Underwater Research Articles

A Coverage Hole Recovery Method for 3D UWSNs Based on Virtual Force and Energy Balance

Underwater wireless sensor networks (UWSNs) have been applied in lots of fields. However, coverage holes are usually caused by complex underwater environment. Coverage holes seriously affect UWSNs’ performance and quality of service; thus, their recovery is crucial for 3D UWSNs. Although most of the current research recovery algorithms demand hole detection, the number of additional mobile nodes is too large, the communication and computing costs are high, and the coverage and energy balance are poor. Therefore, these methods are not suitable for UWSN hole repairing. In order to enhance the performance of hole recovery, a coverage hole recovery method for 3D UWSNs in complex underwater environments based on virtual force guidance and energy balance is proposed. The proposed method closely combines the node energy and considers complex environmental factors. A series of multi-dimensional virtual force models are established based on energy between nodes, area boundaries, zero-energy holes, low-energy coverage holes, underwater terrain, and obstacle forces. Then, a coverage hole recovery method for 3D UWSNs based on virtual force guidance and energy balance (CHRVE) is proposed. In this method, the direction and step size of mobile repairing node movement is guided by distributed computation of virtual forces, and the nodes are driven towards the target location by means of AUV or other carrier devices. The optimal position to improve coverage rate and node force balance is obtained. Simulation experiments show good adaptability and robustness to complex underwater terrain and different environments. The algorithm does not require precise coverage hole boundary detection. Furthermore, it balances network energy distribution significantly. Therefore, this method reduces the frequency of coverage hole emergence and network maintenance costs.

Novel Design and Computational Fluid Dynamic Analysis of a Foldable Hybrid Aerial Underwater Vehicle

Hybrid Aerial Underwater Vehicles (HAUVs), capable of operating effectively in both aerial and underwater environments, offer promising solutions for a wide range of applications. This paper presents the design and development of a novel foldable wing HAUV, detailing the overall structural framework and key design considerations. We employed fluid simulation software to perform comprehensive hydrodynamic and aerodynamic analyses, simulating the vehicle’s behavior during aerial flight, underwater navigation, water entry and exit, and surface gliding. The motion characteristics under different speed and angle conditions were analyzed. Additionally, a physical prototype was constructed, and experimental tests were conducted to evaluate its performance in both aerial and underwater environments. The experimental results confirmed the vehicle’s ability to seamlessly transition between air and water, demonstrating its viability for dual-environment operations.

Multi-Scale Feature Fusion Enhancement for Underwater Object Detection

Underwater object detection (UOD) presents substantial challenges due to the complex visual conditions and the physical properties of light in underwater environments. Small aquatic creatures often congregate in large groups, further complicating the task. To address these challenges, we develop Aqua-DETR, a tailored end-to-end framework for UOD. Our method includes an align-split network to enhance multi-scale feature interaction and fusion for small object identification and a distinction enhancement module using various attention mechanisms to improve ambiguous object identification. Experimental results on four challenging datasets demonstrate that Aqua-DETR outperforms most existing state-of-the-art methods in the UOD task, validating its effectiveness and robustness.

A Comparative Study on Laser Cutting Performance with Varying Speeds at 10 M Underwater

Despite the dismantling structures that are submerged to significant depths of water during the decommissioning of nuclear power plants, there is limited research on deep-water laser cutting processes. A self-designed pressurized chamber was used in this study and successfully conducted the world’s first laser cutting experiment in a simulated 10 m water depth environment. laser cutting was performed in a 10 m underwater environment, and the cutting efficiency was compared to that observed in a 1 m underwater environment. Therefore, A 100 mm thickness of 304 stainless steel was successfully cut underwater, and the highest cutting speed of 100 mm/min was achieved. The result indicates that, as the cutting speed increased during underwater laser cutting, both the heat input and the mass flow rate of the assist gas decreased, resulting in a narrower rear kerf width and an ineffective evacuation of the molten metal.

Superhydrophobic, Multifunctional, and Mechanically Durable Carbon Aerogel Composites for High-Performance Underwater Piezoresistive Sensing.

