57 results about "Underwater vision" patented technology

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 relates to the technical field of underwater visual perception and intelligent detection, and provides a multi-stage feature fusion detection method for a low-illumination underwater target, and the method comprises the steps: obtaining an underwater low-illumination image; the low-illumination image is input to an illumination estimation module, the image is decomposed based on the Retinex theory, an illumination image is extracted, and the low-illumination image and the illumination image are fused pixel by pixel to generate an enhanced input image; inputting the enhanced input image into an image enhancement module for feature extraction and reconstruction; and mapping the image subjected to feature extraction and reconstruction into an image format to obtain a final enhanced result image. According to the invention, the problems of high redundancy interference and low robustness in the prior art are solved.

The invention provides a pseudo-supervised diffusion type underwater image enhancement method based on cross-modal semantic guidance, and the method comprises the following steps: obtaining an original degraded image and an actual reference picture of a UIEB, inputting the original degraded image into a multi-modal large model LLaVA to generate a semantic prompt, and selecting a plurality of different existing underwater enhancement methods, the method comprises the following steps: respectively obtaining a plurality of groups of enhanced images, integrating the enhanced images and semantic prompts into image-text pairs, inputting the image-text pairs into a Zip-CLIP model, calculating the similarity of the image-text pairs to generate a plurality of groups of similarity heat maps, inputting the similarity heat maps and high-order semantic information into a special convolutional neural network architecture, generating a pseudo tag as a weak supervision signal for diffusion model training, and carrying out diffusion model training. And a final enhancement result is obtained. According to the method, through the characteristics of cross-modal semantic alignment and gradual reconstruction of the diffusion model, the image detail reduction and structure retention capability is remarkably improved, a more reliable visual basis is provided for an underwater visual task, and theoretical innovation and practical values are both achieved.

The invention belongs to the technical field of underwater vision measurement, and relates to an underwater binocular vision system refraction parameter self-calibration method, which comprises the following steps of: firstly, constructing a space equation containing a water body refractive index, a refraction interface normal vector, an interface distance and a light path coefficient based on a Snell law; constructing a generalized state vector including an environment parameter, a structure parameter and a path parameter; a system residual equation is established by using different-plane light intersection constraint of binocular vision, and nonlinear iterative least square solution is carried out by constructing a generalized Jacobian matrix so as to realize adaptive calibration of an underwater vision system; according to the method, on the premise that water body parameters do not need to be predicted and high-precision mechanical calibration is not needed, the water body refractive index and the system structure parameters are synchronously solved, self-adaptive calibration of the underwater vision system is achieved, and the three-dimensional measurement precision and robustness in the complex water environment are remarkably improved.

The invention belongs to the technical field of computer vision and ocean detection, and discloses an underwater target detection and tracking system, which comprises an underwater image enhancement module used for preprocessing an original underwater video image and outputting an enhanced image; the improved YOLOv11 target detection module comprises an improved YOLOv11, and the improved YOLOv11 is adopted to receive the enhanced image so as to position the underwater target in real time and output a target bounding box; and the target tracking module based on Transform is used for receiving the video clip of the bounding box and extracting the spatial-temporal characteristics, and realizing target association and trajectory prediction based on the target bounding box and the spatial-temporal characteristics. The invention also discloses an underwater target detection and tracking method. According to the system and the method provided by the invention, the accuracy and the robustness of underwater vision tasks including target detection and tracking can be remarkably improved on the premise of ensuring the real-time performance.

The invention discloses an underwater robot visual positioning method, device and equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: acquiring an original image acquired by a camera, and preprocessing the original image to obtain a processed image; determining a target depth map corresponding to the processed image by using depth estimation; positioning and removing a moving target from the processed image through a dynamic object detection model to obtain a static scene image; the dynamic object detection model is a neural network model obtained by training underwater images in advance; and estimating the pose of the underwater robot in real time through a synchronous positioning and mapping algorithm based on the target depth map and the static scene image, constructing a dense point cloud map, and updating a pose map of the underwater robot. And accurate underwater visual positioning can be realized with low cost.

