Sonar Imaging System Research Articles

The accuracy of length measurements made using imaging SONAR is inversely proportional to the beamwidth

Abstract Multibeam imaging SONARs have been used for a range of measurement applications, such as measurements of fish lengths. This study aimed to quantify the accuracy of imaging SONAR systems, that varied in frequency and beam geometry, to measure the length of synthetic targets positioned perpendicularly. Blueprint Oculus imaging SONAR systems, with four different (centre) frequencies (750 kHz, 1.2 MHz, 2.1 MHz and 3 MHz), were used to measure the length of three targets of nominal lengths: 10 cm, 20 cm and at ranges between 1 and 15.5 m. The effect of beam geometry on measurement error was then examined using regression analysis. This study found that there was an overestimation of the actual length of the target for all measurements that was inversely proportional to the horizontal beamwidth. The measurement error can be reduced by normalising for beamwidth. However, the variation of measurements (i.e. the precision) was found to also increase with range, which was attributed to the increasing beam separation. It is important that the effect of beamwidth on the accuracy of target length measurements, by imaging SONARs is acknowledged in future studies. One approach to mitigating this problem, is to limit the range at which length measurements are made to a beamwidth that produces an estimated level of error that is acceptable for that study.

Transfer Learning and Few-Shot Learning Based Deep Neural Network Models for Underwater Sonar Image Classification With a Few Samples

Acoustic imaging sonar systems are widely used for long-range underwater surveillance in various civilian and military applications. They provide 2-D images of underwater objects, even in turbid water conditions where optical underwater imaging systems fail. Achieving high accuracy in automatic deep learning based underwater image classification remains an open problem due to insufficient data availability, poor image resolution, low signal-to-noise ratio surroundings, etc. In this study, we conduct a comparative analysis of different advanced deep learning approaches, i.e., transfer learning and few-shot learning, to address the problem of automatic object classification in sonar images, using a few samples of data. Specifically, two metric learning-based approaches, i.e., siamese network and triplet network as well as library-based approaches, are studied under the few-shot learning paradigm. Extensive experiments are conducted on a novel custom-made dataset developed in-house, along with the publicly available SeabedObjectsKLSG dataset. In addition, the effectiveness of the sampling technique in handling class imbalance during model training is also investigated in this work. Our experimental results highlight that the few-shot learning based approach is a promising direction for future research on underwater image classification with a few samples.

Taming the data deluge: A novel end‐to‐end deep learning system for classifying marine biological and environmental images

AbstractUnderwater imaging enables nondestructive plankton sampling at frequencies, durations, and resolutions unattainable by traditional methods. These systems necessitate automated processes to identify organisms efficiently. Early underwater image processing used a standard approach: binarizing images to segment targets, then integrating deep learning models for classification. While intuitive, this infrastructure has limitations in handling high concentrations of biotic and abiotic particles, rapid changes in dominant taxa, and highly variable target sizes. To address these challenges, we introduce a new framework that starts with a scene classifier to capture large within‐image variation, such as disparities in the layout of particles and dominant taxa. After scene classification, scene‐specific Mask regional convolutional neural network (Mask R‐CNN) models are trained to separate target objects into different groups. The procedure allows information to be extracted from different image types, while minimizing potential bias for commonly occurring features. Using in situ coastal plankton images, we compared the scene‐specific models to the Mask R‐CNN model encompassing all scene categories as a single full model. Results showed that the scene‐specific approach outperformed the full model by achieving a 20% accuracy improvement in complex noisy images. The full model yielded counts that were up to 78% lower than those enumerated by the scene‐specific model for some small‐sized plankton groups. We further tested the framework on images from a benthic video camera and an imaging sonar system with good results. The integration of scene classification, which groups similar images together, can improve the accuracy of detection and classification for complex marine biological images.