Carbon aerogel piezoresistive sensors (CAPSs), owing to their good thermal stability, self-constructed conductive network, and fast response to pressure, have attracted extensive attention in the field of flexible and wearable electronics in recent years. However, it is still a great challenge for CAPSs to monitor subtle deformations and achieve high-performance underwater piezoresistive sensing. Herein, a superhydrophobic and electrically conductive carbon aerogel composite (CAC) was fabricated by the combination of fluorination of carbon aerogels and decoration of fluorinated halloysite nanotubes (HNTs). Due to the exceptional light absorption and excellent photothermal conversion performance, CAC has a fast and accurate response to temperature with a high-temperature coefficient of resistance (TCR) of -1.06%/°C. The resistance of CAC exhibits a linear response toward compressive strain up to 80% with a high gauge factor of -1.24. Significantly, the CAC sensor can effectively detect tiny deformations, thanks to its excellent waterproof performance, and it enables stable output of sensing signals in an underwater environment. This work provides new insights into the development of superhydrophobic, multifunctional, and mechanically durable piezoresistive sensors with potential applications in underwater flexible electronics.

Adaptive Sliding Mode Control for AUV based on Backstepping and Neural Networks

Abstract Automatic Underwater Vehicle (AUV) inevitably suffers from various interference issues in the marine environment. Due to the limitations of underwater measurement methods and tools, as well as the complexity of AUV's control parameters in the underwater environment, dynamic measurement errors are easily cascaded and amplified, leading to the failure of the control system. In response to this phenomenon, this paper focuses on overcoming the influence of internal and external unknown factors in the tracking and control process of AUV trajectories. The internal factors are mainly the uncertainties generated by the model mismatch problem, and the external factors are mainly from the dynamic ocean currents. The AUV disturbances mainly affect the kinematics and dynamics levels directly or indirectly. In order to achieve the control of internal and external dynamic disturbances, we have designed a control system that employs backstepping (BS) and a sliding mode controller (SMC) with RBF neural networks to achieve the cascaded control of kinematics and dynamics. Comparison of multiple sets of simulations shows that our proposed algorithm has excellent anti-disturbance performance for dynamic conditions with low measurement accuracy.

Research on the generation and evaluation of bridge defect datasets for underwater environments utilizing CycleGAN networks

The surface cracks on the underwater structures critically damages the overall reliability of the structures and reduces their strength. It is significant to monitor these cracks in timely manner. Recently, deep learning algorithms have been used for large scale data study and predictions. However, deep supervised learning algorithms need to get training on large scale data set which is time consuming and difficult to apply on the underwater structures. Therefore, it is highly needed to address these issues. Current research proposes an improved cycle-constraint generative adversarial algorithm for the timely detection of surface cracks in underwater structures. It utilizes an enhanced cycle-consistent generative adversarial network (CycleGAN). The proposed algorithm uses image processing techniques including DeblurGAN and Dark channel prior methods to get quality of dataset from underwater structures. The proposed Algorithm introduces a novel cross-domain VGG-cosine similarity assessment to precisely evaluate the performance of proposed algorithm to retain crack information etc. Moreover, performance of proposed algorithm is evaluated through both qualitative and quantitative methods. The quantitative results are directly obtained from the visual results are presented which are generated by the proposed Algorithm. Whereas, the performance of proposed algorithm based on quantitative results is obtained from metrics including PSNR, SSIM, and FID. Experimental results indicates that the proposed algorithm outperforms the original CycleGAN. End results indicate that the proposed algorithm decreased the value of FID by 20 % and increased the values of PSNR and SSIM by 2.37 % and 3.33 % respectively. Quantitative and qualitative results of the proposed algorithm give significant advantages during creating of surface crack images.