The application discloses a kind of big visual field image side telecentric underwater visual docking imaging optical systems, the optical system includes in sequence: spherical cover window, front group lens, variable diaphragm, rear group lens and filter;Front group lens includes in sequence: front group first lens, front group second lens, front group third lens, front group fourth lens;Rear group lens includes in sequence: rear group first lens, rear group second lens, rear group third lens, rear group fourth lens;Variable diaphragm is located between front group fourth lens and rear group first lens;Object side imaging light beam is imaged on strobe camera target surface in sequence through spherical cover window, front group lens, variable diaphragm, rear group lens and filter.The application has big visual field, short focal length, variable F number and image side telecentricity and the like characteristics, clear imaging is realized by rear group focusing and variable F number, can be used for the close-range underwater visual docking recovery of cross-domain aircraft, ensure that imaging is clear during docking process, and the position solution error caused by image side focusing can be eliminated.

The present invention discloses an underwater cable-tethered beacon long-distance optical navigation system and method, comprising: a blue-green beacon array connected to a power control in a control cabin to form a cable-tethered optical navigation node; the control cabin and the optical navigation node are mounted on a cable-tethered branch to form a map navigation landmark; a submarine cable is connected to onshore and underwater base stations; an underwater camera and a visual recognition module in an underwater robot cabin form an underwater visual recognition system, which receives camera video signals and outputs navigation information to the underwater robot controller; and an optical long-distance navigation algorithm based on the optical navigation node, which obtains navigation information for SLAM positioning through node light source feature extraction, heading calculation, and data fusion. The present invention meets the large-scale, long-distance navigation requirements of underwater robots, has strong adaptability and good stability, and is suitable for long-term navigation and positioning of underwater robots in cable-tethered systems.

The present invention provides a 3D reconstruction method for underwater damage of marine equipment based on vision and IMU fusion. The method includes the steps of performing, in an underwater environment, internal parameter calibration of the left and right cameras of a stereo camera and extrinsic parameter calibration between the left and right cameras of the stereo camera and a platform IMU; calibrating the extrinsic parameter matrices of the drive IMU coordinate system and the laser sensor coordinate system on each axis of the drive system; identifying and roughly locating the damaged area based on the 3D reconstruction of the underwater vision; planning an optimal path for the underwater mobile platform, performing local obstacle avoidance based on the 3D reconstructed point cloud, and controlling the underwater mobile platform to move near the damaged area; planning a trajectory for a drainage system and draining water using the drainage system; and determining the laser position of a laser sensor using drive IMU data to realize a precise 3D reconstruction of the damaged area based on the laser sensor data. This method provides a highly accurate 3D reconstruction of the damaged area, assisting other equipment in autonomous repair and improving the work efficiency of the marine equipment.

The application discloses a method for constructing an underwater static perception model based on terrain fine grid division, and belongs to the underwater visual perception field. The underwater terrain data is finely divided into grids, and the underwater submersible is mapped to the corresponding grid position, so that the static perception model is constructed. In combination with the lake surrounding environment, the lake bottom terrain and the safe navigation depth, a near-non-safe area grid set is constructed, the future grid where the underwater submersible is located is calculated in real time, and if there is an intersection with the non-safe area grid set, an alarm is sent. The model can accurately perceive the underwater static environment and guarantee the safe operation of the underwater submersible.

The invention provides an implicit entropy learning-based underwater visual image efficient reconstruction method and system, and the method comprises the steps: obtaining an input image which is an original image collected by an underwater robot; performing feature extraction on the input image to obtain a high-latitude feature, extracting and obtaining an auxiliary feature, and performing quantization operation on the high-latitude feature and the auxiliary feature; the auxiliary quantization features are coded to generate an auxiliary feature code stream, the auxiliary quantization features are obtained through decoding and recovery, potential context information is decoded and extracted, and an implicit entropy modeling module fuses the context information into distribution prediction of the high-latitude quantization features; encoding the high-latitude quantitative features to generate a high-latitude feature code stream; and decoding and restoring the high-latitude feature code stream to obtain a high-latitude quantization feature, and performing feature decoding on the high-latitude quantization feature by using a decoder to obtain a reconstructed image. According to the method, the problems of insufficient compression performance, poor reconstruction quality and the like of a traditional image compression algorithm in an underwater complex environment can be solved.

