Complex Underwater Environment Research Articles

Online_XKD: An online knowledge distillation model for underwater object detection

Underwater object detection in the field of computer vision faces unique challenges such as color distortion, reduced visibility, and blurred edges caused by aquatic conditions, which decrease the efficiency of traditional methods. Furthermore, the limited memory and computational power of underwater equipment necessitate the development of lightweight and efficient real-time monitoring algorithms. To address these issues, this paper designs an online knowledge distillation(KD) framework based on mutual knowledge transfer, named Online-XKD, which aims to enhance the efficiency and generalizability of knowledge distillation through mutual knowledge exchange while maintaining model lightweightness. The core idea of Online-XKD is an internally designed mutual knowledge transfer structure for online distillation within the detection head, which enhances learning outcomes by optimizing distillation efficiency and broadening generalizability. Additionally, we introduce a mask generation feature knowledge distillation architecture based on the backbone network, which significantly enhances the feature extraction capability of the student model by integrating mask masking and feature generation techniques. Moreover, by incorporating PSA and AUGFPN attention modules, the feature extraction capability in complex underwater environments is further enhanced without adding extra parameters, thereby improving detection accuracy. Experimental results on the URPC2020 dataset demonstrate that Online-XKD enhances the detection accuracy of the student model by 3.6 mAP, exceeding initial KD models by 3.3 mAP, and outperforming most existing KD methods in underwater detection tasks. These results confirm the effectiveness and superiority of Online-XKD in applications of underwater object detection, proving its practical significance and effectiveness in enhancing the feature extraction and generalization abilities of lightweight models.

RTL-YOLOv8n: A Lightweight Model for Efficient and Accurate Underwater Target Detection

Underwater object detection is essential for the advancement of automated aquaculture operations. Addressing the challenges of low detection accuracy and insufficient generalization capabilities for underwater targets, this paper focuses on the development of a novel detection method tailored to such environments. We introduce the RTL-YOLOv8n model, specifically designed to enhance the precision and efficiency of detecting objects underwater. This model incorporates advanced feature-extraction mechanisms—RetBlock and triplet attention—that significantly improve its ability to discern fine details amidst complex underwater scenes. Additionally, the model employs a lightweight coupled detection head (LCD-Head), which reduces its computational requirements by 31.6% compared to the conventional YOLOv8n, without sacrificing performance. Enhanced by the Focaler–MPDIoU loss function, RTL-YOLOv8n demonstrates superior capability in detecting challenging targets, showing a 1.5% increase in mAP@0.5 and a 5.2% improvement in precision over previous models. These results not only confirm the effectiveness of RTL-YOLOv8n in complex underwater environments but also highlight its potential applicability in other settings requiring efficient and precise object detection. This research provides valuable insights into the development of aquatic life detection and contributes to the field of smart aquatic monitoring systems.

Intelligent Control Strategy for Robotic Manta via CPG and Deep Reinforcement Learning

The robotic manta has attracted significant interest for its exceptional maneuverability, swimming efficiency, and stealthiness. However, achieving efficient autonomous swimming in complex underwater environments presents a significant challenge. To address this issue, this study integrates Deep Deterministic Policy Gradient (DDPG) with Central Pattern Generators (CPGs) and proposes a CPG-based DDPG control strategy. First, we designed a CPG control strategy that can more precisely mimic the swimming behavior of the manta. Then, we implemented the DDPG algorithm as a high-level controller that adaptively modifies the CPG’s control parameters based on the real-time state information of the robotic manta. This adjustment allows for the regulation of swimming modes to fulfill specific tasks. The proposed strategy underwent initial training and testing in a simulated environment before deployment on a robotic manta prototype for field trials. Both further simulation and experimental results validate the effectiveness and practicality of the proposed control strategy.

Evaluation Method for Underwater Ultrasonic Energy Radiation Performance Based on the Spatial Distribution Characteristics of Acoustic Power.