Underwater Object Detection Algorithm Based on an Improved YOLOv8

Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the introduction of CIB building blocks into the backbone network, along with the optimization of the C2f structure and the incorporation of large-kernel depthwise convolutions, effectively enhances the model’s receptive field. This improvement increases the capability of detecting multi-scale objects in complex underwater environments without adding a computational burden. Next, the incorporation of a Partial Self-Attention (PSA) module at the end of the backbone network enhances model efficiency and optimizes the utilization of computational resources while maintaining high performance. Finally, the integration of the Neck component from the Gold-YOLO model improves the neck structure of the YOLOv8 model, facilitating the fusion and distribution of information across different levels, thereby achieving more efficient information integration and interaction. Experimental results show that YOLOv8-CPG significantly outperforms the traditional YOLOv8 in underwater environments. Precision and Recall show improvements of 2.76% and 2.06%. Additionally, mAP50 and mAP50-95 metrics have increased by 1.05% and 3.55%, respectively. Our approach provides an efficient solution to the difficulties encountered in underwater object detection.

UUV cluster navigation data extraction fusion positioning algorithm with information geometry

With the rapid development of the ocean economy, underwater unmanned vehicle (UUV) clusters have become the main tool for ocean development, but the complex interference and occlusion in the underwater environment have posed great challenges to the positioning of UUV clusters, becoming a key problem in the application of UUV clusters. To address this issue, this paper proposes a distributed heterogeneous navigation information geometric fusion positioning method for underwater unmanned vehicle clusters based on data extraction(DEFP-IG). This method converts the distributed navigation source information of UUVs into an information geometric probability model, and constructs a factor graph fusion architecture based on data extraction to improve the suppression of interference, thereby achieving high-precision positioning of UUV clusters. Comparing the method proposed in this paper with existing methods in different scenarios, the results show that the proposed method has higher positioning accuracy and good suppression capability for abrupt errors.

Underwater Wireless Communications

Effective underwater wireless communications (UWCs) are essential for a variety of military and civil applications, such as submarine communication and discovery of new natural resources in the underwater environment [...]

Underwater virtual exploration of the ancient port of Amathus

Underwater cultural heritage sites, spanning from submerged settlements to ancient ports and shipwrecks, captivate researchers and the public, providing insight into civilizations along coastlines and riverbanks. However, their accessibility and exploration are hindered by the sea’s physical barrier. Virtual Reality (VR) offers a transformative solution by providing digital accessibility to these underwater artifacts, enabling immersive exploration without physical limitations. VR enables people to embark on virtual tours of these sites, fostering a deeper appreciation of maritime archaeology and cultural heritage. Yet, fully realizing VR’s potential in underwater environments poses challenges, such as realistic virtual reconstruction and accurate simulation of marine life and coral reefs. Photogrammetry emerges as an effective technique for creating detailed 3D models, although underwater conditions often hinder quality outcomes. To address these challenges, our work focuses on digital underwater cultural heritage, presenting a gamified VR exploration of the ancient harbor of Amathus in Cyprus. Through photogrammetry, our VR environment enables users to explore and interact with the historic site seamlessly. Integrated guided tours, procedural generation, and machine learning algorithms enhance realism and user engagement. Evaluation through user studies demonstrates high-quality VR experiences with minimal discomfort, highlighting the efficacy and potential impact of our approach in enhancing underwater exploration and conservation efforts.

Enhancing Underwater SLAM Navigation and Perception: A Comprehensive Review of Deep Learning Integration

Underwater simultaneous localization and mapping (SLAM) is essential for effectively navigating and mapping underwater environments; however, traditional SLAM systems have limitations due to restricted vision and the constantly changing conditions of the underwater environment. This study thoroughly examined the underwater SLAM technology, particularly emphasizing the incorporation of deep learning methods to improve performance. We analyzed the advancements made in underwater SLAM algorithms. We explored the principles behind SLAM and deep learning techniques, examining how these methods tackle the specific difficulties encountered in underwater environments. The main contributions of this work are a thorough assessment of the research into the use of deep learning in underwater image processing and perception and a comparison study of standard and deep learning-based SLAM systems. This paper emphasizes specific deep learning techniques, including generative adversarial networks (GANs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and other advanced methods to enhance feature extraction, data fusion, scene understanding, etc. This study highlights the potential of deep learning in overcoming the constraints of traditional underwater SLAM methods, providing fresh opportunities for exploration and industrial use.

