112 results about "Sonar imagery" patented technology

The invention discloses a sonar image underwater detection method and system based on multi-scale feature fusion and a college up-sampling algorithm, and the method comprises the steps: collecting the foresight sonar data of an underwater typical target, and making a detection training and testing data set of a sonar image; the method comprises the following steps: constructing a feature fusion network comprising an efficient upsampling module, a multi-scale feature fusion module and a small target detection branch, constructing a target detection algorithm model based on YOLOv11, taking the feature fusion network as a neck network of YOLOv11, and removing a deep feature map detection branch of YOLOv11; and training a target detection algorithm model by using the manufactured data set, and inputting a foresight sonar image into the trained model for reasoning to obtain a detection result of the underwater typical target. According to the invention, the accuracy and robustness of an underwater target detection algorithm are improved.

The invention discloses a complex underwater side-scan sonar exploration detection method and device based on a multi-dimensional attention collaborative lightweight anti-noise detection framework, and the device comprises an underwater side-scan sonar imaging device which is used for obtaining a sonar image of a detected target; the computer is connected with the underwater side-scan sonar imaging equipment and comprises a backbone feature extraction network module which is used for processing an input sonar image through a multi-scale edge refining module and outputting a three-scale feature map; the check feature fusion network module is used for realizing cross-channel and cross-space information fusion through the focusing space adaptive local attention module, performing adaptive modulation and deep information aggregation on multi-scale features through a channel frequency aggregation and attention mechanism, and outputting three enhanced feature maps; and the YOLOhead detection head module is used for generating a target detection frame according to the enhanced feature map, and outputting a detection result after non-maximum suppression processing.

A system for generating live sonar images is provided having a first and second sonar transducer assembly. The sonar transducer assemblies each have sonar transducer elements configured to transmit sonar beam(s) into an underwater environment to form respective coverage volumes. The sonar transducer assemblies each define a respective facing direction. The system includes bracket(s) having alignment feature(s). The bracket(s) are configured to mount the sonar transducer assemblies to a watercraft, and the alignment feature(s) are configured to position the sonar transducer assemblies so that the facing directions are different and relative to each other so as to create continuous coverage of the underwater environment. Continuous coverage has an overall coverage volume that is greater than either of the coverage volumes individually. Sonar return data from the sonar transducer elements is used to form a live sonar image representative of sonar returns received from the overall coverage volume.

The invention discloses an imaging method, system and device for correcting range migration based on frequency domain phase multiplication, and the method comprises the following steps: carrying out the range pulse compression of echo data of each receiving array element in a range frequency domain; performing data preprocessing according to a phase center approximation method to obtain equivalent transmit-receive combined synthetic aperture sonar data; deriving a secondary distance compression item at the reference distance in the two-dimensional frequency domain, and performing secondary distance compression processing; deriving a space-variant migration phase term of range migration correction corresponding to each target with different distances; carrying out filtering processing on the two-dimensional frequency domain sonar data; a filtered result is converted to a range-direction time domain and an azimuth-direction Doppler domain, data of targets at corresponding distances are extracted, and the data extracted each time are combined in sequence; and for the data after range migration correction, azimuth pulse compression is carried out in a range time domain and an azimuth Doppler domain, and the data is converted to a two-dimensional time domain to obtain a sonar image.

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.

According to the foresight sonar target detection fusion method provided by the invention, the target detection performance in a foresight sonar image is improved through fusion of a model framework and multi-frame information; the method comprises the following steps: collecting a forward-looking sonar image sequence; image preprocessing including coordinate conversion, denoising and the like; a target detection model is constructed based on deep learning, and single-frame target detection is completed; carrying out detection result fusion on the single-frame image, wherein the detection result fusion comprises coordinate transformation, image slicing, distance matrix construction, fusion threshold setting and the like; performing target tracking and multi-frame fusion on different frames of images; outputting the processed target state information; the detection robustness is improved through a model framework, and detection fusion is performed by using multi-frame information, so that the false alarm phenomenon in forward-looking sonar target detection is reduced; experimental results show that the method can effectively improve the forward-looking sonar target detection performance and reduce the false alarm rate, and has practical application value.