This disclosure relates to a method and system for underwater calibration of composite polarization states based on an equal focal length refraction model. The method includes: approximating the incident angle of light under small-angle conditions by simultaneously applying geometrical optics imaging equations and Snell's law of refraction, obtaining the transformation relationship between the coordinates of the underwater imaging point and the coordinates of the theoretical imaging point in air, and establishing an equal focal length refraction mathematical model for the underwater camera and projector; designing a composite polarization state encoded sinusoidal projection calibration pattern based on this model; utilizing the polarized light projection characteristics, encoding the horizontally polarized light of the green channel of the calibration pattern into horizontal cosine fringes, and the vertically polarized light of the blue channel into vertical sine fringes; superimposing the cosine and sine fringes to generate two sets of orthogonal polarization state fringe patterns, and performing phase calculation and reprojection error optimization based on the equal focal length refraction mathematical model to achieve parameter calibration. This method can significantly improve the calibration accuracy and efficiency of underwater visual measurements.

The invention provides an unmanned monitoring ship based on sonar vision meteorological water quality fusion and a monitoring method thereof, a multi-beam sonar and an underwater camera are used to collect an acoustic image and a visual image of an underwater target, and an embedded main control board is used to realize synchronous registration and fusion of sonar echoes and images. Target detection and recognition are completed based on a deep learning model; meanwhile, a composite water quality probe, an anemorumbometer, a temperature and humidity sensor and an air pressure sensor are used for collecting water body parameters and meteorological parameters in real time to assist in analyzing the water quality change trend; the communication module transmits fusion data to an upper computer in real time through a 5G network, and remote monitoring and intelligent decision making are achieved. According to the invention, through heterogenous information fusion of sonar and underwater vision, the penetrating power of acoustic detection and the high resolution of visual perception are considered, and high-precision detection of fish data in a turbid environment is realized; according to the cross-modal fusion network based on Transform, the target recognition rate and the positioning precision in a complex environment are improved.

This invention discloses an improved YOLO26 underwater target detection method with joint image enhancement. A dedicated underwater image enhancement unit is designed as a pre-task, strengthening image feature representation through a dynamic histogram stretching module and an attention mechanism to output high-quality enhanced images. The YOLO26 network structure is improved by designing a multi-frequency feature reconstruction module and an optimized detection network, achieving collaborative optimization of upstream and downstream tasks between the enhancement and detection units. This method optimizes image quality through enhanced downsampling and upsampling paths. The improved YOLO26 detection network achieves efficient detection and accurate localization of large, medium, and small underwater targets. While ensuring real-time performance and lightweight design, it significantly improves target detection performance under complex underwater conditions, adapting to underwater visual perception scenarios such as underwater search and rescue, marine exploration, and underwater equipment inspection. It provides a practical, generalizable, and easily deployable technical solution for underwater intelligent visual inspection.

The utility model discloses an underwater vision inspection robot integrated with a suspension balance stabilizing device, which relates to the technical field of inspection robots and comprises a machine body and a balance component, a main water tank is mounted on the inner side of the machine body, and the balance component for improving stability is arranged at the upper end of the main water tank. And the balance assembly comprises a power box, a mounting groove, a first telescopic corrugated pipe, a balance water tank and a water passing pipe, the mounting groove is formed in the power box, the outer side of the power box is connected with the first telescopic corrugated pipe, the end of the first telescopic corrugated pipe is connected with the balance water tank, and the water passing pipe is mounted at the lower end of the side edge of the balance water tank. According to the underwater vision inspection robot integrated with the suspension balance stabilizing device, movement of the balance water tanks is controlled through stretching and retracting of the electric push rods in the first telescopic corrugated pipe, the annularly-distributed balance water tanks convey different water amounts in the water passing pipe, suspension balance of the robot body is assisted by the four balance water tanks, and the use stability of the robot body is improved.

