91 results about "Underwater imaging" patented technology

The invention discloses an end-to-end underwater three-dimensional reconstruction method and system based on an underwater imaging model, and belongs to the technical field of underwater image processing. According to the method, deep learning pose estimation is combined with an underwater imaging model; firstly, a multi-frame underwater image sequence is collected as input, a deep neural network is constructed, and the network mainly comprises two core sub-modules: a pose estimation network; secondly, a three-dimensional reconstruction network is adopted, dense point cloud or voxel reconstruction is completed according to the predicted pose and image content, pose estimation and the three-dimensional reconstruction process are integrated in the same system, and overall joint optimization is achieved; and in combination with a self-adaptive underwater imaging model, modeling is performed on physical processes such as underwater illumination attenuation and scattering, so that the reality sense and the accuracy of a reconstruction result are improved. According to the method, high-quality three-dimensional reconstruction of images in a complex underwater environment is realized, and the method can be widely applied to ocean engineering, underwater robots, submarine topography surveying and mapping and underwater cultural relic protection.

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

The invention provides an underwater target detection system and method for an AUV-carried synthetic aperture sonar, and belongs to the technical field of underwater acoustic detection. The method comprises the following steps: transmitting an ultra-wideband sound wave signal to an underwater target, and collecting an echo signal by using receiving array elements which are non-uniformly and sparsely arranged along a track direction; preprocessing the echoes and decomposing the echoes into a plurality of non-overlapping sub-band signals, obtaining an equivalent space response by calculating inter-channel phase differences under each sub-band, and further constructing a joint observation matrix; and high-resolution azimuth scattering intensity distribution is recovered in combination with compressed sensing, and motion compensation is performed by using platform motion data to realize underwater imaging. According to the invention, the dependence of the traditional synthetic aperture technology on long-distance uniform-speed motion is broken through, and high-resolution and high-precision underwater target detection and imaging can still be realized under the condition of high-speed navigation of an AUV (Autonomous Underwater Vehicle).

Disclosed are imaging systems and methods for dimension measurement independent of laser alignment. An example method includes: obtaining an image of the object captured underwater using an underwater imaging system comprising a camera and a laser source, wherein the image includes a representation of the object and a laser spot incident on the object; identifying, in the image, at least two points of interest on the object; determining an image-space distance between the at least two points as represented in the image; determining a location of the laser spot within the image; estimating a camera-to-object distance based on the location of the laser spot within the image and a calibrated spatial relationship between the camera and the laser source; and estimating the physical dimension of the object based on the image-space distance using the camera-to-object distance.

The invention discloses a three-dimensional Gaussian splash reconstruction method for an underwater scene, and belongs to the technical field of computer vision and three-dimensional reconstruction. Comprising the following steps: acquiring a monocular video frame sequence of a target underwater scene, a corresponding camera pose sequence, an initial sparse point cloud, an initial three-dimensional Gaussian point set and learnable physical parameters of an underwater imaging model; in the training process, performing weighted evaluation on a reconstruction error based on a multi-view consistency mechanism of opacity weighting, calculating an importance score of each Gaussian point, and performing densification operation on a three-dimensional Gaussian point set; adopting a staged freezing strategy to cooperatively optimize the three-dimensional Gaussian point set and underwater imaging model parameters; and performing rendering and underwater image synthesis on any new view angle camera pose based on the optimized three-dimensional Gaussian point set and underwater imaging model parameters, and outputting a new view angle synthesized image to represent a reconstruction result. According to the method, the geometric compactness, the visual fidelity and the physical interpretability of an underwater three-dimensional reconstruction result are improved.

The invention discloses an underwater polarization imaging method based on a deep learning algorithm, and the method employs a data collection module, a deep learning module and an image output module, and comprises the steps: collecting a large amount of polarization image data in different underwater scenes through the data collection module; inputting the data into a deep learning module to train a deep learning model to obtain a trained deep learning model; and inputting a newly collected underwater polarization image into the trained deep learning model to obtain a clear underwater imaging picture. Through mass data training, the model generalization ability is high, a good imaging effect on a new scene is achieved, meanwhile, parameters can be adjusted in real time, the imaging quality can be optimized, and high-quality image support is provided for underwater detection and other activities.

