture analysis on the balanced image, evaluating the structural complexity of the sludge, configuring an underwater dark channel prior algorithm, and performing analysis to obtain an ambient light vector and an initial transmissivity graph; constructing a scattering optimization model according to the structural complexity, and optimizing the initial transmissivity graph to obtain an optimized transmissivity graph; performing imaging inverse operation according to the balanced image, the optimized transmissivity image and the ambient light vector to obtain a scattering-removed image; expanding the ambient light vector into an ambient light layer, and identifying the scattering-removed image and the ambient light layer through a color restoration model to obtain a candidate restoration image; recognizing the de-scattering image through a color classification network to obtain a sludge color probability graph; and according to the sludge color probability graph, carrying out weighted fusion on the candidate restoration image to obtain a color calibration image.

The application discloses a kind of unmanned plane vision enhancement system and method based on binocular rotating polarizer, it is related to computer vision and optical imaging technical field, system includes binocular camera module, rotatable polarizer assembly that can be integrally moved into / out of optical path, mechanism for driving its lifting and rotation and main control unit.The method comprises: after moving polarizer into optical path, in a single acquisition cycle, it is driven to rotate at high speed to multiple preset angles and synchronously acquires binocular image sequence;Process image sequence to calculate Stokes parameters and construct three-dimensional polarization feature space in combination with depth information;The feature space is input into multimodal fusion neural network with original image, and enhanced image, semantic segmentation map and optimized depth map are output.The application realizes the quick and flexible switching of polarization mode and ordinary vision mode, and significantly improves the real-time vision perception and enhancement capability of unmanned plane in underwater, strong reflection and other complex environments through software and hardware depth collaboration.

This invention discloses a method and system for visual enhancement of underwater robots in turbid waters, relating to the fields of machine vision and image restoration technology. The method includes: acquiring a sequence of original color images collected in real-time by the underwater robot; mapping the pixel intensity of the original color image sequence to a complex space to construct a complex domain analytic signal; extracting the multi-directional spatial gradient tensor of the original color image, and calculating pseudo-polarization information and environmental characterization parameters accordingly; constructing a virtual phase compensation operator using the pseudo-polarization information and the environmental characterization parameters; wherein the pseudo-polarization information drives the compensation intensity of the virtual phase compensation operator, and the environmental characterization parameters are used to locally weight the compensation intensity. Its beneficial effects include: correcting edge blurring and texture obfuscation caused by wavefront phase disturbances, and significantly improving the edge steepness of structural contours in the image.

The invention discloses a color image vision enhancement method. The method comprises the following steps: acquiring an original color image and separating three color components; a Laplace operator is used to sharpen each component image, a Sobel operator is used to extract horizontal and vertical gradients respectively, the gradients are transformed based on a Gaussian function and normalization processing is completed, natural logarithm operation is carried out on an original component image and a normalized gradient image respectively, two obtained logarithm domain images are added and then exponent recovery is carried out, and a final component image is obtained. Restoring the component image to a gray domain, and performing Sigmoid function transformation on the restored component image; and calculating the global maximum and minimum values of the Sigmoid enhanced images of the three component images, performing linear stretching, and finally combining the stretched color component images to obtain a visual enhanced color image. According to the invention, the permeability of the color image can be improved, the brightness of the color image under the condition of low illumination can be improved, and the color effect matched with the visual habit of human eyes can be kept.

The present application relates to the field of industrial vision enhancement, more specifically, it relates to an underwater image enhancement method based on Transformer and multi-scale spatial domain feature fusion, the present application introduces the mechanism of Transformer, uses self-attention and linear attention mechanism to capture the complex spatial relationship in the image and improve the image quality, at the same time, the multi-scale feature convolution module is designed to extract information of different scales and levels, which can reduce the complexity of the network and improve the calculation efficiency, in addition, an air domain feature fusion module is designed to calculate the attention in the local neighborhood of pixels, thereby improving the calculation efficiency and feature fusion, the method has the advantages of good generalization ability, high robustness and high enhancement efficiency, and can be widely applied in underwater pipeline detection and maintenance, underwater environment monitoring, underwater biological research and other fields, and provides strong support for scientific research and engineering practice in related fields.

