62 results about "Sonar imagery" patented technology

The invention relates to the technical field of underwater target detection, in particular to a target recognition method and system based on a sonar image assisting an optical image. The method comprises the following steps: acquiring an underwater optical image and a sonar image, inputting the underwater optical image and the sonar image into a double-flow multi-scale feature extraction backbone network, and generating an optical multi-scale feature map and a sonar multi-scale feature map; processing the multi-scale feature map through an optical-sonar adaptive feature fusion module to generate enhanced multi-modal fusion features; inputting the multi-modal fusion features into a detection head, and outputting an underwater target detection result; according to the method, the problems of optical-sonar image modal isomerism and space mismatch are effectively solved, and the precision and robustness of underwater target detection are remarkably improved.

The invention provides a sonar intelligent interpretation method and system, and the method comprises the steps: integrating end-side equipment (disposed on a ship-borne platform) and side-side equipment (disposed in a shore-based machine room) to form a sonar intelligent interpretation system; through the intelligent sonar interpretation system, full-process automatic processing of sonar interpretation from data acquisition of sonar images to interpretation is realized, the interpretation efficiency of the sonar images is effectively improved, and in addition, the intelligent sonar interpretation system also can be used for realizing automatic interpretation of the sonar images on the basis of mass sonar image data (namely sonar images stored in a data warehouse) obtained through actual measurement. Model training of the sonar interpretation model and training examination of interpretation personnel are synchronously achieved, a sustainable closed-loop optimization system is formed, and the problems that in traditional sonar interpretation, the interpretation accuracy is low, the data reuse rate is poor, and personnel training examination is difficult are solved.

The invention belongs to the technical field of underwater target detection, and discloses an underwater complex target detection method and system based on generation and detection collaborative optimization, and the method comprises the steps: generating a side-scan sonar simulation sample through employing a CycleGAN, and screening out a high-quality sample through employing a pre-trained YOLO model, so as to construct an enhanced data set; an improved target detection model with YOLOv8 as a baseline is constructed, the model integrates a lightweight backbone network formed by deep separable convolution and an inverted bottleneck structure, a feature fusion neck network adopting element multiplication and residual connection, and an EMA attention module introduced in front of a detection head, and the model is trained by using the enhanced training data set; and performing target detection on an input underwater side-scan sonar image by using the improved YOLOv8 model after training is completed. According to the method, the problems of scarcity of underwater samples and multi-scale detection of targets are effectively solved, and the model lightweight is realized while the detection precision is improved.

The invention relates to the field of image processing, in particular to a fish and shrimp denoising enhancement and intelligent identification method based on a two-dimensional sonar image, which comprises the following steps: extracting a sonar image water background, dividing regions and calculating a signal-to-noise ratio, dynamically adjusting a noise threshold, and denoising to obtain a salient image. And a multi-rotation-angle convolution kernel set is constructed based on a size statistics preset ellipse parameter. And clustering salient image pixel points, setting a bounding rectangle as a suspected target area, and judging the consistency of continuous frame targets through an optical flow algorithm. And dynamically adjusting a convolution kernel weight enhancement image, training a YOLO model to recognize a target, superposing recognition results and providing statistical information. According to the method, the fish and shrimp targets with variable sizes and directions in the sonar image are accurately identified and tracked by utilizing dynamic noise threshold adjustment and a multi-angle convolution kernel set in combination with a denoising enhancement technology based on a signal-to-noise ratio and an optical flow algorithm, the limitation of a traditional method is effectively overcome, and reliable technical support is provided for target detection in a complex underwater environment.

The application discloses a fish resource monitoring method and system based on sonar images and electronic equipment, and the fish resource monitoring method comprises the following steps: acquiring sonar images of a fish pond; detecting fish targets in more than two frames of the sonar images and tracking the fish targets; and determining the number of the fish targets in the fish pond according to the tracking result. Through the above steps, the fish resource monitoring method can accurately monitor the number of the fish targets in the fish pond, and does not cause adverse effects on the growth environment of the fish during the monitoring process.