The invention relates to the technical field of underwater detection, discloses a remote synchronous synthetic aperture sonar cluster acoustic compatibility method, and solves the problem of mutual interference caused when multiple synthetic aperture sonars of the same model carry out detection in the same area. The method comprises the following steps: acquiring the state position and course information of a cluster sonar unit; predicting a future track and a sonar underwater irradiation area based on extended Kalman filtering; an orthogonal frequency modulation emission signal is dynamically generated; on the basis of receiving mixed echoes, carrying out iterative interference cancellation by using known waveform information; and a sparse reconstruction theory is combined to recover a full-bandwidth target echo, and the full-bandwidth target echo is finally used for generating a high-resolution interference-free sonar image. The system comprises a state acquisition module, a track prediction module, a waveform generation module, a transmit-receive control module, an interference cancellation module, a frequency spectrum reconstruction module and an imaging module, and collaborative optimization of sonar interference suppression and underwater high-resolution imaging is realized.

The invention provides a self-supervised multi-task underwater image restoration method based on an underwater imaging model, and the method comprises the following steps: carrying out the color cast correction of an underwater image collected by an underwater robot through an adaptive color balance algorithm, and obtaining an image after the color cast correction; obtaining the transmissivity of the image after the color cast correction through a transmissivity estimation network; enabling the image after color cast correction to pass through a background light estimation network to obtain background light; enabling the image after color cast correction to pass through a clear image reconstruction network to obtain a reconstructed clear image; and the background light and the transmissivity of the reconstructed clear image are synthesized through a Jaffe-McGammery underwater imaging model, and training is carried out in a self-supervision mode to obtain a final enhanced image. According to the method, an end-to-end self-supervised learning architecture is adopted, network training can be completed without large-scale annotation of a data set, the method has the advantages of low training cost and high generalization ability, and efficient image restoration can be realized in a complex and changeable underwater environment.

The invention discloses an underwater high-repetition-frequency pulse laser line scanning imaging system and method, and relates to the technical field of underwater laser imaging, a laser emission module is used for emitting high-repetition-frequency pulse laser and comprises a high-repetition-frequency pulse laser device with the working wavelength of 532 nm and a beam shaping module; wherein the light beam shaping module is composed of a double-axis cone mirror and a cylindrical mirror; the laser imaging module is used for carrying out large-depth-of-field imaging on target reflection echoes in a set time window, comprises a high-speed gating camera, an imaging lens and a narrow-band optical filter, and is constructed based on the Sammer imaging principle; and the synchronous control module is used for performing power supply communication and gating time sequence control to realize time sequence synchronization of the laser and the camera in the system. According to the invention, laser line scanning and range gating imaging technologies are combined, so that the method is suitable for long-distance and large-range environment detection, high-precision three-dimensional reconstruction of a short-distance target can be realized, and the definition, the signal-to-noise ratio and the applicability of underwater imaging are effectively improved.

This application provides an intelligent optical sensing system and method for imaging in turbid underwater environments, belonging to the field of underwater optical imaging. It solves the problem of low efficiency in identifying underwater objects due to the difficulty of obtaining clear images in turbid water with existing equipment. The system includes an optical imaging module, an image acquisition module, and a computing device mounted on a diving device. The computing device runs computer programs for an image preprocessing module, a depth sensing module, and a neural network denoising and recognition module. The optical imaging module generates a structured light field carrying an orbital angular momentum mode based on a spiral phase plate. The image acquisition module acquires the reflection information of the target object after being illuminated by vortex light. The image preprocessing module enhances the acquired image. The depth sensing module obtains the current water pressure using a pressure sensor, and the computing device decomposes the current water depth. The neural network denoising and recognition module performs real-time image recognition and denoising. This application is applied to underwater imaging in turbid water.

