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Side-scan sonar (SSS) images have a wide range of applications in underwater target detection and recognition. However, due to the complexity of the underwater environment, the classification performance of sonar images is usually constrained by issues such as noise and inconspicuous texture features, including speckle noise, sensor noise, and interference from other sources. These noises can degrade the image quality, making it challenging to extract meaningful features and affecting the performance of classification algorithms. To address these challenges, we propose a novel classification model named Shuffle-RDSNet for SSS images. Specifically, we design the residual dual-path shrinkage network (RDSNet), which utilizes a soft thresholding function and determines the threshold by combining two paths to extract features from varying scales. The RDSNet is then integrated with the ShuffleNet V2 network to construct the proposed Shuffle-RDSNet model. This approach effectively mitigates the effect of noise in the feature extraction process and enhances the classification performance of the model. Furthermore, we employ additional techniques, including dilated convolution and depthwise separable convolution (DSC), to optimize the model and further enhance the classification accuracy and performance of the proposed method. Experimental results show that our model outperforms other classification models with a classification accuracy of up to 96.74% on the SSS image dataset, confirming the feasibility and effectiveness of using RDSNet for SSS image classification. [ABSTRACT FROM AUTHOR]

Rapid technological advancement often receives a mix of criticism and welcome implementation. Fishing technologies, such as sonar, are believed to enable anglers to be more efficient and effective in their angling. There are concerns from anglers and managers of increased catch by technology users. We assessed the relationships between technology use—defined as the use of imaging technology such as sonar and underwater cameras—and catch, angler expectations of catch, and trip satisfaction using a dual intercept creel survey. Angling technologies were used by 80% and 79% of intercepted boat and ice anglers, respectively, but only 3.9% of shore anglers. Fishing technologies increased expected catch for game fish anglers, but not panfish anglers, and had no effect on actual catch for either group. Most anglers caught fewer fish than expected, and technology did not improve their ability to meet expectations. Technology use was associated with decreased overall satisfaction among panfish and game fish anglers. These results suggest that concerns about fishing technology increasing catch may not be warranted. Rather, technology use may affect angler expectations and negatively impact angler satisfaction, potentially influencing angler behavior. [ABSTRACT FROM AUTHOR]

The Ocean Networks Canada (ONC) cabled video-observatory at the Barkley Canyon Node (British Columbia, Canada) was recently the site of a Fish Acoustics and Attraction Experiment (FAAE), from May 21, 2022 to July 16, 2023, combining observations from High-Definition (HD) video, acoustic imaging sonar, and underwater sounds at a depth of 645 m, to examine the effects of light and bait on deep-sea fish and invertebrate behaviors. The unexpected presence of at least eight (six recurrent and two temporary) sub-adult male northern elephant seals (Mirounga angustirostris) was reported in 113 and 210 recordings out of 9737 HD and 2805 sonar videos at the site, respectively. Elephant seals were found at the site during seven distinct periods between June 22, 2022 and May 19, 2023. Ethograms provided insights into the seal's deep-sea resting and foraging strategies, including prey selection. We hypothesized that the ability of elephant seals to perform repeated visits to the same site over long periods (> 10 days) was due to the noise generated by the sonar, suggesting that they learned to use that anthropogenic source as an indicator of food location, also known as the "dinner bell" effect. One interpretation is that elephant seals are attracted to the FAAE site due to the availability of prey and use the infrastructure as a foraging and resting site, but then take advantage of fish disturbance caused by the camera lights to improve foraging success. Our video observations demonstrated that northern elephant seals primarily focused on actively swimming sablefish (Anoplopoma fimbria), ignoring stationary or drifting prey. Moreover, we found that elephant seals appear to produce (voluntary or involuntary) infrasonic sounds in a foraging context. This study highlights the utility of designing marine observatories with spatially and temporally cross-referenced data collection from instruments representing multiple modalities of observation. [ABSTRACT FROM AUTHOR]

Side-scan sonar is a principal technique for subsea target detection, where the quantity of sonar images of seabed targets significantly influences the accuracy of intelligent target recognition. To expand the number of representative side-scan sonar target image samples, a novel augmentation method employing self-training with a Disrupted Student model is designed (DS-SIAUG). The process begins by inputting a dataset of side-scan sonar target images, followed by augmenting the samples through an adversarial network consisting of the DDPM (Denoising Diffusion Probabilistic Model) and the YOLO (You Only Look Once) detection model. Subsequently, the Disrupted Student model is used to filter out representative target images. These selected images are then reused as a new dataset to repeat the adversarial filtering process. Experimental results indicate that using the Disrupted Student model for selection achieves a target recognition accuracy comparable to manual selection, improving the accuracy of intelligent target recognition by approximately 5% over direct adversarial network augmentation. [ABSTRACT FROM AUTHOR]

Given the propagation characteristics of sound waves and the complexity of the underwater environment, denoising forward-looking sonar image data presents a formidable challenge. Existing studies often add noise to sonar images and then explore methods for its removal. This approach neglects the inherent complex noise in sonar images, resulting in inaccurate evaluations of traditional denoising methods and poor learning of noise characteristics by deep learning models. To address the lack of high-quality data for FLS denoising model training, we propose a simulation algorithm for forward-looking sonar data based on RGBD data. By utilizing rendering techniques and noise simulation algorithms, high-quality noise-free and noisy sonar data can be rapidly generated from existing RGBD data. Based on these data, we optimize the loss function and training process of the FLS denoising model, achieving significant improvements in noise removal and feature preservation compared to other methods. Finally, this paper performs both qualitative and quantitative analyses of the algorithm's performance using real and simulated sonar data. Compared to the latest FLS denoising models based on traditional methods and deep learning techniques, our method demonstrates significant advantages in denoising capability. All inference results for the Marine Debris Dataset (MDD) have been made open source, facilitating subsequent research and comparison. [ABSTRACT FROM AUTHOR]

