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Capturing clear images in underwater environments is a major challenge in marine engineering. There are many issues to consider in obtaining clear underwater images such as climate, environment, and human factors. The most important reasons are the atomization effect caused by dispersion and the color cast caused by inconsistent energy attenuation of each wavelength when light propagates in water. Recently, deep learning technology has shown impressive performance on underwater image enhancement. The deep learning-based methods apply to the underwater image enhancement tasks. We propose a deep learning model for inferring a degradation model to further improve image dynamic range through a network-guided underwater image enhancement network architecture with multicolor space embedding and convolutional media transfer, fixed an issue with limited dynamic range and brightness in underwater images. Quantitative and qualitative results show that our network performs relatively well in the Underwater Image Enhancement Benchmark (UIEB) [7] dataset compared to other recent methods, and is expected to be applied to different types of underwater work and environments in the future and reduce the degradation problems that often occur with underwater images. The acquisition of high-fidelity imagery in subaqueous environments presents significant technical challenges in marine engineering, encompassing a complex interplay of climatological variables, environmental parameters, and anthropogenic factors. Primary impediments to image clarity comprise the atomization phenomenon induced by optical scattering and chromatic distortion resulting from wavelength-dependent energy attenuation in aqueous media. The procurement of high-resolution underwater imagery is fundamental to numerous scientific applications, including marine biological research, autonomous underwater robotics, and environmental surveillance systems, where precise visual data acquisition substantially augments analytical efficacy. Contemporary developments in deep learning architectures have exhibited remarkable potential for enhancing underwater image quality. In response to these challenges, we present a novel deep learning framework that derives an empirical degradation model, utilizing a network-guided enhancement architecture incorporating multicolor space embedding and convolutional media transfer methodologies to optimize image dynamic range. This methodological approach specifically addresses the limitations in luminance distribution and dynamic range characteristics inherent in subsea imagery. Empirical evaluation of our architectural framework on the standardized Underwater Image Enhancement Benchmark (UIEB) [7] dataset demonstrates statistically significant performance improvements over contemporary methodologies, suggesting broad applicability across diverse submarine environments for mitigating common degradation phenomena. [ABSTRACT FROM AUTHOR]

In this article, an integrated object detection and communication (IODC) system based on single-stage detection framework is first proposed for synthetic aperture radar (SAR) images in underwater environment. Specifically, the combination of the multiscale feature extraction module and semantic information enhance fusion module for SAR images in underwater environment is designed as the semantic encoder and the results prediction module with anchor-free detection method is designed as the semantic decoder. Considering the multiscale feature, the channel encoder composed by a multiscale fusion module and a redundancy module is designed, and the channel decoder is the inverse of the channel encoder. To adapt to the time-varying and complex wireless environment, an adaptive transmission (AT) mechanism based on attention mechanism and knowledge base is proposed for the IODC system. Moreover, considering the actual application requirements, a lightweight design for the IODC system with the AT mechanism is also conducted. The experiment results on the Sonar Common Target Detection dataset show that the proposed IODC system with AT mechanism can achieve nearly 91% average precision at 25 dB, which means the proposed system can achieve the effective integration of the objection detection and communication for SAR images in underwater environment. In the lightweight design, the model parameters of the proposed system can be reduced by up to 20%, with only a 2.22% sacrifice in performance.

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To investigate influence of underwater environment on the effect of underwater drilling and blasting, the action mechanism of underwater drilling and blasting was analyzed by taking the underwater blasting engineering of No.3 pier foundation of New Baishatuo Yangtze River Super-long Bridge in Chongqing, China as research object. Moreover, by using ANSYS/LS-DYNA finite element program, the processes of land drilling & blasting and underwater drilling & blasting were separately numerically simulated. Based on the which, the dynamic response characteristics of land and underwater drilling & blasting were compared by utilizing a blasting funnel test, characteristics of shock waves and inherent characteristics of aqueous medium. Therefore, the influence of underwater environment on drilling and blasting effect was acquired. The results showed that Mises equivalent stress near the water-bottom interface was lower than that of the same location subjected to land drilling and blasting due to the influence of hydraulic pressure from underwater environment. Under the same pressure, the volumes of underwater blasting-induced funnel pits were all lower than those of land blasting-induced funnel pits. With the same charge quantity, the deeper the water, the smaller the volume of blasting induced funnel pits. Compared with land drilling and blasting, the presence of underwater environment can accelerate the attenuation of explosion-induced shock waves to thus further weaken the effect of rock fragmentation in blasting. The research result can further enrich the underwater blasting theory system and provide practical guidance for the construction of underwater drilling and blasting projects. [ABSTRACT FROM AUTHOR]

