9 results about "Side-scan sonar" patented technology

The application provides a side scan sonar file processing method, and relates to the technical field of sonar image processing. The method comprises the following steps: acquiring block information according to the global image size of a side scan sonar file, wherein the block information comprises the total number of columns and the total number of rows of tiles; updating the pixel information of each tile based on the geographical range corresponding to the pulse strip and the tile of the side scan sonar file, wherein the pixel information comprises an image data array, an effective area marker array and a pixel count array; and generating an image file corresponding to each tile, wherein the image file comprises the pixel information, and the image file is used to generate a video corresponding to the side scan sonar file. The embodiment of the application can avoid memory overflow caused by loading global data at one time, greatly reduce memory consumption, independently output an image file for each tile, realize multi-thread parallelism, improve overall processing efficiency, and be suitable for different specifications of sonar files and hardware devices, and has strong engineering landing performance.

The application provides a kind of autonomous underwater vehicle seabed pipeline tracking method and device based on side scan sonar, it is related to underwater pipeline tracking technical field, the method comprises: during the control underwater robot is in pipeline investigation area and carries out comb search, utilize side scan sonar to continuously obtain seabed acoustic image;Pipeline track is detected based on seabed acoustic image, and the initial trajectory of pipeline is determined based on pipeline track;The state estimation of the pipeline is carried out based on the initial trajectory using Kalman filter;Based on state estimation result, the spatial length, confidence and continuity of initial trajectory are determined using trajectory quality evaluation method, and trajectory score is generated;In response to trajectory score meets preset requirement, the initial trajectory is determined as the target tracking trajectory of underwater robot;The front view distance and heading of the underwater robot are dynamically adjusted based on the target tracking trajectory.

The application discloses a kind of underwater robot navigation method, device and equipment based on side scan image matching.It relates to underwater robot navigation technical field, wherein method includes: in response to receiving underwater robot travels to target depth, the side scan sonar image of the underwater robot is preprocessed, and preprocessed image is obtained;Based on the image features of the preprocessed image and reference image in reference image database, feature matching is carried out, and relative pose constraint is obtained;Based on the relative pose constraint, current inertia data, current speed data and current depth data, the running state of the underwater robot is optimized using target fusion optimization method, and the optimized relative pose is obtained;Based on the optimized relative pose, the motion trajectory of the underwater robot is solved, and the navigation trajectory of the underwater robot is obtained.The method of the application can improve the accurate navigation ability of underwater robot in the scene lacking external base station or absolute speed measurement.

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 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 application discloses a kind of sonar image classification methods of combined SLIC superpixel and graph attention network.It includes the following steps: according to the imaging principle of two-dimensional forward-looking sonar and side scan sonar respectively and prior information when imaging: the sonar image after correction is based on the image pre-segmentation of improved DeepLabV3+ network, SLIC superpixel algorithm is used to construct Graph (graph) structure data: construct the sonar image classification model based on GAT (graph attention network), the sonar graph structure data built is sent into network to complete the training and test of model;Verify the importance of pixel feature and spatial position feature.The application discloses a kind of sonar image classification methods of combined SLIC superpixel and graph attention network, which fully utilizes the space position relationship of acoustic shadow area, target area and shadow area by SLIC superpixel method and graph attention network, so as to realize higher accuracy of sonar image classification identification of sonar image by combining pixel feature and spatial geometric feature.

The application discloses a side scan sonar image row-by-row matching and splicing method based on numerical transformation processing, and comprises the following steps: acquiring a first sonar image and a second sonar image; traversing feature data of each row of the first sonar image and the second sonar image, performing one-dimensional wavelet transformation on each row of feature data sequences, and obtaining wavelet coefficients; constructing a feature vector based on the wavelet coefficients; calculating the similarity between the feature vector of each row of feature data in the first sonar image and the feature vectors of all rows of feature data in the second sonar image; identifying matching row pairs in the first sonar image and the second sonar image according to a set similarity threshold, determining a row offset and an overlapping region; aligning the first sonar image and the second sonar image according to the row offset and the overlapping region, and performing image fusion in the overlapping region to generate a panoramic sonar image. The application changes the matching dimension from two dimensions to one dimension, and solves the matching failure problem in a sparse feature environment.

The present application relates to a kind of based on side scan sonar image's Eriocheir sinensis quantity and size evaluation system and equipment, belong to aquaculture monitoring, underwater acoustic detection and image intelligent identification technical field, the system includes side scan sonar collection module, data preprocessing module, bright spot map patch detection module, Eriocheir sinensis candidate target screening module, bright spot-shading identification module, multiple track deduplication module, resource quantity evaluation and visualization module.The method realizes Eriocheir sinensis individual level automatic identification, the assessment of Eriocheir sinensis quantity, size distribution and total resource quantity in pond.Make Eriocheir sinensis breeding pond can not interfere with production, quickly and accurately obtain Eriocheir sinensis quantity and specification structure information in pond, provide data support for feeding management, disease early warning, fishing decision etc..

This invention discloses a side-scan sonar target detection method and apparatus based on adaptive frequency domain sanitization and multi-scale context fusion, belonging to the field of side-scan sonar target detection technology. The method proposes an improved YOLOv11 seabed detection model. An efficient unified perception module and a dynamic multi-scale frequency domain fusion module are introduced into the backbone network. These modules enhance context awareness through multi-scale dilated convolution and adaptively suppress high-frequency noise using frequency domain transformation. The neck network employs a coordinate attention mechanism to strengthen spatial location information and combines a dynamic upsampling device with a cross-feature guided fusion strategy to improve the multi-scale feature fusion effect. Experimental results show that this method can effectively improve target detection accuracy and robustness in complex seabed environments, especially demonstrating superior recognition capabilities for small targets.