In the field of underwater perception and detection, side-scan sonar (SSS) plays an indispensable role. However, the imaging mechanism of SSS results in slow information acquisition and high complexity, significantly hindering the advancement of downstream data-driven applications. To address this challenge, we designed an SSS image generator based on diffusion models. We developed a data collection system based on Autonomous Underwater Vehicles (AUVs) to achieve stable and rich data collection. For the process of converting acoustic signals into image signals, we established an image compensation method based on nonlinear gain enhancement to ensure the reliability of remote signals. On this basis, we developed the first controllable category SSS image generation algorithm, which can generate specified data for five categories, demonstrating outstanding performance in terms of the Fréchet Inception Distance (FID) and the Inception Score (IS). We further evaluated our image generator in the task of SSS object detection, and our cross-validation experiments showed that the generated images contributed to an average accuracy improvement of approximately 10% in object detection. The experimental results validate the effectiveness of the proposed SSS image generator in generating highly similar sonar images and enhancing detection accuracy, effectively addressing the issue of data scarcity. [ABSTRACT FROM AUTHOR]

Introduction: Autonomous Underwater Vehicles (AUVs) are capable of independently performing underwater navigation tasks, with side-scan sonar being a primary tool for underwater detection. The integration of these two technologies enables autonomous monitoring of the marine environment. Methods: To address the limitations of existing seabed detection methods, such as insufficient robustness and high complexity, this study proposes a comprehensive seabed detection method based on a sliding window technique. Additionally, this study introduces a sonar image stitching method that accounts for variations in image intensity and addresses challenges arising from multi-frame overlaps and gaps. Furthermore, an autonomous target perception framework based on shadow region segmentation is proposed, which not only identifies targets in side-scan sonar images but also provides target height measurements. Results: Comprehensive seabed detection method improves accuracy by 31.2% compared to the peak detection method. In experiments, the height measurement error for this method was found to be 9%. Discussion: To validate the effectiveness of the proposed seabed detection method, sonar image stitching method, and target perception framework, comprehensive experiments were conducted in the Qingjiang area of Hubei Province. The results obtained from the lake environment demonstrated the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

The quality of underwater bridge piers significantly impacts bridge safety and long-term usability. To address limitations in conventional inspection methods, this paper presents a sonar-based technique for the three-dimensional (3D) reconstruction and visualization of underwater bridge piers. Advanced MS1000 scanning sonar is employed to detect and image bridge piers. Automated image preprocessing, including filtering, denoising, binarization, filling, and morphological operations, introduces an enhanced wavelet denoising method to accurately extract the foundation contour coordinates of bridge piers from sonar images. Using these coordinates, along with undamaged pier dimensions and sonar distances, a model-driven approach for a 3D pier reconstruction algorithm is developed. This algorithm leverages multiple sonar data points to reconstruct damaged piers through multiplication. The Visualization Toolkit (VTK) and surface contour methodology are utilized for 3D visualization, enabling interactive manipulation for enhanced observation and analysis. Experimental results indicate a relative error of 13.56% for the hole volume and 10.65% for the spalling volume, demonstrating accurate replication of bridge pier defect volumes by the reconstructed models. Experimental validation confirms the method's accuracy and effectiveness in reconstructing underwater bridge piers in three dimensions, providing robust support for safety assessments and contributing significantly to bridge stability and long-term safety assurance. [ABSTRACT FROM AUTHOR]

Aiming at the problem of low accuracy of multi-scale seafloor target detection in side-scan sonar images with high noise and complex background texture, a model for multi-scale target detection using the BES-YOLO network is proposed. First, an efficient multi-scale attention (EMA) mechanism is used in the backbone of the YOLOv8 network, and a bi-directional feature pyramid network (Bifpn) is introduced to merge the information of different scales, finally, a Shape_IoU loss function is introduced to continuously optimize the model and improve its accuracy. Before training, the dataset is preprocessed using 2D discrete wavelet decomposition and reconstruction to enhance the robustness of the network. The experimental results show that 92.4% of the mean average accuracy at IoU of 0.5 (mAP@0.5) and 67.7% of the mean average accuracy at IoU of 0.5 to 0.95 (mAP@0.5:0.95) are achieved using the BES-YOLO network, which is an increase of 5.3% and 4.4% compared to the YOLOv8n model. The research results can effectively improve the detection accuracy and efficiency of multi-scale targets in side-scan sonar images, which can be applied to AUVs and other underwater platforms to implement intelligent detection of undersea targets. [ABSTRACT FROM AUTHOR]

To enhance the performance of visual SLAM in underwater environments, this paper presents an enhanced front-end method based on visual feature enhancement. The method comprises three modules aimed at optimizing and improving the matching capability of visual features from different perspectives. Firstly, to address issues related to insufficient underwater illumination and uneven distribution of artificial light sources, a brightness-consistency recovery method is proposed. This method employs an adaptive histogram equalization algorithm to balance the brightness of images. Secondly, a method for denoising underwater suspended particulates is introduced to filter out noise from images. After image-level processing, a combined underwater acousto–optic feature-association method is proposed, which associates acoustic features from sonar with visual features, thereby providing distance information for visual features. Finally, utilizing the AFRL dataset, the improved system incorporating the proposed enhancement methods is evaluated for its performance against the OKVIS framework. The system achieves a better trajectory estimation accuracy compared to OKVIS and demonstrates robustness in underwater environments. [ABSTRACT FROM AUTHOR]