A thermoelectric generator (TEG) is one of the important energy harvesting sources for wearable electronic devices, which converts waste heat into electrical energy without any external stimuli, such as light or mechanical motion. However, the poor flexibility of traditional TEGs (e.g., Si-based TE devices) causes the limitations in practical applications. Flexible paper substrates are becoming increasingly attractive in wearable electronic technology owing to their usability, environmental friendliness (disposable, biodegradable, and renewable materials), and foldability. The high water-absorbing quality of paper restricts its scope of application due to water failure. Therefore, we propose a high-performance flexible waterproof paper-based thermoelectric generator (WPTEG). A modification method that infiltrates TE materials into cellulose paper through vacuum filtration is used to prepare the TE modules. By connecting the TE-modified paper with Al tape, as well as a superhydrophobic layer encapsulation, the WPTEG is fabricated. The WPTEG with three P–N modules can generate an output voltage of up to 235 mV at a temperature difference of 50 K, which can provide power to portable electronic devices such as diodes, clocks, and calculators in hot water. With the waterproof property, the WPTEG paves the way for achieving multi-scenario applications in humid environments on human skin. [ABSTRACT FROM AUTHOR]

A novel type of underwater pier reinforcement technology was proposed. A non-dispersive mortar (NDM) and stainless steel tube (SS) were adopted to strengthen an underwater concrete column. The strengthened column takes advantage of the good adhesion of NDM and the corrosion resistance of SS. The NDM pouring and curing processes were both conducted in an underwater environment, which simulated the environment of underwater bridge pier reinforcement. An axial compression test was carried out on 16 strengthened columns and 3 unconfined concrete columns to explore the reinforcement effect of SS thickness and NDM type, including the 2–6 mm thickness of SS, the epoxy-based NDM, and the cement-based NDM. The test results indicated that all the specimens showed extrusion expansion failure. The increase in the thickness of SS resulted in a significant improvement in the load capacity of the specimens, and the stress-strain curves showed the strengthening stage and softening stage according to the confinement factor of the SS thickness. After adding cement-based NDM and epoxy-based NDM, the strength of the specimens increased by 6.2–21.4 % and 8.8–31.5 %, respectively. In the case of the same thickness of SS, cement-based NDM showed a better reinforcement effect. The stress-strain curves for the strengthened columns can be divided into three stages. Calculation models were proposed to predict the full stress-strain curves, including the calculation method of the capacity. The calculated results are in good agreement with the experimental data.

In recent years, underwater wireless power transmission technology has developed rapidly. Research on omnidirectional wireless power transmission technology in underwater spatial has become increasingly important. Underwater electromechanical devices in the wireless power transmission process and submarine currents' interference easily cause the radial and axial offset of the transmitting and receiving coils, resulting in changes in the coupling coefficient between the coils, reducing the efficiency and stability of the system energy transmission. The research on the mechanism of underwater spatial omnidirectional wireless power transfer systems has become an urgent problem to be solved in the field of underwater wireless power transmission. To solve this problem, this paper proposes a tri-orthogonal three-dimensional square coil structure that generates an omnidirectional magnetic field in a specific region. An underwater spatial omnidirectional wireless power transfer system is constructed, and theoretical analysis and formula derivation are carried out. Maxwell software is used to simulate the multidimensional electromagnetic field. An experimental verification platform is built, and the experimental results verify the feasibility of the underwater spatial omnidirectional wireless power transfer system, which meets the practical requirements. This design has reference and guiding significance for related industries. [ABSTRACT FROM AUTHOR]

This study reports the analytical approach towards nine coins found in wreck A of Fontanamare for understanding the complex corrosion processes that take place in underwater conditions. Optical microscopy (OM) combined with micro-Raman (µ-Raman) spectroscopy, X-ray diffractometry (XRD), and scanning electron microscopy with energy-dispersive X-ray spectroscopy (SEM and EDS) were used to analyze the nature and the microstructure of the corrosion patina in comparison with the non-degraded state of coins. Three main types of corrosion patina based on copper, lead chloride, and carbonate were identified: black, white, and green, and the obtained results demonstrate the complex corrosion processes that take place in underwater conditions. To better understand the role played by different chemical and physical parameters in the corrosion of bronze artefacts in an underwater environment, this study attempts to understand the nature of the patinas on the coins in relation to the specific structural and environmental parameter variation. [ABSTRACT FROM AUTHOR]

The exploitation and construction of deep sea is a new direction for human development. In order to meet the national strategic development needs, it should be well planned in advance. Deep sea development is more dependent on the marine environment, and the demand for marine environment information is more urgent. This paper analyzed the fields in which the marine environmental service could play a role in the development and construction of deep sea and the corresponding service contents; from the aspects of navigation, hydrographic surveying and charting, and oceanographic survey, it analyzed the current status of the US and China's capabilities in deep sea marine environment support; then the gap between China and the United States in six aspects was analyzed; at last, some proposals were proposed from a disciplinary perspective.

