Anthropogenic waste deposition in aquatic environments precipitates a decline in water quality, engendering pollution that adversely impacts human health, ecological integrity, and economic endeavors. The evolution of underwater robotic technologies heralds a new era in the timely identification and extraction of submerged litter, offering a proactive measure against the scourge of water pollution. This study introduces a refined YOLOv8-based algorithm tailored for the enhanced detection of small-scale underwater debris, aiming to mitigate the prevalent challenges of high miss and false detection rates in aquatic settings. The research presents the YOLOv8-C2f-Faster-EMA algorithm, which optimizes the backbone, neck layer, and C2f module for underwater characteristics and incorporates an effective attention mechanism. This algorithm improves the accuracy of underwater litter detection while simplifying the computational model. Empirical evidence underscores the superiority of this method over the conventional YOLOv8n framework, manifesting in a significant uplift in detection performance. Notably, the proposed method realized a 6.7% increase in precision (P), a 4.1% surge in recall (R), and a 5% enhancement in mean average precision (mAP). Transcending its foundational utility in marine conservation, this methodology harbors potential for subsequent integration into remote sensing ventures. Such an adaptation could substantially enhance the precision of detection models, particularly in the realm of localized surveillance, thereby broadening the scope of its applicability and impact.

To address the issue of increasing navigation errors in low-cost autonomous underwater vehicles (AUVs) operating without assisted positioning underwater, this paper proposes a Virtual GPS Model (VGPSM) based on deep sequence learning. This model is integrated with an Extended Kalman Filter (EKF) to provide a high-precision navigation solution for AUVs. The VGPSM leverages the time-series characteristics of data from sensors such as the Attitude and Heading Reference System (AHRS) and the Doppler Velocity Log (DVL) while the AUV is on the surface. It learns the relationship between these sensor data and GPS data by utilizing a hybrid model of Long Short-Term Memory (LSTM) and Bidirectional Long Short-Term Memory (Bi-LSTM), which are well-suited for processing and predicting time-series data. This approach constructs a virtual GPS model that generates virtual GPS displacements updated at the same frequency as the real GPS data. When the AUV navigates underwater, the virtual GPS displacements generated using the VGPSM in real-time are used as measurements to assist the EKF in state estimation, thereby enhancing the accuracy and robustness of underwater navigation. The effectiveness of the proposed method is validated through a series of experiments under various conditions. The experimental results demonstrate that the proposed method significantly reduces cumulative errors, with navigation accuracy improvements ranging from 29.2% to 69.56% compared to the standard EKF, indicating strong adaptability and robustness.

Inspired by the tubercles on humpback whale flippers, leading-edge tubercles have been incorporated into the design of wings and turbine blades in an attempt to improve their hydrodynamic performance. Although promising improvements, especially in terms of the stall performance, have been demonstrated in the limited research that exists to date, the effectiveness of the leading-edge tubercles seems to be influenced by the base blade. This paper focuses on the introduction of sinusoidal leading-edge tubercles to a base blade developed from the classic NACA0018 airfoil, and numerically investigates the effectiveness of leading-edge tubercles on the hydrodynamics associated with the blade in uniform current with different attack angles. Both the macroscopic parameters, such as the lift and drag forces, and the micro-scale flow characteristics, including the vortex and flow separation, are analyzed. The results indicate that the leading-edge tubercles brings a significant influence on the hydrodynamic forces acting on the blade when subjected to an attack angle greater than 15°. This study also reveals the important role of the turbulence and flow separation on hydrodynamic loading on the blade and the considerable influence of the tubercles on such micro-scale flow characteristics. Although the conditions applied in this work are relatively ideal (e.g., the blade is fixed in a uniform flow and the end effect is ignored), the satisfactory agreement between the numerical and corresponding experimental data implies that the results are acceptable. This work builds a good reference for our future work on the hydrodynamic performance of tidal turbines which adopt this kind of blade for operating in both uniform and shearing currents.

When autonomous underwater vehicles (AUVs) perform underwater tasks, the absence of GPS position assistance can lead to a decrease in the accuracy of traditional navigation systems, such as the extended Kalman filter (EKF), due to the accumulation of errors. To enhance the navigation accuracy of AUVs in the absence of position assistance, this paper proposes an innovative navigation method that integrates a position correction model and a velocity model. Specifically, a velocity model is developed using a dynamic model and the Optimal Pruning Extreme Learning Machine (OP-ELM) method. This velocity model is trained online to provide velocity outputs during the intervals when the Doppler Velocity Log (DVL) is not updating, ensuring more consistent and reliable velocity estimation. Additionally, a position correction model (PCM) is constructed, based on a hybrid gated recurrent neural network (HGRNN). This model is specifically designed to correct the AUV’s navigation position when GPS data are unavailable underwater. The HGRNN utilizes historical navigation data and patterns learned during training to predict and adjust the AUV’s estimated position, thereby reducing the drift caused by the lack of real-time position updates. Experimental results demonstrate that the proposed VM-PCM-EKF algorithm can significantly improve the positioning accuracy of the navigation system, with a maximum accuracy improvement of 87.2% compared to conventional EKF algorithms. This method not only improves the reliability and accuracy of AUV missions but also opens up new possibilities for more complex and extended underwater operations.

This study on soil salinity inversion in coastal tidal flats based on Sentinel-2 remote sensing imagery is significant for improving saline–alkali soils and advancing tidal flat agriculture. This study proposes an improved approach for soil salinity inversion in coastal tidal flats using Sentinel-2 imagery and a new enhanced chaotic mapping adaptive whale optimization neural network (CIWOABP) algorithm. Novel spectral indices were developed to enhance correlations with salinity, significantly outperforming traditional indexes. The CIWOABP model achieved superior validation accuracy (R² = 0.815) and reduced root mean square error (RMSE) and mean absolute error (MAE) compared to other machine learning models. The results enable the precise mapping of salinity levels, aiding salt-tolerant crop cultivation and sustainable agricultural management. This method offers a reliable framework for rapid salinity monitoring and precision farming in coastal regions.