Side-scan Sonar Research Articles

Moats & comets - A methodology to explore for submerged prehistory at sea floor erosional features formed at artificial reefs, Atlantic continental shelf, GeorgiaUSA.

Our primary objective is to demonstrate a method that uses marine processes, principally seafloor scour, to broadly expose prehistoric landforms and associated localities. Too often, the loss of prehistoric strata to scour processes such as ravinement during broad-scale, continental shelf transgressions, are decried without the recognition of the potential for erosion at the local scale can expose those same strata for systematic study. The analysis of sea floor erosional features formed around artificial reefs by turbulent flows in the Georgia Bight have used interferometric side-scan sonar (SSS), Remotely Operated Vehicles (ROVs), ultra-high-definition videography (UHD), and sedimentological, palynological, paleontological, archaeological analytical techniques. These techniques were integrated within a geoarchaeological methodology focused on exploring the Continental Shelf for preserved evidence of prehistoric landforms, flora and fauna once thereon. A stepwise protocol was developed to first gather interferometric side-scan sonar data to be then processed by commercial software packages such as HYPACK Hydrographic and HYSWEEP together with HYSCAN and SONAR Wiz software. The sonar mosaics were imported to ArcGIS/QGIS software to characterize scour features/targets around artificial reefs that had exposed paleo-land surfaces previously obscured by the modern palimpsest sand sheet. Analysis of scours and depressions - “moats and comets” - adjacent to reef structures were evaluated using the profile tool within the HYPACK-HYSWEEP editor package. Symbology for the bathymetric raster layers was edited to provide a clear contrast and measurement at scour features. Measured GIS images were then classified as to scour extent and depth with respect to prevailing current directions responsible for producing turbulence. A ranking of the artificial reefs by factors such as scour depth and extent was used to guide subsequent investigation by ROVs. Sediment cores and grab samples were collected, and those samples underwent particle size analysis (PSA), macro-and micro-botanical analyses. All data – geophysical, GIS, video and geological – were collated and synthesized to produce an assessment of the potential of sea floor scour features to expose previously buried geomorphological/geological, paleontological and/or archaeological features of interest.

A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference

Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.

Object Detection in Side Scan Sonar Images using Local Binary Pattern features

Object Detection in Side Scan Sonar Images using Local Binary Pattern features

Side-Scan Sonar Image Matching Method Based on Topology Representation

In the realm of underwater environment detection, achieving information matching stands as a pivotal step, forming an indispensable component for collaborative detection and research in areas such as distributed mapping. Nevertheless, the progress in studying the matching of underwater side-scan sonar images has been hindered by challenges including low image quality, intricate features, and susceptibility to distortion in commonly used side-scan sonar images. This article presents a comprehensive overview of the advancements in underwater sonar image processing. Building upon the novel SchemaNet image topological structure extraction model, we introduce a feature matching model grounded in side-scan sonar images. The proposed approach employs a semantic segmentation network as a teacher model to distill the DeiT model during training, extracting the attention matrix of intermediate layer outputs. This emulates SchemaNet’s transformation method, enabling the acquisition of high-dimensional topological structure features from the image. Subsequently, utilizing a real side-scan sonar dataset and augmenting data, we formulate a matching dataset and train the model using a graph neural network. The resulting model demonstrates effective performance in side-scan sonar image matching tasks. These research findings bear significance for underwater detection and target recognition and can offer valuable insights and references for image processing in diverse domains.