Multi-beam sonar imaging detection technology is increasingly becoming the mainstream technology in fields such as hydraulic safety inspection and underwater target detection due to its ability to generate clearer images under low-visibility conditions. However, during the multi-beam sonar detection process, issues such as low image resolution and blurred imaging edges lead to decreased target segmentation accuracy. Traditional filtering methods for echo signals cannot effectively solve these problems. To address these challenges, this paper introduces, for the first time, a multi-beam sonar dataset against the background of simulated crack detection for dam safety. This dataset included simulated cracks detected by multi-beam sonar from various angles. The width of the cracks ranged from 3 cm to 9 cm, and the length ranged from 0.2 m to 1.5 m. In addition, this paper proposes a BS-UNet semantic segmentation algorithm. The Swin-UNet model incorporates a dual-layer routing attention mechanism to enhance the accuracy of sonar image detail segmentation. Furthermore, an online convolutional reparameterization structure was added to the output end of the model to improve the model's capability to represent image features. Comparisons of the BS-UNet model with commonly used semantic segmentation models on the multi-beam sonar dataset consistently demonstrated the BS-UNet model's superior performance, as it improved semantic segmentation evaluation metrics such as Precision and IoU by around 0.03 compared to the Swin-UNet model. In conclusion, BS-UNet can effectively be applied in multi-beam sonar image segmentation tasks. [ABSTRACT FROM AUTHOR]

Side-scan sonar plays a crucial role in underwater exploration, and the autonomous detection of side-scan sonar images is vital for detecting unknown underwater environments. However, due to the complexity of the underwater environment, the presence of a few highlighted areas on the targets, blurred feature details, and difficulty in collecting data from side-scan sonar, achieving high-precision autonomous target recognition in side-scan sonar images is challenging. This article addresses this problem by improving the You Only Look Once v7 (YOLOv7) model to achieve high-precision object detection in side-scan sonar images. Firstly, given that side-scan sonar images contain large areas of irrelevant information, this paper introduces the Swin-Transformer for dynamic attention and global modeling, which enhances the model's focus on the target regions. Secondly, the Convolutional Block Attention Module (CBAM) is utilized to further improve feature representation and enhance the neural network model's accuracy. Lastly, to address the uncertainty of geometric features in side-scan sonar target features, this paper innovatively incorporates a feature scaling factor into the YOLOv7 model. The experiment initially verified the necessity of attention mechanisms in the public dataset. Subsequently, experiments on our side-scan sonar (SSS) image dataset show that the improved YOLOv7 model has 87.9% and 49.23% in its average accuracy ( m A P 0.5 ) and ( m A P 0.5:0.95), respectively. These results are 9.28% and 8.41% higher than the YOLOv7 model. The improved YOLOv7 algorithm proposed in this paper has great potential for object detection and the recognition of side-scan sonar images. [ABSTRACT FROM AUTHOR]

Current estimates of the threatened southern distinct population segment of the North American green sturgeon (Acipenser medirostris) combine a plot-sampling density estimator with Dual frequency IDentification SONar (DIDSON) and adaptive resolution imaging sonar (ARIS) sonar data. From 2020 to 2022, we annually collected images of all known green sturgeon aggregations and compared the established method to an N-mixture model using side-scan sonar images. We compared 18 different N-mixture model combinations and chose an overdispersed Poisson model that produced estimated abundances of 742, 1286, and 1208 for 2020–2022, respectively. These numbers are ∼2 times greater than the previous method and, if sustained, would fulfill a key criterion for green sturgeon recovery. N-mixture models are known to be sensitive to violations of assumptions, such as the highly dispersed data from our study that caused serious issues, and we recommend practitioners make judicious use of overdispersion and goodness-of-fit tests and be able to identify parameter confounding between detectability and abundance estimates. For our green sturgeon, we recommend simpler population estimates and to focus future energy on reducing variability in the data collection process. [ABSTRACT FROM AUTHOR]

Super-resolution (SR) is a technique that restores image details based on existing information, enhancing the resolution of images to prevent quality degradation. Despite significant achievements in deep-learning-based SR models, their application in underwater sonar scenarios is limited due to the lack of underwater sonar datasets and the difficulty in recovering texture details. To address these challenges, we propose a multi-scale generative adversarial network (SIGAN) for super-resolution reconstruction of underwater sonar images. The generator is built on a residual dense network (RDN), which extracts rich local features through densely connected convolutional layers. Additionally, a Convolutional Block Attention Module (CBAM) is incorporated to capture detailed texture information by focusing on different scales and channels. The discriminator employs a multi-scale discriminative structure, enhancing the detail perception of both generated and high-resolution (HR) images. Considering the increased noise in super-resolved sonar images, our loss function emphasizes the PSNR metric and incorporates the L2 loss function to improve the quality of the output images. Meanwhile, we constructed a dataset for side-scan sonar experiments (DNASI-I). We compared our method with the current state-of-the-art super-resolution image reconstruction methods on the public dataset KLSG-II and our self-built dataset DNASI-I. The experimental results show that at a scale factor of 4, the average PSNR value of our method was 3.5 higher than that of other methods, and the accuracy of target detection using the super-resolution reconstructed images can be improved to 91.4%. Through subjective qualitative comparison and objective quantitative analysis, we demonstrated the effectiveness and superiority of the proposed SIGAN in the super-resolution reconstruction of side-scan sonar images. [ABSTRACT FROM AUTHOR]

To address the highly demanding assessment of the quantity of fish in cages, a method for estimating the fish quantity in cages based on image sonar is proposed. In this method, forward-looking image sonar is employed for continuous detection in cages, and the YOLO target detection model with attention mechanism as well as a BP neural network are combined to achieve a real-time automatic estimation of fish quantity in cages. A quantitative experiment was conducted in the South China Sea to render a database for training the YOLO model and neural network. The experimental results show that the average detection accuracy mAP50 of the improved YOLOv8 is 3.81% higher than that of the original algorithm. The accuracy of the neural network in fitting the fish quantity reaches 84.63%, which is 0.72% better than cubic polynomial fitting. In conclusion, the accurate assessment of the fish quantity in cages contributes to the scientific and intelligent management of aquaculture and the rational formulation of feeding and fishing plans. [ABSTRACT FROM AUTHOR]