The application provides an underwater visual image efficient reconstruction method and system based on implicit entropy learning, comprising: acquiring an input image, the input image being an original image collected by an underwater robot; performing feature extraction on the input image to obtain high-latitude features, extracting auxiliary features, and performing quantization operation on the high-latitude features and the auxiliary features; encoding the auxiliary quantized features to generate an auxiliary feature code stream, decoding and restoring the auxiliary quantized features, decoding and extracting potential context information, and fusing the context information into distribution prediction of the high-latitude quantized features by an implicit entropy modeling module; encoding the high-latitude quantized features to generate a high-latitude feature code stream; decoding and restoring the high-latitude feature code stream to obtain high-latitude quantized features, and performing feature decoding on the high-latitude quantized features by using a decoder to obtain a reconstructed image. The application can solve the problems of insufficient compression performance and poor reconstruction quality of traditional image compression algorithms in underwater complex environments.

The application discloses a kind of underwater visual positioning methods of illumination robustness based on prior information, belong to underwater visual positioning technical field.The present application focuses on underwater visual positioning research, for the poor stability of underwater visual positioning under the condition of severe illumination change, a kind of underwater visual positioning method based on prior information is designed, the physical properties of environmental brightness information and cooperative target are fully utilized, by screening interest area, dynamic threshold and other methods, effectively solve the problem of target missed detection, false detection and other problems caused by high image background brightness or uneven brightness, improve the segmentation accuracy of target contour, so as to optimize the calculation accuracy of target image coordinates and spatial coordinates;In underwater experiment, the method of the application is verified, reliable detection and accurate positioning of UUV target are realized all day long, all weather, and the robustness of the method of the application to environmental brightness change is verified.

This invention discloses an underwater visual-inertial odometry method based on dynamic sparse attention, belonging to the fields of computer vision and robot navigation. An initial visual feature sequence is obtained from two adjacent underwater images. The dark channel map of the underwater image is calculated and encoded as a transmittance-aware embedding. The transmittance-aware embedding is fused with the position encoding to form a 3D position embedding. The 3D position embedding is added to the initial visual feature sequence and input into a visual encoder composed of N stacked dynamic sparse attention modules, outputting a visual feature sequence. An inertial feature sequence is introduced, and a bidirectional cross-attention mechanism is used to achieve deep interaction and alignment between visual and inertial features. An adaptive gating mechanism is used for weighted fusion, and finally, a regression network is used to predict the six-DOF pose. This invention effectively improves the robustness and accuracy of pose estimation in complex underwater environments by introducing physically prior-guided 3D position embedding, dynamic sparse attention, and deep adaptive fusion.

The invention discloses a muddy water image processing method based on image analysis, and relates to the field of image processing. The method comprises the following steps: S1, obtaining an original turbid underwater image shot by an underwater robot; s2, calculating the weight of saturation and significance features in the original image; s3, the saturation weight, the significance weight and the Laplacian weight are normalized, and an enhanced fusion weight map is generated; and S4, inputting the enhanced fusion image into a CNN-GAN filtering model for de-noising processing. According to the method, the problems of low contrast and complex background interference faced by traditional underwater visual detection are effectively solved, and the observation definition, the positioning precision and the measurement reliability of the underwater robot to the underwater target are remarkably improved.

The invention discloses an underwater target tracking method and system based on adaptive trajectory correction, and the method comprises the steps: constructing an underwater visual target tracking data set, and carrying out the marking of a target position and scene attributes, and obtaining a marked target tracking data set, the underwater visual target tracking data set comprises a plurality of types of underwater target video sequences; an underwater image enhancement module and a self-adaptive trajectory correction module are introduced, and a self-adaptive trajectory correction underwater target tracking model is constructed; and performing target tracking processing on the labeled target tracking data set based on the adaptive trajectory correction underwater target tracking model to obtain a final target tracking result. According to the invention, through image quality optimization and trajectory data stabilization, the tracking precision and robustness of a complex underwater scene are improved. The underwater target tracking method and system based on adaptive trajectory correction can be widely applied to the technical field of underwater visual target tracking.