The invention discloses a sonar image generation method fusing stable diffusion and style migration, and belongs to the field of underwater sonar image generation and image processing. The method comprises the following steps: firstly, making a data set for training a composite image model; thirdly, finely adjusting a large-scale stable diffusion model by using the manufactured data set, so that the large-scale stable diffusion model has the capability of synthesizing sonar images in a specific field; and finally, synthesizing a sonar image with specified content by using a style injection method in a reasoning stage by utilizing the generation capability of the fine tuning weight model. And quantitative and qualitative evaluation analysis is carried out on the synthesized image. According to the image generation method established by the invention, the generated image is closer to the performance of real sonar data, the diversity of the sonar data is remarkably improved, and richer and more reliable data support is provided for subsequent sonar image analysis and application.

The invention discloses an image sonar gain adaptive control method based on an FPGA. The method comprises the following steps: an upper computer sends a control message carrying a manual gain parameter to a sonar signal processing board based on an Ethernet UDP protocol so as to execute manual gain control; a codeword gain and voltage gain cooperative control architecture is provided; pID closed-loop feedback control is carried out based on beam pattern characteristics, and when a manual gain value changing message issued by the Ethernet is received, the adaptive gain compensation value and the PID historical state are cleared, and the manual gain code is switched to an instruction specified value. According to the invention, conflict-free switching between a self-adaptive mode and a manual mode can be realized, and in addition, the quality of sonar images and the stability of gain regulation and control are improved while large dynamic range and high-precision adjustment of time-varying gain are realized.

The invention discloses a sonar image underwater detection method and system based on hierarchical attention feature fusion, and the method comprises the steps: collecting the foresight sonar data of an underwater typical target, and making a detection training and testing data set of a sonar image; constructing a local and global attention processing module, a re-parameterization processing module and a hierarchical feature fusion network; constructing a target detection algorithm model based on YOLOv10, taking the feature fusion network as a neck network of the YOLOv10, and removing a deep feature map detection branch of the YOLOv10; and training a target detection algorithm model by using the manufactured data set, and inputting a foresight sonar image into the trained model for reasoning to obtain a detection result of the underwater typical target. According to the invention, the accuracy and robustness of an underwater target detection algorithm are 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 foresight sonar image enhancement, in particular to a foresight sonar image enhancement method based on feature space conversion and multi-frame fusion, and the method comprises the steps: mapping an original foresight sonar image from a low-quality pixel space to a feature space which is more stable to noise and is more consistent with a heterogeneous image through a feature space conversion module; the forward-looking sonar data self-supervising method comprises the following steps: constructing a self-supervising model to reduce a field gap between a sonar image and a heterogeneous image, so that the self-supervising model more effectively adapts to forward-looking sonar data, the modeling capability of the self-supervising model for unknown degradation factors is enhanced, and consistent input is provided for a multi-frame image fusion enhancement network; complementary information among continuous multi-frame images output by the feature space conversion module is mined and integrated through a multi-frame image fusion enhancement network, fine reconstruction of image details and improvement of image brightness are achieved, and noise in the images is eliminated; according to the technical scheme provided by the invention, the defects that the image brightness is difficult to improve while the noise is suppressed and the high-frequency details of the target cannot be well reserved can be overcome.

The invention belongs to the technical field of image processing, and discloses a side-scan sonar image feature extraction method and system, and the method employs an LF-Net network to carry out the feature point extraction of two adjacent side-scan sonar images, and employs a KNN algorithm to carry out the matching of the feature points extracted by the LF-Net. According to the method, a transfer learning mode is introduced based on the deep learning network LF-Net to solve the sonar image matching problem, side-scan sonar image feature point detection is carried out by constructing the pre-training actual measurement data matching model, and the problem that the generalization ability is poor due to the fact that the number of side-scan sonar image data is small is solved; the KNN algorithm is combined to realize accurate matching of acoustic image feature points, the matching result is subjected to statistical analysis to reduce the influence of mismatching, and the matching robustness is improved. According to the method, two adjacent side-scan sonar images can be accurately matched without too many side-scan sonar image data, and a brand new solution thought is provided for underwater sonar image matching in a complex scene.