Accurately identifying the fish feeding intensity plays a vital role in aquaculture. While traditional methods are limited by single modality (e.g., water quality, vision, audio), they often lack comprehensive representation, leading to low identification accuracy. In contrast, the multimodal fusion methods leverage the fusion of features from different modalities to obtain richer target features, thereby significantly enhancing the performance of fish feeding intensity assessment (FFIA). In this work a multimodal dataset called MRS-FFIA was introduced. The MRS-FFIA dataset consists of 7611 labelled audio, video and acoustic dataset, and divided the dataset into four different feeding intensity (strong, medium, weak, and none). To address the limitations of single modality methods, a Multimodal Fusion of Fish Feeding Intensity fusion (MFFFI) model was proposed. The MFFFI model is first extracting deep features from three modal data audio (Mel), video (RGB), Acoustic (SI). Then, image stitching techniques are employed to fuse these extracted features. Finally, the fused features are passed through a classifier to obtain the results. The test results show that the accuracy of the fused multimodal information is 99.26%, which improves the accuracy by 12.80%, 13.77%, and 2.86%, respectively, compared to the best results for single-modality (audio, video and acoustic dataset). This result demonstrates that the method proposed in this paper is better at classifying the feeding intensity of fish and can achieve higher accuracy. In addition, compared with the mainstream single-modality approach, the model improves 1.5%–10.8% in accuracy, and the lightweight effect is more obvious. Based on the multimodal fusion method, the feeding decision can be optimised effectively, which provides technical support for the development of intelligent feeding systems. • An audio-visual-acoustic dataset containing 7611 labelled audio and video clips was build. • An image sonar was used to obtain video data on the feeding behaviour of fish. • A multimodal fusion scheme for hydrophone, optical, and image sonar data integration was proposed. [ABSTRACT FROM AUTHOR]

For two-dimensional forward-looking sonar imaging, high sidelobes significantly degrade the quality of sonar images. The cosine window function weighting method is often applied to suppress the sidelobe levels in the angular and range dimensions, at the expense of the main lobe resolutions. Therefore, an improved spatially variant apodization imaging method for forward-looking sonar is proposed, to reduce sidelobes without degrading the main lobe resolution in angular-range dimensions. The proposed method is a nonlinear postprocessing operation in which the raw complex-valued sonar image produced by a conventional beamformer and matched filter is weighted by a spatially variant coefficient. To enhance the robustness of the spatially variant apodization approach, the array magnitude and phase errors are calibrated to prevent the occurrence of beam sidelobe increase prior to beamforming operations. The analyzed results of numerical simulations and a lake experiment demonstrate that the proposed method can greatly reduce the sidelobes to approximately −40 dB, while the main lobe width remains unchanged. Moreover, this method has an extremely simple computational process. [ABSTRACT FROM AUTHOR]

Multi beam forward looking sonar plays an important role in underwater detection. However, due to the complex underwater environment, unclear features, and susceptibility to noise interference, most forward looking sonar systems have poor recognition performance. The research on MFLS for underwater target detection faces some challenges. Therefore, this study proposes innovative improvements to the YOLOv5 algorithm to address the above issues. On the basis of maintaining the original YOLOv5 architecture, this improved model introduces transfer learning technology to overcome the limitation of scarce sonar image data. At the same time, by incorporating the concept of coordinate convolution, the improved model can extract features with rich positional information, significantly enhancing the model's detection ability for small underwater targets. Furthermore, in order to solve the problem of feature extraction in forward looking sonar images, this study integrates attention mechanisms. This mechanism expands the receptive field of the model and optimizes the feature learning process by highlighting key details while suppressing irrelevant information. These improvements not only enhance the recognition accuracy of the model for sonar images, but also enhance its applicability and generalization performance in different underwater environments. In response to the common problem of uneven training sample quality in forward looking sonar imaging technology, this study made a key improvement to the classic YOLOv5 algorithm. By adjusting the bounding box loss function of YOLOv5, the model's over sensitivity to low-quality samples was reduced, thereby reducing the punishment on these samples. After a series of comparative experiments, the newly proposed CCW-YOLOv5 algorithm has achieved detection accuracy in object detection mAP@0.5 Reached 85.3%, and the fastest inference speed tested on the local machine was 54 FPS, showing significant improvement and performance improvement compared to existing advanced algorithms. [ABSTRACT FROM AUTHOR]

Researchers are increasingly using biologging equipment (e.g., telemetry receivers, temperature loggers) to characterize the ecology of aquatic ecosystems. This equipment is commonly deployed at a wide range of water depths and greatly expands our capacity to remotely monitor aquatic ecosystems; however, equipment retrieval can be a major challenge. Here, we describe a technological solution to this challenge that uses a combination of live imaging sonar and a remotely operated vehicle to efficiently locate and recover equipment across a wide range of conditions (e.g., turbid water, range of water depths). We provide details on our specific equipment setup (total cost < Can$15,000) used for the recovery of acoustic fish tracking receivers moored to the benthos of Stoney Lake, Ontario, Canada. There are some limitations to this approach, which are discussed. With technological advances and increases in affordability of commercially available products, this approach may be widely applicable to recover biologgers from depth. [ABSTRACT FROM AUTHOR]

To address the requirements of sonar imaging, such as high receiving sensitivity, a wide bandwidth, and a wide receiving angle, an AlN PMUT with an optimized ratio of 0.6 for the piezoelectric layer diameter to backside cavity diameter is proposed in this paper. A sample AlN PMUT is designed and fabricated with the SOI substrate-based bulk MEMS process. The characterization test result of the sample demonstrates a −6 dB bandwidth of approximately 500 kHz and a measured receiving sensitivity per unit area of 1.37 V/μPa/mm2, which significantly surpasses the performance of previously reported PMUTs. The −6 dB horizontal angles of the AlN PMUT at 300 kHz and 500 kHz are measured as 68.30° and 54.24°, respectively. To achieve an accurate prediction of its characteristics when being packaged and assembled in a receive array, numerical simulations with the consideration of film stress are conducted. The numerical result shows a maximum deviation of ±7% in the underwater receiving sensitivity across the frequency range of 200 kHz to 1000 kHz and a deviation of about 0.33% in the peak of underwater receiving sensitivity compared to the experimental data. By such good agreement, the simulation method reveals its capability of providing theoretical foundation for enhancing the uniformity of AlN PMUTs in future studies. [ABSTRACT FROM AUTHOR]