Consider the case where autonomous underwater vehicles (AUVs) are deployed to monitor a 3D underwater environment. This paper tackles the problem of guiding all AUVs to the destination while not colliding with a priori unknown 3D obstacles. Suppose that among all AUVs, only the leader AUV has an ability of locating itself, while accessing a destination location. A follower, an AUV that is not a leader, has no sensors for locating itself. Every follower can only measure the relative position of its neighbor AUVs utilizing its sonar sensors. Our paper addresses distributed controls, so that multiple followers track the leader while preserving communication connectivity. We design controls, so that all AUVs reach the destination safely, while maintaining connectivity in cluttered 3D environments. To the best of our knowledge, our article is novel in developing 3D underwater guidance controls, so that all AUVs equipped with sonar sensors are guided to reach a destination in a priori unknown cluttered environments. MATLAB simulations are used to validate the proposed guidance scheme in underwater environments with many obstacles. [ABSTRACT FROM AUTHOR]

The direction-of-arrival (DoA) estimation algorithms have a fundamental role in target bearing estimation by sensor array systems. Recently, compressive sensing (CS)-based sparse reconstruction techniques have been investigated for DoA estimation due to their superior performance relative to the conventional DoA estimation methods, for a limited number of measurement snapshots. In many underwater deployment scenarios, the acoustic sensor arrays must perform DoA estimation in the presence of several practical problems such as unknown source number, faulty sensors, low values of the received signal-to-noise ratio (SNR), and access to a limited number of measurement snapshots. In the literature, CS-based DoA estimation has been investigated for the individual occurrence of some of these errors but the estimation under joint occurrence of these errors has not been studied. This work investigates the CS-based robust DoA estimation to account for the joint impact of faulty sensors and low SNR conditions experienced by a uniform linear array of underwater acoustic sensors. Most importantly, the proposed CS-based DoA estimation technique does not require a priori knowledge of the source order, which is replaced in the modified stopping criterion of the reconstruction algorithm by taking into account the faulty sensors and the received SNR. Using Monte Carlo techniques, the DoA estimation performance of the proposed method is comprehensively evaluated in relation to other techniques. [ABSTRACT FROM AUTHOR]

With the continuous development and utilization of marine resources, the underwater target detection has gradually become a popular research topic in the field of underwater robot operations and target detection. However, it is difficult to combine the environmental semantic information and the semantic information of targets at different scales by detection algorithms due to the complex underwater environment. In this paper, a cascade model based on the UGC-YOLO network structure with high detection accuracy is proposed. The YOLOv3 convolutional neural network is employed as the baseline structure. By fusing the global semantic information between two residual stages in the parallel structure of the feature extraction network, the perception of underwater targets is improved and the detection rate of hard-to-detect underwater objects is raised. Furthermore, the deformable convolution is applied to capture longrange semantic dependencies and PPM pooling is introduced in the highest layer network for aggregating semantic information. Finally, a multi-scale weighted fusion approach is presented for learning semantic information at different scales. Experiments are conducted on an underwater test dataset and the results have demonstrated that our proposed algorithm could detect aquatic targets in complex degraded underwater images. Compared with the baseline network algorithm, the Common Objects in Context (COCO) evaluation metric has been improved by 4.34%. [ABSTRACT FROM AUTHOR]

The 1940s and 1950s were a revolutionary era in both access to and imagination of the underwater environment in the most modernized nations of the globe. This article examines new possibilities in the exhibition of the underwater environment in film thanks to new dive and filming technologies. It focuses on how the depths are portrayed as a setting in Richard Fleischer's 20,000 Leagues under the Sea (1954), by the Disney studio, and Jacques-Yves Cousteau and Louis Malle's The Silent World (1956). In both films, conventions for portraying terrestrial settings are adapted to the resistant conditions of the aquatic environment. Such adaptation unleashes creativity in filmmaking and results in portrayals in which audiences are at once at home in this alien area of our planet and able to assimilate and enjoy its unprecedented vistas and savage life. [ABSTRACT FROM AUTHOR]