In recent years, underwater wireless ultrasonic energy transmission technology (UWUET) has attracted much attention because it utilizes the propagation characteristics of ultrasound in water. Effectively evaluating the performance of underwater ultrasonic wireless energy transmission is a key issue in engineering design. The current approach to performance evaluation is usually based on the system energy transfer efficiency as the main criterion, but this criterion mainly considers the overall energy conversion efficiency between the transmitting end and the receiving end, without an in-depth analysis of the characteristics of the distribution of the underwater acoustic field and the energy loss that occurs during the propagation of acoustic waves. In addition, existing methods focusing on acoustic field analysis tend to concentrate on a single parameter, ignoring the dynamic distribution of acoustic energy in complex aquatic environments, as well as the effects of changes in the underwater environment on acoustic propagation, such as spatial variability in temperature and salinity. These limitations reduce the usefulness and accuracy of models in complex marine environments, which in turn reduces the efficiency of acoustic energy management and optimization. To solve these problems, this study proposes a method to evaluate the performance of underwater ultrasonic energy radiation based on the spatial distribution characteristics of acoustic power. By establishing an acoustic power distribution model in a complex impedance-density aqueous medium and combining numerical simulation and experimental validation, this paper explores the spatial variation of acoustic power and its impact on the energy transfer efficiency in depth. Using high-resolution spatial distribution data and actual environmental parameters, the method significantly improves the accuracy of the assessment and the adaptability of the model in complex underwater environments. The results show that, compared with the traditional method, this method performs better in terms of the accuracy of the acoustic energy radiation calculation results, and is able to reflect the energy distribution and spatial heterogeneity of the acoustic source more comprehensively, which provides an important theoretical basis and practical guidance for the optimal design and performance enhancement of the underwater ultrasonic wireless energy transmission system.

Equilibrating the impact of fluid scattering attenuation on underwater optical imaging via adaptive parameter learning

Fluid scattering attenuation is a pervasive and intricate physical phenomenon in underwater environment, which challenges optical imaging for various visual tasks. This paper presents a Learnable Physical Imaging Model (LPIM) for equilibrating the impact of the fluid scattering attenuation on underwater optical imaging. Whereas the imaging medium has the liquidity and diversity, an adaptive physical imaging framework is proposed to learn the nonlinear degradation parameter for various complex underwater environment. This adaptive imaging model is jointed by adversarial competition and directional learning strategy, which makes our model learn more excellent properties from the unpaired referring image, and guides the restoration network to directionally adjust the imaging model parameters. To describe the real scenarios better, a multi-branch network, i.e., R-Net, D-Net and BN-net, is developed to learn and capture the color, light intensity and attenuation information. Experimental results on several datasets demonstrate that our approach outperforms the state-of-the-arts in both color balance and visual effect. The LPIM provides an innovative framework designed to counteract image degradation resulting from fluid scattering and attenuation.

Real-time underwater object detection technology for complex underwater environments based on deep learning

Underwater object detection technology is crucial in many marine-related fields, including marine environmental monitoring, marine resource development, and marine ecological protection. However, this technology faces great challenges due to the poor quality of underwater optical images and the varying sizes of underwater objects. Therefore, we proposed an underwater optical detection network (UODN) based on the you only look once version 8 (YOLOv8) framework, which addresses these issues through the cross stage multi-branch (CSMB) module and large kernel spatial pyramid (LKSP) module. The aim of the CSMB module is to extract more features from underwater optical images to address the issue of poor image quality, while the LKSP module is designed to enhance the ability of the network to detect underwater objects of various scales. Furthermore, CSMBDarknet built by CSMB and LKSP can be used as the backbone of other underwater object detection algorithms for underwater feature extraction. Extensive experimental results on the underwater robot professional contest 2020 dataset revealed that the average precision (AP) of UODN increased by 1.0%, the AP50 of UODN increased by 1.1%, and the AP75 of UODN increased by 2.1% compared with those of the original YOLOv8s. Furthermore, UODN outperforms 12 state-of-the-art models on multiple underwater optical datasets, paving the way for future real-time and high-precision underwater object detection.