Emerging underwater survey technologies: A review and future outlook

Emerging underwater survey technologies are revolutionizing the way we explore and understand the underwater world. This review examines the latest advancements in underwater survey equipment, highlighting their operational benefits and potential areas for future development. Recent developments in underwater survey technologies have led to significant improvements in accuracy, efficiency, and data quality. Advanced sonar systems, such as multibeam and sidescan sonars, provide high-resolution images of the seafloor, allowing for detailed mapping of underwater features. Autonomous underwater vehicles (AUVs) equipped with sophisticated sensors and cameras enable precise data collection in challenging environments, such as deep-sea areas or complex underwater structures. The operational benefits of these technologies are vast. They allow for faster surveying, reduced costs, and improved safety for personnel. Additionally, the high-quality data obtained from these surveys can lead to better decision-making in various industries, including offshore energy, marine research, and underwater archaeology. Looking ahead, the future of underwater survey technologies is promising. There is a growing interest in developing integrated systems that combine multiple sensors and data processing capabilities to provide a more comprehensive view of underwater environments. Artificial intelligence and machine learning algorithms are also being increasingly utilized to analyze large datasets and extract valuable insights. However, several challenges remain, such as the need for better underwater communication systems, improved battery life for autonomous vehicles, and enhanced data processing capabilities. Addressing these challenges will be crucial for the continued advancement of underwater survey technologies. In conclusion, the latest advancements in underwater survey technologies offer exciting opportunities for enhancing our understanding of the underwater world. By leveraging these technologies and addressing key challenges, we can unlock new possibilities for underwater exploration and research.

Assessing the importance of sediment characterization on seabed embedment predictions of cylindrical objects

The prediction of object embedment into the seabed is important for the deployment and efficacy of submerged sensors and sensor networks, as well as for other applications such as unexploded ordnance risk assessment, subaquatic search and rescue efforts, or anchors and moorings. The embedment of objects into the seabed during placement depends on the strength parameters of the seabed sediment. However, a detailed characterization of seabed strength can be complex and costly since quality seabed samples and/or in situ testing are required. This study evaluates the importance of a detailed geotechnical seabed sediment characterization over seabed classification from geoacoustic surveying or accepting unknown seabed conditions for the prediction of seabed embedment of cylindrical objects. Monte Carlo simulation was applied to model the uncertainty of sediment strength parameters. The results suggest that prior knowledge of the general sediment type (e.g., fine-grained versus coarse-grained as determined from side scan sonar imaging) significantly improves the precision of the embedment predictions over unknown conditions, reducing the coefficient of variation (CV) in prediction of percent embedment (PE) by about 50 % to 65 %. Performing a detailed geotechnical testing of sediment strength represented a further improvement by 15 % to 35 % over the knowledge of sediment type. Both the mean and standard deviation of PE in fine-grained sediments are at least about 3 times the values for coarse-grained sediments indicating a wider distribution of PE with deeper embedment for cohesive fine-grained soils. The embedment behavior is a function of the method of deployment (free fall with a certain speed versus placement with no speed) in stiff cohesive sediments but is less relevant in soft sediments since PE values are always greater than 100 % (i.e., full embedment). Similarly, in cohesionless sediments, PE is always less than 50 %.

Density and abundance estimation of West Indian manatee, Trichechus manatus, between the states of Ceará and Piauí, Northeast Brazil, using active acoustics

The West Indian manatee is one of the most threatened species in Brazil. The species has currently a patchy distribution from the state of Alagoas to the state of Amapá. The difficulty of observing manatees, especially in estuarine waters, is a challenge for conservation. Therefore, it is necessary to use new methodologies and technologies to solve manatee detection problems in their natural habitats. The goal of this study was to use an active acoustic method of detection to estimate manatee density and abundance in the estuarine complex of the Timonha and Ubatuba rivers, between the states of Ceará and Piauí, northeast Brazil. Data collection was conducted from a wooden boat with an outboard motor using a side-scan sonar. Sonar images were collected along line transects in three regions within the study area. Manatee abundance was calculated using distance sampling (Distance 6.0 software). There were 1,396 transection lines that totaled 863.6 km traveled in the study area. The observed general encounter rate was 0.089 group detected per kilometer traveled. The best detection model was the hazard-rate with no adjustment terms, resulting in a detection probability of 33.7%, and an estimated density for the entire study area of 9.19 manatees per km2. Abundance of manatees in the estuary was estimated to be 37 animals (CV% = 30.2, 95% CI = 21 - 66). The methodology using side-scan sonar proposed here was successful in the detection of manatees in the study area and in defining parameters for its use to estimate the population size using the distance sampling method. This study provides an estimate of manatee density and abundance in a key estuarine system along the northeast coast of Brazil, which can serve as a baseline for future studies and aid in the development of conservation strategies for the species. This is the first time this methodological approach has been used for manatee detection and abundance estimation in Brazil. We recommend the use of side-scan sonar in future West Indian manatee research.