The energy crisis is one of the most critical and urgent problems in modern society; thus, harvesting energy from ubiquitous low-grade heat energy with thermoelectric (TE) materials has become an available strategy in sustainable development. Recently, emerging ionic TE materials have been widely used to harvest low-grade heat energy, owing to their excellent performance in high ionic Seebeck coefficient, low thermal conductivity, and mechanical flexibility. However, the instability of ionic conductive materials in the underwater environment seriously suppresses underwater energy-harvesting, resulting in a waste of underwater low-grade heat energy. Herein, we developed a water-resistant TE ionogel (TEIG) with excellent long-term underwater stability utilizing a hydrophobic structure. Due to the hydrophobic polymer network and hydrophobic ionic liquid (IL), the TEIG exhibits high hydrophobicity and antiswelling capacity, which meets the requirement of environment stability for underwater thermoelectric application. Furthermore, the water resistance endows the TEIG with great thermoelectric performances in the underwater environment, including satisfactory ionic Seebeck coefficient, outstanding durability, and superior salt tolerance. Therefore, this investigation provides a promising strategy to design water-resistant TE materials, enabling a remarkable potential in harvesting low-grade heat energy under water. [ABSTRACT FROM AUTHOR]

This paper investigates the underwater optical image restoration method under the background of underwater target detection based on optical vision in AUVs. The light source used for AUV detection is different when the AUV operates in different depths. The natural light source is used in shallow water and the artificial light source is used in deep water. This paper investigates underwater optical image restoration in these two light conditions. Aiming at the problem of image blurring in underwater optical images, the traditional underwater image restoration method based on scattering model can obtain satisfactory image restoration performance in natural light conditions. However, it cannot obtain the same image restoration result in artificial light conditions. To solve this problem, this paper presents an improved underwater optical image restoration method based on the scattering model. The scattering model and power spectrum are used to solve the initial parameters of the filter, and the parameters are optimized based on an evaluation index. The index of image definition is introduced to evaluate the restoration performance and to achieve the satisfactory image restoration result in both natural light and artificial light conditions. The effectiveness of the presented method is verified by experiments. [ABSTRACT FROM AUTHOR]

Ocean current energy bears the characteristics of wide distribution and high energy density. Harvesting current energy as the in-situ power for distributed ocean sensors will greatly enhance the sustainability of ocean sensing. In many parts of the ocean, the current speed is not desirable (low or unstable) for conventional current energy harvesting devices. Here, an underwater blade-free triboelectric nanogenerator (UBF-TENG) is developed to effectively utilize the low-speed currents through the flow-induced vibration (FIV). The power generation unit is fully enclosed inside the shell, effectively reducing the direct impact and underwater corrosion. The study demonstrates that within the established experimental parameters space, the optimized UBF-TENG can facilitate a start-up speed as low as 0.14 m/s. The UBF-TENG has achieved a maximum open-circuit voltage output of 250 V and a maximum short-circuit current output of 2.2μA at 0.76 m/s. The charging capacity of the UBF-TENG is tested with various capacitors, demonstrating the application potential of the UBF-TENG as an in-situ power supply underwater. [Display omitted] • The UBF-TENG utilizes the flow-induced vibration, allowing it to facilitate a start-up current speed as low as 0.14 m/s. • The device's power generation unit is fully enclosed inside the shell, which effectively reduces the direct impact and corrosion underwater. [ABSTRACT FROM AUTHOR]

This study reports a composite coating with noise reduction and corrosion resistance properties. A series of PU-based organic coating matrices, whose performance was validated with different compositions, were first prepared. Subsequently, Ti 3 C 2 T x -PANI composite fillers, possessing both porous and layered structures, were successfully synthesized and incorporated into the optimal-performing organic coating matrices. Experimental testing and finite element analysis demonstrated that the prepared composite coating could reduce environmental noise levels by approximately 25 to 30 dB through leveraging its internal microcavity structure and superb damping performance. Furthermore, due to the barrier effect against corrosive media infiltration provided by the fillers, the impedance modulus of the coating remained 109 Ω·cm2 after being immersed in 3.5 wt% NaCl solution for 50 days. In summary, this coating with an outstanding corrosion resistance and noise reduction performance, demonstrates huge potential for applications in reducing noise and preventing corrosion in marine equipment. • The performance of different components of SPUs was compared to select the most suitable composite coating matrix. • The porous PANI and Ti 3 C 2 T x enhance both the sound absorption capability and the damping capability of the coating matrix. • The filler provides a physical barrier against corrosive media ensuring the coating has long-term corrosion resistance. [ABSTRACT FROM AUTHOR]