CCW-YOLOv5: A forward-looking sonar target method based on coordinate convolution and modified boundary frame loss.

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.

Depth control of bionic robotic fish based on fuzzy PID algorithm

Abstract This paper proposes a staged depth control method based on fuzzy PID control to improve the execution of various underwater tasks by robotic fish in pectoral and caudal fin co-propulsion modes in complex underwater environments. The method obtains appropriate PID parameters and regulates the corresponding CPG models by introducing a fuzzy controller into the PID control framework. The method follows the fuzzification, fuzzy rule setting, and defuzzification steps. During the approach phase, the robotic fish adjusts the amplitude and offset of its pectoral fin. In the cruise phase, it adjusts the angle of attack of the pectoral fin to achieve staged depth control. Simulation and analysis results indicate that the staged fuzzy PID control is more effective than pure fuzzy control and PID control. It reduces delay time by 38.5% and 59.4%, optimizes rise time by 31.1% and 44.1%, reduces peak time by 49.7% and 44.6%, and reduces steady-state error by 42.4% and 76.6%. Therefore, the staged fuzzy PID control provides a faster response and greater stability, making it an effective method for depth control of underwater robots.

Path Planning Techniques for Underwater Robots Based on Obstacles in Subsea Oil Pipelines

With the continuous growth of global energy demand, the safety and maintenance of underwater oil pipelines have become a focal point of international concern. Autonomous Underwater Vehicles (AUVs) are increasingly pivotal in inspection, maintenance, and repair of oil pipelines. This paper explores path planning techniques for underwater robots based on obstacles in subsea oil pipelines to enhance operational efficiency and safety. Employing theoretical analysis and simulation, the study systematically addresses key issues in path planning, including environmental modeling, obstacle recognition, path generation, and optimization. By constructing complex underwater environment models and integrating advanced algorithms such as improved ant colony optimization, genetic algorithms, and particle swarm optimization, the research investigates path planning strategies for AUVs under various obstacle distributions. The results demonstrate that the proposed path planning techniques effectively handle obstacles in subsea oil pipelines, significantly improving planning efficiency and robustness. Additionally, the feasibility of multi-robot collaborative path planning is discussed, providing theoretical support for future advancements in underwater robotics. Overall, this study offers new technological insights for the maintenance of underwater oil pipelines and is crucial for enhancing the intelligence level of underwater operations.

Ship-Radiated Noise Separation in Underwater Acoustic Environments Using a Deep Time-Domain Network

Ship-radiated noise separation is critical in both military and economic domains. However, due to the complex underwater environments with multiple noise sources and reverberation, separating ship-radiated noise poses a significant challenge. Traditionally, underwater acoustic signal separation has employed blind source separation methods based on independent component analysis. Recently, the separation of underwater acoustic signals has been approached as a deep learning problem. This involves learning the features of ship-radiated noise from training data. This paper introduces a deep time-domain network for ship-radiated noise separation by leveraging the power of parallel dilated convolution and group convolution. The separation layer employs parallel dilated convolution operations with varying expansion factors to better extract low-frequency features from the signal envelope while preserving detailed information. Additionally, we use group convolution to reduce the expansion of network size caused by parallel convolution operations, enabling the network to maintain a smaller size and computational complexity while achieving good separation performance. The proposed approach is demonstrated to be superior to the other common networks in the DeepShip dataset through comprehensive comparisons.