Enhanced Depth Control and Stability in Submersible Pylon Inspection Robots Using IMU-Based Extended Kalman Filter and PID Control

The research developed a Submersible Pylon Inspection Robot (SPIR) to clean and inspect underwater structures automatically, facing challenges in stability and depth of operation. Integrating the Inertial Measurement Unit (IMU) with the Extended Kalman Filter (EKF), as well as implementing customized PID controls, aims to improve the accuracy and stability of SPIR in dynamic underwater environments. The results show that the SPIR control system can follow the reference depth at shallow depths well, but has difficulty maintaining stability at deeper depths. The position error graph on the Z-axis shows the initial fluctuation that decreases over time, reflecting the increased calibration. The use of drive motors also shows different working patterns, with some motors active in depth settings and others for stabilization. This study shows that the integration approach of IMU, EKF, and PID control can improve SPIR performance, although further adjustments are required to meet the extreme challenges in underwater environments.

Development of Attentive-Based Trans-UNet and Cycle Generative Adversarial Networks for Underwater Image Enhancement

Researchers across the globe have been investigating underwater photographs and a way to capture pictures with outstanding clarity for the past couple of decades. Also, improving the obtained photographs is an exhausting task. Usually, applied underwater image-capturing devices are unable to acquire high-resolution photos underwater, and their maintenance is extremely costly. The underwater images include multiple flaws because of biological processes like attenuation and scattering. These photographs experience color distortion, blurriness, and low contrast effects. Technologies that utilize deep learning have become more popular among several research studies and have gradually grown in impact on society. Several techniques demand sets of training photos, but gathering such expected sets can be challenging because of the complex nature of the underwater environment. Generating and restoring an image from water is a difficult task that has gained prominence in recent days. By lowering graininess, adjusting, and refining the photos using deep learning models, the major goal is to enhance underwater images. To accomplish this objective, an intelligent attention-based deep learning model is proposed. In the first stage, the unrefined images are gathered from typical data sources. Further, the collected underwater images are fed into the model of Attentive-based Trans-UNet-CycleGAN (ATUNet-CGAN), where the Transformer-based UNet model is integrated with the Cycle Generative Adversarial Networks (GANs). Also, the attention mechanism process is involved in Trans-UNet-CycleGAN for improving the superiority of submarine images. Finally, the performance of the model is validated using different metrics and correlated among baseline approaches. Therefore, the proposed methodology outperforms the exploitation of better enhancement of image quality.

Self-powered wireless sensor networks based on the radioisotope thermoelectric generator for aquatic temperature monitoring

This study focuses on the development of ultra-long-life wireless sensor network (WSN) node based on the thermoelectric effect for use in areas such as aquatic temperature monitoring. A long-lasting radioisotope thermoelectric generator (RTG) was used as the energy source for the WSN node to address issues such as short runtime and pollution associated with traditional chemical batteries and solar cells. A compact RTG was fabricated by developing thermoelectric modules with high adaptability to cylindrical heat sources. The electrical output performance at the ocean surface and in the underwater environment was analyzed, with the maximum output power reaching 2326.6 μW. An integrated circuit module comprising direct current to direct current (DC-DC) boost converters, control switch circuits, and supercapacitors was employed to power the WSN node from the RTG directly. Powered by the RTG, a WSN node for monitoring ocean temperature was also constructed. Its performance was evaluated under different environmental conditions by investigating the effects of RTG’s output, sensor data variations, distance, and other factors on WSN node stability. Results demonstrate that the designed self-powered WSN node can operate stably and continuously at the ocean surface and in underwater environments, thereby showing its promising prospects.

Automatically Differentiable Higher-Order Parabolic Equation for Real-Time Underwater Sound Speed Profile Sensing

This paper is dedicated to the acoustic inversion of the vertical sound speed profiles (SSPs) in the underwater marine environment. The method of automatic differentiation is applied for the first time in this context. Representing the finite-difference Padé approximation of the propagation operator as a computational graph allows for the analytical computation of the gradient with respect to the SSP directly within the numerical scheme. The availability of the gradient, along with the high computational efficiency of the numerical method used, enables rapid inversion of the SSP based on acoustic measurements from a hydrophone array. It is demonstrated that local optimization methods can be effectively used for real-time sound speed inversion. Comparative analysis with existing methods shows the significant superiority of the proposed method in terms of computation speed.