The invention relates to the technical field of underwater imaging, provides a Fourier lamination underwater imaging system and method based on adaptive phase retrieval, and effectively solves the problem of insufficient imaging resolution caused by a strong scattering effect of a water body and aperture limitation of an optical system in a traditional underwater imaging technology. According to the method, the Fourier lamination imaging technology is applied to the underwater environment for the first time, and the spatial resolution is improved by 2.24 times compared with that of a traditional method in combination with the self-adaptive phase retrieval algorithm. Further experiments show that even under the strong scattering condition, the scheme can still keep the resolution performance obviously superior to that of a traditional method. The invention provides a novel underwater high-resolution imaging technical scheme, the imaging performance of marine resource exploration and underwater engineering facility detection can be remarkably improved, and the method has important application value.

The invention relates to the technical field of underwater imaging equipment, and discloses an underwater variable baseline stereoscopic vision imaging system, which comprises a mounting plate, a lifting mechanism is arranged at the lower part of the mounting plate, the lifting mechanism is used for driving a direction adjusting cylinder to lift, a direction adjusting mechanism is arranged in the direction adjusting cylinder, and the direction adjusting mechanism is used for adjusting the direction of the direction adjusting cylinder. The direction adjusting mechanism is used for adjusting the direction of the groove frame, the groove frame is provided with an angle adjusting mechanism, the angle adjusting mechanism is used for adjusting the angle of the shell, continuous adjustment of the baseline length of the left camera and the baseline length of the right camera can be synchronously achieved, and the visual angle included angle between the two cameras is adjusted in a linkage mode. It is ensured that the imaging system always maintains a stable imaging geometrical relationship and reasonable field-of-view overlapping; the design has the advantages of being compact in structure, easy and convenient to control, sensitive in response and the like, is particularly suitable for underwater stereoscopic vision imaging tasks in a dynamic change environment, and has good engineering application prospects.

This invention provides an underwater long-range ultra-high-definition polarization imaging system and method, relating to the field of data processing technology. The system comprises, from bottom to top, an optical sensing layer, a physical computing layer, an AI inference enhancement layer, an adaptive control layer, and a decision optimization layer, forming a hierarchical control structure where data flows upward and strategies are passed downward. The optical sensing layer employs a non-uniform polarization sampling array, a spectral polarization joint coding unit, and a dual-modal illumination unit to simultaneously acquire intensity, multi-directional polarization, multispectral images, and depth information. The physical computing layer calculates polarization parameters in real time and inverts water turbidity within the image signal processor hardware pipeline. The AI ​​inference enhancement layer embeds the physical model as a differentiable module into a neural network to achieve scattering suppression and detail enhancement. The adaptive control layer adopts a time-scale separated dual-closed-loop architecture. This invention can adapt to different water turbidity levels, output ultra-high-definition images in real time, and significantly improve the quality of long-range underwater imaging.

The invention discloses an underwater binocular camera calibration method based on laser assistance, and the method comprises the steps: firstly constructing an underwater stereoscopic vision binocular refraction-removing imaging model according to the Snell's law, and correcting an underwater imaging error; secondly, acquiring a camera internal reference matrix by using a Zhang Zhengyou calibration method; then, a laser range finder is used for projecting laser to the calibration plate to form a light spot, the center pixel coordinate of the light spot is extracted through a gray gravity center method, the three-dimensional coordinate of the light spot is calculated in combination with laser ranging data, and the initial value of an external parameter matrix of the binocular camera is solved; and finally, introducing a Bayesian optimization algorithm, and optimizing the constraint equation by taking minimization of a three-dimensional coordinate calculation error as a target so as to obtain an exact solution of an external parameter matrix of the camera. Through organic combination of a deep learning technology and binocular stereo vision, the parameter calibration precision of the underwater binocular stereo vision camera is improved, a reliable technical guarantee is provided for subsequent underwater concrete structure defect quantification, and the fine monitoring requirement in a complex water environment is met.

This invention discloses an underwater image enhancement method and system based on physical priors and target features, belonging to the field of image processing technology. To address the technical problem that existing underwater image enhancement methods struggle to simultaneously achieve imaging physical consistency, visual quality, and usability for downstream target recognition tasks, this invention extracts physical prior information from underwater images, including transmittance maps and background illumination maps; acquires target features from underwater images; extracts image features from underwater images through an enhancement network; and fuses and reconstructs image features, physical prior information, and target features to obtain an enhanced image; trains the enhancement network; and uses the trained enhancement network to process underwater images to obtain the enhanced image. This invention achieves high-quality visual restoration while satisfying the physical constraints of underwater imaging, and significantly improves the accuracy of underwater target recognition and the performance of related downstream tasks.