Decoupled variational retinex for reconstruction and fusion of underwater shallow depth-of-field image with parallax and moving objects

Underwater imaging often suffers from poor quality due to the complex underwater environment and limitations of hardware equipment, leading to images with shallow depth of field and moving objects, which pose a challenge for information fusion of image sequences from the same underwater scene. To effectively address these problems, we propose a decoupled variational Retinex method for reconstructing and fusing underwater shallow depth of field images. Specifically, we first construct a module that adopts the decoupled variational Retinex model to adjust pixel dynamic range and luminance components, enhance non-local properties’ extraction with higher-order data constraints, and significantly improve image quality. Then, we develop a precision alignment strategy for image sequences by calculating and correcting control point deviations in the overlapping areas, achieving accurate registration of the image sequences, and effectively reconstructing scenes with parallax. Moreover, scenes with moving objects within the image sequence are reconstructed by redistributing overlapping areas. We design a novel cost function based on the neighborhood information of seams, which facilitates iterative optimization of these solved seams. This process improves the segmentation accuracy within these regions, achieving more precise scene reconstruction. Compared with state-of-the-art approaches, our method demonstrates superior performance in rectifying degraded image quality and reconstructing visually appealing images, with the resulting reconstructed images showing enhanced subjective visual quality.

DDformer: Dimension decomposition transformer with semi-supervised learning for underwater image enhancement

Vision-guided Autonomous Underwater Vehicles (AUVs) have gradually become significant tools for human exploration of the ocean. However, distorted images severely limit the visual ability, making it difficult to meet the needs of complex underwater environment perception. Fortunately, recent advancements in deep learning have led to rapid developments in underwater image enhancement. The emergence of the Transformer architecture has further enhanced the capabilities of deep learning. However, the direct application of Transformer to underwater image enhancement presents challenges in computing pixel-level global information and extracting local features. In this paper, we present a novel approach that merges dimension decomposition Transformer with semi-supervised learning for underwater image enhancement. To begin, dimension decomposition attention is proposed, which enables Transformer to compute global dependencies directly at the original scale and correct color distortions effectively. Concurrently, we employ convolutional neural networks to compensate for Transformer's limitations in extracting local features, thereby enriching details and textures. Subsequently, a multi-stage Transformer strategy is introduced to divide the network into high- and low-resolution stages for multi-scale global information extraction. It helps correct color distortions while enhancing the network's focus on regions with severe degradation. Moreover, we design a semi-supervised learning framework to reduce the reliance on paired datasets and construct a corresponding multi-scale fusion discriminator to enhance the sensitivity to input data. Experimental results demonstrate that our method outperforms state-of-the-art approaches, showcasing excellent learning and generalization capabilities on subjective perception and overall evaluation metrics. Furthermore, outstanding results highlight the significant improvements it brings to downstream visual engineering applications. The code of the proposed DDformer is available at https://github.com/ZhiGao-hfuu/DDformer.

EF-UODA: Underwater Object Detection Based on Enhanced Feature

The ability to detect underwater objects accurately is important in marine environmental engineering. Although many kinds of underwater object detection algorithms with relatively high accuracy have been proposed, they involve a large number of parameters and floating point operations (FLOPs), and often fail to yield satisfactory results in complex underwater environments. In light of the demand for an algorithm with the capability to extract high-quality features in complex underwater environments, we proposed a one-stage object detection algorithm called the enhanced feature-based underwater object detection algorithm (EF-UODA), which was based on the architecture of Next-ViT, the loss function of YOLOv8, and Ultralytics. First, we developed a highly efficient module for convolutions, called efficient multi-scale pointwise convolution (EMPC). Second, we proposed a feature pyramid architecture called the multipath fast fusion-feature pyramid network (M2F-FPN) based on different modes of feature fusion. Finally, we integrated the Next-ViT and the minimum point distance intersection over union loss functions in our proposed algorithm. Specifically, on the URPC2020 dataset, EF-UODA surpasses the state-of-the-art (SOTA) convolution-based object detection algorithm YOLOv8X by 2.9% mean average precision (mAP), and surpasses the SOTA ViT-based object detection algorithm real-time detection transformer (RT-DETR) by 2.1%. Meanwhile, it achieves the lowest FLOPs and parameters. The results of extensive experiments showed that EF-UODA had excellent feature extraction capability, and was adequately balanced in terms of the number of FLOPs and parameters.