The invention relates to the technical field of vision correction instruments, and provides a movable multi-light-source teenager vision correction instrument which comprises two parallel installation cylinders, sleeve frames are fixedly connected to the two ends of the two installation cylinders, and the two sleeve frames at the same end are rotationally connected through a damping shaft; light training modules are arranged on the inner sides of the two mounting cylinders and comprise fixed frames fixedly connected to the inner sides of the mounting cylinders, movable frames are slidably connected to the inner sides of the fixed frames, LED light assemblies are arranged at one ends of the movable frames, and driving parts used for driving the movable frames to slide in the axis directions of the mounting cylinders are arranged on the inner sides of the fixed frames; a control module electrically connected with the LED lamplight assembly and the driving piece is arranged on the inner side of the sleeve frame. By setting three modes of visual tracking, visual function and vision enhancement training for different vision defects, the problems of fixed light source, single-color stimulation, single training mode and the like in the existing vision correction equipment are solved.

System and method for operating a vehicle. A gaze sensor receives an image of the driver's face. An environment sensor receives environmental data relating to an environmental state and a distant object, a human-machine interface, and a processor. The processor is configured to receive a user profile for the driver, where the user profile specifies a visible area and a blind spot in the driver's field of vision; to determine the driver's gaze state from the image; to determine the location of the distant object from the environmental data; to analyze the gaze state and the environmental data to determine that a distant object is in the driver's blind spot; and, at the human-machine interface, to execute a corrective action to improve the visibility of the distant object in the blind spot.

The invention discloses an unmanned aerial vehicle vision enhancement system and method based on a binocular rotating polarizer, and relates to the technical field of computer vision and optical imaging, and the system comprises a binocular camera module, a rotatable polarizer assembly capable of integrally moving in / out of a light path, a mechanism for driving the rotatable polarizer assembly to lift and rotate, and a main control unit. The method comprises the following steps: after moving a polarizer into a light path, driving the polarizer to rotate at a high speed to a plurality of preset angles in a single acquisition period and synchronously acquiring a binocular image sequence; processing the image sequence to calculate a Stokes parameter and constructing a three-dimensional polarization feature space in combination with depth information; and inputting the feature space and the original image into a multi-modal fusion neural network, and outputting an enhanced image, a semantic segmentation image and an optimized depth image. According to the invention, rapid and flexible switching between a polarization mode and a common visual mode is realized, and the real-time visual perception and enhancement capability of the unmanned aerial vehicle in complex environments such as underwater and strong reflection is significantly improved through deep cooperation of software and hardware.

The present disclosure relates to cantilevered imaging modality wearable optical systems that provide for optimal ergonomics coupled with vision enhancement and vision magnification. Methods of use, devices, and kits are also contemplated.

The invention discloses a multi-laser fusion point cloud three-dimensional target detection method based on two-dimensional vision enhancement, and the method comprises the steps: carrying out the three-dimensional universal obstacle recognition of point clouds scanned by a single-line mechanical laser radar and a solid-state laser radar based on a local grid map and a light tracing method; calculating the terrain clearance of the three-dimensional universal obstacle identified by the solid-state laser radar; splicing the images shot by the plurality of cameras to form a two-dimensional panoramic image; performing target detection and semantic segmentation on the two-dimensional panoramic image by using a two-dimensional deep learning detection model and a segmentation model; and finally, projecting an identification result of the three-dimensional universal obstacle to the two-dimensional panoramic image, performing association matching with the two-dimensional detection frame, and performing semantic enhancement processing on the successfully matched three-dimensional universal obstacle to obtain independent three-dimensional targets corresponding to different semantic entities. The method can realize omnibearing perception and accurate measurement of the height of the obstacle, and is low in calculation complexity and strong in dynamic environment adaptability.

The invention belongs to the technical field of remote sensing image enhancement, and provides a stereoscopic vision enhancement method for a large image of a low-resolution remote sensing image in order to further enhance image processing of the large image of the remote sensing image, which comprises the following steps of: performing block processing on a global low-resolution original remote sensing image, and calculating an illumination intensity matrix of each pixel point; and the illumination intensity matrix and the remote sensing image are fused, the image is enhanced in combination with topographic data, the three-dimensional feeling and readability of the landform are enhanced, and the remote sensing image with better visual expressive force is generated.

The invention discloses a power grid wiring diagram verification method based on a multi-modal large model, and the method comprises the steps: recognizing a to-be-verified power grid wiring diagram image through a computer vision model, and obtaining initial primitive information which comprises a primitive type and a primitive position; based on the initial primitive information, adding a non-occlusion visual mark to a corresponding primitive in the power grid wiring diagram image, and generating a visual enhancement image; based on the initial primitive information, retrieving a related target naming rule from a power grid naming specification knowledge base through an improved Monte Carlo tree search algorithm; and on the basis of the vision enhancement image and a target naming rule, identifying error information in the initial primitive information through a multi-modal large model, and completing the verification of the power grid wiring diagram. According to the invention, high-precision graph-to-model conversion and logic verification of the wiring diagram are realized.