A Lightweight underwater detector enhanced by Attention mechanism, GSConv and WIoU on YOLOv8

The underwater target detection is the most important part of monitoring for environment, ocean, and other fields. However, the detection accuracy is greatly decreased by the poor image quality resulted from the complex underwater environments. The storage and computing power of underwater equipments are not enough for complex underwater target detection technology. Therefore, many YOLO series algorithms have been applied to underwater target detection. On the basis of YOLOv8, a lightweight underwater detector enhanced by Attention mechanism, GSConv and WIoU, AGW-YOLOv8, is proposed in this paper. Firstly, by the combination of limited contrast adaptive histogram equalization and wavelet transform(LCAHE-WT), the fidelity and detail of images are improved; Secondly, by CBAM, the key channel features can be effectively extracted with retaining spatial information to improve the performance of the network when dealing with complex image tasks; Thirdly, by GSConv, composed of depth-wise separable convolution and regular convolution, the model parameters and computational complexity are reduced; Fourth, the SE attention mechanism is integrated into the C2f module of the neck, and the channel dimension is weighted to make the network more focus on important features, and to further enhance the feature extraction capability; Finally, by the dynamic nonmonotonic mechanism of WIoU, the gradient gain can be reasonably distributed, the harmful gradients of extreme samples can be reduced, and the generalization ability and overall performance of the model are improved. By the experiments on the URPC2020 data-set, it can been proved that the mAP of AGW-YOLOv8 can reaches 82.9%, is 2.5% higher than that of YOLOv8; and the parameters is 2.95M, is lower than 3.01M of YOLOv8.

PAFPT: Progressive aggregator with feature prompted transformer for underwater image enhancement

The optical characteristics of underwater environments often result in distorted underwater images, which makes it difficult to meet the needs of complex underwater environment perception. The demand for obtaining high-quality underwater images presents challenges for existing underwater image enhancement methods. In this paper, a progressive aggregator with feature prompted transformer for underwater image enhancement is presented. First, a feature prompted transformer module consisting of a feature convolutional transposed attention and a prompted feedforward neural network is proposed. It integrates feature attention mechanisms and adjusts input features through prompt weights to better focus on global information while enhancing local details, enriching texture, removing blurring, and correcting color deviation. Concurrently, a progressive aggregator consisting of four stages of up-sampling aggregation blocks is constructed to transfer features effectively and facilitate the aggregation of features of different scales at different stages to realize comprehensive attention to image details and semantic information. Besides, a region reconstruction unit is designed, and a reconstruction attention mechanism is introduced to pay attention to the bottleneck localized region to suppress noise. The performance of our method is thoroughly evaluated on public underwater image datasets: EUVP, UIEB, RUIE and SQUID. The quantitative and qualitative results indicate that our method outperforms nine state-of-the-art methods in both subjective perception and evaluation metrics, demonstrating excellent learning and generalization abilities. Additionally, the excellent results reflect the significant performance gains it brings to downstream visual applications.

Pretrained U-Net: in-depth analysis of binary image segmentation in underwater marine environment

Pretrained U-Net: in-depth analysis of binary image segmentation in underwater marine environment

Positioning Systems for Unmanned Underwater Vehicles: A Comprehensive Review

Positioning systems are integral to Unmanned Underwater Vehicle (UUV) operation, enabling precise navigation and control in complex underwater environments. This paper comprehensively reviews the key technologies employed for UUV positioning, including acoustic systems, inertial navigation, Doppler velocity logs, and GPS when near the surface. These systems are essential for seabed mapping, marine infrastructure inspection, and search and rescue operations. The review highlights recent technological advancements and examines the integration of these systems to enhance accuracy and operational efficiency. It also addresses ongoing challenges, such as communication constraints, environmental variability, and discrepancies between theoretical models and field applications. Future trends in positioning system development are discussed, with a focus on improving reliability and performance in diverse underwater conditions to support the expanding capabilities of UUVs across scientific, commercial, and rescue missions.