An imaging algorithm for high-resolution imaging sonar system

An imaging algorithm for high-resolution imaging sonar system

Designing a Geodesic Faceted Acoustical Volumetric Array Using a Novel Analytical Method

We present a novel analytical method as an efficient approach to design a geodesic-faceted array (GFA) for achieving a beam performance equivalent to that of a typical spherical array (SA). GFA is a triangle-based quasi-spherical configuration, which is conventionally created using the icosahedron method imitated from the geodesic dome roof construction process. In this conventional approach, the geodesic triangles have nonuniform geometries due to some distortions that occur during the random icosahedron division process. In this study, we took a paradigm shift from this approach and adopt a new technique to design a GFA that is based on uniform triangles. The characteristic equations that relate the geodesic triangle with a spherical platform were first developed as functions of the operating frequency and geometric parameters of the array. Then, the directional factor was derived to calculate the beam pattern associated with the array. A sample design of GFA for a given underwater sonar imaging system was synthesized through an optimization process. The GFA design was compared with that of a typical SA, and a reduction of 16.5% in the number of array elements was recorded in the GFA at a nearly equivalent performance. Both arrays were modeled, simulated, and analyzed using the finite element method (FEM) to validate the theoretical designs. Comparison of the results showed a high degree of compliance between the FEM and the theoretical method for both arrays. The proposed novel approach is faster and requires fewer computer resources than the FEM. Moreover, this approach is more flexible than the traditional icosahedron method in adjusting geometrical parameters in response to desired performance outputs.

Use of multibeam imaging sonar for observation of marine mammals and fish on a marine renewable energy site.

Environmental data is crucial for planning, permitting, execution and post construction monitoring of marine renewable energy projects. In harsh conditions in which marine renewable energy is harvested, integrated monitoring platforms comprising multibeam imaging sonar systems coupled with other sensors can provide multiparametric data of the marine environment surrounding marine renewable energy installations. The aim of this study was to test the possibilities of observing the occurrence of fish and marine mammals using a multibeam imaging sonar system deployed at a wave power test site. The results obtained from a ten-day data set proved the platform as suitable for long time underwater monitoring and also revealed that the occurrence of fish and marine mammals was distributed across characteristic time and space domains. Large fish [>0.4 m] frequently occurred at night-time and near the benthic zone. Small fish [<0.2 m] frequently occurred during daylight and within the pelagic zone. The occurrence of seals was periodically distributed along a daily cycle, with intervals of 1-2 hours between maxima and minima. In conclusion, the use of multibeam imaging sonar can be a reliable technique for the qualitative and quantitative observations of fish and marine mammals in general and at marine renewable energy sites specifically, including protected and economically important species.

Imaging sonar performance estimation

Imaging sonar systems including forward looking sonars, synthetic aperture sonars, and real aperture sonars produce large quantities of data and are often connected to automated target recognition (ATR) algorithms. Performance of these algorithms degrades, sometimes significantly, with changes in environmental reverberation, seafloor sediment characteristics, and target characteristics. However, most ATR algorithms have no capability to assess their own performance, flag data that is invalid, or convey when their results should not be trusted or utilized by humans or autonomous algorithms. Performance estimation algorithms can be used in conjunction with ATR to supplement these shortcomings. Estimates of signal-to-noise ratio, image contrast, and resolution can be used to assess deviation from nominal performance capabilities, while regressing ATR performance against imagery characteristics or other through-the-sensor metrics can be used to estimate false alarm rate.

Attentive SOLO for Sonar Target Segmentation

Imaging sonar systems play an important role in underwater target detection and location. Due to the influence of reverberation noise on imaging sonar systems, the task of sonar target segmentation is a challenging problem. In order to segment different types of targets in sonar images accurately, we proposed the gated fusion-pyramid segmentation attention (GF-PSA) module. Specifically, inspired by gated full fusion, we improved the pyramid segmentation attention (PSA) module by using gated fusion to reduce the noise interference during feature fusion and improve segmentation accuracy. Then, we improved the SOLOv2 (Segmenting Objects by Locations v2) algorithm with the proposed GF-PSA and named the improved algorithm Attentive SOLO. In addition, we constructed a sonar target segmentation dataset, named STSD, which contains 4000 real sonar images, covering eight object categories with a total of 7077 target annotations. The experimental results show that the segmentation accuracy of Attentive SOLO on STSD is as high as 74.1%, which is 3.7% higher than that of SOLOv2.