The invention belongs to the technical field of underwater detection, and particularly discloses an expanded underwater vision system and a network based on dual-mode cascade networking, and the system comprises a housing, and a camera module, a calculation module, a storage module, a switch and a power module which are disposed in the housing. A plurality of watertight connectors and transparent glass are arranged on the shell in a sealing manner; the camera module, the storage module and the switch are respectively in communication connection with the calculation module; the power supply module is provided with a voltage input port and a voltage output port, and the power supply module is used for providing electric energy through the voltage output port; each watertight connector is provided with a communication interface and a power supply interface which are respectively in communication connection with the switch and electrically connected with the voltage input port of the power supply module; the communication interface of at least one watertight connector is an Ethernet interface, and the communication interface of at least one watertight connector is an optical fiber interface. According to the invention, the convenience of underwater detection can be effectively improved.

This application belongs to the field of remote bolt manipulation technology and discloses a remote loosening and gripping device for control rod guide cylinder bolts in reactor internals. It includes a tool rotation positioning base, on which are mounted bolt loosening and gripping tools and an underwater vision-assisted positioning module arranged on the same circumference. The circumference diameter is the same as the distance between the control rod guide cylinder axis and the diameter of the guide cylinder bolt. This application arranges the bolt loosening and gripping tool, the underwater vision-assisted positioning module, the guide cylinder bolt, and the bolt locking cap on a circumference of the same size, with a 90° positional relationship to each other. When the underwater vision-assisted positioning module aligns with a guide cylinder bolt using underwater vision technology, it sets the current circumferential position to zero, and the bolt loosening and gripping tool completes its initial position calibration. The encoder on the tool rotation positioning base can then accurately locate the positions of all bolts.

This invention discloses a multi-functional robot for ship maintenance and anti-piracy defense, belonging to the field of marine special robots and intelligent ship operation and maintenance technology. This multi-functional robot comprises two types of isomorphic modular robots: one type is a ship maintenance robot, equipped with underwater visual recognition, hull rust and dirt removal, flexible grinding, and localized spot coating functions, used for routine underwater anti-corrosion and anti-fouling maintenance of ships; the other type is a cutting and welding anti-piracy defense robot, integrating underwater cutting and welding mechanisms, capable of completing underwater damage repairs of ships, while simultaneously using the cutting mechanism to cut and stop pirate boarding boats, external components, and climbing devices, thus possessing both maintenance and anti-piracy defense functions. This invention adopts a robot division of labor and collaborative design. The maintenance end adopts a multi-machine collaborative operation mode, which is highly compatible with the self-developed multi-machine collaborative anti-corrosion and anti-fouling process. The maintenance and defense end realizes the integration of cutting, welding and anti-piracy functions. It has a reasonable structure, strong practicality and easy deployment. It can complete the entire process of underwater operation and maintenance and offshore security operations without stopping the ship or entering the dry dock. It is applicable to underwater maintenance and security defense of military and civilian ships throughout the entire life cycle and has significant market application value and creativity.

The application discloses a cloud-edge collaborative zebra fish three-dimensional trajectory tracking method and system, belongs to the underwater visual perception computing technical field, and aims to solve the technical problem of how to stably and timely track and reconstruct the three-dimensional trajectories of multiple zebra fishes in multiple incubators. The method comprises the following steps: constructing a target recognition model based on a YOLOv5 algorithm, and performing the following on the edge side: obtaining the position information of the zebra fishes through the trained target recognition model, associating the position information and the ID of the zebra fishes through an OC-SORT tracking algorithm, and timely uploading the frame number of the current video image and the ID and position information of each zebra fish in the video image to the cloud side; three-dimensional trajectory construction: calculating the weight of the edges in the directed graph on the basis of realizing trajectory matching through the construction of the directed graph, and realizing the three-dimensional matching of the movement trajectories of the zebra fishes under two different visual angles through multi-view information data fusion.