Accurate underwater target detection is crucial for the operation of autonomous underwater vehicles (AUVs), enhancing their environmental awareness and target search and rescue capabilities. Current deep learning-based detection models are typically large, requiring substantial storage and computational resources. However, the limited space on AUVs poses significant challenges for deploying these models on the embedded processors. Therefore, research on model compression is of great practical importance, aiming to reduce model parameters and computational load without significantly sacrificing accuracy. To address the challenge of deploying large detection models, this paper introduces an automated pruning method based on dependency graphs and successfully implements efficient pruning on the YOLOv7 model. To mitigate the accuracy degradation caused by extensive pruning, we design a hybrid distillation method that combines output-based and feature-based distillation techniques, thereby improving the detection accuracy of the pruned model. Finally, we deploy the compressed model on an embedded processor within an AUV to evaluate its performance. Multiple experiments confirm the effectiveness of our proposed method in practical applications. [ABSTRACT FROM AUTHOR]

Fish object detection and counting in pelagic fisheries face many challenges in complex environments. Sonar imaging technology offers a solution because it generates high-resolution images underwater. In this paper, we propose a sonar-based fish object detection and counting method using an improved YOLOv8 combined with BoT-SORT to address issues such as missed detection, false detection, and low accuracy caused by complex factors such as equipment motion, light changes, and background noise in pelagic environments. The algorithm utilizes the techniques of lightweight upsampling operator CARAFE, generalized feature pyramid network GFPN, and partial convolution. It integrates with the BoT-SORT tracking algorithm to propose a new region detection method that detects and tracks the schools of fish, providing stable real-time fish counts in the designated area. The experimental results indicate that while focusing on maintaining a lightweight design, the improved algorithm achieved a 3.8% increase in recall and a 2.4% increase in mAP0.5 compared to the original algorithm. This significantly impacts scientific and rational fishery planning, marine resource protection, and improved productivity. At the same time, it provides important data support for marine ecological monitoring, environmental protection, and fishery management, contributing to sustainable fishery development and marine ecology preservation. [ABSTRACT FROM AUTHOR]

High-quality forward-looking sonar images are the basic guarantee for underwater object detection and classification of autonomous underwater vehicle (AUV). However, the sonar image has been suffering from two main problem with complex and changeable underwater environment: high speckle noises and the lack of high frequency information. In this paper, a two-stage sonar image enhancement algorithm based on convolutional autoencoder is proposed to solve the above two problems to allow low-frequency sonar images to obtain resolutions approximate to high-frequency sonar images. For the high speckle noise, we proposed a convolutional denoising autoencoder based speckle reduction method for low-frequency sonar image to avoid noise enhanced as the image enhancement process. Skip connections and newly designed loss function is incorporated to better suppress noise with varying degrees. To solve the problem of insufficient high frequency information, a convolutional sparse autoencoder is further introduced to achieve image super resolution enhancement. In order to verify the effectiveness of the enhancement network proposed in this paper, we conducted extensive experimental analysis have been conducted from two aspects: image enhancement effect and underwater target detection effect. Specifically, underwater sonar images have been collected was conducted based on our self-owned AUV platform equipped with a dual frequency forward looking sonar in a water tank for network training. And three datasets are constructed for speckle reduction, high-frequency restoration, and underwater target detection. Through extensive experimental verification, our proposed enhancement method achieves high performance improvement on speckle reduction and high-frequency information restoration than the state-of-the-art image speckle reduction and image enhancement algorithms with a better PSNR, SSIM, and EPI. Finally, the YOLO V5 network model is used for underwater target detection and the experimental results show that combining image enhancement networks can effectively alleviate the problems of false and missed detections in the original network, which greatly improving the detection accuracy of the algorithm with a high detection speed. All the experiments have shown that the method proposed in this paper can effectively improve the underwater perception ability of sonar equipment. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Side‐scan sonar is a lightweight acoustic sensor that is frequently deployed on autonomous underwater vehicles (AUVs) to provide high‐resolution seafloor images. However, using side‐scan images to perform simultaneous localization and mapping (SLAM) remains a challenge when there is a lack of 3D bathymetric information and discriminant features in the side‐scan images. To tackle this, the authors propose a feature‐based SLAM framework using side‐scan sonar, which is able to automatically detect and robustly match keypoints between paired side‐scan images. The authors then use the detected correspondences as constraints to optimise the AUV pose trajectory. The proposed method is evaluated on real data collected by a Hugin AUV, using as a ground truth reference both manually‐annotated keypoints and a 3D bathymetry mesh from multibeam echosounder (MBES). Experimental results demonstrate that this approach is able to reduce drifts from the dead‐reckoning system. The framework is made publicly available for the benefit of the community. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Side-scan sonar (SSS) is now a prevalent instrument for large-scale seafloor topography measurements, deployable on an autonomous underwater vehicle (AUV) to execute fully automated underwater acoustic scanning imaging along a predetermined trajectory. However, SSS images often suffer from speckle noise caused by mutual interference between echoes, and limited AUV computational resources further hinder noise suppression. Existing approaches for SSS image processing and speckle noise reduction rely heavily on complex network structures and fail to combine the benefits of deep learning and domain knowledge. To address the problem, RepDNet, a novel and effective despeckling convolutional neural network is proposed. RepDNet introduces two re-parameterized blocks: the Pixel Smoothing Block (PSB) and Edge Enhancement Block (EEB), preserving edge information while attenuating speckle noise. During training, PSB and EEB manifest as double-layered multi-branch structures, integrating first-order and secondorder derivatives and smoothing functions. During inference, the branches are re-parameterized into a 3 x 3 convolution, enabling efficient inference without sacrificing accuracy. RepDNet comprises three computational operations: 3 x 3 convolution, element-wise summation and Rectified Linear Unit activation. Evaluations on benchmark datasets, a real SSS dataset and Data collected at Lake Mulan aestablish RepDNet as a well-balanced network, meeting the AUV computational constraints in terms of performance and latency. [ABSTRACT FROM AUTHOR]