YOLO‐UOD: An underwater small object detector via improved efficient layer aggregation network

AbstractAccurate detection of underwater objects is a key indicator technology to effectively enhance the field of marine development and application, and is of great importance to various fields including marine military defense and seafood aquaculture. Efficient and rapid detection of underwater targets is a crucial technological challenge in this field. To meet the challenges posed by these issues, this study applies the convolutional omni‐efficient layer aggregation network (CO‐ELAN) module to the detector backbone to improve the ability of the network structure to acquire underwater objects from image information. The module improves the feature representation of gradient branching through a multi‐dimensional dynamic convolution and attention mechanism. In terms of loss calculation, the optimized normalized Wasserstein distance approach is used to predict the box distribution probabilistic modelling method to determine comparable distances to the ground box and obtain better samples of small target labels. Here, an underwater image enhancement algorithm based on white balance and underwater blur fusion is used to obtain clear images that enable improved detector performance. After the verification experiment on the URPC2018 dataset, it is found that the detector has better underwater detection ability compared with other detectors in the complex underwater environment. The proposed method achieves a 2.4% improvement over the YOLOv7 baseline model, while reducing computation costs by 5%.

An Obstacle Avoidance Strategy for AUV Based on State-Tracking Collision Detection and Improved Artificial Potential Field

This paper proposes a fusion algorithm based on state-tracking collision detection and the simulated annealing potential field (SCD-SAPF) to address the challenges of obstacle avoidance for autonomous underwater vehicles (AUVs) in dynamic environments. Navigating AUVs in complex underwater environments requires robust autonomous obstacle avoidance capabilities. The SCD-SAPF algorithm aims to accurately assess collision risks and efficiently plan avoidance trajectories. The algorithm introduces an SCD model for proactive collision risk assessment, predicting collision risks between AUVs and dynamic obstacles. Additionally, it proposes a simulated annealing (SA) algorithm to optimize trajectory planning in a simulated annealing potential field (SAPF), integrating the SCD model with the SAPF algorithm to guide AUVs in obstacle avoidance by generating optimal heading and velocity outputs. Extensive simulation experiments demonstrate the effectiveness and robustness of the algorithm in various dynamic scenarios, enabling the early avoidance of dynamic obstacles and outperforming traditional methods. This research provides an accurate collision risk assessment and efficient obstacle avoidance trajectory planning, offering an innovative approach to the field of underwater robotics and supporting the enhancement of AUV autonomy and reliability in practical applications.

Precise underwater fish measurement: A geometric approach leveraging medium regression

Accurate estimation of fish length is crucial aquaculture processes. Precise and real-time estimation of fish length aids fish farmers in formulating effective farming strategies. Existing methods for estimating the length of underwater fish have made significant advancements in overcoming the interferences present in complex underwater environments. Nevertheless, the measurement results of some methods continue to be limited by variations in the refractive index of multiple media and deviations in imaging angles, resulting in measurement errors and diminished practicality. Consequently, we propose a measurement method that aims to address the aforementioned challenges in fisheries aquaculture by providing more accurate and cost-effective solutions. This method initially introduces the concept of regressing multiple media into a single medium to correct underwater depth estimation and enhance underwater imaging quality. Subsequently, an accurate plane fitting algorithm is applied to precisely fit the fish body plane and extract its corresponding normal vector information. Finally, taking into account the light propagation path and accounting for the limitations of precise values, we utilize the geometric relationship between the length of the bounding box and the actual length of the fish to calculate a more accurate estimation of fish body length. Experimental validation showcases the applicability of our measurement method to diverse fish species and substantiates its exceptional performance in both experimental and real-world scenarios. The maximum average relative errors for each group are within 8%, and the average relative error is 3.28% under the exact value constraints presented in the experiments. This method makes a significant contribution to fish length estimation and offers valuable insights for applications in fisheries and aquaculture.