The present invention discloses an OSIC detection method for an underwater optical Massive MIMO communication system with misaligned transmission and reception. After estimating the gain of each sub-channel using the LS channel estimation algorithm, the offset direction of the transceiver can be determined based on the magnitude of each DC gain in the channel gain matrix. The detection column order and the order within the column are then determined based on the offset direction, and interference elimination is performed to complete the detection of each signal. The present invention sorts according to the offset direction, preferentially detecting signals without interference, and then detecting signals with interference, greatly reducing the error propagation caused by the sorting, making the subsequent stage more accurate in subtracting the detected signal, and making the remaining signal used in the subsequent stage have less interference, thereby improving the bit error rate performance, so as to solve the problem of increased bit error rate of the UWOC system caused by misalignment of the transceiver and offset and diffusion of the imaging spot in the underwater imaging optical Massive MIMO communication system.

The invention belongs to the technical field of underwater image enhancement perception, and particularly relates to a double-branch cross decoupling deepwater image enhancement method and system and a storage medium. The method comprises the following steps: acquiring an original underwater image, and inputting the original underwater image into a multi-stage encoder; extracting a general feature map in a multistage encoder, setting a detail reconstruction branch and a color correction branch, performing branch-oriented feature enhancement, performing gating modulation cross fusion on the enhanced features, and outputting a feature map; inputting the feature map into a multi-stage decoder and a prediction head to obtain a transmissivity map and a three-dimensional vector of global background light; and substituting the transmissivity graph and the three-dimensional vector of the global background light into an underwater imaging model formula for inverse processing to obtain a clear deepwater image. The problems that the image is easy to degrade in a deepwater non-uniform illumination environment, and an existing underwater image enhancement method depends on big data, is complex in training or consumes time in post-processing, and is difficult to adapt to ARV lightweight deployment requirements are solved.

The application discloses an end-to-end underwater three-dimensional reconstruction method and system based on an underwater imaging model, and belongs to the technical field of underwater image processing.The application combines deep learning pose estimation with an underwater imaging model; firstly, a plurality of underwater image sequences are collected as input to construct a deep neural network, the network mainly comprises two core sub-modules: one is a pose estimation network; and the other is a three-dimensional reconstruction network, according to the predicted pose and image content, dense point cloud or voxel reconstruction is completed, the pose estimation and the three-dimensional reconstruction process are integrated in the same system to realize overall joint optimization; and the adaptive underwater imaging model is combined to model the physical processes such as underwater light attenuation and scattering, so that the realism and accuracy of the reconstruction result are improved.The application realizes high-quality three-dimensional reconstruction of images in a complex underwater environment, and can be widely applied to ocean engineering, underwater robots, seabed topographic mapping and underwater cultural relic protection.

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.

This invention discloses an underwater image enhancement method based on cross-knowledge embedding learning. It embeds three types of cross-knowledge—red channel knowledge, contrast knowledge, and gradient knowledge—of underwater images into a deep learning model. Guided by multiple knowledge bases, it improves the adaptability of the deep learning model to underwater optical scenes, comprehensively solves the problems of wavelength selective attenuation and forward and backward scattering in the underwater imaging process, and effectively improves the data quality of underwater images.

The present invention relates to the field of artificial intelligence and visual detection technology, and specifically to a live fish injury detection method based on AI and multi-eye visual underwater imaging technology. The method comprises the following steps: providing a multi-eye visual imaging device to collect clear and complete fish body images, performing image denoising, grayscale conversion, contrast enhancement, and normalization operations on the collected images to obtain preprocessed fish body images; constructing a convolutional neural network based on ResNet-50 as a main framework as a live fish injury grading detection model, improving the convolutional neural network based on ResNet-50 as a main framework, and then inputting the preprocessed fish body images into the live fish injury grading detection model for training. The present invention can effectively reduce the overfitting problem, optimize the feature extraction effect, improve the classification accuracy, solve the problem of class imbalance in underwater images, and thus improve the accuracy of detection.