Estuaries present a unique interface between ocean and freshwater systems and support key fish habitats. While the role of estuaries for juvenile fish has been studied extensively, the daily movement patterns of large fish and the exchange of fishes between estuary and ocean are less understood, particularly in developed estuaries. This study examines the quantity of fish biomass transiting through an important commercial fishing estuary in New South Wales, Australia, over 4 months, at four sites, and across diel periods. We used an imaging sonar across diurnal and nocturnal sampling events to reveal the size of fish, their movement patterns, and the biomass flux occurring throughout the estuary. Across ~ 60 h of footage, > 66,200 fish were counted and measured. Fish activity was the greatest at night, with 77% of fish biomass flux (kg h−1) occurring during nocturnal sampling. The total observed fish biomass over the 60 h amounted to ~ 15,500 kg, with the highest activity recorded at the estuary mouth at night (736 ± 463 kg h−1). Overall, mean fish length was the greatest at night (25.2 ± 2.0 cm); however, fish observed during the day at the estuary mouth had the highest mean length (27.8 ± 6.9 cm). Extrapolated across the width of the estuary, total fish movement ranged to a maximum of 47 Tons h−1 in the lower estuary (Stockton) and a minimum of 0.4 Tons h−1 in the upper estuary (Throsby Creek Lower). These results demonstrate the importance of estuaries, despite their level of development, in supporting the daily movement patterns of fish biomass between habitats, estuary, and ocean. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Autonomous Underwater Vehicle (AUV)-based multibeam bathymetry, sub-bottom profiles, and side-scan sonar images were collected in 2009 and 2010 to map the geomorphic features along the axial zone of a canyon (referred to as C4) within the canyon system developed on the northern slope of the South China Sea. These data significantly improved the spatial resolution of acoustic data, leading to a better understanding of the sedimentary processes within the modern canyon system. The bathymetric data reveal that sections across the canyon axis exhibit either asymmetrical or symmetrical characteristics, which differ from the overall asymmetrical pattern of the entire canyon. This suggests that the overall asymmetrical pattern of the canyon is not primarily due to axial incision. Various morphological elements were identified along the canyon axis, including failure scars, undulating features, knickpoints, flat terraces, furrows, and mass transport deposits (MTDs). Landslides, predominantly located in the upper canyon, were formed after at least 5000 years BP. Between the beginning of the canyon and a water depth of approximately 1300 m, there are alternating flat terraces and knickpoints. The large knickpoints' low slope gradients are likely formed by the presence of undulating features. The internal configurations of undulating features suggest that they are depositional structures rather than sediment deformation. The formation of small-scale furrows below a depth of 1200 m may be associated with occasional gravity flows down the canyon. It is suggested that the canyon was generally inactive during the Holocene but experienced sporadic processes of sediment erosion, transport, and re-deposition in the axial zone that were triggered by landslide events occasionally in the upper canyon. [ABSTRACT FROM AUTHOR]

AbstractPonds are a unique agricultural irrigation method with a long history in the Taoyuan area. However, most of them lack accurate spatial information, such as water depth, water surface area, and effective water storage capacity. In this study, the water depth of pond was measured using an unmanned surface vehicle, which has the advantage of being able to operate in shallow waters and in areas that are inaccessible to manned boats. The results show that it can rapidly provide 3D point cloud and an underwater 50 cm gridded digital elevation model with an accuracy of 6.7 cm, which can be used to identify structural defects at the pond bed and the embankment and to accurately estimate the water surface area and effective water storage capacity. The nests with diameters of 30 to 70 cm dug by male tilapia to attract female tilapia for spawning can also be found. In addition, the ground-penetrating radar and electrical resistivity tomography were carried out to successfully investigate the electricity-related properties of the internal structure of the embankment and the leakage locations of pond. In conjunction with the maintenance experience of the Taoyuan Management Office, a risk reference index was proposed for the safety management.

For many aquatic animals, distributional patterns, trophodynamic relationships, and reproductive activity are driven by complex biotic and abiotic ecosystem interactions that influence behaviour. Linking behavioural information to environmental stimuli and stressors can, therefore, help to anticipate population and ecosystem responses to changing conditions and inform management. However, behavioural information is challenging to obtain because many sampling gears do not provide adequate spatial or temporal resolution, or potentially alter behaviours. Traditionally, most behavioural studies have been laboratory experiments, while behaviour in situ has often been inferred indirectly. Advancements in imaging sonar technology enable the study of in situ behaviours with the potential to address many understudied relationships. In this review we discuss applications of imaging sonar among a meta-analysis of 155 studies of aquatic organisms in their environments. We evaluate the performance of imaging sonar for studying inter- and intra-specific interactions, associations with complex and sensitive habitats and low-visibility environments, and to evaluate traditional fisheries sampling gears. We examine the data processing and analytical methods used to refine taxonomic resolution, manage time use and autocorrelation through sub-sampling, extract behavioural metrics applied to ecological processes, and for automating abundance estimates and image classification with the goal of providing a resource for researchers. [ABSTRACT FROM AUTHOR]