The Study of Selecting a Test Area for Validating the Proposal Specification of InSAS(Interferometric Synthetic Aperture Sonar)

This paper provides a case study of development testing and evaluation of design goal of Interferometric SAS (Synthetic Aperture Sonar) system that is developing supported by Civil-Military Technology Cooperation Center in offshore fields. For Deep water operating capabilities evaluation, We have surveyed candidate field, bathymetric mapping and target identification over 200 m depth, East Sea. In testing phase, We have provided environmental information of testing field include water column, seabed and weather condition in real time. And to compare excellency of developing InSAS, we have gather commercial imaging sonar system data with same target. This case study will support the Test Readiness Review of future underwater surveillance system developing via investigate marine testing field environment, testing facilities and planning.

Gain and Phase Calibration of Uniform Rectangular Arrays Based on Convex Optimization and Neural Networks

A calibration method based on convex optimization (CVX) and neural networks is proposed for the large planar arrays of phased array three-dimensional imaging sonar systems. The method only needs an acoustic calibration source at an unknown position in the far field, and the direction of arrival (DOA) and gain and phase error are jointly estimated. The method uses a CVX algorithm to solve an optimization problem and initially estimates the DOA of the calibration source robustly. Subsequently, according to the estimation results, a neural network is used for fitting to obtain off-grid DOA estimation of the calibration source. Thereafter, spatial matched filtering is performed to obtain the gain and phase residual estimations. The root mean square error (RMSE) of the beam pattern calibrated by the method for uniform planar arrays can reach a value of 4.9542 × 10−5. The experimental results demonstrate the efficiency of the proposed method for gain and phase calibration.

Underwater Terrain Image Stitching Based on Spatial Gradient Feature Block

At present, underwater terrain images are all strip-shaped small fragment images preprocessed by the side-scan sonar imaging system. However, the processed underwater terrain images have inconspicuous and few feature points. In order to better realize the stitching of underwater terrain images and solve the problems of slow traditional image stitching speed, we proposed an improved algorithm for underwater terrain image stitching based on spatial gradient feature block. First, the spatial gradient fuzzy C-Means algorithm is used to divide the underwater terrain image into feature blocks with the fusion of spatial gradient information. The accelerated-KAZE (A-KAZE) algorithm is used to combine the feature block information to match the reference image and the target image. Then, the random sample consensus (RANSAC) is applied to optimize the matching results. Finally, image fusion is performed with the global homography and the optimal seam-line method to improve the accuracy of image overlay fusion. The experimental results show that the proposed method in this paper effectively divides images into feature blocks by combining spatial information and gradient information, which not only solves the problem of stitching failure of underwater terrain images due to unobvious features, and further reduces the sensitivity to noise, but also effectively reduces the iterative calculation in the feature point matching process of the traditional method, and improves the stitching speed. Ghosting and shape warping are significantly eliminated by re-optimizing the overlap of the image.

Fish Segmentation in Sonar Images by Mask R-CNN on Feature Maps of Conditional Random Fields.

Imaging sonar systems are widely used for monitoring fish behavior in turbid or low ambient light waters. For analyzing fish behavior in sonar images, fish segmentation is often required. In this paper, Mask R-CNN is adopted for segmenting fish in sonar images. Sonar images acquired from different shallow waters can be quite different in the contrast between fish and the background. That difference can make Mask R-CNN trained on examples collected from one fish farm ineffective to fish segmentation for the other fish farms. In this paper, a preprocessing convolutional neural network (PreCNN) is proposed to provide “standardized” feature maps for Mask R-CNN and to ease applying Mask R-CNN trained for one fish farm to the others. PreCNN aims at decoupling learning of fish instances from learning of fish-cultured environments. PreCNN is a semantic segmentation network and integrated with conditional random fields. PreCNN can utilize successive sonar images and can be trained by semi-supervised learning to make use of unlabeled information. Experimental results have shown that Mask R-CNN on the output of PreCNN is more accurate than Mask R-CNN directly on sonar images. Applying Mask R-CNN plus PreCNN trained for one fish farm to new fish farms is also more effective.

Research on the module of color image sonar system based on imx6ull

Underwater sonar imaging system has a wide range of application prospects, and both military and civil are increasingly valued. This paper introduces the module design of a color image sonar system based on IMX6ULL and completes the imaging of the sonar system through experiments. The main frequency and sampling frequency of the system are improved and applied to the TKIS-I color image sonar digital system. High-performance A / D is used for data acquisition and storage, and RS485 is used for long-distance transmission to the host computer to realize display and imaging. It is mainly composed of a core board, PWM signal module, communication module, sampling module, and motor drive module. The processing speed and imaging effect of the improved system are significantly improved.