The application provides a diving mirror for enhancing underwater environment visibility in real time, comprising a camera, a laser lamp, a lens, a semi-transparent display, a fixing band and a mirror frame; the lens is embedded in the mirror frame, the camera is arranged on the upper edge of the mirror frame, the laser lamp is arranged on both sides of the mirror frame, the semi-transparent display is arranged on the inner side of the mirror frame, and the both ends of the fixing band are connected with the both ends of the mirror frame. The diving mirror for enhancing underwater environment visibility in real time is disclosed, the underwater environment is acquired, enhanced and displayed in real time through the underwater high-definition camera and the semi-transparent display, so that the diver can still obtain clear and real images in the case of low illumination, excessive color deviation and serious blur, the problem that the diver's underwater vision is limited is solved, and the diver can more efficiently complete underwater work.

The invention relates to a composite polarization state underwater calibration method and system based on an equal-focal-length refraction model, and the method comprises the steps: carrying out the approximate processing of a light incident angle under a small angle condition through a simultaneous geometric optical imaging equation and the Snell refraction law, obtaining a conversion relation between the coordinates of an underwater imaging point and the coordinates of a theoretical imaging point in the air, and carrying out the calculation of the conversion relation; establishing an equal focal length refraction mathematical model of the underwater camera and the projector; designing a composite polarization state coding sine projection calibration pattern based on the model; encoding horizontal polarized light of a green channel of the calibration pattern to cosine fringes in the horizontal direction and encoding vertical polarized light of a blue channel to sine fringes in the vertical direction by using the projection characteristic of polarized light; and the cosine fringes and the sine fringes are superposed to generate two groups of cross-polarization state fringe patterns, and phase calculation and re-projection error optimization are performed on the cross-polarization state fringe patterns based on an equal-focal-length refraction mathematical model to realize parameter calibration. According to the method, the calibration precision and efficiency of underwater vision measurement can be remarkably improved.

This application discloses an underwater robot visual localization method, apparatus, device, and storage medium, relating to the field of computer technology. The method includes: acquiring an original image captured by a camera; preprocessing the original image to obtain a processed image; determining a target depth map corresponding to the processed image using depth estimation; locating and removing moving targets from the processed image using a dynamic object detection model to obtain a static scene image; the dynamic object detection model is a neural network model pre-trained using underwater images; and estimating the underwater robot's pose in real time based on the target depth map and the static scene image using a simultaneous localization and mapping algorithm, constructing a dense point cloud map, and updating the underwater robot's pose map. This method enables accurate underwater visual localization at low cost.

The present invention discloses an underwater differentiated beacon optical positioning system and method. The system comprises a differentiated positioning array consisting of a long-range primary beacon and a short-range secondary beacon, connected to a power supply within a control cabin. An underwater camera and a visual recognition module within an underwater robot cabin form an underwater visual recognition system. The visual recognition module receives camera video signals and outputs positioning information to the underwater robot controller. The system also includes a neural network-based positioning recognition algorithm that processes the differentiated beacon array image through a recognition, extraction, tracking, and pose estimation computational framework to obtain real-time positioning information. The system meets the real-time requirements for underwater robot mobile positioning, exhibits strong adaptability and stability, and can effectively extend the range of underwater optical positioning, making it suitable for underwater robot navigation and positioning.

The invention discloses an underwater image sharpening method based on a feedback network and reinforcement learning optimization, and belongs to the technical field of underwater image processing. The method comprises the following steps: acquiring underwater images in various underwater environments and constructing a feedback data set; designing an underwater image sharpening network based on a feedback network and reinforcement learning optimization, wherein the network comprises an adaptive enhancement module, a scoring feedback network and a reinforcement learning optimization enhancement network; training a score feedback network by using the feedback data set; and based on a score feedback network and a reinforcement learning optimization mechanism, carrying out adaptive training on the underwater image sharpening network, and finally generating a high-quality underwater image. According to the method, automatic scoring is performed through a feedback network, an image sharpening strategy is dynamically optimized in combination with reinforcement learning, the color recovery, contrast enhancement and detail retention capabilities are improved, a clear and natural underwater image can still be generated in a high-turbidity and complex illumination environment, and the effectiveness and robustness of underwater visual perception are improved.