The present disclosure is related to systems, methods, computing device readable media, and devices for providing enhanced vision to persons, users, or patients with low vision, particularly low vision in a center of the user's field of view (FOV). This also contains the ability for the system to connect to the cloud to access Gen AI & Multimodal Models for visual assistance and control in conjunction with the vision enhancement.

The invention discloses a multi-modal equipment entity identification method based on multi-level semantics and vision enhancement, and relates to the field of information processing. According to the multi-modal equipment entity identification method based on the multi-level semantics and vision enhancement, the multi-modal equipment entity identification method based on the multi-level semantics and vision enhancement is provided, and accurate identification of the equipment entity is achieved by integrating information of two modalities of a text and an image. The core of the method is to construct multi-level semantic coding and a visual scene graph, and realize cross-modal graph structure alignment, feature aggregation, dynamic gating multi-modal fusion and span-level entity classification, so that complementary information of texts and images is fully utilized, and the problem of inaccurate recognition caused by incomplete single-modal information is effectively solved; and the accuracy and robustness of entity recognition are improved.

The utility model provides a kind of subway track geometric dimension rapid measuring device, including support plate, the upper surface of support plate is fixedly connected with fixed plate, rotationally connected with screw rod between two fixed plates, the outer surface of screw rod is threadedly connected with movable seat, the upper surface of movable seat is fixedly connected with first hydraulic telescopic rod, and the telescopic end of first hydraulic telescopic rod is fixedly connected with movable plate;Measuring instrument is fixedly connected on the upper surface of movable plate, and measuring instrument includes laser radar module, vision enhancement module and inertial measurement module.The utility model can flexibly adjust the horizontal position and height of measuring instrument, solve the problem that traditional device cannot adjust height and lead to insufficient measurement accuracy, meet the multi-dimensional measurement demand under different track scenes, and measuring instrument is set with laser radar module, vision enhancement module and inertial measurement module, meet the high-precision requirement of subway track geometric dimension measurement.

The embodiment of the invention relates to the technical field of vehicle perception, and discloses a visual field enhancement method and device based on a formation system, the method is applied to a first vehicle of the formation system, the formation system comprises the first vehicle and a second vehicle, and the method comprises the following steps: obtaining perceived object data fed back by the second vehicle; converting the sensed object data from the second vehicle coordinate system to the first vehicle coordinate system to obtain first vehicle enhanced data; and displaying the sensing object corresponding to the first vehicle enhancement data. By applying the technical scheme of the invention, the visual field of vehicles in the formation system can be effectively expanded, and the perception ability of a driver to the surrounding environment is improved.

An automotive transportation vehicle has a passenger cabin with a plurality of seating positions for respective persons to be seated. A display system in the vehicle has a plurality of display zones, wherein each display zone is visualizable along respective vectors from one or more of the seating positions. A dominant eye is identified for a particular occupant seated in a respective seating position when there is a differential vision preference between a right eye and a left eye of the particular occupant. Display content is generated to be displayed to the particular occupant. A display zone is selected for displaying the display content wherein the selected display zone corresponds to a respective vector from the seating position of the particular occupant matching the dominant eye of the particular occupant.

The invention relates to the technical field of foreign matter recognition, in particular to a coal conveying belt foreign matter recognition system based on machine vision, and the system comprises an operation environment self-adaptive vision enhancement and motion reference construction module which is used for obtaining an original belt surface image sequence, building a belt surface coordinate system and a motion reference model, and outputting a vision enhancement frame set and a motion consistency mask; the semantic-space-time consistency foreign matter recognition and coal-like false detection suppression module is used for generating a candidate foreign matter target set based on space-time association and appearance semantic joint modeling, performing suppression by using an interference target and outputting a screened foreign matter target set; the credible alarm and linkage disposal and closed-loop optimization module is used for generating credible alarm information and forming a disposal instruction set and disposal feedback information; foreign matter samples and disposal results are sorted into a retraining sample set, and model parameters are updated offline or online, so that closed-loop optimization is completed. According to the method, under the conditions that the coal flow and the belt surface move rapidly and are locally shielded, the accuracy can still be kept, and missing detection and false alarm are avoided.