The invention discloses a three-dimensional sonar phase self-correction method, and relates to the technical field of underwater acoustic engineering and array signal process.The method comprises the steps that a three-dimensional sonar image is generated through sonar original multi-channel signals, a plurality of strong display point targets which can be separated from the three-dimensional sonar image are extracted, and the signal-to-noise ratio of the strong display point targets is higher than a threshold value; sound path distance difference phase compensation is carried out on echo signals of each strong display point target, a channel common phase deviation is estimated by using a compensated residual complex signal, then an image entropy and a target peak value are set as convergence criteria, the criteria are deeply combined with an iterative reweighting mechanism, step-by-step optimization is carried out to obtain the phase deviation estimation precision, and the estimation precision of the channel common phase deviation is improved. And finally, obtaining an optimal inherent phase deviation, and carrying out global phase correction on the multi-channel echo signal by using the optimal inherent phase deviation. According to the invention, real-time online self-correction can be realized, the problem of imaging blur caused by phase inconsistency between channels is effectively compensated, and the contrast ratio of three-dimensional sonar imaging and the target visualization quality are remarkably improved.

The present invention includes systems and methods for improving the process of detecting and classifying objects of interest using UUVs to survey the ocean. According to a particular embodiment, the present invention provides a more cost-efficient process by employing preconfigured heuristic operational scenarios, or supervised machine learning to fine-tune this process by integrating the use of environmental variables relating to the water in which data is gathered, and more particularly for classifying sonar images of the seafloor.

The application provides a sonar image target detection method and system, comprising the following steps: step 1: constructing a front-view sonar image dataset, collecting original images and preprocessing the original images; using a cross-domain image conversion model to generate synthetic sonar images; labeling the images and dividing them into a training set and a test set; step 2: constructing an improved YOLO network structure, including a backbone network, a neck network and a detection head; the backbone network outputs multi-scale feature maps to the neck network; the neck network introduces a high-level screening feature fusion pyramid module with a spatial attention mechanism, weights and fuses the features and suppresses noise, and outputs enhanced feature maps to the detection head; step 3: using the training set to train the model; using the validation set to evaluate the performance in terms of the average precision mean mAP and saving the optimal model; step 4: using the test set to load the optimal model for inference, obtaining the detection result, calculating the mAP for evaluation, and visualizing and rendering the result on the original image.

The present application relates to the field of image processing, especially to a fish and shrimp denoising enhancement and intelligent identification method based on two-dimensional sonar images, comprising: extracting a sonar image water body background, dividing regions and calculating a signal-to-noise ratio, dynamically adjusting a noise threshold, and obtaining a saliency image through denoising. Based on size statistics, preset ellipse parameters are constructed, and a multi-rotation angle convolution kernel set is constructed. The saliency image pixel points are clustered, and the circumscribed rectangle is set as a suspected target area. The consistency of the target in the continuous frame is judged through an optical flow algorithm. The convolution kernel weight is dynamically adjusted to enhance the image, the YOLO model is trained to identify the target, the identification result is superimposed, and statistical information is provided. Through the use of dynamic noise threshold adjustment and multi-angle convolution kernel set, combined with the denoising enhancement technology based on the signal-to-noise ratio and the optical flow algorithm, the fish and shrimp targets with variable size and direction in the sonar image are accurately identified and tracked, and the limitations of traditional methods are effectively overcome, providing reliable technical support for target detection in complex underwater environments.

The application provides a sonar image three-dimensional reconstruction method based on target shadow and gray scale, which comprises the following steps: Step 1, converting the original sonar image into a gray scale image; Step 2, carrying out denoising treatment on the image; Step 3, carrying out shadow edge calibration; Step 4, segmenting a target region; Step 5, eliminating small target regions; Step 6, solving the pitch angle of the target region; and Step 7, reconstructing the target region. The application uses the multi-beam forward-looking sonar imaging principle as a research object, analyzes the transmission, reflection and reception process of sound waves, realizes a reverse reasoning using sound propagation, and obtains the three-dimensional reconstruction of the target object in the sonar image.