An underwater image restoration method based on polarization degree image clustering belongs to the field of image processing, and comprises the following steps: constructing a physical model of underwater imaging; a polarization image is collected, a Stokes vector is calculated, and meanwhile the polarization degree and the polarization angle of the scene are calculated according to the Stokes vector; calculating a brightest polarization image and a darkest polarization image; performing coarse clustering and fine clustering on the polarization degree image, and separating a background region from a target region; and local adaptive optimization is carried out based on an adaptive optimization method of an evaluation index, and image restoration is carried out by using a gain item. The background area can be automatically selected without manual intervention; the back scattering value of each small block can be estimated and optimized in a robust mode through iteration based on evaluation given by the evaluation indexes, the target polarization degree and the background polarization degree of the image are adjusted according to block characteristics, and the recovery effect of the whole image is improved. According to the method, a clear underwater image can be recovered, the visibility and the contrast ratio of the image are improved, and color distortion is corrected at the same time.

The invention discloses a lattice field flatness detection system and method based on an unmanned aerial vehicle laser fusion image, and relates to the technical field of flatness detection, and the system comprises a laser array detector, a CMOS image sensor, a positioning system, an embedded microcontroller, a storage chip, a power circuit, and a multi-rotor unmanned aerial vehicle system. The method specifically comprises the following steps: controlling a laser array to emit a detection light signal underwater through an embedded microcontroller, and synchronously controlling a CMOS image sensor to collect a water layer image under laser detection; and an embedded microcontroller is used for carrying out storage, preprocessing, threshold segmentation, morphological processing, light pillar detection and length calculation on the image data to obtain flatness data of the grid field mud surface after water soil preparation. Therefore, by the adoption of the lattice field flatness detection system and method based on the unmanned aerial vehicle laser fusion image, flatness data can be obtained through the underwater imaging light beam, the principle is clear, calculation is simple, and processing and calculation can be conveniently carried out in an edge terminal.

The invention relates to an underwater low-light image recovery method and device, electronic equipment and a storage medium, and the method comprises the steps: obtaining and processing an original underwater polarization image, and calculating an orthogonal polarization image; three parallel depth image prior networks are constructed and initialized, and are respectively used for generating direct transmission light and orthogonal polarization backscattered light; the network is jointly optimized under polarization physical constraints, and accurate decoupling and illumination recovery of scattering components are realized by minimizing a total loss function formed by reconstruction loss, polarization differential loss, polarization degree reconstruction loss and adaptive stretching loss; and recovering a final underwater clear image according to an underwater imaging model by using direct transmission light and backscattering light generated by the optimized network. According to the method, advantages of a physical imaging model and data driving are effectively fused, and robust and high-quality underwater image restoration is realized specially for complex degradation of an underwater low-light scene.

The invention discloses a panchromatic organic room temperature phosphorescence (RTP) material system constructed based on a molecular isomerization strategy, and a preparation method and application thereof. The positions of nitrogen atoms in a tricyclic fused aromatic skeleton are accurately regulated and controlled to construct structural isomers including benzoindole [g], [e] and [f] and carbazole, and red, yellow, green and blue panchromatic RTP emission is realized. The compound can be efficiently synthesized by a one-pot solvent-free mechanochemical method, is doped in polymers such as PVA (Polyvinyl Alcohol) to construct a stable phosphorescent composite material, and shows a phosphorescent life as long as 4.23 seconds, an efficient TSFRET effect and adaptability to various matrixes. Theoretical and crystallographic analysis reveals a regulation and control mechanism of a heterogeneous structure on luminescence performance. The method is suitable for the fields of anti-counterfeiting, underwater imaging, security identification and the like, and a new path for constructing the panchromatic phosphorescent material is provided.