Underwater small target detection based on dynamic convolution and attention mechanism

In ocean observation missions, unmanned autonomous ocean observation platforms play a crucial role, with precise target detection technology serving as a key support for the autonomous operation of unmanned platforms. Among various underwater sensing devices, side-scan sonar (SSS) has become a primary tool for wide-area underwater detection due to its extensive detection range. However, current research on target detection with SSS primarily focuses on large targets such as sunken ships and aircraft, lacking investigations into small targets. In this study, we collected data on underwater small targets using an unmanned boat equipped with SSS and proposed an enhancement method based on the YOLOv7 model for detecting small targets in SSS images. First, to obtain more accurate initial anchor boxes, we replaced the original k-means algorithm with the k-means++ algorithm. Next, we replaced ordinary convolution blocks in the backbone network with Omni-dimensional Dynamic Convolution (ODConv) to enhance the feature extraction capability for small targets. Subsequently, we inserted a Global Attention Mechanism (GAM) into the neck network to focus on global information and extract target features, effectively addressing the issue of sparse target features in SSS images. Finally, we mitigated the harmful gradients produced by low-quality annotated data by adopting Wise-IoU (WIoU) to improve the detection accuracy of small targets in SSS images. Through validation on the test set, the proposed method showed a significant improvement compared to the original YOLOv7, with increases of 5.05% and 2.51% in mAP@0.5 and mAP@0.5: 0.95 indicators, respectively. The proposed method demonstrated excellent performance in detecting small targets in SSS images and can be applied to the detection of underwater mines and small equipment, providing effective support for underwater small target detection tasks.

Multi-AUV coverage path planning algorithm using side-scan sonar for maritime search

Coverage search is widely applied in the maritime military and civilian fields. To improve search efficiency and detection accuracy, we propose an effective and rapid multi-autonomous underwater vehicle (AUV) coverage path planning (ER-MCPP) algorithm using side-scan sonar (SSS). First, the given search region was optimized and modeled using grid decomposition. Next, a Voronoi diagram was used to divide and allocate the regions according to the initial position of the AUV. Subsequently, based on the idea of guidance, navigation, and control (GNC), a coverage search algorithm for a single AUV was proposed, including a path planning method combining improved lawn mower and outward-spiral methods, an integrated navigation method based on Kalman filtering, and a path tracking method considering ocean currents. In addition, we proposed a fault-tolerant control method. When an AUV fails unexpectedly, the other AUVs can accomplish a coverage search for the region through cooperation. Simulation experiments were performed using the MATLAB software. The results demonstrate that the proposed algorithm is valid. Compared with other methods, our algorithm has better robustness, finds targets more rapidly, and has a higher search efficiency. The practicality of the proposed algorithm is verified through field tests.

Sample Augmentation Method for Side-Scan Sonar Underwater Target Images Based on CBL-sinGAN

The scarcity and difficulty in acquiring Side-scan sonar target images limit the application of deep learning algorithms in Side-scan sonar target detection. At present, there are few amplification methods for Side-scan sonar images, and the amplification image quality is not ideal, which is not suitable for the characteristics of Side-scan sonar images. Addressing the current shortage of sample augmentation methods for Side-scan sonar, this paper proposes a method for augmenting single underwater target images using the CBL-sinGAN network. Firstly, considering the low resolution and monochromatic nature of Side-scan sonar images while balancing training efficiency and image diversity, a sinGAN network is introduced and designed as an eight-layer pyramid structure. Secondly, the Convolutional Block Attention Module (CBAM) is integrated into the network generator to enhance target learning in images while reducing information diffusion. Finally, an L1 loss function is introduced in the network discriminator to ensure training stability and improve the realism of generated images. Experimental results show that the accuracy of shipwreck target detection increased by 4.9% after training with the Side-scan sonar sample dataset augmented by the proposed network. This method effectively retains the style of the images while achieving diversity augmentation of small-sample underwater target images, providing a new approach to improving the construction of underwater target detection models.