1. Name of the product in this design: Graphical User Interface for Displaying Sonar Detection Information in Electronic Equipment. 2. Purpose of this design: An electronic device. 3. The key design feature of this product is its graphical user interface. 4. The picture or photo that best illustrates the key design points: Design 1 front view. 5. Design 1 is designated as the basic design. 6. Purpose of the graphical user interface: After the sonar is introduced into the water, it emits ultrasonic waves to the surrounding area (within a range of 130°×20°) through the sonar head, and acquires and displays environmental information and target distribution within the underwater sound wave range in real time. 7. Human-computer interaction method of graphical user interface: Design 1's main view is the sonar acquisition operation interface. By clicking various control buttons, sonar images are acquired. The sonar acquisition results can be viewed in real time on the interface. Clicking the hexagonal icon in the upper right corner enters the "Sonar Settings" interface of the changing state diagram of Design 1; Design 2's main view is the sonar acquisition operation interface. By clicking various control buttons, sonar images are acquired. The sonar acquisition results can be viewed in real time on the interface. Clicking the hexagonal icon in the upper right corner enters the "Sonar Settings" interface of the changing state diagram of Design 2.

The invention discloses an unmanned ship full-coverage connection topological optimization path planning method and system for underwater target search and detection, a storage medium and electronic equipment, and the method comprises the steps: firstly carrying out the environment modeling and rasterization of a detection region, and obtaining a free grid set and neighborhood state information; the method comprises the following steps: establishing a mixed integer linear programming model containing geometric continuity constraints and topological structure rewards to obtain a global optimal rectangular decomposition scheme under the balance of geometric boundary cost and topological connection rewards; establishing a minimum turning spanning tree to minimize the turning times of the coverage path by judging the connection type and turning cost between the rectangles; and finally, generating a smooth coverage trajectory in combination with the kinematics constraint of the unmanned ship. According to the method, the problem of sonar image distortion caused by too many turning times in the existing coverage path planning method is solved, the global turning times of the coverage path are reduced, the problem of detection precision decline caused by the fact that acoustic imaging conditions are damaged due to frequent turning maneuvering in the traditional path is solved, and the detection path quality is optimized.

The invention belongs to the technical field of underwater sonar imaging and intelligent identification, and relates to a virtual sonar image generation method and system. The method comprises the following steps: mapping a real optical target image into a virtual sonar target image by adopting a vision-text fusion network; embedding the virtual sonar target image into the real sonar background image to generate a combined image; and background compensation and texture consistency adjustment are carried out on the combined image by using a virtual sonar image background reconstruction network to obtain a virtual sonar image with a background. According to the invention, the bottleneck that real sonar data acquisition is limited by environment, cost and equipment conditions is broken through; meanwhile, the target type and number can be flexibly expanded, and the problems that sonar samples are insufficient in category and unbalanced in distribution are effectively solved.

The application discloses a kind of side-scan sonar image seabed cable zero-sample rapid detection method, belong to seabed cable detection technical field, comprising the following steps: step one, establish neural network model;Step two, data fusion enhancement: to existing public side-scan sonar image data data fusion enhancement, generate experimental data set;Step three, data set annotation: to data set is marked;Step four, model training: neural network is trained;Step five, model test: using measured data to the network model after training is tested;The application, by establishing neural network model, and to existing public side-scan sonar image data data fusion enhancement, generate experimental data set, to data set is marked and neural network is trained, using measured data to the network model after training is tested, this method combines data enhancement method, realizes the zero-sample training and rapid detection of neural network.