Evading collisions in three-dimensional underwater environments is critical in exploration of an Autonomous Underwater Vehicle (AUV). In underwater environments, AUV measures an obstacle surface by utilizing a three-dimensional active sonar. This article addresses reactive collision evasion control by considering extended obstacles. Here, an extended obstacle is an arbitrary obstacle that can generate any number of measurements and not a point target generating at most one measurement. Considering 3D environments, our manuscript considers collision evasion with both moving obstacles and static obstacles. The proposed reactive collision evasion controllers are developed by considering hardware limits, such as the maximum speed or acceleration limit of an AUV. We further address how to make an AUV move towards a goal, while avoiding collision with extended obstacles. As far as we know, the proposed collision evasion controllers are novel in handling collision avoidance with an extended obstacle, in the case where an AUV measures 3D-obstacle boundaries by utilizing sonar sensors. The effectiveness of the proposed controllers is demonstrated by MATLAB simulations. [ABSTRACT FROM AUTHOR]

A novel underwater spliceable basalt fiber-reinforced polymer (BFRP) shell-confined concrete structure was proposed that rapidly reinforces underwater piers by lapping the FRP shell with adhesive underwater combined with nondispersive mortar (NDM) filling. Thirty-six prefabricated BFRP shells combined with nondispersive mortar reinforced concrete columns (PFRCs) were made for axial compression testing. The effects of the BFRP layer number, lap length and pouring environment on the specimen performance were compared, and different damage patterns and stress strain relationship curves were obtained. The test results reveal that the PFRCs exhibited two failure modes: BFRP fracture failure in nonlapped segments and BFRP bond adhesive peeling failure in lap segments, and the specimens with more BFRP layers and shorter lap lengths were prone to BFRP bond adhesive peeling failure. From 2 to 4 layers, the ultimate stress and ultimate strain values increased by 1.18–1.91 times and 5.18–10.32 times, respectively, demonstrating increasing the number of BFRP layers is a more effective means to improve the structural performance. The ultimate strength of the underwater reinforced specimens reached more than 70 % of that of the corresponding specimens on land, which demonstrated the feasibility of using prefabricated FRP-molded shell reinforcing technology for submerged piers. Finally, the calculation model of an FRP-confined concrete column is evaluated, and the full stress strain curves of the PFRCs are proposed. • The prefabricated BFRP shells combined with non-dispersive mortar reinforced concrete columns（PFRCs）were presented. • A lapping adhesive-filling device for the joint of the prefabricated BFRP shell was proposed. • Influences of FRP layers, lap length and pouring environment were evaluated. • Two failure modes were obtained and ultimate stress and strain were evaluated. • Stress-strain curves calculation models for PFRCs were proposed. [ABSTRACT FROM AUTHOR]

Take the 3D reconstruction of underwater targets as the background, this study proposes an experimental design plan for the three-dimensional reconstruction of underwater targets based on monocular vision. First, a monocular camera is used to rotate and shoot an underwater object from multiple angles without the need to pre-calibrate the camera to generate an initial set of image inputs. Then, problems such as a decrease in the number of feature point extractions in the underwater image, an increase in the mismatch ratio due to color distortion, and low contrast ratio, by using the proposed feature matching SIFT HC method, the gray difference between pixels and the color contrast between pixels are minimized by bimodal distribution function to obtain the gray image to be matched. On this basis, Helinger distance is used as similarity measure for feature matching to effectively maintain the original image color contrast, improve the number of effective feature points in adjacent images, and eliminate the false matching point pairs to improve the matching rate of feature points. Finally, the 3D point cloud is reconstructed to effectively restore the surface details of the target object. Experimental results show that the experimental equipment is simple, without pre-calibrate the camera, and can effectively restore the 3D structure and surface details of the target object in view of the color distortion of the underwater image. [ABSTRACT FROM AUTHOR]

This paper investigates the underwater optical image restoration method under the background of underwater target detection based on optical vision in AUVs. The light source used for AUV detection is different when the AUV operates in different depths. The natural light source is used in shallow water and the artificial light source is used in deep water. This paper investigates underwater optical image restoration in these two light conditions. Aiming at the problem of image blurring in underwater optical images, the traditional underwater image restoration method based on scattering model can obtain satisfactory image restoration performance in natural light conditions. However, it cannot obtain the same image restoration result in artificial light conditions. To solve this problem, this paper presents an improved underwater optical image restoration method based on the scattering model. The scattering model and power spectrum are used to solve the initial parameters of the filter, and the parameters are optimized based on an evaluation index. The index of image definition is introduced to evaluate the restoration performance and to achieve the satisfactory image restoration result in both natural light and artificial light conditions. The effectiveness of the presented method is verified by experiments.