Exploration and identification of ancient Roman shipwreck located at Quseir, Red Sea

Recent marine geophysical investigations in front of the Myos Hormos Port (also known as Quseir al-Qadim) on the Red Sea coast of Egypt indicated the existence of an ancient Roman shipwreck, among other features on the seafloor. The study showed that side scan sonar imaging is a powerful technique for classifying the seafloor and detecting submerged targets among other seafloor features with resembling acoustic signatures. Integrating bathymetric data with sonar imageries displayed the seafloor topography in front of the ancient port where reef habitats were dominant. The Autonomous Underwater Vehicle was efficient in mapping closed and near-shore areas like bays. This vehicle provided high-resolution bathymetric and sonar data for the semi-closed bay area located at the entrance of the ancient port. Acoustic measurements were done for the sunken shipwreck using sonar software, and a structural model was established for the ∼ 32 m long sunken ship. The model suggested the presence of a pile of shipload gathered around the shipwreck. Results can contribute to the development of Egypt's tourism industry by promoting underwater cultural heritage preservation, as the discovery of an ancient Roman shipwreck is a significant finding that can attract tourists worldwide.

Sonar Image Target Detection Based on Simulated Stain-like Noise and Shadow Enhancement in Optical Images under Zero-Shot Learning

There are many challenges in using side-scan sonar (SSS) images to detect objects. The challenge of object detection and recognition in sonar data is greater than in optical images due to the sparsity of detectable targets. The complexity of real-world underwater scanning presents additional difficulties, as different angles produce sonar images of varying characteristics. This heterogeneity makes it difficult for algorithms to accurately identify and detect sonar objects. To solve these problems, this paper presents a novel method for sonar image target detection based on a transformer and YOLOv7. Thus, two data augmentation techniques are introduced to improve the performance of the detection system. The first technique applies stain-like noise to the training optical image data to simulate the real sonar image environment. The second technique adds multiple shadows to the optical image and 3D data targets to represent the direction of the target in the sonar image. The proposed method is evaluated on a public sonar image dataset, and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed. The experimental results show that our method achieves better precision.

Optimizing AUV Navigation Using Factor Graphs with Side-Scan Sonar Integration

For seabed mapping, the prevalence of autonomous underwater vehicles (AUVs) employing side-scan sonar (SSS) necessitates robust navigation solutions. However, the positioning errors of traditional strapdown inertial navigation system (SINS) and Doppler velocity log (DVL) systems accumulated significantly, further exacerbated by DVL’s susceptibility to failure in complex underwater conditions. This research proposes an integrated navigation approach that utilizes factor graph optimization (FGO) along with an improved pre-integration technique integrating SSS-derived position measurements. Firstly, the reliability of SSS image registration in the presence of strong noise and feature-poor environments is improved by replacing the feature-based methods with a Fourier-based method. Moreover, the high-precision inertial measurement unit (IMU) pre-integration method could correct the heading errors of SINS significantly by considering the Earth’s rotation. Finally, the AUV’s marine experimental results demonstrated that the proposed integration method not only offers improved SSS image registration and corrects initial heading discrepancies but also delivers greater system stability, particularly in instances of DVL data loss.

Three-dimentional reconstruction of underwater side-scan sonar images based on shape-from-shading and monocular depth fusion

Modern marine research requires high-precision three-dimensional (3D) underwater data. Underwater environments experience severe visible light attenuation, which leads to inferior imaging compared with air. In contrast, sound waves are less affected underwater; hence side-scan sonar is used for underwater 3D reconstruction. Typically, the shape-from-shading algorithm (SfS) is widely used to reconstruct surface normal or heights from side-scan sonar images. However, this approach has challenges because of global information loss and noise. To address these issues, this study introduces a surface-normal fusion method. Specifically, we propose a frequency separation SfS algorithm using a discrete cosine transform, which provides a surface-normal map with less noise. We then fuse the surface-normal map with a novel depth estimation network to achieve high-precision 3D reconstruction of underwater side-scan sonar images. We conducted experiments on synthetic, NYU-depth-v2, and real side-scan sonar datasets to demonstrate the effectiveness of the proposed method.