The application belongs to the technical field of computer vision and target detection, and discloses a sonar target detection method based on neural architecture search technology, which comprises the following steps: constructing and preprocessing a data set; performing neural architecture search based on Zero-shot of the maximum differential entropy principle to obtain an optimal CNN-Transformer backbone architecture; generating high-quality Query through Query selection based on different scale features of various targets of a sonar image extracted by the CNN-Transformer backbone network; decoding Query by a decoder part to predict a sonar target; training through a hybrid loss function of multi-task collaborative optimization; and outputting position, category and confidence information of the target. The sonar target detection performance is significantly improved through architecture innovation, noise-resistant design and theoretical breakthrough by using the above sonar target detection method based on neural architecture search technology.

The present application relates to underwater target detection technical field, specifically to a kind of target identification method and system based on sonar image auxiliary optical image;Method includes: obtaining underwater optical image and sonar image, input double-flow multi-scale feature extraction backbone network, generate optical multi-scale feature map and sonar multi-scale feature map;Through optical-sonar adaptive feature fusion module, multi-scale feature map is handled, and enhanced multimodal fusion feature is generated;Multi-modal fusion feature is input detection head, and underwater target detection result is output;The present application effectively solves optical-sonar image modal heterogeneity and spatial mismatch problem, significantly improves the precision and robustness of underwater target detection.

The invention relates to a multi-beam data screening optimization processing method based on image sonar. Aiming at the problems of weak target signal annihilation and insufficient image sharpness caused by a traditional arithmetic average method, the method adopts a maximum value screening strategy to carry out multi-beam data fusion. The method comprises the following specific steps: carrying out preprocessing and TVG compensation on sonar original echo data; obtaining a plurality of beam data through parallel beam forming; for each distance unit, traversing and comparing the signal intensity of all beams, and selecting the maximum value as the final pixel value of the unit; and then median filtering noise reduction and configurable point picking processing are carried out. According to the method, the detection probability of a weak target and a point target can be remarkably improved, a sonar image with high contrast and high sharpness is generated, and meanwhile, different detection tasks are flexibly adapted by integrating multiple fusion and point selection modes. The method is efficient in calculation, is easy to realize on embedded platforms such as a DSP (Digital Signal Processor), an FPGA (Field Programmable Gate Array) and the like, and has good engineering application value.

The invention discloses an underwater target tracking method and system fusing sonar and a camera. According to the underwater target tracking method, underwater tracking is conducted on a tracking target through the underwater target tracking system composed of an underwater holder fixing platform, the underwater camera, the sonar, a switch, an image processor and an underwater steering engine. In the target tracking process, an optical image and a sonar image are collected respectively, and the processed optical image and sonar image are obtained after optical image sectorization preprocessing, acousto-optic registration and image enhancement are carried out in sequence; performing high and low frequency decomposition and adaptive weighted fusion on the processed optical image and sonar image to obtain a final fused image; and the mass center position of the tracked target is obtained based on the final fusion image, and a combined control strategy is provided to drive the holder to steer stably and quickly, so that the underwater camera and the sonar can be continuously aligned with the tracked target.

The application discloses a kind of based on dual attention mechanism's underwater side-scan sonar image recognition method and system, comprising: obtaining the monitoring image of underwater side-scan sonar equipment collection;Monitoring image is input to the EDA-Net model of pre-set and obtains underwater target detection result, wherein, EDA-Net model includes improved backbone network, improved neck network and head network;The improved backbone network is based on YOLOv11 framework construction, wherein double path heteromeric attention module is set;Improved neck network is provided with bottleneck convolution-parameterless attention module;The head network is used for target detection according to the feature map output by improved neck network.This application can more accurately identify target detail texture characteristics under the condition of small sample, multi-scale data set, improve the recognition accuracy and stability of model to multi-scale target in complex underwater environment.

The application discloses a kind of based on multi-scale feature fusion and high school upsampling algorithm's underwater detection method and system of sonar image, the method includes: collecting underwater typical target forward-looking sonar data, and the detection training and test data set of sonar image are made to obtain;Build the feature fusion network including efficient upsampling module, multi-scale feature fusion module and small target detection branch, build the target detection algorithm model based on YOLOv11, the neck network of YOLOv11 is regarded as feature fusion network, and the deep feature map detection branch of YOLOv11 is removed;The data set made is used to train target detection algorithm model, and forward-looking sonar image is input into the model trained and inquired, to obtain the detection result of underwater typical target.The application improves the accuracy and robustness of underwater target detection algorithm.