With the development of underwater sonar detection technology, simultaneous localization and mapping (SLAM) approach has attracted much attention in underwater navigation field in recent years. But the weak detection ability of a single vehicle limits the SLAM performance in wide areas. Thereby, cooperative SLAM using multiple vehicles has become an important research direction. The key factor of cooperative SLAM is timely and efficient sonar image transmission among underwater vehicles. However, the limited bandwidth of underwater acoustic channels contradicts a large amount of sonar image data. It is essential to compress the images before transmission. Recently, deep neural networks have great value in image compression by virtue of the powerful learning ability of neural networks, but the existing sonar image compression methods based on neural network usually focus on the pixel-level information without the semantic-level information. In this paper, we propose a novel underwater acoustic transmission scheme called UAT-SSIC that includes semantic segmentation-based sonar image compression (SSIC) framework and the joint source-channel codec, to improve the accuracy of the semantic information of the reconstructed sonar image at the receiver. The SSIC framework consists of Auto-Encoder structure-based sonar image compression network, which is measured by a semantic segmentation network's residual. Considering that sonar images have the characteristics of blurred target edges, the semantic segmentation network used a special dilated convolution neural network (DiCNN) to enhance segmentation accuracy by expanding the range of receptive fields. The joint sourcechannel codec with unequal error protection is proposed that adjusts the power level of the transmitted data, which deal with sonar image transmission error caused by the serious underwater acoustic channel. Experiment results demonstrate that our method preserves more semantic information, with advantages over existing methods at the same compression ratio. It also improves the error tolerance and packet loss resistance of transmission. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Differences between conventional sonar and Multiple-Input Multiple-Output (MIMO) sonar systems arise in achieving high angular and range resolution. MIMO sonar uses Matched Filtering (MF) with well-correlated transmitted signals to enhance spatial resolution by obtaining virtual arrays. However, imperfect correlation characteristics yield high sidelobe values, which hinder accurate target localization in underwater imagery. To address this, a Compressed Sensing (CS) method is proposed by reconstructing echo signals to suppress correlation noise between orthogonal waveforms. A shifted dictionary matrix and a deterministic Discrete Fourier Transform (DFT) measurement matrix are used to multiply received echo signals to yield compressed measurements. A sparse recovery algorithm is applied to optimize signal reconstruction before joint transmit–receive beamforming forms a 2D sonar image in the angle-range domain. Numerical simulations and lake experimental results confirm the effectiveness of the proposed method, by obtaining a lower sidelobe sonar image under sub-Nyquist sampling rates as compared with other approaches. [ABSTRACT FROM AUTHOR]

Fish are a critical component of marine biology; therefore, the accurate identification and counting of fish are essential for the objective monitoring and assessment of marine biological resources. High‐frequency adaptive resolution imaging sonar (ARIS) is widely used for underwater object detection and imaging, and it quickly obtains close‐up video of free‐swimming fish in high‐turbidity water environments. Nonetheless, processing the massive data output using imaging sonars remains a major challenge. Here, the authors developed an automatic image‐processing programme that fuses K‐nearest neighbour background subtraction with DeepSort target tracking to automatically track and count fish. The automatic programme was evaluated using four test data sets with different target sizes and observation ranges and differently deployed sonars. According to the results, the approach successfully counted free‐swimming fish targets with an accuracy index of 73% and a completeness index of 70%. Under appropriate conditions, this approach could replace time‐consuming semi‐automatic approaches and improve the efficiency of imaging sonar data processing, while providing technical support for future real‐time data processing. [ABSTRACT FROM AUTHOR]

Multibeam echosounders, as the most commonly used bathymetric equipment, have been widely applied in acquiring seabed topography and underwater sonar images. However, when interference occurs in the water column, traditional bottom detection methods may fail, resulting in discontinuities in the bathymetry and distortion in the sonar images. To solve this problem, we propose an anti-interference bottom detection method based on deep learning models. First, the variation differences of backscatter strengths at different incidence angles and the failure conditions of traditional methods were analyzed. Second, the details of our deep learning models are explained. And these models were trained using samples in the specular reflection, scatter reflection, and high-incidence angle regions, respectively. Third, the bottom detection procedures of the along-track and across-track water column data using the trained models are provided. In the experiments, multibeam data with strong interferences in the water column were selected. The bottom detection results of the along-track water column data at incidence angles of 0°, 35°, and 60° and the across-track ping data validated the effectiveness of our method. By comparison, our method acquired the correct bottom position when the traditional methods had inaccurate or even no detection results. Our method can be used to supplement existing methods and effectively improve bathymetry robustness under interference conditions. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

In the realm of ocean engineering and maintenance of subsea structures, accurate underwater distance quantification plays a crucial role. However, the precision of such measurements is often compromised in underwater environments due to backward scattering and feature degradation, adversely affecting the accuracy of visual techniques. Addressing this challenge, our study introduces a groundbreaking method for underwater object measurement, innovatively combining image sonar with stereo vision. This approach aims to supplement the gaps in underwater visual feature detection with sonar data while leveraging the distance information from sonar for enhanced visual matching. Our methodology seamlessly integrates sonar data into the Semi-Global Block Matching (SGBM) algorithm used in stereo vision. This integration involves introducing a novel sonar-based cost term and refining the cost aggregation process, thereby both elevating the precision in depth estimations and enriching the texture details within the depth maps. This represents a substantial enhancement over existing methodologies, particularly in the texture augmentation of depth maps tailored for subaquatic environments. Through extensive comparative analyses, our approach demonstrates a substantial reduction in measurement errors by 1.6%, showing significant promise in challenging underwater scenarios. The adaptability and accuracy of our algorithm in generating detailed depth maps make it particularly relevant for underwater infrastructure maintenance, exploration, and inspection. [ABSTRACT FROM AUTHOR]