Comparison of multi-beam bathymetric system and 3D sonar system in underwater detection of beach obstacles

The instrument detection methods of underwater part of beach obstacle mainly include multi-beam bathymetric system and three-dimensional panoramic imaging sonar system. SeaBat T50-P multi beam bathymetry and BV5000-1350 sonar system are widely used in the area of underwater detection. The underwater part of beach obstacles is measured and analyzed, and the application scope of both equipment is discussed. The results show that the BV5000-1350 sonar system can be used to detect underwater incomplete obstacles with wider application scope, high accuracy, convenient installation of instruments, and no auxiliary equipment such as navigation, positioning and attitude sensor is needed; SeaBat T50-P multi-beam bathymetric system has high requirements for the structure of the measured object, moderate accuracy, and is more suitable for large-scale underwater obstacle survey.

The Influence of a Deep‐Water Intrusion on the Distribution of Chrysaora melanaster in the Southeastern Bering Sea

AbstractScyphozoan jellyfish are conspicuous components of marine ecosystems, which during a bloom, can impact food web structure and economically important fisheries. Jellyfish biomass in the southeastern Bering Sea (SEBS) is primarily composed of Chrysaora melanaster and has varied widely over the past four decades, yet the underlying causes of these biomass fluctuations remain unclear. The present study investigated the spatial and seasonal dynamics of C. melanaster along the Alaska Peninsula in the SEBS using an adaptive resolution imaging sonar system and nets in June–July and September 2018. The abundance of C. melanaster was high in coastal waters near the Alaska Peninsula, with peak densities occurring east of Unimak Island in both sampling periods. The current pattern revealed by an acoustic Doppler current profiler showed that cold, deep water from Bering Canyon flowed onto the shelf east of Unimak Pass. Differences in the strength of this cross‐isobath, deep‐water intrusion were observed during the two sampling periods. This flow may have influenced the distribution of C. melanaster by constraining the mixed coastal water near of Unimak Island in June‐July and by transporting medusae westward, into deeper water in September, after the deep‐water intrusion weakened. The present study provides a new mechanistic understanding of how regional‐scale ocean variability in the SEBS affects C. melanaster distribution.

Optimization of Sparse Cross Array Synthesis via Perturbed Convex Optimization.

Three-dimensional (3-D) imaging sonar systems require large planar arrays, which incur hardware costs. In contrast, a cross array consisting of two perpendicular linear arrays can also support 3-D imaging while dramatically reducing the number of sensors. Moreover, the use of an aperiodic sparse array can further reduce the number of sensors efficiently. In this paper, an optimized method for sparse cross array synthesis is proposed. First, the beamforming of a cross array based on a multi-frequency algorithm is simplified for both near-field and far-field. Next, a perturbed convex optimization algorithm is proposed for sparse cross array synthesis. The method based on convex optimization utilizes a first-order Taylor expansion to create position perturbations that can optimize the beam pattern and minimize the number of active sensors. Finally, a cross array with 100 + 100 sensors is employed from which a sparse cross array with 45 + 45 sensors is obtained via the proposed method. The experimental results show that the proposed method is more effective than existing methods for obtaining optimum results for sparse cross array synthesis in both the near-field and far-field.

Optimized Design for Sparse Arrays in 3-D Imaging Sonar Systems Based on Perturbed Bayesian Compressive Sensing

Sparse planar array designs significantly reduce the hardware complexity and computational overhead in phased array 3-D imaging sonar systems. Recently, Bayesian compressive sensing (BCS) theory has been applied for synthesizing maximally sparse arrays, in which case, the problem is formulated as a pattern matching technique in a probabilistic fashion. Thus, in this study, a perturbed BCS-based method with a minimum inter-element spacing constraint is proposed. In particular, a modified BCS technique is first employed to synthesize the primary sparse array. Then, the position perturbations of the elements are determined via the first-order Taylor expansion to increase their degrees of freedom (DOFs). The position perturbations allow for continuous element arrangement, which compensates for the imperfection of discrete candidate sample locations. After that, an improved element merging technique combined with convex optimization was adopted to establish constraints on element spacing while mitigating beam pattern degradation. In practice, this is a feasible approach, which merges elements close to each other, leading to a sparser array with a minimum inter-element spacing. Our numerical results confirm the validity of the proposed method in matching accuracy, array sparsity, and constraining the minimum spacing between elements.