The invention is suitable for the technical field of image visual processing, and provides an edge visual enhancement processing method and system in a complex environment, and the method comprises the steps: obtaining an initial edge sharpening gain parameter and historical operation data of a target camera when the target camera faces a specified water surface, a water surface fluctuation sample formed after vehicles which belong to the same designated water surface but have different reverse wind forces pass through is screened out from the historical operation data; according to the method, the definition and the contrast ratio of the vehicle edge can still be kept under the complex illumination and environment superposition condition, the stability and the accuracy of traffic flow detection and recognition are remarkably improved, and therefore more reliable data support is provided for intelligent traffic management.

The application relates to the technical field of intelligent inspection and computer vision, in particular to a road lamp inspection and distribution anomaly detection method based on a UAV and a night vision enhancement model, wherein the UAV flies along a preset track at a low altitude, synchronously collects night road images and self-positioning attitude data, carries out multi-channel vision enhancement processing on the collected night road images, inputs an improved YOLO model to complete lamp target detection, outputs lamp candidate frames and confidence, constructs an input tensor through multi-channel feature fusion, and embeds CBAM in a C2f module of a Backbone part of YOLOv8. The road lamp inspection and distribution anomaly detection method based on the UAV and the night vision enhancement model has high night detection precision, through the synergistic improvement of multi-channel vision enhancement, a CBAM attention mechanism and a brightness weighted loss function, the lamp detection accuracy of the model in a strong interference night scene reaches more than 93%, the false detection rate is reduced to less than 5%, and the false detection problem caused by strong light interference at night is effectively solved.

The invention discloses a self-adaptive fixation point display method and system based on content awareness and user calibration, and the method comprises the steps: carrying out the calibration of a visual attention range when the system is started for the first time, and obtaining the individualized initial fixation area parameters of a user; the position of a fixation point is obtained in real time through an eye movement tracking module, and eyeball movement states are classified; identifying a content semantic category of the current display picture; according to the content semantic category and the initial fixation area parameters, dynamically determining geometric parameters of the high-fidelity rendering area; executing rendering operation for reducing power consumption outside the high-fidelity rendering area; and monitoring content change in the area, and when key non-periodic change is detected, performing temporary vision enhancement operation on the changed area. According to the method, the clear display area can be dynamically adjusted according to the screen content and the personalized visual features of the user, significant energy saving is achieved, meanwhile, it is ensured that the user does not miss important information through an intelligent visual enhancement mechanism, and display efficiency, power consumption control and user experience are considered.

The utility model relates to the technical field of wearable vision enhancement equipment, in particular to a head fixing structure for wearable vision enhancement equipment, which comprises a vision enhancement equipment body, and the left part and the right part of the outer surface of the vision enhancement equipment body are fixedly connected with ear hanging strips. The ends, away from the vision enhancement equipment body, of the two lug hanging strips are fixedly connected with elastic belts, and the end, away from the lug hanging strips, of the elastic belt on the left side is fixedly connected with a connecting mechanism. According to the head fixing structure for the wearable visual enhancement equipment, the head shape of a user can be prevented from being disordered when the head fixing structure is worn from the position above the head, the user does not need to worry about the image problem when wearing the equipment, a good image can be kept in formal business negotiation or daily social activities, and the user experience is improved. The applicability of the device in various scenes is improved, the wearing stability can be improved, and device displacement caused by head shaking is avoided.

The invention relates to the technical field of intelligent lighting, and discloses a photoelectron-driven intelligent lighting and visual enhancement system, which comprises a hardware layer and a software layer, and each layer realizes intelligent regulation and control and visual quality enhancement of scene lighting through a core data interaction mode of hardware acquisition-software processing-control feedback. According to the photoelectron-driven intelligent illumination and vision enhancement system, the spectrum-adjustable illumination module carries red, green, blue and amber LEDs and is matched with an exclusive heat dissipation structure to ensure that the spectrum is stable and parameters are adjustable, and the vision enhancement composite algorithm module outputs an enhanced image and quantizes a vision entropy weight coefficient through a three-layer collaborative architecture. The system and the method are linked with the main control module, the visual enhancement result reversely optimizes the spectrum and brightness parameters of illumination, closed-loop optimization of visual quality and illumination parameters is formed, and the problems of insufficient adaptability of spectrum regulation and visual enhancement and unstable visual enhancement effect are solved.