The invention discloses a decoupling cooperative enhancement method for an underwater image degradation factor, and belongs to the technical field of underwater image enhancement. Aiming at the problem that an underwater imaging environment is complex and changeable, and light propagation is influenced by wavelength selective absorption and suspended particle scattering to cause image degradation, a degradation factor is approximately decomposed into relatively independent scattering degradation and color cast degradation, and a binarization strategy is introduced to compress the storage and calculation amount of data, so that the image degradation is realized. And global information is compensated in back propagation through decoupling learning, and data compression precision loss is recovered. According to the decoupling collaborative optimization method, scattering and color problems are distributed to a physical compensation module and a spectrum compensation module, learning information is isolated through a gating mechanism, and degeneration decoupling is achieved; the loss function is combined with the subtasks and the global loss to compensate the binarization precision loss of the submodules, and finally, the modules are cascaded to process and recover the degraded image.

The invention belongs to the technical field of intelligent fishery and underwater imaging, and discloses a distance-adjustable matte background plate and body width estimation camera device for an intelligent fishing system, which comprises a distance-adjustable matte background plate arranged in a recognition box and a body width estimation camera mounted near a sensor. The background plate is driven by a waterproof electric driver to realize dynamic adjustment of the imaging distance; and the control processor automatically adjusts the position of the background plate based on the fish body width and the water body turbidity parameter so as to obtain the optimal imaging effect. Scale marks are arranged on the surface of the background plate and used for measuring the length of a fish body. The background plate can be in a linear type or a front-arc and rear-straight combined type, and has the function of expelling fish bodies when being slowly propelled. The method can significantly improve the fish identification success rate under the natural water body condition, and is suitable for scientific research, resource protection and smart fishery application.

The invention discloses an underwater range gating dynamic imaging method based on meta-heuristic attitude estimation, and relates to the technical field of underwater imaging, and the method comprises the steps: building an underwater range gating laser imaging system, and obtaining a continuous space-time matched original scanning image sequence through synchronous control of laser emission, scanning and gating exposure; selecting a reference frame from an original scanning image sequence, determining an effective imaging area, modeling inter-frame rigid body motion by adopting a six-degree-of-freedom pose parameter, and constructing a global optimization objective function based on a gray mean square error; and designing a self-adaptive weighted pixel loss function, distributing weights for pixels through normalized gray and gradient information, and inhibiting the influence of a highlight area. According to the method, the meta-heuristic optimization thought is applied to the 6D pose estimation problem of underwater range gating laser imaging, a 6D pose optimization framework suitable for a high-sparseness and high-constraint scene is constructed based on PSO, and the fundamental challenges of scarcity of feature points and insufficient data sets are overcome.

The invention discloses an underwater scene semantic segmentation method based on SAM2 physics driven fusion and adaptive prompt, and relates to the field of deep learning. Selecting underwater scene images and depth maps and real semantic segmentation images corresponding to the underwater scene images, and forming a training set; introducing the classical underwater imaging physics model into RGB-D semantic segmentation, and constructing a convolutional neural network; pre-processing a training set, and inputting the pre-processed training set into the convolutional neural network for training to obtain a corresponding predicted underwater scene semantic segmentation image; calculating and predicting a loss function value between the underwater scene semantic segmentation image and the corresponding real semantic segmentation image, and training for multiple times until the loss function value accords with a preset threshold value to obtain an underwater scene semantic segmentation network model; and inputting an image to be segmented into the underwater scene semantic segmentation network model to obtain a segmentation result. According to the method, high-precision segmentation of a complex underwater scene is realized, and the automation degree and robustness of segmentation are improved.

This invention discloses an underwater range-gated dynamic imaging method based on metaheuristic attitude estimation, belonging to the field of underwater imaging technology. The method includes: constructing an underwater range-gated laser imaging system; acquiring a continuous, spatiotemporally matched sequence of original scanned images by synchronously controlling laser emission, scanning, and gated exposure; selecting reference frames from the original scanned image sequence and determining the effective imaging region; modeling inter-frame rigid body motion using six-degree-of-freedom pose parameters; constructing a global optimization objective function based on gray-level mean square error; and designing an adaptive weighted pixel loss function, assigning weights to pixels by normalizing gray-level and gradient information to suppress the influence of bright areas. This invention applies metaheuristic optimization ideas to the 6D pose estimation problem of underwater range-gated laser imaging, constructing a 6D pose optimization framework suitable for highly sparse and highly constrained scenarios based on PSO, overcoming the fundamental challenges of feature point scarcity and insufficient datasets.