Sedimentation processes and morphological changes in a dredge pit and surrounding environment on Ship Shoal in the northern Gulf of Mexico

The use of dredged material from continental shelves and estuaries for degraded barrier islands has become a common practice worldwide. South Pelto block on eastern Ship Shoal (SS) off the coast of Louisiana has been dredged for high-quality sand resources, including Caminada Dredge Pit (CDP) and multiple pits in East Timbalier borrow areas (ET) adjacent to CDP. To understand how these dredge pits change and sediments infill over time, multiple surveys were conducted, including bathymetry, sidescan sonar, subbottom CHIRP, and sediment samples were collected for grain size analysis.Although there is no nearby muddy sediment, sidescan and grain size data both show low-reflectivity areas inside CDP became larger over time, indicating continual supply from suspended mud. Subbottom data show decreasing pit wall slopes over time and degassing over patchy low reflectivity areas on the pit bottom. Arc-chord rugosity analysis was performed with emphasis applied to ET to examine the difference between cutter and hopper dredge techniques. It was revealed that dredging from the hopper created a higher rugosity value than the cutter, leading to a shorter recovery time for the benthic environment. Bathymetric results reveal approximately 20 m to 60 m of horizontal outward pit wall migrations over 3–5 years. Hurricanes Marco, Zeta, and Ida as well as Tropical Storm Claudette passed the study site (within 50 Km) during the study period which increased wind-wave energies sufficiently to develop new sand ripples discovered on sidescan sonar data near ET and CDP. By integrating various datasets and investigating the effects of tropical cyclones on a dredged area in SS, this study contributes valuable insights that fill existing knowledge gaps and enhance decision-making for future management strategies.

Inferring benthic megafaunal sediment reworking activity in relation to bottom water oxygen in Barkley Canyon, NE Pacific from video and acoustic imaging analysis

Sediment reworking activity influences benthic functioning expressed as nutrient fluxes and carbon cycling. Multiple studies have addressed sediment reworking based on observations from individual instrument types (e.g., video camera, side-scan sonar, multibeam), but none have considered reworking based on two or more complementary instruments. We therefore analyzed deep-sea megafaunal and reworked sediment traces by combining and comparing observations from non-invasive instruments, an underwater video camera and a rotary sonar, as part of the node of Ocean Networks Canada's NEPTUNE cabled observatory, located at 396 m depth in Barkley Canyon Upper Slope, NE Pacific Ocean. Specifically, we examined sediment traces and benthic megafaunal communities during two different sampling periods (May and September 2013) and at two different spatial scales of analysis. The camera images (∼0.5 m2) documented significantly lower megafaunal density during the period of reduced oxygen concentrations (May). Although we did not observe significant differences in sediment trace diversity and density between the two study periods, we were interested in how the relief traces generated by unidentified gastropods (likely the family Solariellidae) influenced sediment mixing. Relief traces showed higher density in low oxygen (May, 1.87 count m−2) than in high oxygen (September, 1.17 count m−2) conditions. Sonar images (∼1268 m2), which lacked sufficient resolution to allow identification of benthic organisms, documented distributions of biological pits (regions of low backscatter), a significantly greater proportion of bioturbated seafloor area, and increased in pit size with increased oxygen levels. Pits dominated sonar images at this location and persisted through the two study periods. Most sonar field of view subsections showed a significant increase in circularity of pit shapes, likely explained by increased reworked sediment with increasing oxygen concentration. We conclude that, except for relief burrows, higher reworked sediment area coincided with the highest oxygen concentration, which aligns with previously established reduced metabolic activity by benthos adapted to oxygen minimum zones. Furthermore, we emphasize the complementarity of the two imaging techniques (video and sonar) in understanding deep-sea benthic ecosystem dynamics, as well as the importance of considering multiple spatial scales when investigating proxies of bioturbation activity.