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 invention discloses an automatic detection method and system for a small target sonar image, and relates to the technical field of underwater target detection, and the method comprises the steps: collecting a small target sonar image with accurate target information; based on a YOLO11 model, a three-channel cross-dimensional interactive attention mechanism is added to the front end of a detection head, an improved YOLO11 model is generated, model training is carried out through the small target sonar image, a detection model is constructed, and the detection model is used for detecting the underwater small target through the sonar image. The automatic detection performance of the three-channel cross-dimensional interactive-YOLO11 model for the small-target side-scan sonar image is optimal in similar models, and the automatic detection requirement of the small-target sonar image can be met.

The invention discloses an underwater forward-looking sonar image target detection method and device, and the method comprises the steps: obtaining underwater forward-looking sonar data, carrying out the target detection of a current frame of forward-looking sonar image of the underwater forward-looking sonar data, and obtaining a first target detection result; based on the current frame of forward-looking sonar image, predicting a next frame of forward-looking sonar image of the current frame of forward-looking sonar image by a Kalman filter to obtain a prediction detection result; acquiring a next frame of forward-looking sonar image of the current frame of forward-looking sonar image as the current frame of forward-looking sonar image, and performing target detection on the current frame of forward-looking sonar image to obtain a second target detection result; and matching the prediction detection result with the second target detection result, and determining the trajectory of the target according to the matching result and the first target detection result. The method can effectively identify a shielded target or distinguish target tracks with similar features.

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.

The application belongs to the technical field of image processing, and discloses a side-scan sonar image feature extraction method and system, which extracts feature points of two adjacent side-scan sonar images by using an LF-Net network, and matches the feature points extracted by the LF-Net by using a KNN algorithm. The application introduces a transfer learning mode based on a deep learning network LF-Net to solve the sonar image matching problem, detects feature points of side-scan sonar images by constructing a pre-training measured data matching model, and solves the problem of poor generalization ability caused by the small amount of side-scan sonar image data. The application realizes accurate matching of sonar image feature points by combining the KNN algorithm, reduces the influence of false matching by statistically analyzing the matching results, and improves the robustness of matching. The application can accurately match two adjacent side-scan sonar images without too much side-scan sonar image data, and provides a new solution for underwater sonar image matching in complex scenes.

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 discloses a sonar target processing method based on heterogeneous filtering detection and level set segmentation; the method is as follows: 1. Image acquisition; 2. Denoising processing; 3. Superpixel image segmentation; 4. Heterogeneous filtering target detection; 5. Adaptive threshold processing; 6. Target fine segmentation; According to the imaging characteristics of side-scan sonar, the present invention adopts a step-by-step heterogeneous filtering method to process the image. On the one hand, it effectively removes the imaging effect of uneven intensity in the sonar image, and on the other hand, it effectively enhances the target bright area and dark area of the image, effectively improving the false alarm correct rate of target detection. The adaptive threshold processing in the present invention determines the threshold according to the local information characteristics of the image after heterogeneous filtering, directly performs region segmentation on the filtered image, and obtains the initial contour of the target. The fine segmentation based on level set in the present invention uses the result of threshold processing as the initial contour, combines the superpixel boundary constraint, and drives the segmentation contour to the superpixel boundary to obtain an accurate target contour.

The present invention discloses a method and system for detecting underwater small targets in sonar images under complex backgrounds. The method comprises: denoising the collected sonar images using a multi-level median filtering method; performing complex terrain masking on the denoised sonar images to extract highlight texture maps and shadow texture maps of the terrain; performing high-order moment image segmentation on the denoised sonar images, and combining the complex terrain masking results to perform background suppression; clustering the processed connected domains using three different-sized detection windows to associate split targets and suppress ridge terrain; extracting highlight areas and shadow areas, calculating highlight pixel density and shadow pixel density respectively, and obtaining a detection score through pixel density weighted calculation to detect small targets. The method is not restricted by seabed terrain and adaptively adjusts the threshold according to the required false alarm rate and actual acoustic image intensity. It has the characteristics of high accuracy, low false alarm rate, and strong adaptability.