The invention provides an underwater structure defect segmentation model construction method, and the method comprises the steps: obtaining a sonar image data set, constructing a segmentation network, dividing a feature map outputted by an encoder into non-overlapping local blocks, calculating the similarity between the blocks, obtaining a spatial redundancy index, taking the index as an auxiliary loss item, and combining the auxiliary loss item with main segmentation loss, thereby obtaining a segmentation model. And training the segmentation network by adopting a dynamic loss training strategy. By dividing the local blocks and calculating the similarity, the spatial redundancy degree in the feature map is explicitly quantified; by taking a redundancy index as auxiliary loss, the model is directly constrained to reduce repeated feature expression; and balancing the main and auxiliary loss weights through a dynamic loss training strategy. Under the combined action of the technical features, the model learns the feature representation with higher discrimination capability while keeping the original structure, the problem of feature redundancy caused by low contrast and complex background of the sonar image is effectively solved, and the recognition precision of defect boundaries and fine structures is improved.

The invention relates to a subsea pipeline side-scan sonar detection method and device and electronic equipment, and relates to the technical field of multifunctional underwater robot target detection, and the method comprises the steps: inputting a side-scan sonar image into a deep learning model for feature extraction, determining a pipeline prediction result and a pipeline prediction confidence coefficient according to a prediction feature tensor, and obtaining a pipeline prediction result; identifying a pipeline target in the side-scan sonar image, and carrying out informatization processing on the pipeline target to generate a pipeline information dictionary; and performing post-processing optimization on the pipeline information dictionary, performing coordinate conversion on the pixel-level pipeline target image coordinates in the post-processed and optimized pipeline information dictionary in combination with the real-time navigation data, and resolving to obtain the longitude and latitude coordinates of the pipeline target. False detection targets which do not conform to pipeline features are effectively eliminated, so that the false alarm rate is reduced, and weak targets or subtle features of buried pipelines can be captured from complex sonar images.

The invention relates to an underwater three-dimensional terrain reconstruction method based on forward-looking sonar, electronic equipment and a storage medium, and the method comprises the following steps: 1, carrying out the preprocessing of a forward-looking sonar image sequence, carrying out the coding, extraction and compression of a plurality of frames of continuous sonar images through a convolutional neural network, and obtaining acoustic feature vectors; step 2, acquiring an ontology sensing data sequence of the unmanned ship, and performing time sequence modeling on the ontology sensing data by adopting a gating circulation unit network to generate a motion state vector; according to the method, acoustic feature vectors and motion state vectors are extracted through a deep learning model, historical information is fused by using a feature fusion network, and finally, an underwater three-dimensional terrain model is output through a terrain reconstruction network.

This invention discloses a frequency-space collaborative sensing method and system for target detection in sonar images. The method includes the following steps: acquiring sonar images, preprocessing them using contrast-limited adaptive equalization, and dividing them into training and validation sets; constructing an initial target detection network and training it based on the training and validation sets to obtain the target detection network; acquiring the sonar image to be detected and inputting it into the target detection network for target detection to obtain the detection result. The contrast-limited adaptive equalization grayscale transformation preprocessing of this invention enhances the contrast of sonar images, making target boundaries clearer, textures more stable, and background stripes and speckles more distinguishable, effectively improving the model's detection accuracy.

A marine electronic device for a watercraft is configured to present live sonar imagery of an underwater environment. The device includes a display, a processor, a memory, and a communication interface for receiving sonar return data from a sonar transducer assembly comprising multiple sonar transducer arrays. The processor generates a first live sonar image based on first sonar return data representing a first volume of the underwater environment, wherein the entire portion of the first live sonar image is continually updated in real time. The device receives user input defining a portion of the first live sonar image corresponding to a second, smaller volume within the first volume, and generates a second live sonar image based on second sonar return data that is a subset of the first sonar return data. The second live sonar image is presented on the display to provide a focused view of the underwater environment.