Sonar image intelligent processing in seabed pipeline detection: review and application

Subsea pipelines rely primarily on imaging sonar for detection and identification. We analyze the imaging principles of side scan sonar, multi-beam sonar, synthetic aperture sonar, seafloor penetrating sonar and forward-looking sonar. We discuss their effectiveness in detecting seabed pipelines, as well as their limitations in image recognition capabilities. As intelligent algorithms have become increasingly important in the field of image processing, we review the sonar image intelligent detection and recognition algorithms in the past six years and summarize the internal principles and application effects of classic algorithms such as Scale-Invariant Feature Transform, K-means algorithm, and constant false-alarm rate that currently show good application prospects. Simultaneously, we review the particular strengths exhibited by these algorithms, such as contour feature extraction, image segmentation and clustering, target recognition under background noise, etc. The research on intelligent processing of sonar images opens up a new way to solve the difficult problem of the seabed targets detection and recognition.

Automatic Seabed Target Segmentation of AUV via Multilevel Adversarial Network and Marginal Distribution Adaptation

The real-time segmentation results of side-scan sonar (SSS) images can realize the intelligent perception of autonomous underwater vehicles (AUVs). However, there remains a compelling challenge that the models trained with training data may not generalize well in practical applications due to the various marine conditions/working devices. This article proposes a real-time cross-domain segmentation adaptation scheme for SSS images based on 1) adversarial training via min-max training to distribute SSS images in the output space and 2) marginal distribution adaption to improve the segmentation performance by minimizing the measured distribution distance. To further enhance the adapted model, the multi-layer feature discriminators are established to realize the fine-grained alignment. Experiments on the real-world SSS datasets demonstrate that our proposed model outperforms state-of-the-art methods and achieves the actual industrial application for AUV.

Unveiling Underwater Structures: MobileNet vs. EfficientNet in Sonar Image Detection

Deep learning techniques have led to an increased use of Convolutional Neural Networks (CNN) in recognizing images for marine surveys and classifying underwater objects. Applying CNN for the automatic identification of targets in side-scan sonar (SSS) images can boost accuracy and efficiency. Using transfer learning and deep learning as lenses, this work explores the field of underwater item detection. Comparing performance of MobileNet and EfficientNet in performing the task of underwater item classification from sonar images is the central research objective of this study. These two well-known convolutional neural networks were the subject of a thorough investigation to help explain this. In order to fine-tune pre-trained models on a dataset of sonar images, the study approach used the transfer learning technique. We conducted tests on MobileNet and EfficientNet and thoroughly assessed each of their accuracy levels. This study has two main conclusions. First, showing promise as a strong model for underwater object identification, EfficientNet performed exceptionally well in the classification challenge.

RPFNet: Recurrent Pyramid Frequency Feature Fusion Network for Instance Segmentation in Side-Scan Sonar Images

Side-scan sonar (SSS) is an essential acoustic sensor device for obtaining underwater information. The instance segmentation of sonar images can effectively locate and detect underwater objects. Although various CNN-based frameworks have achieved promising results in natural image instance segmentation, the noise interference, highlight shadow, and blurred edge in sonar images bring more significant challenges for sonar image instance segmentation. To solve these problems, we propose a novel recurrent pyramid frequency feature fusion network (RPFNet), which mainly consists of the pyramid frequency feature fusion network (PFN), recurrent residual attention mechanism (RRAM), mask prediction module, and semantic segmentation module. By enhancing and fusing different frequency features of SSS images, PFN can effectively extract fine-grained features and reduce background information interference. The RRAM uses residual structure and attention mechanism to enhance the correlation of different frequency features and improve nonlinear feature representation ability. The mask prediction and semantic segmentation modules are used to discriminate object instance categories and generate corresponding semantic segmentation results. We extensively evaluate the proposed framework on the real-scenes sonar image dataset. The evaluation results demonstrate that the proposed method outperforms the state-of-the-art methods in various evaluation metrics. Code is available at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/darkseid-arch/SonarRPFNet</uri> .