Object detection of continuous sonar image sequences has become an efficient way for underwater environment exploration. However, the task always suffers from the influence of th e complex underwater environment. In particular, the existing algorithms mainly focus on image-based detection and can not balance the detection speed and accuracy in continuous sonar image sequences. To solve the problem, this paper proposes a novel automatic detection algorithm based on deep learning for continuous sonar image sequences. Firstly, the convLSTM (convolution Long Short-Term Memory) is improved to fuse sonar features obtained from the cross-detection model, which are from three aspects: 1) The original convolution is replaced by depthwise separable convolution; 2) The original network input is divided into G groups and processed by group convolution; 3) A connection layer between the Bottleneck convolution layer and output is added to further capture feature information between frames. Then, to fully extract sonar features, a cross-detection network is established by fusing two different feature extraction networks MobileNetV3-Large and MobileNetV3-Small. Finally, we combine thecross-detection network with the improved convLSTM to establish the whole model, which can fully extract and utilize temporal information in continuous sonar image sequences. The experimental results show that the proposed model has effectively improved the detection speed in sonar image sequences at 150 FPS, simultaneously keeping an 85.8% mAP. [ABSTRACT FROM AUTHOR]

Sonar imaging technology is widely used in the field of marine and underwater monitoring because sound waves can be transmitted in elastic media, such as the atmosphere and seawater, without much interference. In underwater object detection, due to the unique characteristics of the monitored sonar image, and since the target in an image is often accompanied by its own shadow, we can use the relative relationship between the shadow and the target for detection. To make use of shadow-information-aided detection and realize accurate real-time detection in sonar images, we put forward a network based on a lightweight module. By using the attention mechanism with a global receptive field, the network can make the target pay attention to the shadow information in the global environment, and because of its exquisite design, the computational time of the network is greatly reduced. Specifically, we design a ShuffleBlock model adapted to Hourglass to make the backbone network lighter. The concept of CNN dimension reduction is applied to MHSA to make it more efficient while paying attention to global features. Finally, CenterNet's unreasonable distribution method of positive and negative samples is improved. Simulation experiments were carried out using the proposed sonar object detection dataset. The experimental results further verify that our improved model has obvious advantages over many existing conventional deep learning models. Moreover, the real-time monitoring performance of our proposed model is more conducive to the implementation in the field of ocean monitoring. [ABSTRACT FROM AUTHOR]

Traditional sonar image target detection analysis has problems such as long detection time, low detection accuracy and slow detection speed. To solve these problems, this paper will use the multi-feature fusion sonar image target detection algorithm based on the particle swarm optimization algorithm to analyze the sonar image. This algorithm uses the particle swarm algorithm to optimize the combination of multiple feature vectors and realizes the adaptive selection and combination of features, thus improving the accuracy and efficiency of sonar image target detection. The results show that: when other conditions are the same, under the particle group optimization algorithm, the sonar image multiple feature detection algorithm for three sonar image detection time between 4s-9.9s, and the sonar image single feature detection algorithm of three sonar image detection time between 12s-20.9s, shows that the PSO in multiple feature fusion sonar image target detection with better performance and practicability, can be effectively applied to the sonar image target detection field. [ABSTRACT FROM AUTHOR]

This article introduces a control architecture designed for the development of Hybrid Remotely Operated Underwater Vehicles. The term "Hybrid" characterizes Remotely Operated systems capable of autonomously executing specific operations. The presented architecture maintains teleoperation capabilities while enabling two fully autonomous applications. The approach emphasizes the implementation of reactive navigation by exclusively utilizing data from a Mechanical Scanned Imaging Sonar for control decisions. This mandates the control system to solely react to data derived from the vehicle's environment, without considering other positioning information or state estimation. The study involves transforming a small-scale commercial Remotely Operated Underwater Vehicle into a hybrid system without structural modifications, and details the development of an intermediate Operational Control Layer responsible for sensor data processing and task execution control. Two practical applications, inspired by tasks common in natural or open-water aquaculture farms, are explored: one for conducting transects, facilitating monitoring and maintenance operations, and another for navigating toward an object for inspection purposes. Experimental results validate the feasibility and effectiveness of the authors' hypotheses. This approach expands the potential applications of underwater vehicles and facilitates the development of Hybrid Remotely Operated Underwater Vehicles, enabling the execution of autonomous reactive tasks. [ABSTRACT FROM AUTHOR]

The use of traditional in situ methods for underwater surveys to map freshwater mussel habitats is limited by challenges such as water transparency, depth and high labour demands. In this study, adaptive resolution imaging sonar (ARIS) was applied to monitor mussel distribution and abundance.In contrast to conventional quadrat surveys, this acoustic survey is non‐invasive and enables direct observation of mussels to allow their survival status to be determined in turbid water. ARIS produces high‐quality acoustic images that facilitate the creation of a distribution map for broader‐scale monitoring, especially if paired with deep learning methods such as the YOLOv4 algorithm for automatic mussel detection and classification.ARIS was successfully applied to surveying over 2000 m2 of Lake Izunuma in Miyagi, Japan. In one site, a high mussel abundance of ~0.6 individuals/m2 was detected, while other sites had low densities. The detection model achieved a mean average precision of 0.97.The survey results were used to generate a distribution map of living mussels. This study illustrates the feasibility of using an acoustic video camera with an open‐source deep learning algorithm to monitor mussels and other benthos in turbid water. [ABSTRACT FROM AUTHOR]

Underwater 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. [ABSTRACT FROM AUTHOR]

Data-driven reduced order models (ROMs) have recently emerged as an efficient tool for the solution of inverse scattering problems with applications to seismic and sonar imaging. One requirement of this approach is that it uses the full square multiple-input/multiple-output (MIMO) matrix-valued transfer function as the data for multidimensional problems. The synthetic aperture radar (SAR), however, is limited to the single-input/single-output (SISO) measurements corresponding to the diagonal of the matrix transfer function. Here we present a ROM-based Lippmann-Schwinger approach overcoming this drawback. The ROMs are constructed to match the data for each source-receiver pair separately, and these are used to construct internal solutions for the corresponding source using only the data-driven Gramian. Efficiency of the proposed approach is demonstrated on 2D and 2.5D (3D propagation and 2D reflectors) numerical examples. The new algorithm not only suppresses multiple echoes seen in the Born imaging but also takes advantage of their illumination of some back sides of the reflectors, improving the quality of their mapping. [ABSTRACT FROM AUTHOR]