A method for stitching forward-looking sonar images while retaining information is provided. In this application, forward-looking sonar is used as underwater detection equipment and acquired forward-looking sonar images are stitched together. A phase correlation method is employed for estimating displacements between images to determine the position of each single forward-looking sonar image within the stitched image. A method based on local statistics is used for image blending to obtain a stitched forward-looking sonar image that retains information. The method for stitching forward-looking sonar images proposed in this application adapts to intra-frame and inter-frame artifacts caused by non-ideal sonar imaging configurations, overcoming the drawbacks of image quality degradation due to the intra-frame and inter-frame artifacts. The method enhances the amount of information contained in the stitched image, which can assist observers in conducting rapid underwater exploration.

This invention discloses a method and system for target detection in sonar fish school images based on an improved YOLOv11 model. The method includes: acquiring a pre-processed sonar image of a fish school inside an aquaculture vessel; constructing a trained improved YOLOv11 model with embedded CARSA mechanism based on the sonar image of the fish school; and performing fish target detection on the sonar image of the fish school to be detected based on the improved YOLOv11 model. Based on the above data processing flow, this invention effectively improves the feature representation ability and noise suppression ability of sonar fish school targets while maintaining the lightweight overall model structure, thereby improving the accuracy and robustness of fish school target detection and meeting the real-time and stability requirements of aquaculture vessels in practical applications.

The invention relates to the technical field of cage culture, and provides a method for improving the accuracy of a biometric device of a profile motion culture cage, which comprises the following steps of: acquiring all sonar images and corresponding direction information acquired in a rotation process; feature points are extracted based on the netting contour; filtering the feature points, and generating a sparse point set of the net cage contour by difference values; calculating a body-park radius shape coefficient based on the point set, and performing evaluation after waiting for a plurality of time intervals; the contour feature points are mapped to a three-dimensional coordinate system with sonar as an original point based on the direction angle; and the distance between the net cage contour and the sonar scanning boundary. By evaluating the deformation degree of the net, the method can effectively distinguish whether the state of the netting is good or not in data collected by equipment in one-time profile operation, biomass evaluation can be accurately carried out, if the net is seriously deformed, the equipment can give an alarm and suggest a user to carry out measurement again when the water flow is low, and the measurement efficiency is improved. And evaluation data with relatively low accuracy given by the system due to network deformation interference can be effectively avoided.

The application belongs to the technical field of underwater sonar imaging and intelligent identification, and relates to a virtual sonar image generation method and system. The method comprises: mapping a real optical target image to a virtual sonar target image by using a vision-text fusion network; embedding the virtual sonar target image into a real sonar background image to generate a combined image; and performing background compensation and texture consistency adjustment on the combined image by using a virtual sonar image background reconstruction network to obtain a virtual sonar image with a background. The application breaks through the bottleneck of real sonar data collection being limited by environment, cost and equipment conditions; at the same time, the target type and quantity can be flexibly expanded, effectively solving the problems of insufficient sonar sample categories and unbalanced distribution.

A method for forming a sonar image is provided comprising receiving first sonar data from a sonar transducer assembly when it is oriented in a first orientation, receiving first orientation data, and causing an actuator to generate movement of the sonar transducer assembly so it moves from the first orientation to a second orientation. The method includes receiving second sonar data from the sonar transducer assembly when it is oriented in the second orientation, receiving second orientation data, and creating a sonar image using the first sonar data, the second sonar data, the first orientation data, and the second orientation data. The sonar image includes first and second image portions including image data representative of the first and second sonar data. The first image portion is positioned relative to the second image portion to portray a real-world differential position of the first sonar data relative to the second sonar data.