Sparse Reconstruction-Based Inverse Scattering Imaging in a Shallow Water Environment

Bistatic sonar or multistatic sonar system can collect more scattering information of targets than a monostatic sonar system. In this paper, sparse learning via iterative minimization method (SLIM) is introduced to distinguish wave components for time-domain (TD) back-propagation (BP) inverse scattering imaging improvement. Unlike the prevailing high central frequency (>100 kHz) and wideband imaging sonar systems, a relatively low-frequency band (1–10 kHz) is considered here. Due to the low sidelobe output of SLIM, the investigated object’s surface in TD-BP image is much clearer in an ideal two-dimensional free field case. Furthermore, when the environmental information is known, this sparse reconstruction-based channel deconvolution method can be implemented to recognize, categorize the main propagating paths and then rectify their time of arrivals. Compared with the phase conjugation-based channel deconvolution method, the proposed approach’s results have fewer sidelobes and higher signal-to-background ratio in the simulation.

Distributed Convex Optimization Compressed Sensing Method for Sparse Planar Array Synthesis in 3-D Imaging Sonar Systems

Synthesis of sparse planar arrays can effectively reduce hardware costs and computational complexity in phased array 3-D imaging sonar systems. Traditional stochastic methods, such as simulated annealing, require multiple experiments and parameter adjustments to obtain optimal results. Methods based on compressed sensing (CS) can overcome this defect. However, when applied to large arrays, CS methods require vast computational complexity and may not obtain optimal sparse results because of violating the restricted isometry property. To make CS methods more practical, a distributed convex optimization CS method is proposed here for the sparse planar array synthesis in 3-D imaging sonar systems. This method is based on the CS theory, solving the minimum number of active elements under certain beam pattern constraints using the iterative reweighted l 1-norm minimization algorithm. Then, a multistage distributed framework is proposed to decompose the array into multistage subarrays, and the array synthesis is performed sequentially for each stage subarray to reduce computational complexity and obtain higher sparsity rates. Some applications of sparse planar array synthesis are employed to evaluate the efficiency of the proposed method.

Detection of Visual Signatures of Marine Mammals and Fish within Marine Renewable Energy Farms using Multibeam Imaging Sonar

Techniques for marine monitoring have been greatly evolved over the past decades, making the acquisition of environmental data safer, more reliable and more efficient. On the other hand, the marine renewable energy sector has introduced dissimilar ways of exploring the oceans. Marine energy is mostly harvested in murky and high energetic places where conventional data acquisition techniques are impractical. This new frontier on marine operations brings the need for finding new techniques for environmental data acquisition, processing and analysis. Modern sonar systems, operating at high frequencies, can acquire detailed images of the underwater environment. Variables such as occurrence, size, class and behavior of a variety of aquatic species of fish, birds, and mammals that coexist within marine energy sites can be monitored using imaging sonar systems. Although sonar images can provide high levels of detail, in most of the cases they are still difficult to decipher. In order to facilitate the classification of targets using sonar images, this study introduces a framework of extracting visual features of marine animals that would serve as unique signatures. The acoustic visibility measure (AVM) is here introduced as technique of identification and classification of targets by comparing the observed size with a standard value. This information can be used to instruct algorithms and protocols in order to automate the identification and classification of underwater targets using imaging sonar systems. Using image processing algorithms embedded in Proviwer4 and FIJI software, this study found that acoustic images can be effectively used to classify cod, harbour and grey seals, and orcas through their size, shape and swimming behavior. The sonar images showed that cod occurred as bright, 0.9 m long, ellipsoidal targets shoaling in groups. Harbour seals occurred as bright torpedo-like fast moving targets, whereas grey seals occurred as bulky-ellipsoidal targets with serpentine movements. Orca or larger marine mammals occurred with relatively low visibility on the acoustic images compared to their body size, which measured between 4 m and 7 m. This framework provide a new window of performing qualitative and quantitative observations of underwater targets, and with further improvements, this method can be useful for environmental studies within marine renewable energy farms and for other purposes.