A Novel Obstacle Traversal Method for Multiple Robotic Fish Based on Cross-Modal Variational Autoencoders and Imitation Learning.

Precision control of multiple robotic fish visual navigation in complex underwater environments has long been a challenging issue in the field of underwater robotics. To address this problem, this paper proposes a multi-robot fish obstacle traversal technique based on the combination of cross-modal variational autoencoder (CM-VAE) and imitation learning. Firstly, the overall framework of the robotic fish control system is introduced, where the first-person view of the robotic fish is encoded into a low-dimensional latent space using CM-VAE, and then different latent features in the space are mapped to the velocity commands of the robotic fish through imitation learning. Finally, to validate the effectiveness of the proposed method, experiments are conducted on linear, S-shaped, and circular gate frame trajectories with both single and multiple robotic fish. Analysis reveals that the visual navigation method proposed in this paper can stably traverse various types of gate frame trajectories. Compared to end-to-end learning and purely unsupervised image reconstruction, the proposed control strategy demonstrates superior performance, offering a new solution for the intelligent navigation of robotic fish in complex environments.

A novel multi-source navigation algorithm based on factor graph in complex underwater environments of polar regions

Autonomous underwater vehicle (AUV) is an unmanned, cableless, self-propelled, autonomous navigation and control vehicle that maneuvers under unmanned or minimally monitored and intervened conditions. To solve the problem of increasing navigation accuracy error caused by time-varying measurement noise of AUV auxiliary sensors in complex underwater environments of polar regions, a multi-source navigation algorithm for AUV based on improved factor graph is proposed. The algorithm first adds a confidence function to the standard factor graph algorithm, which is used to adjust the covariance matrix of the navigation sensor to suppress the problem of time-varying measurement noise. Then, a sliding window is added to the factor graph algorithm and only nodes within the sliding window are optimized to ensure real-time performance. Finally, considering that inertial navigation has high accuracy in a short period of time, inertial navigation iterations are performed on nodes that are about to be marginalized within the sliding window to improve navigation accuracy. The analyses of the simulation experiment results show that the algorithm can effectively cope with the time-varying problem of measurement noise and ensure the accuracy and robustness of the AUV navigation system in the complex marine environment.

AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection

In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifically addresses the impact of non-ideal imaging factors on detection accuracy in complex underwater environments. To mitigate the influence of noise on object detection performance, we propose AMSP Vortex Convolution (AMSP-VConv) to disrupt the noise distribution, enhance feature extraction capabilities, effectively reduce parameters, and improve network robustness. We design the Feature Association Decoupling Cross Stage Partial (FAD-CSP) module, which strengthens the association of long and short range features, improving the network performance in complex underwater environments. Additionally, our sophisticated post-processing method, based on non-maximum suppression with aspect-ratio similarity thresholds, optimizes detection in dense scenes, such as waterweed and schools of fish, improving object detection accuracy. Extensive experiments on the URPC and RUOD datasets demonstrate that our method outperforms existing state-of-the-art methods in terms of accuracy and noise immunity. AMSP-UOD proposes an innovative solution with the potential for real-world applications. Our code is available at https://github.com/zhoujingchun03/AMSP-UOD.

Research on Underwater Image Enhancement Algorithm

In recent years, with the continuous expansion of human activity areas, the Marine pollution caused by human activities has become increasingly serious, and the large influx of agricultural emissions and household garbage needs to rely on underwater equipment to clean up. However, to identify garbage from the complex underwater environment, it is necessary to study the underwater image enhancement algorithm. Therefore, this paper takes underwater plastic floaters as the research object, starts with the optical imaging model of underwater images, analyzes the process and mechanism of underwater optical imaging, and its impact on image quality, so as to better understand the causes of image degradation. Based on white balance algorithm, histogram equalization algorithm, bilateral filter algorithm and gamma correction method, the underwater image recognition image is improved. The principle of these methods to enhance underwater image is analyzed and applied to further improve the effect of underwater image enhancement.