For aquaculture resource evaluation and ecological environment monitoring, the automatic detection and identification of marine organisms is critical; however, due to the low quality of underwater images and the characteristics of underwater biological detection, the lack of abundant features can impede traditional hand-designed feature extraction approaches or CNN-based object detection algorithms, particularly in complex underwater environments. Therefore, the goal of this study was to perform object detection in underwater environments. This study developed a novel method for capturing feature information by adding the convolutional block attention module (CBAM) to the YOLOv5 backbone network. The interference of underwater organism characteristics in object characteristics decreased and the output object information of the backbone network was enhanced. In addition, a self-adaptive global histogram stretching algorithm (SAGHS) was designed to eliminate degradation problems, such as low contrast and color loss, that are caused by underwater environmental features in order to restore image quality. Extensive experiments and comprehensive evaluations using the URPC2021 benchmark dataset demonstrated the effectiveness and adaptivity of the proposed methods. Additionally, this study conducted an exhaustive analysis of the impacts of training data on performance.

The trajectory planning algorithm of underwater vehicle near-bottom cruise is important to scientific investigation, industrial inspection, and military affairs. An autonomous underwater vehicle (AUV) often faces the problems of complex underwater environment and large cruise area in a real environment, and some robots must hide themselves during the cruise. However, to the best of our knowledge, few studies have focused on trajectory planning algorithms for AUVs with multiple constraints on large-scale maps. The currently used algorithms are not effective at solving length-constraint problems, and the mainstream trajectory planning algorithms for robots cannot be directly applied to the needs of underwater vehicle sailing near the bottom. Therefore, we present a bounded ridge-based trajectory planning algorithm (RA*) for an AUV to go on a near-bottom cruise. In the algorithm, we design a safety map based on a spherical structure to ensure the safe operation of the robot. In addressing the length-constraint problem and large-scale map planning problem, this paper proposes a two-stage framework for RA*, which designs map compression and parallel computation using a coarse-fine planning framework to solve the large-scale trajectory planning problem and uses a bounded search method to meet the trajectory planning requirements of length constraint. In this study, experiments based on the virtual ocean ridge are conducted, and the results validate the effectiveness and efficiency of the proposed RA* with MCPC algorithm framework.

Underwater Image Enhancement (UIE) aims to restore image quality by mitigating inherent degradations in underwater imaging systems. While existing learning-based methods show promise, they face limitations in separating and processing frequency components, effectively fusing domain information, and balancing the enhancement of structures and details. To resolve these limitations, we propose CDF-UIE, a novel network that leverages and fuses cross-domain information for mitigating the degradation in underwater images. CDF-UIE first performs domain decoupling of input features using the proposed Spatial-Frequency Decoupling (SFD) block. Then we design an innovative Cross-Domain Fusion (CDF) block, which effectively bridges the spatial and frequency domain features through the cross-domain attention mechanism. To produce stable and detailed enhanced outputs, we exploit the coarse and fine-scale information in the image reconstruction stage. Additionally, we introduce a multiscale objective function that incorporates pixel-level, structural, and perceptual constraints to guide the enhancement process. We conduct extensive experiments on six diverse real-world underwater image datasets. Comprehensive experiments and real-world application tests demonstrate that CDF-UIE significantly outperforms existing methods, offering promising future applications in various underwater scenarios. The source code is available at https://github.com/hpzhan66/CDF-UIE.

Side-scan sonar (SSS) has become an important tool for ocean exploration due to its practicality and reliability. Existing approaches for SSS image classification mainly rely on deep convolutional neural networks (DCNNs). Although effective, DCNN-based methods fail to classify SSS images with specific target samples that are completely absent from the training data. In this work, a global context external-attention network (GCEANet) is proposed to produce pseudo-SSS images corresponding to the absent categories for zero-shot SSS image classification. First, the optical and acoustic images are taken as content and style image inputs to the network, respectively. Second, a global context (GC) block is proposed to extract the context semantics of the style features and fuse them with the content features. Finally, an external attention (EA) block is proposed to extract the potential correlation between content and style features to generate pseudo-SSS images with optical content. In addition, a contrastive loss based on image reconstruction is proposed to optimize the cross-modal feature space fusion. Extensive experiments demonstrate the effectiveness of our approach in efficiently generating realistic pseudo-SSS samples. The experimental results on real SSS sample datasets show that our method outperforms other state-of-the-art (SOTA) methods in the field of zero-shot SSS image classification. The code is available at https://github.com/baizhongyu/GCEANet.

Following the development of autonomous underwater vehicles (AUVs), multiple trajectory-based submarine target information collection constitutes one of the key technologies that significantly influence underwater information collection ability and deployment efficiency. In this paper, we propose an underwater information collection AUV, O-AUV, that can perceive the omnidirectional area and could detect a larger area than the traditional AUV. A 3D sensing model for the O-AUV is proposed to describe the complex underwater information collection spaces. Thereafter, a cube-based environment model involving candidate observation point calculation methods are suggested to adapt the O-AUV model. A voyage cost map is also built according to the multi-AUV path planning for a common submarine mission that must traverse numerous mission targets in complex environments through the R-Dijkstra algorithm. Specifically, the voyage planning problem is solved through a critical algorithm called ANSGA (accelerated NSGA-II algorithm), which in turn, is developed by modifying the non-dominated sorting genetic algorithm (NSGA-II) to accelerate the optimization rate for the Pareto solution. Experiments are carried out in MATLAB, and the results verify the validity of the proposed O-AUV+ANSGA algorithm framework.

Side-scan sonar (SSS) is attracting increasing attention in ocean exploration for its utility and stability on autonomous underwater vehicles (AUVs). Existing SSS image recognition methods mainly employ deep neural networks (DNNs) for various tasks. However, the effectiveness of DNN-based approaches is limited in situations with zero samples. In this letter, a multi-feature fusion self-attention network (MFSANet) is proposed to generate SSS images of novel categories, transforming this problem into a conventional supervised learning problem. Specifically, optical-acoustic image pairs are used as inputs to the network to synthesize pseudo-SSS images. First, the shallow and deep features of the input images are extracted by different layers of the encoder. Then, the long-range dependencies of the acoustic images are efficiently modeled with the proposed simplified self-attention module (SSAM). Finally, the acoustic features are appropriately aggregated to each position of the optical features to efficiently generate pseudo-SSS samples for training the classification network. In addition, a novel contrastive loss is proposed to optimize the cross-modal feature space distribution. Experimental results demonstrate that our method can efficiently generate high-quality pseudo-SSS samples, which improves the accuracy of zero-shot SSS image recognition.