Research on underwater acoustic sensor networks has become a compelling field in recent years since resources on land are being depleted. Therefore, robust and efficient routing protocols are needed to ensure the reliability of message gathering and transmitting in underwater sensor networks. In the process of biological foraging in nature, creatures such as insects can find paths while adapting to dynamic environmental conditions through group cooperation, which provides a new perspective for research on routing protocols. In this study, the authors proposed an ant colony algorithm‐based routing protocol (ACAR). In ACAR, the pheromone with a novel physical meaning and concentration changing mechanism is utilised to guide ants (path establishing packets) to the sink node. In addition, node depth information is used to decrease redundancy in the underwater environment. The routing process can be summarised in three parts: pheromone list setup, routing decision and damaged path repair. Simulation results, carried out on an underwater simulator based on NS‐2, showed that ACAR outperforms other schemes with regards to network lifetime with a relatively better delivery ratio and latency in the proposed network scenarios. [ABSTRACT FROM AUTHOR]

A distributed short-term predictive control method is proposed to be applied to autonomous underwater vehicle (AUV) clusters for underwater cooperative hunting tasks. To support AUV clusters in unknown underwater dynamic environments, the constraints of information acquisition, AUV motion constraints, collision avoidance, and smoothness requirements of motion paths in the tasks are considered. First, a distributed multi-constraint optimization model including target attraction, interactions between AUVs, continuous obstacle avoidance, and control effort is proposed. Effective information between AUVs is extended under limited information conditions. Second, each AUV solves the distributed optimization problem and determines the desired trajectory in real time using information from the prediction horizon. In the simulation experiments, the proposed method is demonstrated to provide at least 12.19%, 11.08%, and 79.89% improvement in terms of task completion time, energy consumption, and path smoothing, respectively, when compared to other methods under the same information conditions. The robustness of the proposed method under different noise conditions is verified by positioning noise experiments, and the task can still be accomplished even if the noise level reaches 75% of the safe distance. The adaptability of the proposed method is demonstrated by simulations in different environments. • A distributed multi-constraint optimization model for AUV cluster cooperative hunting tasks is developed. • The effective information among AUVs is extended under the limited information condition. • The possibility of local optimal is reduced. Continuous obstacle avoidance in the whole process is realized. • The task can still be accomplished when the noise level reaches 75% of the safe distance. [ABSTRACT FROM AUTHOR]

The use of underwater autonomous vehicles has been growing, allowing the performance of tasks that cause inherent risks to Human, namely in inspection processes near to structures. With growth in usage of systems with autonomous navigation, visual acquisition methods have also gotten more developed because, they have appealing cost and they also show interesting results when operate at a short distance. It is possible to improve the quality of navigation through visual SLAM techniques which can map and locate simultaneously and its key aspect is the detection of revisited areas. These techniques are not usually applied to underwater scenarios and, therefore, its performance in environment is unknown. The paper presents a more reliable navigation system for underwater vehicles, resorting to some visual SLAM techniques from literature. The results, conducted in a realistic scenario, demonstrated the ability of the system to be applied to underwater environment.

: One of the main issues for the navigation of underwater robots consists in accurate vehicle positioning, which heavily depends on the orientation estimation phase. The systems employed to this end are affected by different noise typologies, mainly related to the sensors and to the irregular noise of the underwater environment. Filtering algorithms can reduce their effect if opportunely configured, but this process usually requires fine techniques and time. This paper presents DANAE++, an improved denoising autoencoder based on DANAE (deep Denoising AutoeNcoder for Attitude Estimation), which is able to recover Kalman Filter (KF) IMU/AHRS orientation estimations from any kind of noise, independently of its nature. This deep learning-based architecture already proved to be robust and reliable, but in its enhanced implementation significant improvements are obtained in terms of both results and performance. In fact, DANAE++ is able to denoise the three angles describing the attitude at the same time, and that is verified also using the estimations provided by an extended KF. Further tests could make this method suitable for real-time applications in navigation tasks. [ABSTRACT FROM AUTHOR]

Various types of knowledge and features have been explored for level set-based segmentation. On the ground, the prior knowledge and carefully-designed features perform well to identify the foreground-background contrast, which improves the performance of the segmentation method for complicated and distorted data. However, this is not the case for underwater environments, since the features available on the ground are not suitable for challenging underwater environments. Thus, underwater image segmentation currently lags behind ground-based segmentation. In this paper, novel cues and a suitable model formulation for object segmentation from underwater images are proposed. We consider the special haze effect over underwater images and extract an informative feature (transmission feature) from haze condensation. The saliency feature is also used for underwater object segmentation. Consequently, in our method, the object-background difference can be presented by these features on two levels, i.e., the edge-level transmission and region-level saliency features. These two types of features are integrated into a unified level set formulation to propose a solution that handles the challenging issues in underwater object segmentation. The experimental comparisons of our method with other methods comprehensively demonstrate the satisfactory performance of our method.

