149 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 provides a feature weight adaptive super-pixel segmentation method based on a sonar image. The method comprises the following steps: Step 1, converting an original sonar image into a grayscale image; step 2, denoising processing is carried out on the image by using guide filtering; step 3, extracting specific features of the sonar image; step 4, carrying out superpixel segmentation preprocessing of automatic adjustment of feature weights; step 5, carrying out maximum flow-minimum cut superpixel aggregation; step 6, carrying out aggregation on the super pixels through a preset threshold value; and Step 7, separating the target from the background to obtain a segmentation result. According to the method, a segmentation technology suitable for the target object area of the acoustic image can be realized, and a better image foundation is laid for subsequent target object disease type judgment and volume and area measurement through analysis of the segmented area, so that segmentation is more accurate, the type of the target object is easier to judge, and the size of the disease is more accurately measured.

The invention belongs to the technical field of sonar image processing, and relates to a target identification method based on sonar image segmentation and a computing device.The target identification method based on sonar image segmentation comprises the following steps that 1) a data set of existing sonar images is acquired; 2) building a target identification model composed of an encoder and a decoder; 3) inputting the data set into the target recognition model from the encoder, and performing initial training on the target recognition model; 4) performing sample augmentation on categories with a small number of samples in the data set to obtain an augmented feature domain data set; 5) using the augmented feature domain data set to carry out fine tuning training on the target identification model; and 6) after the target identification model is finely adjusted and trained, inputting a newly obtained sonar image into the target identification model to obtain a segmentation image of the target. The method can achieve the accurate recognition of a target.

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 belongs to the technical field of sonar image small target detection, and particularly relates to a foresight sonar image small target detection and recognition algorithm. Comprising the following steps: step 1, inputting sonar beam domain data; 2, detecting the position of a small target through a constant false alarm algorithm; step 3, extracting target HOG features of the sonar image through an HOG algorithm; and 4, identifying the type of the detected target through an SVM algorithm, and carrying out image classification. The algorithm is high in operation efficiency, good in timeliness and good in detection and recognition capability.

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 discloses a CML-sinGAN-based side-scan sonar underwater target image sample amplification method, and belongs to the technical field of side-scan sonar image target recognition and amplification and deep learning. The method specifically comprises the steps that firstly, an eight-layer-scale pyramid network structure is designed according to the characteristics of a side-scan sonar image, and the diversity of a generated image is improved while image textures are fully learned; secondly, fusion is carried out in a generator, and information dispersion is reduced while target learning is enhanced; then, a loss function based on L1loss is introduced into the discriminator, the discrimination capability of the discriminator on the authenticity of the target is enhanced, and unreal false pictures are removed; and finally, based on the conversion model, carrying out high-quality amplification on the existing small sample side-scan sonar image, and carrying out YOLOv7 identification detection by using the amplified image. The method provides a new way for side-scan sonar small target sample amplification and a high-performance underwater target detection model.

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.

A system comprising: at least one hardware processor; and one or more software modules are configured to, when executed by the at least one hardware processor, receive a selection of a mission marker; display a diver's position, heading, depth and course history for the selected mission marker; and interface with a multibeam imaging sonar that uses a 12 degree aperture on a 750 k frequency band to improve the resolution and clarity of the sonar image, as the narrow beam width allows for more precise targeting and detection of objects.

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.

The invention discloses a sonar image super-resolution reconstruction method and system based on a generative adversarial network, and the method comprises the steps: constructing the generative adversarial network which comprises a generator and a discriminator; acquiring a first sonar image data set, and preprocessing the first sonar image data set to obtain a second sonar image data set; inputting the second sonar image data set into the generative adversarial network for training, and performing parameter updating on the generative adversarial network through a preset total loss function to obtain a trained super-resolution reconstruction model; and obtaining a sonar image to be detected, and inputting the sonar image to be detected into the super-resolution reconstruction model to obtain a super-resolution reconstruction result. The method can accurately capture the detail features of the underwater sonar image, further recovers the real texture details of the underwater sonar image, and can be widely applied to the technical field of sonar image reconstruction.

The invention discloses a weak target detection method and system for a sonar image and a computing device, and relates to the technical field of sonar image recognition, and the weak target detection method comprises the steps: carrying out the Gamma modeling of a smoothed sonar image; designing a constant false alarm detection window for detecting the modeled sonar image; carrying out binarization processing on the detected sonar image to estimate a background; the binary images are clustered; after an object conforming to brightness characteristics in a sonar image is extracted, whether the object is a real moving target or a false target is judged. Through the above steps, in a complex and changeable environment, the weak target detection method can also provide robustness detection, and the weak target in the sonar image can be accurately and efficiently detected.

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 invention discloses a side-scan sonar image submarine cable zero sample rapid detection method, and belongs to the technical field of submarine cable detection, and the method comprises the following steps: 1, building a neural network model; step 2, data fusion enhancement: carrying out data fusion enhancement on the existing public side-scan sonar image data to generate a data set for an experiment; step 3, data set labeling: labeling the data set; 4, model training: training the neural network; step 5, model testing: testing the trained network model by using actual measurement data; according to the method, the neural network model is established, data fusion enhancement is performed on the existing open side-scan sonar image data, the data set for experiment is generated, the data set is labeled, the neural network is trained, and the trained network model is tested by using the actually measured data. And zero sample training and rapid detection of the neural network are realized.

The invention relates to a high-noise side-scan sonar image super-resolution processing technology and device based on a diffusion model, and the method comprises the steps: S1, constructing a sonar image super-resolution processing model based on the diffusion model, and the sonar image super-resolution processing model comprises an encoder and a decoder; the method comprises the following steps: carrying out unified size adjustment, normalization processing and specific channel extraction on a sonar side-scan image to generate training input of a codec; the method comprises the following steps: defining and optimizing a loss function of a codec; s2, generating a degraded image through preprocessing and downsampling of a sonar side-scan image, placing the degraded image into a trained encoder to generate potential representation and condition information, further training a UNet model in combination with a diffusion model, and improving noise prediction precision through a gradient descent method; s3, inputting the degraded image xlow and the water column region information c into an encoder tau to generate condition information d, and performing reverse diffusion operation by using a UNet network in combination with a randomly generated initial noise state zT; according to the method, the resolution and the quality of the sonar image are effectively improved by simulating the process of gradually recovering from a seriously degraded image to a high-quality image, and the method has the advantages that the method is high in resolution and quality, high in robustness and high in robustness, and the method is suitable for being applied to the field of sonar imaging and can be widely applied to the field of sonar imaging. The problem of poor image quality caused by high noise and low resolution of sonar images is solved, and the accuracy and effectiveness of subsequent image analysis are remarkably improved.

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.