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 invention relates to a forward-looking sonar image denoising method based on guiding attention. The invention relates to the technical field of image processing, and the method comprises the steps: collecting a foresight sonar image, extracting uniform noise blocks and non-uniform noise blocks from the image, and constructing a sonar image data set; the uniform noise blocks and the non-uniform noise blocks are used for training a denoising model, so that the model learns a mapping relation from the non-uniform noise blocks to the uniform noise blocks; a sonar image is used as input, a denoising model is used for generating a coarse noise graph, the noise graph is refined through an adjustment factor to obtain a fine noise graph, and the sonar image and the fine noise graph are subtracted to obtain a denoised image. According to the invention, the sonar image is directly used to train the deep learning model, so that the denoising model can accurately learn the mapping relation between the sonar image and the clean image. The model is guided to estimate noise mapping by guiding attention, so that the model can keep target details in the image to the maximum extent.

The invention discloses an underwater target detection method fusing lightweight multi-scale and flexible attention, and relates to the technical field of seabed side-scan sonar image processing. According to the method, the self-developed multi-scale feature extraction block is used for providing a rich receptive field for the model; a soft channel attention module is utilized to enable the network to be easily focused on an important channel of a feature map; the bottleneck structure is fused, and the feature channel is compressed in the middle and rear part of the network to reduce the scale of the network; and meanwhile, direct use of an upper sampling method, a lower sampling method or a pooling method is avoided, and a multi-scale convolution kernel and a Softmax function are used for carrying out dimension reduction and fusion on the features, so that the extracted features are prevented from being damaged.

The invention discloses a sonar image imaging method, system and device based on a two-step interpolation method. The sonar image imaging method comprises the following steps: calculating a two-dimensional frequency domain system function; second-order approximation is carried out on the distance-direction instantaneous frequency, and a high-order phase approximation error is calculated; for the approximated function, calculating the maximum range migration of the scene and the multiple # imgabs0 # to be interpolated in the subsequent steps, and performing first zero padding on the front end and the rear end of the sonar data in a range direction time domain; performing high-order phase error compensation and range-direction matched filtering in a two-dimensional frequency domain for the zero-filled data; calculating the total number # imgabs 1 # of the range-direction frequency domain data, supplementing # imgabs 2 # zero values to the rear end of the range-direction frequency domain data, and converting the range-direction frequency domain data to a range-direction time domain; aiming at the data of the distance-Doppler domain, according to the distance migration amount corresponding to each distance target, adopting a rounding-off interpolation method to carry out distance migration correction; and carrying out azimuth matched filtering in the distance-Doppler domain and converting to a two-dimensional time domain to obtain a high-resolution image.

The invention provides a side-scan sonar target detection method and device. The method provided by the invention comprises the steps of constructing a training sample set, and training a target detection model based on the training sample set; the target detection model comprises a backbone network, a neck network and a detection network, the backbone network is used for extracting features from an image, the convolution module is used for extracting a feature map of the image, the attention mechanism module generates a first feature map carrying weight information according to an attention value, and the neck network is used for enhancing the first feature map to obtain a second feature map; the detection network is used for detecting the second feature map to obtain a detection result; and performing target detection on a to-be-detected sonar image by using the trained target detection model to obtain a target detection result. The side-scan sonar target detection method and device provided by the invention are used for solving the problem that a traditional target detection method is poor in detection precision and robustness when detecting a side-scan sonar image.

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.

A method for accurately segmenting submarine cables from side-scan sonar images comprises the following steps: inputting an image I1, processing the image I1 using a cable feature automatic extraction filter based on a curvelet transform, extracting cable-related information, and obtaining a feature image I2; pixel-by-pixel multiplying the image I1 by the feature image I2 to obtain a feature image I3; using a Hough transform to find no less than three straight line segments in the feature image I3, recording the angle α of the longest line segment in the image, constructing linear structural units in the same direction as the cables in the image, performing noise elimination and binarization processing on the feature image I3 to obtain a binary image I4; performing cable morphology restoration on the binary image I4 to obtain a cable morphology restoration image I5, and retaining only the largest connected area in the image as the cable segmentation result for the submarine side-scan sonar image. The method solves the problems in side-scan sonar image segmentation that a large amount of data is required for training, high image clarity requirements are required, target color information needs to be obtained, and high image size requirements are required.

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.