Research on underwater acoustic sensor networks has become a compelling field in recent years since resources on land are being depleted. Therefore, robust and efficient routing protocols are needed to ensure the reliability of message gathering and transmitting in underwater sensor networks. In the process of biological foraging in nature, creatures such as insects can find paths while adapting to dynamic environmental conditions through group cooperation, which provides a new perspective for research on routing protocols. In this study, the authors proposed an ant colony algorithm‐based routing protocol (ACAR). In ACAR, the pheromone with a novel physical meaning and concentration changing mechanism is utilised to guide ants (path establishing packets) to the sink node. In addition, node depth information is used to decrease redundancy in the underwater environment. The routing process can be summarised in three parts: pheromone list setup, routing decision and damaged path repair. Simulation results, carried out on an underwater simulator based on NS‐2, showed that ACAR outperforms other schemes with regards to network lifetime with a relatively better delivery ratio and latency in the proposed network scenarios. [ABSTRACT FROM AUTHOR]

Automatic salient object detection over images is an important step for object detection and recognition. Many existing salient object detection methods are performed excellently on ground, but, it remains a challenge to detect salient objects in water. Different from common scenes on ground, challenges to underwater salient object detection are posed by poor underwater image quality, uncontrolled underwater objects and environments. Till now, most of existing methods are hindered from adaptability to difficult underwater environments, due to the strong light attenuation and scattering effects. To this end, we propose a novel underwater salient object detection method by combining 2D and 3D visual features. For feature combination, a novel detection model is established by mathematically stimulating the biological vision mechanism of aquatic animals. This model, apart from the 2D visual features i.e. the color and intensity, extracts 3D depth features to arouse the depth sensitivity in the three-dimensional space. These 2D and 3D visual features are combined by our biologically inspirited model, generating comprehensive salient object detection results. Here, aiming to correctly estimate 3D depth features from underwater images, a regional method is used to respectively extract 3D depth features in two regions, namely the artificial light and natural light regions. Evaluations show the diverse and comprehensive performances of various features for underwater salient object detection. High accuracy of our proposed method is demonstrated by comparing to state-of-the-art saliency detection methods on public underwater benchmarks acquired in diverse underwater environments. [ABSTRACT FROM AUTHOR]

Underwater Image Enhancement (UIE) is a challenging problem due to the complex underwater environment. Traditional UIE methods can hardly adapt to various underwater environments. Deep learning-based UIE methods are more powerful but often rely on a large deal of real-world underwater images with distortion-free reference images. This gives rise to two issues: First, the reference images are highly uncertain because the ground-truth images cannot be are captured directly in underwater environment. Second, learning-based methods may lack generalization ability for diverse underwater environments. To tackle these issues, we propose HPUIE-RL, a hierarchical probabilistic UIE model facilitated by reinforcement learning. This model integrates UNet with hierarchical probabilistic modules to produce various enhanced candidate images that reflect the uncertainty of the enhancement. Then, a reinforcement learning fine-tuning framework is designed to fine-tune the pretrained model in an unsupervised manner, which responds to the dynamic underwater environment. Experiments on real-world datasets from diverse underwater environments demonstrate that our HPUIE-RL model outperforms state-of-the-art UIE methods regarding visual and quantitative performance and generalizability. • A novel hierarchical probabilistic model with reinforcement learning (HPUIE-RL) is proposed. • A reinforcement learning-based fine-tuning framework is developed for unsupervised optimization. • The HPUIE-RL model demonstrates superior visual and quantitative performance. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of object recognition from colorless 3D point clouds in underwater environments. It presents a performance comparison of state-of-the-art global descriptors, which are readily available as open source code. The studied methods are intended to assist Autonomous Underwater Vehicles (AUVs) in performing autonomous interventions in underwater Inspection, Maintenance and Repair (IMR) applications. A set of test objects were chosen as being representative of IMR applications whose shape is typically known a priori. As such, CAD models were used to create virtual views of the objects under realistic conditions of added noise and varying resolution. Extensive experiments were conducted from both virtual scans and from real data collected with an AUV equipped with a fast laser sensor developed in our research centre. The underwater testing was conducted from a moving platform, which can create deformations in the perceived shape of the objects. These effects are considerably more difficult to correct than in above-water counterparts, and therefore may affect the performance of the descriptor. Among other conclusions, the testing we conducted illustrated the importance of matching the resolution of the database scans and test scans, as this significantly impacted the performance of all descriptors except one. This paper contributes to the state-of-the-art as being the first work on the comparison and performance evaluation of methods for underwater object recognition. It is also the first effort using comparison of methods for data acquired with a free floating underwater platform. [ABSTRACT FROM AUTHOR]

Vision measurement systems have a reliable performance on ground, but it remains a challenge to apply commonly-used vision measurement systems (i.e. multi-camera systems and laser systems) in underwater environments. One of the most challenging issues is the transformation from an unscaled measurement to a scaled result, which is achieved by a calibration method and determinate the strategy used for underwater vision measurement. This paper proposes a novel monocular underwater calibration method underlying a simple underwater vision measurement system. Underwater unscaled measurement results are calculated by the dark channel prior model. These results are then processed by our calibration method, transforming the unscaled measurements to accurately scaled results. These measurement results finally are used to estimate the scaled 3D structure of underwater objects. Experimental results under natural open water show that our proposed method is reliable and efficient. [ABSTRACT FROM AUTHOR]

An underwater structure reinforcement process was studied. The construction method is to first install a sleeve and then perform a composite grouting. The method has the advantages of simple construction process short construction period small construction difficulty high bonding degree of the epoxy composite grouting material and the original structure surface. Through the repair tests under three different environments of drying fresh water and artificial seawater it is found that the underwater repair composite grouting material have good repairing effect to the substrates under different environments. The construction process construction material preparation and construction quality assurance measures of this technology are also discussed which provides experience for underwater structure reinforcement and repair construction. [ABSTRACT FROM AUTHOR]

Turbid underwater environment poses great difficulties for the applications of vision technologies. One of the biggest challenges is the complicated noise distribution of the underwater images due to the serious scattering and absorption. To alleviate this problem, this work proposes a deep pixel‐to‐pixel networks model for underwater image enhancement by designing an encoding–decoding framework. It employs the convolution layers as encoding to filter the noise, while uses deconvolution layers as decoding to recover the missing details and refine the image pixel by pixel. Moreover, skip connection is introduced in the networks model in order to avoid low‐level features losing while accelerating the training process. The model achieves the image enhancement in a self‐adaptive data‐driven way rather than considering the physical environment. Several comparison experiments are carried out with different datasets. Results show that it outperforms the state‐of‐the‐art image restoration methods in underwater image defogging, denoising and colour enhancement. [ABSTRACT FROM AUTHOR]

Exploring the vast expanse of oceans, Earth's largest realm and a vital resource repository, has intrigued humanity, transcending disciplinary boundaries. Recent breakthroughs in science and technology have propelled us from surface endeavors into the depths of the underwater world. However, a host of challenges emerges beneath the waves, encompassing energy constraints, limited sensing capabilities, and communication obstacles for submerged equipment. These challenges, which resonate across diverse fields, demand innovative solutions to advance scientific pursuits and practical applications. Triboelectric nanogenerators (TENGs), pioneered by Zhong Lin Wang in 2012, represent a revolutionary leap in energy harvesting and self-powered sensing technologies. These devices have fostered exploration and innovation in various domains, including marine science and technology. In response to the unique challenges posed by the underwater environment, a specialized application of TENGs has emerged: underwater TENGs. These innovative devices hold the potential to transform underwater exploration by increasing energy harvesting, expanding sensing capabilities, and enhancing communication systems. This comprehensive review aims to demystify the domain of underwater TENG technology, delving into the theories, design strategies, and recent breakthroughs that underpin this emerging field. This review also contemplates the future trajectories of underwater TENGs, envisioning their transformative role in elevating underwater research and technology. [Display omitted] • A comprehensive review aims to demystify the domain of underwater TENG technology. • A holistic overview of the progress in the field of underwater TENGs. • A broad outlook on the future development of underwater TENG technology. [ABSTRACT FROM AUTHOR]

With the characteristics of high transmission delay and mobility, distributed time synchronization of underwa-ter sensor networks can be very challenging. The existing works cannot get high accuracy, because most of them ignore the long transmission latency, or dynamic changing tra ission delay caused by the mobility. A mobility model was built considering the effect of mobility and long transmission latency on time synchronization, and a time synchronization parameters equation was formed according to the mobili model. Thus, based on the equation, a time synchronization algorithm (MM-sync) was proposed. The experiment results show that MM-sync can reduce the consumption of energy, and can get higher accuracy than state-of-art solutions in high density underwater sensor networks with rapid movement.